Minimize Landsat-8

Landsat-8 / LDCM (Landsat Data Continuity Mission)

Spacecraft     Launch    Mission Status     Sensor Complement    Ground Segment    References

The Landsat spacecraft series of NASA represents the longest continuous Earth imaging program in history, starting with the launch of Landsat-1 in 1972 through Landsat-7 with the ETM+ imager (launch April 15, 1999). With the evolution of the program has come an increased emphasis on the scientific utility of the data accompanied by more stringent requirements for instrument and data characterization, calibration and validation. This trend continues with LDCM, the next mission in the Landsat sequence. The enhancements of the Landsat-7 system, e.g., more on-board calibration hardware and an image assessment system and personnel, have been retained and improved, where required, for LDCM. Aspects of the calibration requirements are spread throughout the mission, including the instrument and its characterization, the spacecraft, operations and the ground system. 1) 2)

The following are the major mission objectives: 3)

• Collect and archive moderate-resolution, reflective multispectral image data affording seasonal coverage of the global land mass for a period of no less than five years.

• Collect and archive moderate-resolution, thermal multispectral image data affording seasonal coverage of the global land mass for a period of no less than three years.

• Ensure that LDCM data are sufficiently consistent with data from the earlier Landsat missions, in terms of acquisition geometry, calibration, coverage characteristics, spectral and spatial characteristics, output product quality, and data availability to permit studies of land cover and land use change over multi-decadal periods.

• Distribute standard LDCM data products to users on a nondiscriminatory basis and at no cost to the users.

Background: In 2002, the Landsat program had its 30th anniversary of providing satellite remote sensing information to the world; indeed a record history of service with the longest continuous spaceborne optical medium-resolution imaging dataset available anywhere. The imagery has been and is being used for a multitude of land surface monitoring tasks covering a broad spectrum of resource management and global change issues and applications.

In 1992 the US Congress noted that Landsat commercialization had not worked and brought Landsat back into the government resulting in the launches of Landsat 6 (which failed on launch) and Landsat 7. However there was still much conflict within the government over how to continue the program.

In view of the outstanding value of the data to the user community as a whole, NASA and USGS (United States Geological Survey) were working together (planning, rule definition, forum of ideas and discussion among all parties involved, coordination) on the next generation of the Landsat series satellites, referred to as LDCM (Landsat Data Continuity Mission). The overall timeline foresaw a formulation phase until early 2003, followed by an implementation phase until 2006. The goal was to acquire the first LDCM imagery in 2007 - to ensure the continuity of the Landsat dataset [185 km swath width, 15 m resolution (Pan) and a new set of spectral bands]. 4) 5) 6) 7) 8) 9) 10) 11)

The LDCM project suffered some setbacks on its way to realization resulting in considerable delays:

• An initial major programmatic objective of LDCM was to explore the use of imagery purchases from a commercial satellite system in the next phase of the Landsat program. In March 2002, NASA awarded two study contracts to: a) Resource21 LLC. of Englewood, CO, and b) DigitalGlobe Inc. of Longmont, CO. The aim was to formulate a proper requirements set and an implementation scenario (options) for LDCM. NASA envisioned a PPP (Public Private Partnership) program in which the satellite system was going to be owned and operated commercially. A contract was to be awarded in the spring of 2003. - However, it turned out that DigitalGlobe lost interest and dropped out of the race. And the bid of Resource21 turned out to be too high for NASA to be considered.

• In 2004, NASA was directed by the OSTP (Office of Science and Technology Policy) to fly a Landsat instrument on the new NPOESS satellite series of NOAA.

• In Dec. 2005, a memorandum with the tittle “Landsat Data Continuity Strategy Adjustment” was released by the OSTP which directed NASA to acquire a free-flyer spacecraft for LDCM - thus, superseding the previous direction to fly a Landsat sensor on NPOESS. 12)

However, the matter was not resolved until 2007 when it was determined that NASA would procure the next mission, the LDCM, and that the USGS would operate it as well as determine all future Earth observation missions. This decision means that Earth observation has found a home in an operating agency whose mission is directly concerned with the mapping and analysis of the Earth’s surface allowing NASA to focus on advancing space technologies and the future of man in space.

Overall science objectives of the LDCM imager observations are:

• To permit change detection analysis and to ensure consistency of the LDCM data with the Landsat series data

• To provide global coverage of the Earth's land surfaces on a seasonal basis

• To acquire imagery at spatial, spectral and temporal resolutions sufficient to characterize and understand the causes and consequences of change

• To make the data available to the user community.

The procurement approach for the LDCM project represents a departure from a conventional NASA mission. NASA traditionally specifies the design of the spacecraft, instruments, and ground systems acquiring data for its Earth science missions. For LDCM, NASA and USGS (the science and technology agency of the Department of the Interior, DOI) have instead specified the content, quantity, and characteristics of data to be delivered.


Figure 1: History of the Landsat program (image credit: NASA) 13)

Legend to Figure 1: The small white arrow within the Landsat-7 arrow on this timeline indicates the collection of data without the Scan Line Corrector.

“The Landsat series of satellites is a cornerstone of our Earth observing capability. The world relies on Landsat data to detect and measure land cover/land use change, the health of ecosystems, and water availability,” NASA Administrator Charles Bolden told the Subcommittee on Space Committee on Science, Space and Technology U.S House of Representatives in April 2015.

“With a launch in 2023, Landsat-9 would propel the program past 50 years of collecting global land cover data,” said Jeffrey Masek, Landsat-9 Project Scientist at Goddard. “That’s the hallmark of Landsat: the longer the satellites view the Earth, the more phenomena you can observe and understand. We see changing areas of irrigated agriculture worldwide, systemic conversion of forest to pasture – activities where either human pressures or natural environmental pressures are causing the shifts in land use over decades.”

Landsat-8 successfully launched on Feb. 11, 2013 and the Landsat data archive continues to expand. — Landsat-9 was announced on April 16, 2015. The launch is planned for 2023. 14)

Dec. 31, 2015: NASA has awarded a sole source letter contract to BACT (Ball Aerospace & Technologies Corporation), Boulder, Colo., to build the OLI-2 (Operational Land Imager-2) instrument for the Landsat-9 project. 15)


In April 2008, NASA selected GDAIS (General Dynamics Advanced Information Systems), Inc., Gilbert, AZ, to build the LDCM spacecraft on a fixed price contract. An option provides for the inclusion of a second payload instrument. LDCM is a NASA/USGS partnership mission with the following responsibilities: 16) 17) 18) 19)

• NASA is providing the LDCM spacecraft, the instruments, the launch vehicle, and the mission operations element of the ground system. NASA will also manage the space segment early on-orbit evaluation phase -from launch to acceptance.

• USGS is providing the mission operations center and ground processing systems (including archive and data networks), as well as the flight operations team. USGS will also co-chair and fund the Landsat science team.

In April 2010, OSC (Orbital Sciences Corporation) of Dulles VA acquired GDAIS. Hence, OSC will continue to manufacture and integrate the LDCM program as outlined by GDAIS. Already in Dec. 2009, GDAIS successfully completed the CDR (Critical Design Review) of LDCM for NASA/GSFC. 20) 21)


Figure 2: Artist's rendition of the LDCM spacecraft in orbit (image credit: NASA, OSC)

The LDCM spacecraft uses a nadir-pointing three-axis stabilized platform (zero momentum biased), a modular architecture referred to as SA-200HP. The SA-200HP (High Performance) bus is of DS1 (Deep Space 1) and Coriolis mission heritage. The spacecraft consists of an aluminum frame and panel prime structure.

The spacecraft is 3-axis stabilized (zero momentum biased). The ADCS (Attitude Determination and Control Subsystem) employs six reaction wheels, three torque rods and thrusters as actuators. Attitude is sensed with three precision star trackers (2 of 3 star trackers are active), a redundant SIRU (Scalable Inertial Reference Unit), twelve coarse sun sensors, redundant GPS receivers (Viceroy), and two TAMs (Three Axis Magnetometers).

- Attitude control error (3σ): ≤ 30 µrad

- Attitude knowledge error (3σ): ≤ 46 µrad

- Attitude knowledge stability (3σ): ≤ 0.12 µrad in 2.5 seconds; ≤ 1.45 µrad in 30 seconds

- Slew time: 180º any axis: ≤ 14 minutes, including settling; 15º roll: ≤ 4.5 minutes, including settling.

Key aspects of the satellite performance related to imager calibration and validation are pointing, stability and maneuverability. Pointing and stability affect geometric performance; maneuverability allows data acquisitions for calibration using the sun, moon and stars. For LDCM, an off nadir acquisition capability is included (up to 1 path off nadir) for imaging high priority targets (event monitoring capability).
Also, the spacecraft pointing capability will allow the calibration of the OLI using the sun (roughly weekly), the moon (monthly), stars (during commissioning) and the Earth (at 90° from normal orientation, a.k.a., side slither) quarterly. The solar calibration will be used for OLI absolute and relative calibration, the moon for trending the stability of the OLI response, the stars will be used for Line of Sight determination and the side slither will be an alternate OLI and relative gain determination methodology. 22) 23)

C&DH (Command & Data Handling) subsystem: The C&DH subsystem uses a standard cPCI backplane RAD750 CPU. The MIL-STD-1553B data bus is used for onboard ADCS, C&DH functions and instrument communications. The SSR (Solid State Recorder) provides a storage capacity of 4 Tbit @ BOL and 3.1 Tbit @ EOL.

The C&DH subsystem provides the mission data interfaces between instruments, the SSR, and the X-band transmitter. The C&DH subsystem consists of an IEM (Integrated Electronics Module), a PIE (Payload Interface Electronics), the SSR, and two OCXO (Oven Controlled Crystal Oscillators).


Figure 3: Photo of the EM SSR (Solid State Recorder), image credit: NASA


Figure 4: Block diagram of the C&DH subsystem (image credit: NASA, USGS, Ref. 87)

- The IEM subsystem provides the command and data handling function for the observatory, including mission data management between the PIE and SSR using FSW on the Rad750 processor. The IEM is block redundant with cross strapped interfaces for command and telemetry management, attitude control, SOH (State of Health) data and ancillary data processing, and for controlling image collection and file downlinks to the ground.

- The SSR subsystem provides for mission data and spacecraft SOH storage during all mission operations. The OCXO provides a stable, accurate time base for ADCS fine pointing.

- The C&DH accepts encrypted ground commands for immediate execution or for storage in the FSW file system using the relative time and absolute time command sequences (RTS, ATS respectfully). The commanding interface is connected to the uplink of each S-band transceiver, providing for cross-strapped redundancy to the C&DH. All commands are verified onboard prior to execution. Real-time commands are executed upon reception, while stored commands are placed in the FSW file system and executed under control of the FSW. Command counters and execution history are maintained by the C&DH FSW and reported in SOH telemetry.

- The IEM provides the command and housekeeping telemetry interfaces between the payload instruments and the ADCS components using a MIL-STD-1553B serial data bus and discrete control and monitoring interfaces. The C&DH provides the command and housekeeping interfaces between the CCU (Charge Control Unit), LCU (Load Control Unit) , and the PIE boxes.

- The PIE is the one of the key electrical system interfaces and mission data processing systems between the instruments, the spacecraft C&DH, SSR, and RF communications to the ground. The PIE contains the PIB (Payload Interface Boards ) for OLI (PIB-O) and TIRS (PIB-T).

Each PIB contains an assortment of specialized FPGAs (Field Programmable Gate Arrays) and ASICs, and each accepts instrument image data across the HSSDB for C&DH processing. A RS-485 communication bus collects SOH and ACS ancillary data for interleaving with the image data.


Figure 5: Block diagram of PIB (image credit: USGS, NASA)

- Data compression: Only the OLI data, sent through the PIB-O interface, implements lossless compression, by utilizing a pre-processor and entropy encoder in the USES ASIC. The compression can be enabled or bypassed on an image-by-image basis. When compression is enabled the first image line of each 1 GB file is uncompressed to provide a reference line to start that file. A reference line is generated every 1,024 lines (about every 4 seconds) to support real-time ground contacts to begin receiving data in the middle of a file and decompressing the image with the reception of a reference line.

- XIB (X-band Interface Board): The XIB is the C&DH interface between the PIE, SSR, and X-band transmitter, with the functional data path shown in Figure 6.

The XIB receives real-time data from the PIE PIB-O and PIB-T and receives stored data from the SSR via the 2 playback ports. The XIB sends mission data to the X-band transmitter via a parallel LVDS interface. The XIB receives a clock from the X-band transmitter to determine the data transfer rates between the XIB and the transmitter to maintain a 384 Mbit/s downlink. The XIB receives OLI realtime data from the PIB-O board, and TIRS real-time data from the PIB-T board across the backplane. The SSR data from the PIB-O and PIB-T interfaces are multiplexed and sent to the X-Band transmitter through parallel LVDS byte-wide interfaces.


Figure 6: X-band mission data flow (image credit: USGS, NASA)

- SSR (Solid Ste Recorder): The SSR is designed with radiation hard ASIC controllers, and up-screened commercial grade 4GB SDRAM (Synchronous Dynamic Random Access Memory) memory devices. Protection against on-orbit radiation induced errors is provided by a Reed-Solomon EDAC (Error Detection and Correction) algorithm. The SSR provides the primary means for storing all image, ancillary, and state of health data using a file management architecture. Manufactured in a single mechanical chassis, containing a total of 14 memory boards, the system provides fully redundant sides and interfaces to the spacecraft C&DH.

The spacecraft FSW (Flight Software) plays an integral role in the management of the file directory system for recording and file playback. FSW creates file attributes for identifier, size, priority, protection based upon instructions from the ground defining the length of imaging in the interval request, and its associated priority. FSW also maintains the file directory, and creates the ordered lists for autonomous playback based upon image priority. FSW automatically updates and maintains the spacecraft directory while recording or performing playback, and it periodically updates the SSR FSW directory when no recording is occurring to synchronize the two directories (Ref. 87).

TCS (Thermal Control Subsystem): The TCS uses standard Kapton etched-foil strip heaters. In general, a passive, cold-biased system is used for the spacecraft. Multi-layer insulation on spacecraft and payload as required. A deep space view is provided for the instrument radiators.

EPS (Electric Power Subsystem): The EPS consists of a single deployable solar array with single-axis articulation capability and with a stepping gimbal. Triple-junction solar cells are being used providing a power of 4300 W @ EOL. The NiH2 battery has a capacity of 125 Ah. Use of unregulated 22-36 V power bus.

The onboard propulsion subsystem provides a total velocity change of ΔV = 334 m/s using eight 22 N thrusters for insertion error correction, altitude adjustments, attitude recovery, EOL disposal, and other operational maintenance as necessary.

The spacecraft has a launch mass of 2780 kg (1512 kg dry mass). The mission design life is 5 years; the onboard consumable supply (386 kg of hydrazine) will last for 10 years of operations.

Spacecraft platform

SA-200HP (High Performance) bus

Spacecraft mass

Launch mass of 2780 kg; dry mass of 1512 kg

Spacecraft design life

5 years; the onboard consumable supply (386 kg of hydrazine) will last for 10 years of operations

EPS (Electric Power Subsystem)

- Power: 4.3 kW @ EOL (End of Life)
- Single deployable solar array with single-axis articulation capability
- Triple-junction solar cells
- NiH2 battery with 125 Ah capacity
- Unregulated 22 V - 36 V power bus
- Two power distribution boxes

ADCS (Attitude Determination &
Control Subsystem)

- Actuation: 6 reaction wheels and 3 torque rods
- Attitude is sensed with 3 precision star trackers, a redundant SIRU (Scalable Inertial Reference Unit),
12 coarse sun sensors, redundant GPS receivers (Viceroy), and 2 TAMs (Three Axis Magnetometers)
- Attitude control error (3σ): ≤ 30 µrad
- Attitude knowledge error (3σ): ≤ 29 µrad
- Attitude knowledge stability (3σ): ≤ 0.12 µrad in 2.5 seconds
- Attitude jitter: ≤ 0.28 µrad, 0.1-1.0 Hz
- Slew time, 180º pitch: ≤ 14 minutes, inclusive settling
- Slew time, 15º roll: ≤ 4.5 minutes, inclusive settling

C&DH (Command & Data Handling)

- Standard cPCI backplane RAD750 CPU
- MIL-STD-1553B data bus
- Solid state recorder provides a storage capacity of 4 TB @ BOL and 3.1 TB @ EOL

Propulsion subsystem

- Total velocity change of ΔV = 334 m/s using eight 22 N thrusters
- Hydrazine blow-down propulsion module

Table 1: Overview of spacecraft parameters


Figure 7: Two views of the LDCM spacecraft (without solar arrays) and major components (image credit: NASA, USGS)

RF communications: Earth coverage antennas are being used for all data links. The X-band downlink uses lossless compression and spectral filtering. The payload data rate is 440 Mbit/s. The X-band RF system consists of the X-band transmitter, TWTA (Travelling Wave Tube Amplifier), DSN (Deep Space Network) filter, and an ECA (Earth Coverage Antenna). The serial data output is set at 440.825 Mbit/s and is up-converted to 8200.5 MHz. The TWTA amplifies the signal such that the output of the DSN filter is 62 W. The DSN filter maintains the signal’s spectral compliance. An ECA provides nadir full simultaneous coverage, utilizing 120º half-power beamwidth, for all in view ground sites below the spacecraft's current position with no gimbal or actuation system. The system is designed to handle up to 35 separate ground contacts per day as forecasted by the DRC-16 (Design Reference Case-16).

The X-band transmitter is a single customized unit, including the LDPC FEC algorithms, the modulator, and up converter circuits. The transmitter uses a local TXCO (Thermally Controlled Crystal Oscillator) as a clock source for tight spectral quality and minimum data jitter. This clock is provided to the PIE XIB to clock mission data up to a 384Mbit/s data rate to the transmitter. The X-band transmitter includes an on-board synthesized clock operating at 441.625 Mbit/s coded data rate using the local 48 MHz clock as a reference. Using the on-board FIFO buffer, this architecture provides a continuous data flow through the transmitter (Ref. 87).

The S-band is used for all TT&C functions. The S-band uplink is encrypted providing data rates of 1, 32, and 64 kbit/s. The S-band downlink offers data rates of 2, 16, 32, RTSOH; 1 Mbit/s SSOH/RTSOH GN; 1 kbit/s RTSOH SN. Redundant pairs of S-band omni’s provide transmit/receive coverage in any orientation. The S-band is provided through a typical S-band transceiver, with TDRSS (Tracking and Data Relay Satellite System) capability for use during launch and early orbit and in case of spacecraft emergencies.

Onboard data transmission from an earth-coverage antenna:

• Real-time data received from PIE (Payload Interface Electronics) equipment

• Play-back data from SSR (Solid State Recorder)

• To three LGN (LDCM Ground Network) stations

- NOAA Interagency Agreement (IA) to use Gilmore Creek Station (GLC) near Fairbanks, AK

- Landsat Ground Station (LGS) at USGS/EROS near Sioux Falls, SD

- NASA contract with KSAT for Svalbard; options for operational use by USGS (provides ≥ 200 minutes of contact time)

• To International Cooperator ground stations (partnerships of existing stations currently supporting Landsat).


Figure 8: Photo of the EM X-band transponder (left) and AMT S-band transponder (right), image credit: NASA


Figure 9: Alternate view of the deployed LDCM spacecraft showing the calibration ports of the instruments TIRS and OLI (image credit: NASA/GSFC)


Figure 10: The LDCM spacecraft with both instruments onboard, OLI and TIRS (image credit: USGS) 24)

Launch: The LDCM mission was launched on February 11, 2013 from VAFB, CA. The launch provider was ULA (United Launch Alliance), a joint venture of Lockheed Martin and Boeing; use of the Atlas-V-401 the launch vehicle with a Centaur upper stage. 25) 26)

Note: Initially, the LDCM launch was set for July 2011. However, since this launch date was considered as too optimistic, NASA changed the launch date to the end of 2012. This new launch delay buys some time for an extra sensor with TIR (Thermal Infrared) imaging capabilities.

Orbit: Sun-synchronous near-circular orbit, altitude = 705 km, inclination = 98.2º, period = 99 minutes, repeat coverage = 16 days (233 orbits), the nominal LTDN (Local Time on Descending Node) equator crossing time is at 10:00 hours. The ground tracks will be maintained along heritage WRS-2 paths. At the end of the commissioning period, LDCM is required to be phased about half a period ahead of Landsat 7. 27)

Figure 11: Anatomy of Landsat 8. Have you ever wondered what all the parts of a satellite do? This video identifies a few of the main components onboard Landsat 8 and tells you about their role in flying the satellite and capturing images of the Earth's surface below (video credit: USGS, NASA) 28)

Figure 12: The Landsat Data Continuity Mission (LDCM), a collaboration between NASA and the USGS (U.S. Geological Survey), will provide moderate-resolution measurements of Earth's terrestrial and polar regions in the visible, near-infrared, short wave infrared, and thermal infrared. There are two instruments on the spacecraft, the Thermal InfraRed Sensor (TIRS) and the Operational Land Imager (OLI). LDCM will provide continuity with the nearly 40-year long Landsat land imaging data set, enabling people to study many aspects of our planet and to evaluate the dynamic changes caused by both natural processes and human practices (video credit: NASA, USGS) 29)

Note: As of February 2020, the previously single large Landsat-8 file has been split into four files, to make the file handling manageable for all parties concerned, in particular for the user community.

This article covers the Landsat-8 mission and its imagery in the period 2021, in addition to some of the mission milestones.

Landsat-8 imagery in the period 2020

LandSat-8 imagery in the period 2019

Landsat-8 imagery in the period 2018

Landsat-8 imagery in the period 2017 to June 2013

Mission status and some imagery of 2021

• April 14, 2021: Eruptions at La Soufrière volcano have propelled ash and gas high into the air over the Caribbean islands of Saint Vincent and Barbados. The eruption—the volcano’s first explosive event since 1979—prompted thousands of people to evacuate. 30)


Figure 13: Explosive activity has propelled ash and gas high into the air over the Caribbean islands of Saint Vincent and Barbados. The recent bout of explosive activity began on April 9, 2021. At about 10:30 a.m. local time that day, the Operational Land Imager (OLI) on Landsat-8 acquired this image of volcanic ash billowing from La Soufrière. The plume obscures the volcano below, a peak that stands 1178 meters (3,864 feet) above sea level on the northern side of Saint Vincent (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey and MODIS data from NASA EOSDIS LANCE and GIBS/Worldview. Story by Kathryn Hansen)

- According to Jean-Paul Vernier, an atmospheric scientist with NASA’s Earth Applied Sciences Disasters Program, activity was apparent months before the explosive eruptions. It started with an effusive eruption in which magma that reached the surface slowly built up a lava dome. In April, the dome “finally turned out a massive explosion without many precursor signs,” Vernier said. Explosive eruptions result from the rapid expansion of pressurized gasses trapped in the rock or magma; the pressure violently breaks rocks apart and produces a plume of rock, ash, and gas.


Figure 14: Winds carried much of the ash and gas east from Saint Vincent. On the afternoon of April 10, 2021, the MODIS instrument on NASA’s Aqua satellite acquired this image showing ash reaching Barbados, 190 km (120 miles) away. Clouds (white) are also abundant in this view (image credit: NASA Earth Observatory)

- These images show ash aloft in the atmosphere, but some of it fell back to the ground. According to news reports, ashfall has blanketed parts of Saint Vincent and Barbados. It also has threatened food and water supplies on Saint Vincent and has reduced visibility, which can complicate evacuation efforts. People displaced to the island’s southern side—away from the volcano and generally safer—still had to contend with falling ash and poor air quality.

- Scientists are investigating the extent and height reached of the ash and gas plume. They think some ash has risen all the way into the stratosphere, where strong winds can potentially carry it great distances. Other satellite instruments have detected sulfur dioxide reaching Cape Verde, an archipelago in the central Atlantic Ocean. Sulfur dioxide near ground level can irritate the human nose and throat; higher in the atmosphere it can make sulfuric acid aerosols and, in extreme cases, lead to a cooling effect.

- The NASA Disasters team is working with several science institutions and agencies to continue assessing the eruption and to make data available to emergency responders and aid groups. “Our program has been working with stakeholders in the region since the first signs of the eruption,” Vernier said.

• April 3, 2021: Located along the southwest coast of South Korea, Sinan County attracts people from many walks of life. Its world-renowned tidal flats host unique marine life as well a thriving salt production industry. Meanwhile, purple-painted islands draw tourists from around the country. 31)

- Sinan County includes more than 1,000 islands, about a quarter of all islands in the country. The majority are surrounded by shallow tidal flats that are alternately covered or exposed by the rise and fall of tides. Depending on the time of the year, the flats can be muddier, sandier, or a combination of both. Finer mud tends to build in the zones during the summer, then erodes in the winter. Monsoons and strong waves in the winter create sandier flats.


Figure 15: From expansive tidal flats to purple-painted towns, southwestern South Korea features unique ecosystems for salt production, wildlife, and tourists. The images show portions of Sinan County, or Shinan-gun, on October 15, 2020. The images were acquired by the OLI instrument on the Landsat-8 satellite (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- Reclaimed mudflats are also used for commercial salt production. The region’s fresh air, clean seawater, and abundant sunshine create prime conditions for making salt. Salt production begins by storing sea water in reservoirs and moving it to evaporation ponds (appearing as checkered fields) to naturally increase the water’s salinity with the help of the Sun and wind. On crystallization ponds, the saline water turns into salt crystals, which are stored in silos for two to three years to remove the bitter-tasting solution and improve the taste.

- Shinean sea salt contains low concentrations of sodium chloride, but relatively high amounts of moisture, calcium, potassium, magnesium, and sulfuric acid ions that help bring out the flavor in traditional Korean foods. Jeungdo Island (Figure 16), which contains the most extensive mudflat in South Korea, is home to the country’s largest sun-dried salt producer. The island also contains a salt museum and sea salt ice cream shop.


Figure 16: Detail 1. The tidal flats, or getbol in Korean, are highly productive ecosystems. The mineral-rich sediments are full of microorganisms that attract marine animals such as clams and mud octopuses. The flats serve also as an important stopover for many migratory birds (image credit: NASA Earth Observatory)


Figure 17: Detail 2. The image shows another unique aspect of the region: the brightly-colored Banwol and Bakji Islands. Nicknamed the "the purple islands," they are known for displays of purple paint on their buildings, roofs, phone booths, and bridge. There is even a restaurant that serves purple food. The purple complements the native bellflowers called campanula, which cover the landscape in lilac. The Korean government launched the purple initiative to improve tourism on the two islands, which collectively have a population of around 250 people. Since 2018, more than 490,000 people have visited Banwol and Bakji (image credit: NASA Earth Observatory)

• March 25, 2021: Persistent, heavy rain fell for several days in late summer in New South Wales, Australia, leading to the region’s worst flooding in six decades. The Australian Bureau of Meteorology reported that areas around Sydney and in the Hunter and Mid North Coast regions were drenched with 400 to 600 mm (16 to 24 inches) of rain across four days, with the most extreme totals approaching one meter. 32)

- Water levels rose to major flood levels along the Clarence, Gwydir, Mehi, Lower Hunter, Manning, and Colo rivers, among others. The Hawkesbury-Nepean River system around Sydney saw its highest crests since 1961. At least 40,000 people were evacuated and several died across New South Wales (NSW) state, while farmers suffered significant crop and livestock losses.

- Upstream from Sydney, the Warragamba Dam has been overflowing since March 20 and is expected to continue doing so for a week. The BBC reported: “Warragamba Dam discharged 500 gigalitres on Sydney—equivalent to the volume of Sydney Harbour.” The downstream Hawkesbury-Nepean valley has several choke points that cause river water to pile up and rise onto floodplains west of Sydney in what emergency management authorities refer to as a bathtub effect.


Figure 18: Persistent heavy rain raised rivers to levels not seen since 1961. On March 23, 2021, the Operational Land Imager (OLI) on Landsat-8 acquired a natural-color image of flooding in the Hawkesbury-Nepean River system along the western edge of Sydney [image credit: NASA Earth Observatory, images by Lauren Dauphin, using modified Copernicus Sentinel data (2021), processed by ESA and analyzed by the National Central University of Taiwan in collaboration with NASA-JPL and Caltech. Landsat data from the U.S. Geological Survey and topographic data from the Shuttle Radar Topography Mission (SRTM). Story by Michael Carlowicz]


Figure 19: The flood proxy maps (Figures 19 and 20) highlight areas of the Mid North Coast region that were likely to be flooded (indicated in blue) on March 20, 2021. The maps were derived from synthetic aperture radar (SAR) data acquired by the Copernicus Sentinel-1 satellites, operated by the European Space Agency (ESA). The maps were created by the National Central University of Taiwan in collaboration with the Advanced Rapid Imaging and Analysis (ARIA) team at the Jet Propulsion Laboratory and Caltech. The ARIA team is supported by NASA’s Earth Science Disasters Program (image credit: NASA Earth Observatory)


Figure 20: Radar signals can penetrate cloud cover, allowing researchers to observe landscapes that are obscured from other satellite sensors. The team created the maps by comparing radar observations collected before and after the rainfall. Specifically, the researchers look for changes in brightness: if a normally rough ground surface is replaced with a smooth water surface, the brightness of those pixels will change (image credit: NASA Earth Observatory)

- Many of the areas affected by floods in March 2021 were afflicted with extreme drought and wildfire in the summer of 2020. Burn-scarred landscapes often produce more runoff during extreme rain events because the heat from fires reduces the capacity of the soil to absorb and hold on to water. Furthermore, fire strips away plants and trees that could intercept raindrops before they reach the ground.

Figure 21: The animation shows rainfall rates and accumulation across eastern Australia from March 16-23, 2021. Those data are overlaid on shades of white and gray from NOAA infrared satellite observations of cloudiness. The rainfall data are remotely-sensed estimates that come from the Integrated Multi-Satellite Retrievals for GPM (IMERG), a product of the Global Precipitation Measurement (GPM) mission. Rainfall rates are marked in blue, while accumulation is represented in green. Due to averaging of the satellite data, local rainfall amounts may be significantly higher when measured from the ground (video credit: NASA)

- Preliminary estimates from NASA’s IMERG analysis indicate that more than 600 mm (24 inches) of rain fell just off the coast across the week, with accumulations in coastal areas exceeding 400 mm (16 inches). The region usually sees 1000 to 1500 mm (40 to 60 inches) of rainfall in a year.

- La Niña patterns in the tropical Pacific have brought more rain than usual to eastern and southeastern Australia this summer. That extra rain likely left the soils and waterways with less capacity for absorbing new rainfall in March.

• March 24, 2021: Few rivers carry as much sediment as the Huang He (Yellow River) in China. The name itself comes from the muddy color of the water—a consequence of the river’s upper and middle reaches flowing through a region in northwestern China with unusually fine and powdery soil called loess. 33)

- All of the silt in the water supercharges the river’s ability to build new land at its delta, the area where it dumps its sediment into the shallows of the Bo Hai Sea. In this pair of Landsat images, note how much the easternmost lobe of the delta changed shape between 1989 and 2020 as the river delivered new sediment to some parts of the delta and erosion ate away at older coastlines. (Read our Yellow River Delta World of Change story to see more imagery of the delta.)

- One of the most noticeable changes resulted from a diversion project that Chinese engineers completed in 1996, blocking the main channel and steering water and sediment to the northeast. The project’s purpose was to create new land in an area with offshore oil and gas to make the resource easier to extract. Before completion, new land formed along a rounded peninsula oriented to the southeast; afterward, the abandoned channel narrowed and new land began forming to the northeast, even as erosion ate away at parts of the older peninsula.

- Other features in this area have seen equally dramatic changes. Aquaculture and salt evaporation ponds—the green and blue rectangular features along the coasts—have proliferated. So has oil drilling infrastructure (small rectangular features) due to the rapid expansion of Shengli Oil Field, now China’s largest. Several smooth-edged sea walls and dykes have been built along the coast in an attempt to protect the new oil, aquaculture, and other infrastructure from encroaching tides.

- On the youngest land, different types of vegetation&mdash:notably the cordgrass Spartina alterniflora—have spread widely, creating dense new pockets of green in the 2020 image. The invasive cordgrass first reached the Yellow River Delta in the late-1980s, and began to spread rapidly in the intertidal zone in the early 2000s. While the grass does stabilize the shoreline, it has crowded out a local reed species (Phragmites australis) and an annual plant (Suaeda salsa), significantly reducing how much carbon the delta ecosystem stores and increasing methane emissions. By replacing S. salsa, the cordgrass has also made the area less habitable for certain rare birds, including red-crowned cranes and black-billed gulls.

- Like many deltas around the world, the Yellow River Delta faces growing pressure from the sea for several reasons. By 2020, many of the coastlines shown here had retreated inland by a few kilometers as the sea overwhelmed tidal mud flats and marshes. This is partly because the delta itself is sinking. Freshly deposited mud naturally settles and compresses over time.

- Human activity—particularly the pumping of groundwater for aquaculture—has accelerated the process. Though less influential, the process of pumping oil from below the surface and bringing in heavy equipment may have contributed to the subsidence as well. For much of the area shown in this image, scientists have reported subsidence rates of 20 millimeters (0.8 inches) per year. Layered onto both phenomena is global warming and sea level rise. Warming ocean water and the addition of fresh water to the oceans from melting ice sheets and glaciers is thought to contribute about 3 millimeters of sea level rise per year in this area.

- Finally, the Yellow River now carries only a tenth of the sediment that it did during the 1960s and about half of what it did in the 1980s. Several dams, erosion-control projects, and reforestation projects in upstream farming areas now trap much of the water and sediment that would otherwise reach the delta naturally.

- Efforts to flush sediment from clogged reservoirs and to scour sediment from the river bed led to a spurt of accelerated land formation in the delta between 2002-2014. However, the volume of sediment reaching the delta began dwindling in 2014 as coarser sediments coated and “armored” the river channel in key areas, preventing additional scouring. Since 2014, the delta has once again begun to lose more land each year than it gains.


Figure 22: Landsat-4 TM image of the Yellow River delta acquired on February 13, 1989 (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)


Figure 23: Landsat-8 OLI image of the Yellow River delta acquired on 24 October 2020. Changes in sediment load, vegetation, and the river’s course have brought stark changes to this dynamic river delta (image credit: NASA Earth Observatory)

• March 22, 2021: Come summer, Utahns will flock to the state’s lakes and reservoirs to boat, swim and picnic along the shore. And every week, if not every day, scientists like Kate Fickas of Utah State University in Logan will use satellite images and other data to monitor recreation sites to check for rapid growth of algae into a bloom, and make sure the water is safe for people and pets. 34)


Figure 24: Data from the Landsat-8 satellite can help resource managers identify potentially harmful algal blooms in water bodies like Utah Lake, seen here (image credit: NASA/USGS)

- From the vantage point of space, satellites, including the NASA and U.S. Geological Survey’s (USGS) Landsat 8, can help scientists identify lakes where a bloom has formed. It’s a complicated data analysis process, but one that researchers are automating to assist resource managers in identifying potential problems.

- “I grew up swimming in the Willamette River in Oregon, and diving in lakes over the summer,” said Fickas. “So it means a lot to me that I’m able to not only help develop algorithms for monitoring cyanobacteria blooms, which is interesting in itself, but to be able to take that next step and keep the public safe, and allow them to safely recreate and enjoy the water the way that I do.”

Figure 25: From space, satellites such as the NASA and USGS Landsat 8 can help scientists identify where an algal bloom has formed in lakes or rivers. It’s a complicated data analysis process, but one that researchers are automating so resource managers around the country can use the satellite data to identify potential problems (video credit: NASA's Goddard Space Flight Center)

- Blooms are made up of naturally occurring algae, phytoplankton, and cyanobacteria that explode in number under the right conditions: warm temperatures, lots of nutrients, and calm waters. Many water bodies in Utah meet those conditions, Fickas said, especially with warming temperatures due to climate change, as well as nutrient-rich runoff from agricultural fields and other sources.

- Satellites including Landsat-8 and ESA's (the European Space Agency) Sentinel-3 can detect when a lake changes color due to the mats of greenish organisms – allowing scientists like Fickas to tell water managers where to test to see if the waters are harmful or not. The two satellites have different strengths: Sentinel-3 collects data on individual lakes more frequently and measures wavelengths of light that are more indicative of phytoplankton, but Landsat-8 has a higher spatial resolution, so it can observe smaller lakes and identify specific problem areas within a larger lake.

- Landsat satellite-based detection of a 2017 bloom in Lake Utah helped save an estimated $370,000 in healthcare and related costs for the area, according to a 2020 study published in the journal GeoHealth. The case study builds on a larger multi-agency project to track algal blooms.

- When Landsat-8 measures a bloom, it detects chlorophyll-a, a green pigment found in phytoplankton, said Nima Pahlevan, a scientist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. Water is a tricky thing for Landsat satellites to measure since it’s dark compared to brightly reflective trees, buildings, and other landscapes. But Landsat-8 is more sensitive than its predecessors, able to distinguish between many more intensities of light, essentially detecting more shades of green.

- “Any increased level of chlorophyll-a over the norm could be alarming, and that’s what we’re looking for from the satellite data,” he said.

- Nima and his team have developed an algorithm to take the data collected by Landsat-8 over lakes, analyze it, and create a product that tells local water or recreation managers where that increase in chlorophyll-a might be. To get from the raw data to the usable product involves multiple steps, including accounting for atmospheric particles and gases that might otherwise skew the results.

- “Not everyone has access to the computing power to be able to process satellite images, or the time or expertise,” Pahlevan said. “Having these products readily available to the community will significantly increase the number of people who can use the satellite data products.”

- These Landsat aquatic reflectance products are still provisional, he stressed, but they are newly available from the USGS, which provides all Landsat data as well as other data products for free.

- While this data product could help decision-makers spot potential problem areas for boaters and swimmers, other Landsat data products measure things like forested areas, burned areas, and snow cover.

- “Data products convert the complex observations made by the instrument to the kind of information people need,” said Jeff Masek of NASA Goddard, project scientist for the upcoming Landsat 9 satellite. “They allow allow people who aren’t as familiar with remote sensing complexities to make use of the data.”

- Landsat-9, which is scheduled to launch in September 2021, has all the attributes of Landsat-8 that allow it to quantify chlorophyll-a, and will have added capabilities to distinguish between even more intensities of light reflecting from water bodies and other surfaces. Scientists are looking forward to future satellites as well. Landsat Next, the satellite following Landsat-9, could have additional capabilities that allow it to better detect the specific organisms that cause harmful blooms, and not just benign phytoplankton growth that doesn’t release any concerning toxins.

- “We’re seeing more water quality issues around the world,” Masek said, “which is why we’re so interested in the capability to monitor them.”

• March 17, 2021: Humans have inhabited Egypt’s Sinai Peninsula since prehistoric times. As a land bridge between Asia and Africa, the Sinai has provided a path to countless travelers, conquerors, and settlers over the centuries. The southwestern region still has traces of some of the peninsula’s earliest inhabitants, from fragments of an ancient alphabet to remnants of turquoise mines. 35)


Figure 26: The mountains in the southwestern Sinai Peninsula hold ancient relics of temples and turquoise mining. This detail image shows the southwest Sinai on March 11, 2021, as captured by the OLI instrument on Landsat-8. Mountains dominate the region, making it difficult terrain to traverse (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Caption by Kasha Patel)

- The Sinai Peninsula has a dry desert climate, yet is also one of the colder provinces of Egypt due to its topography and relatively high elevation. Not many animals live in the area, but species of ibex, gazelles, wildcats, jackals, and sand foxes have been spotted there. Shrubs grow on steep slopes in the south, while succulents and salt-tolerant plants are found on coastal plains. The mountains of the Sinai have long been a destination for human hermits and mystics. Today, people make a living on the peninsula through the petroleum industry, agriculture, mining, fishing, and tourism.


Figure 27: The wider image shows the intersection of the mountains and El Ramla, the largest sand desert in the southern part of the Sinai Peninsula (image credit: NASA Earth Observatory)

- Archeologists estimate the earliest inhabitants in the southwestern Sinai were miners who excavated copper and turquoise deposits around 3,500 B.C.E. Two popular mining locations were Serabit el-Khadim and Wadi Maghareh (also known as the “Valley of Caves”). In many cases, the miners were slaves captured by Egyptians in war. They mined turquoise by hollowing out portions of the mountains, and then transported the mineral to the Egyptian mainland. The turquoise was used for jewelry and color pigments. Ancient Egyptians called the Sinai Mafkat, meaning “Country of Turquoise.”

- Serabit el-Khadim is well-known today for its ancient ruins. Excavators have found scattered relics of a temple, including a red sandstone sphinx. Dedicated to the goddess Hathor, the temple is one of the few known monuments to a pharaoh in the Sinai.

- The temple ruins also contain inscriptions believed to be precursors to an alphabet. The scripts were hieroglyphic signs—symbols were used to represent sounds. For example, linguists determined an inscription on the sphinx read “mahbalt,” meaning “beloved of the Lady.” An ox-head character is thought to be a forerunner of the letter a in the Latin alphabet. The script may also have been used to write the names of miners and keep track of their labors. There are also multiple engravings near the temple, including drawings of ships carrying turquoise.

• March 9, 2021: Though it covers just 1 percent of Earth’s land surfaces, Indonesia’s rainforest is believed to shelter 10 percent of the world’s known plant species, 12 percent of mammal species, and 17 percent of bird species. Spread across 18,000 islands, it covers an area large enough to make it the world’s third-largest rainforest, trailing only those in the Amazon and Congo basins. 36)


Figure 28: The data used in this earlier image was acquired by the Thematic Mapper (TM) on Landsat 5 in 2002 (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey and forest loss data from the University of Maryland. Story by Adam Voiland)

- While satellite data indicate that Indonesia has had high rates of forest loss in recent decades, the situation seems to be changing. Deforestation declined significantly between 2017-2019, according to data from Global Forest Watch. The forest change data used in the analysis was collected by Landsat satellites and processed by a team from the University of Maryland.

- But even as deforestation slows on major Indonesian islands such as Sumatra and Kalimantan, there are signs of a shift to other areas. One of those areas is Papua (also called Western New Guinea). Papua’s rugged terrain and scarcity of transportation infrastructure has led to less development and economic growth than in other parts of Indonesia. But in some parts of the island, there has been noticeable new activity in the past decade.


Figure 29: While the region has seen less deforestation than other parts of Indonesia, large-scale clearing is still evident. The image shows forest clearing along the Digul River near Banamepe, an area that was cleared between 2011 and 2016.This image was acquired by the Operational Land Imager (OLI) on Landsat-8 in 2020 (image credit: NASA Earth Observatory)


Figure 30: This map, based on forest change data from the University of Maryland, shows part of southern Papua where lowland rainforest and swamp forest have been cleared to establish several large plantations. While large-scale deforestation has been happening in this area for about two decades, several particularly large plots were cleared in the past few years, including some near the river town Tanahmerah.

- The smaller, more scattered clearings along rivers are likely associated with selective logging, natural shifts in water courses, and small-scale clearing by subsistence farmers, explained remote sensing scientist David Gaveau, the author of a new study about deforestation trends in Papua. In the lower third of the map, an area where forests transition into the Trans-Fly savanna and grasslands, some of the changes are likely associated with seasonal fires.

- “The slowdown in Sumatra and Kalimantan is due, at least in part, to the exhaustion of available suitable land for plantation agriculture and increasing land prices on these islands,” explained Kemen Austin, an analyst with the non-profit research organization RTI International and the author of a 2019 study about the drivers of deforestation in Indonesia. “Papua is seen as the next frontier, and recent investments in infrastructure have made plantation agriculture in the region more economically compelling.”

- According to Gaveau’s analysis of two decades of Landsat data, nearly 750,000 hectares of forest were cleared in Papua between 2001-2019—about 2 percent of the island’s forests. Of that total, the analysis found that about 28 percent was cleared for industrial plantations (oil palm and pulpwood), 23 percent for shifting cultivation, 16 percent for selective logging, 11 percent for rivers and lakes expanding or changing course, 15 percent for urban expansion and roads, 5 percent for fires, and 2 percent for mining. (Shifting cultivation is a type of farming where fields are only used temporarily and then left to regrow naturally for a number of years before being cleared again.)

- Biological surveys have been rare on the relatively undeveloped New Guinea, so the island’s immense biodiversity remains only partly catalogued and understood. Since the island was once connected to Australia, it is home to unusual marsupials, such as tree kangaroos and forest wallabies. Among the island’s more notable animals are two species of egg-laying mammals (monotremes) called echidna.

• March 8, 2021: Since late January 2021, blue-green algae have spread across Lake Burrinjuck in New South Wales, Australia. Authorities issued warnings to stay out of the lake, which usually attracts many people for waterskiing and fishing around this time of the year. 37)

- Blue-green algae, also known as cyanobacteria, typically appear as greenish clumps or scum on the surface of the water and have a strong musty odor. They occur naturally in modest numbers but can reproduce quickly under favorable circumstances—namely sufficient sunlight, stagnant water, and high amounts of dissolved nutrients, such as fertilizer runoff.

- Blue-green algae blooms can be fatal for pets and can cause stomach problems, rashes, and even vomiting for humans, if ingested. They could also harm the fish population at the lake, which is known for its golden perch, Murray cod, rainbow trout, and more. When the algae die, they sink to the bottom of the lake, where they are decomposed by bacteria. If the concentrations of algae and bacteria are high enough, the process can deplete oxygen concentrations in the water, causing fish to suffocate.

- Based on algal samples, the state-owned water supplier and river operator WaterNSW issued alerts in late January and February to stay out of the water and to stop recreational activities in the lake. As of March 2, it reported lower concentrations of algae but still advised people not to drink untreated lake water and to exercise caution if partaking in water activities.

- Longtime local residents told The Canberra Times that the algal outbreaks were the worst they have seen in more than a decade. According to WaterNSW, the blooms were somewhat unusual since the lake is located in a cooler part of the state and the Burrinjuck Dam recently received a large inflow of water. A spokesperson for WaterNSW said, however, the inflows may have brought in nutrients from other catchments.


Figure 31: On February 10, 2021, the Operational Land Imager (OLI) on Landsat-8 captured imagery of algae blooms in Lake Burrinjuck and the Murrumbidgee River (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

• March 3, 2021: Antarctica’s Brunt Ice Shelf finally calved a large iceberg in February 2021, two years after rifts opened rapidly across the ice and raised concerns about the shelf’s stability. 38)


Figure 32: The break was first detected by GPS equipment on February 26, 2021, and then confirmed the next day with radar images from the European Space Agency’s Sentinel-1A satellite. On March 1, clouds were sparse enough for the Operational Land Imager (OLI) on Landsat-8 to acquire this natural-color image of the new iceberg [image credit: NASA Earth Observatory image by Joshua Stevens, using Landsat data from the U.S. Geological Survey and data © OpenStreetMap contributors via CC BY-SA 2.0. Story by Kathryn Hansen with information from Christopher Shuman (NASA GSFC/UMBC JCET)]

- Named A-74, the berg spans about 1270 km2 (490 square miles), or about twice the size of Chicago. That’s a large piece of ice for the Brunt Ice Shelf, but Antarctica is known for churning out some enormous bergs. For comparison, Iceberg A-68A was almost five times that size when it calved from the Larsen C Ice Shelf in 2017.

- A-74 broke from the ice shelf northeast of the McDonald Ice Rumples—an area where the flow of ice is impeded by an underwater formation that causes pressure waves, crevasses, and rifts to form at the surface. The rift that spawned the new berg appeared near the rumples in satellite images in September 2019, and it advanced across the ice shelf with remarkable speed during the austral summer of 2020-2021.

- “I would not have thought that this rift could go zipping across the northeast side of the Brunt Ice Shelf and cause a significant calving—all in a tiny fraction of the time it has taken Chasm 1 to extend toward the ice rumples from the south,” said Christopher Shuman, a University of Maryland, Baltimore County, glaciologist based at NASA’s Goddard Space Flight Center.

- Chasm 1 is a separate rift located south of the ice rumples and the Halloween Crack. After decades of growth and then a rapid acceleration in 2019, that rift appeared poised to spawn its own iceberg, prompting safety concerns for researchers “upstream” at the British Antarctic Survey’s Halley VI Research Station. This section of the shelf is still holding on, but when it eventually breaks the berg will likely measure about 1700 km2 (660 square miles).

- Scientists are waiting to see how the complex structure responds to the recent calving. “The Halloween Crack may or may not be the first to respond,” Shuman said. “We’ll be closely watching that pinning point for changes to the larger Brunt Ice Shelf remnant.”

- It also remains to be seen what will become of the new iceberg. Most likely, it will eventually get caught up in the Weddell Gyre—similar to the fate of A-68. But first it needs to be pushed offshore, and to date it does not appear to have moved very far.

• February 25, 2021: Sheer, glacier-covered ridges separated by gorges soar over the Chamoli district in northern India. On the morning of February 7, 2021, this spectacular terrain in Uttarakhand turned deadly when a torrent of rock, ice, sediment, and water surged through the Rishiganga River valley past multiple villages and slammed into two hydropower stations. 39)


Figure 33: On February 21, 2021, the Operational Land Imager (OLI) on Landsat-8 captured a view of the landscape in the wake of the event. In the image, natural-color Landsat-8 data were overlaid on a digital elevation model from the Shuttle Radar Topography Mission (SRTM) to depict the rugged topography [image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey and topographic data from the Shuttle Radar Topography Mission (SRTM). Story by Adam Voiland, with information from Dan Shugar (University of Calgary) and Christopher Shuman (NASA GSFC/UMBC JCET)]

- The scale of the damage in the Himalayan district was devastating. Hundreds of people were swept away by the chaotic rush of water and debris. Dozens of people, many of them workers at the power plants, lost their lives; others ended up trapped in tunnels, prompting dramatic rescue attempts. Numerous homes, bridges, and roads were ruined.


Figure 34: The torrent of debris from a mountain in the Himalaya devastated remote valleys in Uttarakhand. The image pair above shows a closeup of the same area before and after the debris flow. Note the dark scar near the origin of the landslide and the trail of dust and debris that blanketed the valley walls downstream (image credit: NASA Earth Observatory)

- Initially, there was some confusion about what caused the catastrophe, but a group of remote sensing scientists have mined satellite data for clues to fill in the sequence of events.

- Months before the landslide, satellite images showed a crack opening on an ice-covered flank of Ronti, a 6,029-meter (19,780-foot) mountain peak. On February 7, 2021, a huge chunk of a steep slope broke off from the peak, bringing down part of a hanging glacier perched on the ridge. After freefalling for roughly two kilometers, the rock and ice shattered as it slammed into the ground, producing an enormous landslide and dust cloud. As the accelerating rock and ice raced through Ronti Gad and then Rishiganga River valley, it picked up glacial sediments and melted snow. All the materials mixed into a fast-moving slurry that overwhelmed the river and churned wildly as it rushed through the river valley.

- What triggered the rock and hanging glacier to fall in Uttarakhand remains an open question. University of Calgary geomorphologist Dan Shugar is among a group of scientists trying to find an answer to that and other questions about the disaster. As part of the effort, they are analyzing several types of meteorological, geologic, and modeling data to supplement and contextualize the satellite imagery. They hope to determine what role weather conditions, the tectonic environment, and shifting climate conditions might have played in priming the rock and ice for collapse.

- “Unfortunately, there were no weather stations that we know of that were nearby, but we are looking at things like whether cycles of ongoing freezing and thawing may have weakened the rock,” said Shugar. “Climate change may have even helped destabilize the rock face through increased water infiltration over a period of years and by thawing permafrost. For now, we can hypothesize about these possibilities, but careful work is required to understand exactly what happened.”

• February 23, 2021: Earth science satellites are generally used to observe certain features of the planet—landforms, atmospheric chemistry, ocean patterns. But at the same time, they periodically show us things that few people have seen or even looked for. 40)

- In February 2020, our team noticed a twitter message with a peculiar and beautiful image from Russia near 66 degrees north latitude. It turned into a scientific detective story and an unresolved case.


Figure 35: In this OLI image on Landsat-8, acquired on 15 September 2016, stripe patterns twist and turn around the hills of the northern Central Siberian Plateau. On steeper hills, the stripes form tight loops that spiral from the top of the hill to the bottom. As they descend toward the riverbanks, they start to fade. Eventually, the stripes disappear at lower elevations and at latitudes. There are several possible causes for the distinctive striping pattern, and the answers vary by the season and by the expertise of the researcher (image credit: NASA Earth Observatory, images by Joshua Stevens, using Landsat data from the U.S. Geological Survey and topographic information from the ArcticDEM Project at the Polar Geospatial Center, University of Minnesota. Story by Andi Brinn Thomas, with Mike Carlowicz)


Figure 36: OLI image on Landsat-8, acquired on 29 October 2020. Researchers are puzzling over a distinctive striping pattern in the Central Siberian Plateau (image credit: NASA Earth Observatory)

- There are several possible causes for the distinctive striping pattern, and the answers vary by the season and by the expertise of the researcher.

- This portion of the Central Siberian Plateau lies within the Arctic Circle, where air temperatures remain below freezing for most of the year. Much of the landscape is covered in permafrost that can stretch tens to hundreds of meters below the surface. There are different levels of intensity, but this area generally has permafrost coverage for 90 percent of the year.

- The land does occasionally thaw, and cycles of freezing and thawing are known to create polygon, circle, and stripe patterns on the surface (referred to as “patterned ground”). In the case of the images above, the stripes could be elongated circles stretched out on the slopes by such thawing cycles. Yet studies have shown that this type of striping usually occurs at a much smaller scale and tends to be oriented downslope.


Figure 37: OLI images on Landsat-8, acquired in the period 15 September 2016 and 29 October 2020 (image credit: NASA Earth Observatory)

- To geomorphologists, the nature of the soil offers another explanation for the stripes. In regions this cold, soils can turn into Gelisols—soils with permafrost in their top two meters and often with darker and lighter layers distinguished by more organic matter or more mineral and sediment content. As the ground freezes and thaws, the layers break up and mix vertically in a process called cryoturbation. The persistent freezing and thawing action through the seasons can cause layers to align in a striping pattern. Different tundra vegetation—lichens, low shrubs, and moss—might grow preferentially on these Gelisol layers, accentuating the stripes we see from above. But this hypothesis has not been proven at large scales.


Figure 38: Several rivers cut across the plateau, including the Markha, and as the stripe pattern moves closer to the river, it starts to fade. This could be a result of sediment buildup along the riverbanks from millions of years of erosion (image credit: NASA Earth Observatory)

- From a geologist’s perspective, the different stripes appear similar to sedimentary rock layers. Thomas Crafford of the U.S. Geological Survey called the pattern “layer cake geology,” where sedimentary rock layers have been exposed and dissected by erosion. As snowmelt or rain travels downhill, pieces of sedimentary rock are chipped away and sent down to the ravines below. Such erosion could cause a step-like pattern that appears as stripes from space similar to a slice of layer cake. This pattern is also referred to as “cliff and bench topography.”

- In the winter Landsat image of Figure 36, snow causes the striping pattern to stand out more than in other seasons. The benches would be the lighter stripes (covered in snow) and the cliffs would be darker stripes. The Arctic digital elevation map above, based on data from the ArcticDEM Project, gives a clearer perspective on the possible cliff and bench features.

- “It looks like small canyons, maybe like the Badlands of South Dakota. The horizontal striping appears to be different layers of sedimentary rock,” said Walt Meier, an ice specialist at the U.S. National Snow and Ice Data Center. “The shape of the erosion pattern looks a bit different than standard sedimentary erosion, but my guess is that is due to the permafrost. The rivers are eroding through frozen ground. There could also be some effect from frost heaves affecting the topography.”

- Louise Farquharson, an Arctic geologist at the University of Alaska-Fairbanks, pointed to a region in northern Alaska with a very similar stripe pattern that could be formed by a similar process.

• February 15, 2021: For much of the year, an efflorescent salt crust makes Lake Lefroy stand out as a bright, white spot in satellite images. But after heavy rains, the ephemeral lake in Western Australia takes on a different look. 41)


Figure 39: When the OLI instrument on Landsat-8 acquired this natural-color image on February 9, 2021, water had pooled in the playa’s lowest points. The rain fell as part of a tropical low that soaked the Eastern Goldfields region in early February. The water was discolored by some combination of suspended sediments from the region’s red soils, light reflecting off the rust-colored lake bed, or bacterial activity in the salty water (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)

- The smaller pools of green water in the center of the lake are areas where a mine discharges groundwater, a process called mine dewatering. The mine, built along a causeway that bisects the lake, taps into rich deposits of gold and nickel. Mining pits, roads, tailing ponds, and other mining infrastructure are visible along the causeway.


Figure 40: Spectacular patterns emerged as stormwater pooled on the salt crust of this ephemeral lake in Western Australia (image credit: NASA Earth Observatory)

- Large volumes of water do not persist for long in Lake Lefroy because the region’s hot, dry climate encourages evaporation. While water pooled in early February in a pattern that resembles a tropical fish, it’s unlikely the pattern will last. Lake Lefroy is frequently reshaped by changes in the prevailing winds that transport water back and forth between different parts of the playa. Nor are fish often found in these waters. Aside from certain flies, small crustaceans, phytoplankton, and algae, not much thrives in the hypersaline and impermanent waters.

• February 12, 2021: Beneath Earth’s crust lies 2,900 km (1,800 miles) of viscous mineral and rock known as the mantle. Famous and fanciful literature aside, no human is likely to visit the mantle or deep interior of Earth. But at Gros Morne National Park, people can step on fragments of the mantle without having to dig an inch. 42)

- Gros Morne provides some of the world’s best exhibits of the process of plate tectonics. The park contains a portion of the Long Range Mountains, a subrange of the Canadian Appalachians that dates back to around 1.2 billion years ago, when present-day North America collided with another continent. Those mountains have since eroded and left behind the gneiss and granite peaks of the Long Range. The park contains some of the tallest peaks of the Long Range mountains, including Big Level and Gros Morne Mountain (French for “great somber”).

- The Tablelands, located on the south end of the park, are considered one of its most striking features. The flat-topped, rust-colored land is rich with peridotite rock from the upper part of Earth’s mantle. The rock was thrust towards the surface around 500 million years ago through a process known as subduction. When two plates on Earth’s crust collide, one is often pushed back (subducted) toward the mantle. Standing out amid the lush green park, the yellowish-red Tablelands played a crucial role in confirming the theory of plate tectonics.

- The Canadian Space Agency has also studied the area to aid in the search for life beyond Earth. Scientists study how microscopic life forms can survive in the iron-rich Tablelands to better understand how they might survive on the extreme environment on Mars.

- Gros Morne National Park also features some recent geologic history at the Western Brook Pond. The freshwater fjord was carved by advancing glaciers tens of thousands of years ago during the most recent ice age. After the glaciers melted and receded, the land rebounded and cut off the outlet from the sea. Saltwater was slowly and naturally flushed from the 30 km (20-mile) long pond. Today, the fjord is surrounded by steep rock walls up to 600 meters (2,000 feet) high and contains nearly pure fresh water. The setting is a favorite for photographers.

- Today, the park is protected by the Canada National Parks Act. One of the biggest natural threats to the park is a large moose population, which is five to 20 times higher here than in other parts of Canada. Introduced into the area about 100 years ago, the hungry population has eaten through large portions of the boreal forest and hindered regrowth.


Figure 41: A geologist’s dream, Gros Morne National Park is one of the few places where you can set foot on the Earth's mantle without digging an inch. On October 3, 2017, the OLI instrument on Landsat-8 acquired natural-color imagery of Gros Morne National Park. The UNESCO World Heritage site covers 1,800 km2 (690 square miles) in the Great Northern Peninsula of western Newfoundland (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)


Figure 42: A detailed view of the Tablelands, in the southern portion of the Gros Morne National Park (image credit: NASA Earth Observatory)

• February 9, 2021: Snow is not as rare as you might think in the Hawaiian Islands. But it never stops being beautiful. 43)

- Starting with a moderate storm on January 18, 2021, snow has fallen three times on the highlands of Hawai'i in the past three weeks. The snow cover has persisted on Mauna Kea and Mauna Loa—the two tallest volcanoes in the island chain—since January 25. Some snow also briefly crowned Haleakalā volcano (elevation 10,000 feet/3000 meters) on the island of Maui.


Figure 43: Three storms in three weeks have left abundant snow atop Hawaii’s tallest volcanic mountains. On February 6, 2021, the OLI instrument on Landsat-8 acquired natural-color images of the “Big Island” of Hawai'i with abundant snow on its two tallest peaks. Nearly every year, Mauna Kea and Mauna Loa (elevation above 13,600 feet/4200 meters) receive at least a dusting that lasts a few days. Sometimes, like this year, it is more like a winter blanket of snow (image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey and data from the National Snow and Ice Data Center. Story by Michael Carlowicz)


Figure 44: The bar chart below shows the Normalized Difference Snow Index (NDSI) for Hawai'i as observed by NASA’s Terra satellite. NDSI incorporates a blend of visible light and shortwave infrared to assess the amount of snow within a given geographic area. The chart shows the combined NDSI for Mauna Loa (teal) and Mauna Kea (blue) for the first week of February in each year from 2001 to 2021. The combined weekly NDSI in 2021 for the two volcanoes is the highest since 2014 and second-highest in the record (image credit: NASA Earth Observatory)

- According to news and social media accounts, Hawaiians have found their way up the volcanic mountains with snowboards and boogie boards to sled through the fluffy white blanket. Others have filled their pickup truck beds to bring snow down to friends. Hawaiian weather blogger Weatherboy posted several photos from the scene.

- Snowfall in Hawai'i is often associated with a weather phenomenon referred to as a Kona low. Winds that typically blow out of the northeast shift and blow from the southwest. The winds from the leeward or “Kona” side draw moisture from the tropical Pacific, turning it from rain to snow as the air rises up into the high elevations.

- With the recent snowfall in Hawai'i, Florida is now the only state that has not yet seen snow this winter, according to The Weather Channel.

• February 3, 2021: In late January 2021, Tropical Cyclone Eloise caused widespread damage and heavy flooding in central Mozambique. The storm displaced more than 16,000 people, damaged around 17,000 houses, and killed more than a dozen people across a few countries in southeast Africa. 44)


Figure 45: These images show flooding on January 30, 2021, seven days after Eloise made landfall near the coastal city of Beira. The images from December 2019 are provided to compare the area under non-flooded conditions in the same season. The false-color images, acquired by the Operational Land Imager (OLI) on Landsat-8, use a combination of visible and infrared light (bands 7-5-3) to help differentiate flood water (dark blue), bare land (brown), and vegetation (bright green), image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel.

- After crossing northern Madagascar and before making landfall on mainland Africa, Eloise slightly strengthened due to warm waters in the Mozambique Channel. Stations in Beira recorded 25 cm (10 inches) of rain in 24 hours. Several rivers burst their banks, and roads became impassable. Tens of thousands of hectares of farmland were submerged in brown water, which could affect harvest this April. The storm, which brought winds up to 160 kilometers (100 miles) per hour, also blew over trees, power lines, and signs.

- Most of the areas hit by Eloise are still recovering from cyclones Idai and Kenneth in 2019, which claimed hundreds of lives. When Eloise hit, some villages were already flooded. In Dec. 2020, Beira and other surrounding areas endured heavy rains and flooding from severe weather.


Figure 46: Landsat-8 image of Mozambique on 27 December 2019 (image credit: NASA Earth Observatory)


Figure 47: Landsat-8 image of Mozambique on 30 January 2021 (image credit: NASA Earth Observatory)

- After making landfall in Mozambique, Eloise continued across southern Africa, though in a weakened state. The storm caused damage and flooding to South Africa, Eswatini, and Zimbabwe.

• January 30, 2021: Gold has been found on every continent except Antarctica, but the lustrous yellow metal is not exactly ubiquitous. The element (Au on the periodic table) is actually quite rare, accounting for just one out of every billion atoms in Earth’s crust. But in places such as the Central Aldan ore district in the Russian Far East—where concentrations of the precious metal have been discovered — mining operations are large enough to be seen from space. 45)


Figure 48: On September 11, 2019, the OLI instrument on Landsat-8 acquired this natural-color image showing part of the ore district in the Republic of Sakha (Yakutia). The image is centered about 25 kilometers (15 miles) northwest of the gold-mining town of Aldan, and about 450 kilometers southwest of the regional capital city, Yakutsk (image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)

- Central Aldan is one of Russia’s largest gold ore districts, with the mineral occurring in numerous deposits, or “lodes,” in the fractured rock. One of the largest lodes lies in the Kuranakh deposit, a shallow, ribbon-like orebody (up to 50 meters thick and 25 kilometers long) sandwiched between Cambrian limestone below and Jurassic sandstone above. Mining sites developed to to extract this gold are visible in the detailed images of Figures 49 and 50).


Figure 49: In places where concentrations of the precious metal have been discovered, mining operations are large enough to be seen from space (image credit: NASA Earth Observatory)

- The Kuranakh gold deposit was discovered in 1947, and a moderate amount of gold was extracted by 1955. Ten years later, large-scale open-pit mining began and continues today. Open-cut, drilling, and blasting techniques are now used to access the ore, which is processed at an onsite mill. In 2019, the Kuranakh mine produced 224,700 ounces of refined gold.

- Not all of the region’s gold shows up as lode deposits. In areas where a lode has been eroded, pieces of gold can become concentrated by rivers and streams into placer deposits.

- To excavate the placer, bucket-lined dredges scoop up material in the front and dump the tailings behind in curved piles. The accumulation of arc-shaped piles forms the long, maze like-pattern, which is visible in the image above. From April to December in the 2019 mining season, three dredges extracted 18,600 ounces of gold from the Bolshoy Kuranakh placer deposit.


Figure 50: This detail image, centered west of the town of Nizhny Kuranakh, shows the excavation site of buried placer along a tributary of the Aldan River (image credit: NASA Earth Observatory)

• January 20, 2021: Two years after the Brunt Ice Shelf seemed poised to produce a berg twice the size of New York City, the ice is still hanging on. But the calving of one, maybe two, large icebergs is inevitable. The question is: when? Ice scientists are watching to see if a rapidly accelerating crack will cause the shelf to rip apart before the sunlit summer season ends. 46)


Figure 51: The OLI instrument on Landsat-8 acquired this image of the Brunt Ice Shelf on January 12, 2021. The ice flows away from the Antarctic mainland and floats on the eastern Weddell Sea. The main shelf area has long been home to the British Antarctic Survey’s Halley Research Station, from which scientists study Earth, atmospheric, and space weather processes (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)

- The breaking, or “calving,” of icebergs from ice shelves is part of a natural, cyclical process of growth and decay at the limits of Earth’s ice sheets. As glacial ice flows from land and spreads out over the sea, shelf areas farthest from shore grow thinner. These areas are stretched thin, and can be melted from above or below, making them more prone to forming rifts and eventually breaking away. The Brunt Ice Shelf appears to be in a period of instability, with cracks spreading across its surface.

- The major rifts are visible in the wide view of Figure 51. In late October 2016, the “Halloween crack” appeared and rapidly extended eastward. In early 2019, Chasm 1 extended northward as fast as 4 km per year. Now, a new crack is zippering across the shelf north of the Halloween crack, far faster than the fissure to its south.

- “It is impossible to know exactly what caused this new rift to extend so quickly,” said Christopher Shuman, a University of Maryland, Baltimore County, glaciologist based at NASA’s Goddard Space Flight Center. “It’s likely that fracture dynamics near the McDonald Ice Rumples played a role, as they did in the quick propagation of the ‘Halloween Crack’ in 2016. The unusual mix of ice blocks and mélange in this part of the Brunt Ice Shelf ‘system’ is another factor.”

- The rumples are the result of ice that flows over an underwater formation, where the bedrock rises high enough to reach into the underside of the floating ice shelf. This rocky formation impedes the flow of ice and causes pressure waves, crevasses, and rifts to form at the surface.

- All of these cracks, combined with a recent speed up at the leading edge of the ice shelf (detected by ESA’s Sentinel-1), point to an instability that is likely to spawn a new iceberg or two. The exact timing is uncertain, but until the break occurs and the shelf has been reformed, Halley Research Station is being kept minimally staffed for safety reasons. In 2016-2017, the Halley VI station was relocated to a safer location (Halley VIa) upstream of the then-growing Chasm 1.

- “I think we are going to see big changes here,” Shuman said. And with more than two months left of sunlight, changes should be visible in natural-color satellite images for a while longer before the onset of winter darkness.


Figure 52: The detailed view shows the new rift growing away from an area known as the McDonald Ice Rumples. The rift shows up in satellite images as early as September 2019, when it had grown just over 2 kilometers longer during the austral winter. But the biggest growth just occurred recently. Between November 18 and December 22, 2020, the rift grew in length by about 20 kilometers. Then it jogged toward the north and grew an additional 8 kilometers by January 12, 2021 (image credit: NASA Earth Observatory)

• January 19, 2021: Smooth, stationary clouds are occasionally reported by the public as sightings of “unidentified flying objects.” But these clouds are not as mysterious as they might first seem. 47)


Figure 53: On December 29, 2020, the OLI instrument on Landsat-8 acquired these images of soft-edged clouds hovering over the Eisenhower Range of Antarctica’s Transantarctic Mountains. The range is bounded to the north by Priestley Glacier and to the south by Reeves Glacier, both of which feed into the Nansen Ice Shelf on Terra Nova Bay [image credit: NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen with image interpretation by Bastiaan Van Diedenhoven (NASA GISS/Columbia) and Jan Lenaerts (CU Boulder)]

- The clouds have the hallmarks of lenticular clouds that can form along the crests of mountain waves. Mountain waves form when fast moving wind is disturbed by a topographic barrier—in this case, the Eisenhower Range. Air is forced to flow up and over the mountains, causing waves of rising and falling air downwind of the range. The rising air cools and water vapor condenses into clouds. Conversely, falling air leads to evaporation.

- Adding to their mystique, this cloud type appears to stay put—sometimes for hours—defying the strong horizontal winds. In reality, the clouds are constantly building around the crest of the wave and then dissipating just beyond.


Figure 54: Detail image of smooth, soft-edged clouds hovered over the Eisenhower Range in Victoria Land, Antarctica (image credit: NASA Earth Observatory)

- In the United States, lenticular clouds are particularly common around the Rocky Mountains. They have been known to occur over Antarctic mountains, too, but there are not many witnesses besides satellites. The white-on-white color of clouds over ice make the Antarctic versions harder to discern, even in satellite images. This natural-color image has been enhanced with infrared light to separate the white clouds from the white snow and ice below. The clouds also threw rounded shadows on the landscape.

- Still, a few people have witnessed lenticular clouds in Antarctica firsthand. Scientists working with NASA’s Operation IceBridge shot photos of the phenomenon near Mount Discovery in 2013 and over Penny Ice Cap in 2015.

• January 11, 2021: With its population rising three times faster than the national average, the Charleston metropolitan area in South Carolina is among the fastest growing places in the United States. 48)


Figure 55: Large tracts of coastal forests and farmland have been cleared and developed in recent decades to accommodate new residents to the area. The pair of natural-color Landsat images above—this image from 1985 (on Landsat-5, TM) and the image of Figure 56 from 27 December 2020—show some of the changes. Forests and marshes appear green; developed areas are gray. Places where widespread development has occurred include James Island, Johns Island, Daniel Island, West Ashley, and Mount Pleasant (image credit: NASA Earth Observatory images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey)


Figure 56: Sea level rise and new development are on a collision course in South Carolina lowcountry.. Charleston metropolitan area observed by OLI on Landsat-8 on 27 December 2020 (image credit: NASA Earth Observatory)

- A similar story is playing out in cities all across the United States, but the Charleston area stands out in one critical way—much of the new development has happened on low-lying land that is especially vulnerable to sea level rise and flooding. Older, more established parts of Charleston—often on slightly higher land but surrounded by water on three sides—faces similar challenges. As one form of remediation, local and federal government officials are moving forward with plans to build a seawall to protect the city’s historic downtown from encroaching water.

- “Other southeastern coastal cities face similar problems but with one caveat: the lowcountry of South Carolina is low,” said Norman Levine, director of the Santee Cooper GIS Laboratory and Lowcountry Hazards Center at the College of Charleston. “Over one-third of all homes are built on land that sits below 10 feet (3 meters) of elevation.”

- However, hurricane storm surges up to 9 feet have been measured in the past, and climatologists expect surges to grow larger as global climate warms and storms become more intense.

- High tide, or “nuisance flooding,” is already far more common now than it was decades ago, according to Dale Morris, the coauthor of a 2019 report that assessed the region’s flood risks. On average, Charleston saw 10 to 25 tidal floods per year in the 1990s. There were 89 such events in 2019 and 69 in 2020, he said. In other words, the city now sees tidal flooding every 4 to 5 days.

- Both problems are amplified by sea level rise. Relative sea level in Charleston has risen by 10 inches (25 cm) since 1950, with an acceleration to 1 inch (3 cm) every 2 years since 2010.

- “If you look at a lot of the recent development, it impinges upon or is in low-lying floodplains and adjacent land,” said Morris. “These areas used to flood and no one really noticed. Now they flood and impact people’s lives, resources, and livelihoods.”

- The report offers some general principals and recommendations for future development. Development should respect the landscape's natural drainage patterns and soil qualities. Coastal forests—which sponge up water—should be preserved wherever possible. And according to the report authors, development on the lowest-lying areas should not happen.

- “We are not saying don’t develop at all,” said Morris. “We are saying to develop wisely, carefully, sensibly given the current and future flood risks. Those risks are not going to decrease.”

• January 7, 2021: Popocatépetl volcano—the name is Aztec for “smoking mountain”—is one of Mexico’s most active volcanoes. The glacier-clad stratovolcano has been erupting since January 2005, with daily low-intensity emissions of gas, steam, and ash. 49)


Figure 57: Ash and gas emissions continue from one of Mexico’s most active volcanoes. On January 2, 2021, the Operational Land Imager (OLI) on Landsat-8 captured this image of a plume rising from Popocatépetl (nicknamed El Popo), image credit: NASA Earth Observatory image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel

- On January 6, the Washington Volcanic Ash Advisory Center (VAAC) reported a volcanic ash plume that rose to around 6,400 meters (21,000 feet) above the volcano. Mexico’s National Center for Prevention of Disasters (CENAPRED), which continuously monitors Popo, warned people not to approach the volcano or its crater due to falling ash and rock fragments. Some ashfall was blown downwind to the city of Puebla, located about 45 kilometers (30 miles) away from the volcano.

- At 5,426 meters (17,802 feet) above sea level, Popocatépetl is the second tallest volcano in Mexico (after Citlaltépetl). It is comprised of alternating layers of volcanic ash, lava, and rocks from earlier eruptions. The volcano is located around 70 kilometers (40 miles) southeast of Mexico City and more than 20 million people live close enough to be affected by a major eruption. However, most of the eruptions in the past 600 years have been relatively mild.

Landsat-8 Initial imagery until May 2013 when Landsat-8 was declared operational

Landsat-8 is operational — LDCM was officially renamed to Landsat-8. On May 30, 2013, NASA transferred operational control of the Landsat-8 satellite to the USGS (U.S. Geological Survey ) in Sioux Falls, S.D. This marks the beginning of the operational phase of the Landsat-8. The USGS now manages the satellite flight operations team within the Mission Operations Center, which remains located at NASA’s Goddard Space Flight Center in Greenbelt, MD.

The mission carries on a long tradition of Landsat satellites that for more than 40 years have helped to study how Earth works, to understand how humans are affecting it and to make wiser decisions for the future. The USGS will collect at least 400 Landsat-8 scenes every day from around the world to be processed and archived at the USGS/EROS (Earth Resources Observation and Science Center) in Sioux Falls. 50)

• May 22, 2013: One of two new spectral bands identifies high-altitude, wispy cirrus clouds that are not apparent in the images from any of the other spectral bands. The March 24, 2013, natural color image of the Aral Sea, for example, appears to be from a relatively clear day. But when viewed in the cirrus-detecting band, bright white clouds appear. 51)

The SWIR band No 9 (1360-1390 nm) is the cirrus detection band of the OLI (Operational Land Imager) instrument. Cirrus clouds are composed of ice crystals. The radiation in this band bounces off of ice crystals of the high altitude clouds, but in the lower regions, the radiation is absorbed by the water vapor in the air closer to the ground. The information in the cirrus band is to alert scientists and other Landsat users to the presence of cirrus clouds, so they know the data in the pixels under the high-altitude clouds could be slightly askew. Scientists could instead use images taken on a cloud-free day, or correct data from the other spectral bands to account for any cirrus clouds detected in the new band.

Figures 58 and 59 are simultaneous OLI observations of the same area of the Aral Sea region in Central Asia which illustrate the power of interpretation of a scene. The cirrus clouds of Figure 59 are simply not visible in the natural color image of Figure 58. This new analysis feature will give scientists a better handle to study the changing environment.


Figure 58: Natural color image of the Aral Sea region observed on March 24, 2013 (image credit: NASA)


Figure 59: Cirrus cloud detection band image of the Aral Sea region observed on March 24, 2013 (image credit: NASA)

• May 9, 2013: Availability of free long-term Landsat imagery to the public. Today, Google released more than a quarter-century of images, provided free to the public, of Earth taken from space and compiled into an interactive time-lapse experience. Working with data from the Landsat Program managed by the USGS (U.S. Geological Survey), the images display a historical perspective on changes to Earth's surface over time. 52) 53) 54) 55)

The long-term archive of Landsat images of every spot on Earth is a treasure trove of scientific information that can form the basis for a myriad of useful applications by commercial enterprises, government scientists and managers, the academic community, and the public at large.

In 2009, Google started working with USGS to make this historic archive of Earth imagery available online. Using Google Earth Engine technology, the Google team sifted through 2,068,467 images—a total of 909 terabytes of data—to find the highest-quality pixels (e.g., those without clouds), for every year since 1984 and for every spot on Earth. The team then compiled these into enormous planetary images, 1.78 terapixels each, one for each year.

• May 6, 2013: As the LDCM satellite flew over Indonesia's Flores Sea on April 29, it captured an image of Paluweh volcano spewing ash into the air. The satellite's OLI instrument detected the white cloud of smoke and ash drifting northwest, over the green forests of the island and the blue waters of the tropical sea. The TIRS (Thermal Infrared Sensor) on LDCM picked up even more. 56) 57)


Figure 60: An ash plume drifts from Paluweh volcano in Indonesia in this image, taken April 29, 2013 with OLI (image credit: NASA)

By imaging the heat emanating from the 5-mile-wide volcanic island, TIRS revealed a hot spot at the top of the volcano where lava has been oozing in recent months (Figure 61).


Figure 61: This thermal image was taken by the TIRS instrument on April 29, 2013 (image credit: USGS, NASA)

Legend to Figure 61: A bright white hot spot, surrounded by cooler dark ash clouds, shows the volcanic activity at Paluweh volcano in the Flores Sea, Indonesia. The image of Paluweh also illuminates TIRS' abilities to capture the boundaries between the hot volcanic activity and the cooler volcanic ash without the signal from the hot spot bleeding over into pixels imaging the cooler surrounding areas.

• May 2, 2013: All spacecraft and instrument systems continue to perform normally. LDCM continues to collect more than 400 scenes per day and the U.S. Geological Survey Data Processing and Archive System continues to test its ability to process the data flow while waiting for the validation and delivery of on-orbit calibration, which convert raw data into reliable data products. 58)

• On April 12, 2013, LDCM (Landsat Data Continuity Mission) reached its final altitude of 705 km. One week later, the satellite’s natural-color imager (OLI) scanned a swath of land 185 km wide and 9,000 km long. 59) 60)

• Since April 4, 2013, LDCM is on WRS-2 (Worldwide Reference System-2),


Figure 62: These images show a portion of the Great Salt Lake, Utah as seen by LS-7 (left) and LS-8 (LDCM) satellites (right); both images were acquired on March 29, 2013 (image credit: USGS, Ref. 60)

Legend to Figure 62: On March 29-30, 2013, the LDCM was in position under the Landsat 7 satellite. This provided opportunities for near-coincident data collection from both satellites. The images below show a portion of the Great Salt Lake in Utah, and the Dolan Springs, Arizona area, the latter of which is used in Landsat calibration activities. 61)

• March 21, 2013: Since launch, LDCM has been going through on-orbit testing. The mission operations team has completed its review of all major spacecraft and instrument subsystems, and performed multiple spacecraft attitude maneuvers to verify the ability to accurately point the instruments. 62)

- As planned, LDCM currently is flying in an orbit slightly lower than its operational orbit of 705 km above Earth's surface. As the spacecraft's thrusters raise its orbit, the NASA-USGS team will take the opportunity to collect imagery while LDCM is flying under Landsat 7, also operating in orbit. Measurements collected simultaneously from both satellites will allow the team to cross-calibrate the LDCM sensors with Landsat 7's Enhanced Thematic Mapper-Plus instrument.

- After its checkout and commissioning phase is complete, LDCM will begin its normal operations in May. At that time, NASA will hand over control of the satellite to the USGS, which will operate it throughout its planned five-year mission life. The satellite will be renamed Landsat 8. USGS will process data from OLI and TIRS and add it to the Landsat Data Archive at the USGS Earth Resources Observation and Science Center, where it will be distributed for free via the Internet.


Figure 63: First image of LDCM released in March 2013 (image credit: NASA) 63)

Legend to Figure 63: The first image shows the meeting of the Great Plains with the Front Ranges of the Rocky Mountains in Wyoming and Colorado. The natural-color image shows the green coniferous forest of the mountains coming down to the dormant brown plains. The cities of Cheyenne, Fort Collins, Loveland, Longmont, Boulder and Denver string out from north to south. Popcorn clouds dot the plains while more complete cloud cover obscures the mountains.
The image was observed on March 18, 2013 using data from OLI (Operational Land Imager) bands 3 (green), 5 (near infrared), and 7 (short wave infrared 2) displayed as blue, green and red, respectively.

• March 18, 2013: First day of simultaneous OLI and TIRS Earth imaging (Ref. 60).

• Feb. 21, 2013: The LDCM mission operations team successfully completed the first phase of spacecraft activation. All spacecraft subsystems have been turned on, including propulsion, and power has been supplied to the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) instruments. 64)

• LDCM will go through a check-out phase for the next three months. Afterward, operational control will be transferred to NASA's mission partner, the USGS (U.S. Geological Survey), and the satellite will be renamed to Landsat-8. The data will be archived and distributed free over the Internet from the EROS (Earth Resources Observation and Science) center in Sioux Falls, S.D. Distribution of Landsat-8 data from the USGS archive is expected to begin within 100 days of launch.

• The LDCM spacecraft separated from the rocket 79 minutes after launch and the first signal was received 3 minutes later at the ground station in Svalbard, Norway. The solar arrays deployed 86 minutes after launch, and the spacecraft is generating power from them (Ref. 25).

Minimize Landsat 8 continued

Sensor complement: (OLI, TIRS)

Background: In 2008 the TIRS (Thermal Infrared Sensor) instrument was still regarded an option to the LDCM mission. However, in Dec. 2009, the US government confirmed that TIRS would be developed and would be on board the LDCM spacecraft. In the spring of 2010, TIRS passed the CDR (Critical Design Review). 65) 66)

The OLI and TIRS data are merged into a single data stream. Together the OLI and TIRS instruments on LDCM replace the ETM+ instrument on Landsat-7 with significant enhancements.


Figure 64: Photo of the EM PIE (Payload Interface Electronics) equipment, image credit: NASA

OLI (Operational Land Imager):

Already in July 2007, NASA had awarded a contract to BATC (Ball Aerospace Technology Corporation) of Boulder, CO, to develop the OLI (Operational Land Imager) key instrument for LDCM. The BATC contract terms call for the design, development, fabrication and integration of one OLI flight model. Furthermore, the company is also required to test, deliver and provide post-delivery support and five years of on-orbit support for the instrument.

The multispectral and moderate resolution OLI instrument has similar spectral bands to the ETM+ (Enhanced Thermal Mapper plus) sensor of Landsat-7. It includes new coastal aerosol (443 nm, band 1) and cirrus detection (1375 nm, band 9) bands, though it does not have a thermal infrared band.

The following list provides an overview of the most important observation requirements for the OLI instrument: 67)

• The specifications require delivery of data covering at least 400 Landsat scenes/day (185 km x 180 km) for the US archive. The data are to be acquired in a manner that affords seasonal coverage of the global land mass. Data are required for the heritage reflective Thematic Mapper (TM) spectral bands plus two new bands, a blue band for coastal zone observations and a short wave infrared band for cirrus cloud detection.

• 30 m GSD (Ground Sample Distance) for VIS/NIR/SWIR, 15m GSD for PAN data.

• The specifications do not require thermal data (TIR band), representing a departure from the TM (Thematic Mapper) heritage. The specification also requires data providing a 30 m GSD (Ground Sample Distance) for each of the multispectral bands. Note: The TIR band was deselected due to the extra cost of active cooling.

• An edge response slope is also specified for the image data from each spectral band. The edge response is defined as the normalized response of the image data to a sharp edge as expressed in a Level 1R VDP (Validation Data Product). An edge response slope of 0.027 is required for bands 1 through 7, a slope of 0.054 is required for the panchromatic band, band 8, and a slope of 0.006 for the cirrus band, band 9.

• All instrument source data will be quantized to 12 bit resolution.

Band Nr

Band Name

Spectral range (nm)

Use of data


Radiance (W/m2 sr μm), typical



New Deep Blue


Aerosol/coastal zone

30 m









30 m
(TM heritage bands)






























Minerals/litter/no scatter






Image sharpening

15 m






Cirrus cloud detection

30 m



Table 2: NASA/USGS requirements for LDCM imager spectral bands

• The WRS-2 (Worldwide Reference System-2) defines Landsat scenes as 185 km x 180 km rectangular areas on the Earth's surface designated by path and row coordinates. This heritage system is used to catalogue the data acquired by the Landsat 4, 5, and 7 satellites and will also be used for the LDCM.

• Provide “standard”, orthorectified data products within 24 hours of observation (products available via the web at no cost)

• Data calibration consistent with previous Landsat missions

• Continue IC (International Cooperator) downlinks

• Support priority imaging and a limited off-nadir collection capability (± 1 path/row).


ETM+ (Landsat-7)

Band Nr

Wavelength (µm)

GSD (m)

Band No.

Wavelength (µm)

GSD (m)

8 (PAN)

0.500 - 0.680


8 (PAN)

0.52 - 0.90



0.433 - 0.453






0.450 - 0.515



0.45 - 0.52



0.525 - 0.600



0.53 - 0.61



0.630 - 0.680



0.63 - 0.69






0.78 - 0.90



0.845 - 0.885






1.360 - 1.390






1.560 - 1.660



1.55 - 1.75



2.100 - 2.300



2.09 - 2.35


OLI does not include thermal imaging capabilities

6 (TIR)

10.40 - 12.50


Figure 65: Spectral parameter comparison of OLI and ETM+ instruments


Figure 66: OLI and ETM spectral bands (image credit: NASA)

OLI instrument:

The OLI design features a multispectral imager with a pushbroom architecture (Figure 67) of ALI (Advanced Land Imager) heritage, a technology demonstration instrument flown on the EO-1 spacecraft of NASA (launch Nov. 21, 2000). A pushbroom implementation is considered to be more geometrically stable than the whiskbroom scanner of the ETM+ instrument. As a tradeoff of this architecture selection, the imagery must be terrain corrected to ensure accurate band registration.. 68) 69) 70) 71) 72)


Figure 67: Schematic view of the OLI instrument design (image credit: BATC)

The FPA (Focal Plane Assembly) consists of 14 FPMs (Focal Plane Modules). This is a consequence of the pushbroom architecture selection for OLI leading to a different set of geometric challenges than a cross-track whiskbroom implementation. Instead of using a small focal plane and a scanning mirror, 14 FPMs are required to cover the full Landsat cross-track field of view. Each FPM contains nine spectral bands in along-track (Figure 68). The along-track spectral band separation leads to an approximately 0.96-second time delay between the leading and trailing bands. This time delay creates a small but significant terrain parallax effect between spectral bands, making band registration more challenging.

The along-track dimension of the OLI focal plane (see Figure 69) also makes it desirable to “yaw steer” the spacecraft. This means that the spacecraft flight axis is aligned with the ground (Earth fixed) velocity vector, rather than with the inertial velocity vector, in order to compensate for cross-track image motion due to Earth rotation.

Although the pushbroom architecture requires many more detectors and a correspondingly larger focal plane, it also allows for a much longer detector dwell time (~4 ms for OLI vs. 9.6 µs for ETM+), leading to much higher signal-to-noise ratios. The lack of moving parts in the pushbroom design also allows for a more stable imaging platform and good internal image geometry.


Figure 68: Schematic view of the FPM layout concept (image credit: BATC, USGS)


Figure 69: Orientation of the FPMs in the FPA (Focal Plane Assembly) of the OLI instrument (image credit: BATC)

Each FPM contains detectors for each spectral band, silicon for the VNIR bands and HgCdTe for the SWIR bands and a butcher-block filter assembly to provide the spectral bands.

OLI features about 6500 active detectors per multispectral band and 13000 detectors for the panchromatic band. These detectors are organized as blocks ~500 multispectral (1000 panchromatic) detectors wide within 14 focal plane modules (FPMs) that make up the focal plane assembly. Each module has its own butcher-block assembly spectral filter. This provides significantly improved signal to noise performance, but complicates the process of radiometrically matching the detectors responses. Similarly, the lack of a scan mirror removes the need for knowledge of its movement, but requires knowledge of the detectors locations across a much larger focal plane (Ref. 2).

Observation technique

Pushbroom imager

Spectral bands

9 bands in VNIR/SWIR covering a spectral range from 443 nm to 2300 nm


- Four-mirror off-axis telescope design with a front aperture stop
- Use of optical bench
- Telecentric design with excellent stray light rejection

FPA (Focal Plane Assembly)

- Consisting of 14 sensor chip assemblies mounted on a single plate
- FPA is passively cooled
- Hybrid silicon / HgCdTe detectors
- Butcher block filter assembly over each SCA (Sensor Chip Assembly)

Swath width (FOV=15º)

185 km

GSD (Ground Sample Distance)

15 m for PAN data; 30 m for VNIR/SWIR multispectral data

Data quantization

12 bit


- Solar calibrator (diffuser) used once/week
- Stimulation lamps used to check intra-orbit calibration
- Dark shutter for offset calibration (used twice per orbit)
- Dark detectors on focal plane to monitor offset drift

Instrument, mass, power, size


Table 3: Overview of OLI instrument parameters

The OLI will provide global coverage by acquiring ~400 scenes per day in six VNIR and three SWIR bands, all at 12 bit radiometric resolution. In addition to these bands, there will be a tenth band consisting of covered SWIR detectors, referred to as the ‘blind’ band, that will be used to estimate variation in detector bias during nominal Earth image acquisitions. The OLI bands are distributed over 14 SCAs (Sensor Chip Assemblies) or FPMs, each with 494 detectors per 30 m band and twice as many for the 15 m panchromatic band - totaling in over 75000 imaging detectors. 73)

OLI calibration:

The OLI calibration subsystem (Figures 70 and 71) consists of two solar diffusers (a working and a pristine), and a shutter. When positioned so that the sun enters the solar lightshade, the diffusers reflect light diffusely into the instruments aperture and provide a full system full aperture calibration. The shutter, when closed, provides a dark reference. In addition, two stim lamp assemblies are located at the front aperture stop. Each lamp assembly contains three lamps (per redundant configuration) that are operated at constant current and monitored by a silicon photodiode. The lamp signal goes through the full telescope system. Additionally, the OLI focal plane will include masked HgCdTe detectors, that is, detectors that will be blocked from seeing the Earth’s radiance (Ref. 2). 74) 75)

Solar diffusers:

- Full-aperture full system Spectralon diffuser, designed to be used at different frequencies to aid in tracking the system and diffuser changes. The pristine diffuser will be used to check degradation of main diffuser.

- The primary solar diffuser will nominally be deployed every 8 days to track the calibration of the OLI sensor and perform detector-to-detector normalization.

- The solar diffuser based calibration requires a spacecraft maneuver to point the OLI solar calibration aperture towards the sun. The pristine diffuser will be used on a less frequent basis, about every six months, as a check on the primary diffuser's degradation.

Stimulation lamps:

- Multi—bulbed tungsten lamp assemblies, that illuminate the OLI detectors through the full optical system, similarly designed to be used at different frequencies to separate lamp and system changes. The working lamp will be used daily for intra-orbit calibration/characterization; the reference lamp set approximately monthly, and the pristine lamp set approximately twice a year.

- The lamb assembly can also be compared to solar diffuser measurements to check stability.

Dark shutter:

- Used twice per orbit for offset calibration

• Dark detectors on focal plane to monitor offset drift

• Linearity checked by varying detector integration time.

The LDCM operational concept also calls for the spacecraft to be maneuvered every lunar cycle to view the moon, providing a "known" stable source for tracking stability over the mission. A side-slither maneuver, where the spacecraft is rotated 90º to align the detector rows with the velocity vector, is also planned. These data will provide an additional method to assess the detector-to-detector radiometric normalization.

Pre-launch spectro-radiometric characterization and calibration (Ref. 74):

The spectral characterization of the OLI instrument is being performed at the component, focal plane module and fill instrument levels. The components, which have all completed testing, include detector witness samples, spectral filters prior to dicing into flight filter sticks, the focal plane assembly window witness samples and telescope mirror witness samples.

The FPM (Focal Plane Module) level tests, which are also complete, are specifically designed to characterize the spectral out-of-band response. The FPM level tests measure the spectral response of all the detectors by illuminating the full focal plane at approximately the correct cone angle.

An integrating sphere is used in the pre-launch radiance calibration of the OLI. The traceability of the calibration of this sphere will start with the 11" OLI transfer sphere directly calibrated at the NIST Facility for Spectroradiometric Calibration (FASCAL). While still at NIST, this OLI transfer sphere is checked by independently NIST calibrated University of Arizona (UAR VNIR transfer radiometer), NASA and NIST (Government Transfer Radiometers) radiometers. Also, the Ball Standard Radiometer (BSR), that has filters matching the OLI bands, views the sphere.


Figure 70: OLI block diagram illustrating the calibration subsystem in front of the telescope (image credit: NASA, BATC)


Figure 71: Blow-up of the calibration subsystem illustrating the solar diffuser and shutter assemblies (image credit: NASA, BATC)


Figure 72: Illustration of the OLI instrument (image credit: NASA, BATC)

In Nov. 2008, the OLI instrument passed the ICDR (Instrument Critical Design Review). 76)


Figure 73: Photo of the completed OLI instrument with electronics (image credit: BATC, NASA, USGS)

Delivery of the OLI instrument in the summer of 2011 (Ref. 3).

TIRS (Thermal Infrared Sensor)

The TIRS instrument is providing continuity for two infrared bands not imaged by OLI. NASA/GSFC is building the TIRS instrument inhouse. TIRS is a late addition to the LDCM mission, the requirements call for a GSD (Ground Sample Distance of 120 m for the imagery; however, the actual GSD will be 100 m.

The LDCM ground system will merge the data from both sensors into a single multispectral image product. These data products will be available for free to the general public from the USGS enabling a broad scope of scientific research and land management applications. 77) 78)

TIRS is a QWIP (Quantum Well Infrared Photodetector) based instrument intended to supplement the observations of the OLI instrument. The TIRS instrument is a TIR (Thermal Infrared) imager operating in the pushbroom mode with two IR channels: 10.8 µm and 12 µm. The two spectral bands are achieved through interference filters that cover the FPA (Focal Plane Assembly). The pushbroom implementation increases the system sensitivity by allowing longer integration times than whiskbroom sensors. The two channels allow the use of the “split-window” technique to aid in atmospheric correction.


Figure 74: Functional block diagram of TIRS (image credit: NASA, Ref. 75)

The focal plane consists of three 640 x 512 QWIP GaAs arrays mounted on a silicon substrate that is mounted on an invar baseplate. The two spectral bands are defined by bandpass filters mounted in close proximity to the detector surfaces. The QWIP arrays are hybridized to ISC9803 readout integrated circuits (ROICs) of Indigo Corporation. The focal plane operating temperature will be maintained at 43 K (nominally). 79) 80) 81)

Instrument type

Pushbroom imager

Two channel thermal imaging instrument

10.8 and 12.0 µm band centers


10.3-11.3 µm,
11.5-12.5 µm

GSD (Ground Sample Distance)

100 m (nominal), 120 m (requirement)

Swath width

185 km, FOV = 15º

Operating cadence

70 frames/s

Instrument calibration

- Scene select mirror to select between 2 calibration sources
- Two full aperture calibration sources: onboard internal calibration and space view


- Three SCA (Sub-Chip Assembly) QWIP detectors built in-house at Goddard
- FPA consists of three 640 x 512 detector arrays
- Pixel size of 25 µm producing an IFOV of 142 µrad
- The FPA consists of an invar “spider” which is bonded to the silicon interface board
containing the QWIPs and on which the “daughter boards” are mounted.
- Actively cooled FPA operating at 43 K
- Two-stage cryocooler provided by BATC


- The telescope is a 4-element refractive lens system.
- Passively cooled telescope operating at 185 K

Telescope f number


Data quantization

12 bit

Instrument mass, size, power

236 kg, approx: 80 cm x 76 cm x 43 cm, 380 W

Table 4: TIRS instrument parameters

QWIP detector: The development of the QWIP detector technology has made great strides in the first decade of the 21st century. In 2008, NASA/GSFC revised the design of the infrared detector concept of the TIRS (Thermal Infrared Sensor) imager, under development for the LDCM (Landsat Data Continuity Mission). The initially considered HgCdTe-based detector design was changed to a QWIP design due to the emergence of broadband QWIP capabilities in the MWIR and TIR (LWIR) regions of the spectrum. The introduction of QWIP technology for an operational EO mission represents a breakthrough made possible through collaborative efforts of GSFC, the Army Research Lab and industry (Ref. 80).

An important advantage of GaAs QWIP technology is the ability to fabricate arrays in a fashion similar to and compatible with the silicon IC technology. The designer’s ability to easily select the spectral response of the material from 3 µm to beyond 15 µm is the result of the success of band-gap engineering. 82)

Advantages of QWIP technology:

- Large lattice-matched substrates

- Mature materials technology

- No unstable mid-gap traps

- Inherently, radiation hard.


Figure 75: QWIP quantum state diagram (image credit: NASA/JPL)


Figure 76: TIRS 10-13 µm QWIP spectral response requirement (image credit: NASA)


Figure 77: Overview of the TIRS focal plane layout (image credit: NASA, Ref. 75)

The three arrays are precisely aligned to each other in the horizontal and vertical directions (to within 2 µm). There is a requirement that the detection region within the QWIP array be within 10 µm of a common focal plane altitude. This specification is challenging since it includes surface non-uniformities of the baseplate, substrate, the QWIP/ROIC hybrid and the epoxy bond lines between these components. Nonetheless, since there are three discreet arrays they must all fall within a single focus position.

The filter bands are further confined to specific regions of the QWIP array. Although each array contains 512 rows, after all the operational requirements are satisfied (frame rate, windowing, co-registration, scene reconstruction, etc.) only 32 rows are available under each filter band separated by 76 rows of occluded pixels (for dark current subtraction). Once all these requirements are incorporated into the focal plane design, eligible rows on any given array are pre-determined. Of these eligible rows, there must be three that can be combined to make two perfect rows, or preferably, at least two perfect rows (that is, rows where all pixels meet every specification).

TRL (Technology Readiness Level) tests: An important and essential process for qualifying new or previously unused technology in a NASA space mission is the technology readiness level demonstration. There are nine levels with level 6 (TRL 6) being the level at which new hardware must be demonstrated. Typically, this means qualification in the environment which the instrument will be subjected through out the mission; radiation effects, vibration, thermal cycling and (in some cases) shock. Both the readout and QWIP hybrids were subjected to gamma, proton and heavy ion radiation equivalent to 35 krad or almost 10 times the expected mission dose. At these levels and at the operating temperature of 43 K minimal effects were observed and none were considered to be a mission risk.

A fully functioning focal plane assembly was subjected to 40 thermal cycles from 300 K to 77 K and back to 300 K. Every tenth cycle went to 43 K to collect the array performance data. After the completion of the 40 cycles there was essentially no change in any of the three QWIP arrays (2 grating QWIP hybrids and one C-QWIP hybrid). - The final environmental test performed was vibration to simulate the effect of the launch. Since this is a qualification test the vibration loads are specified 3db above the expected loads. The focal plane assembly was subjected to a series of vibration input loads including x, y and z-axis random vibration for 2 minutes/axis, a sine sweep and sine burst test (15 g at 20 Hz). No failures occurred and this assembly and the overall design was certified by an independent review panel as having met the requirements for TRL 6.


Figure 78: Schematic view of the FPA (Focal Plane Assembly), image credit: NASA


Figure 79: Photos of the FPA (image credit: NASA)

Legend to Figure 79: The left photo of the FPA is without filters showing the 3 QWIPs in the center. The daughter boards are the red and green assemblies to the left and right, respectively. The invar spider is the component with the 4 arms. - The right picture of the FPA comes with the filters attached. Note that there are two filters over each array with a thin dark strip between them.

Optical system: The imaging telescope is a 4-element refractive lens system. A scene select mechanism (SSM) rotates a scene mirror (SM) to change the field of regard from a nadir Earth view to either an on-board blackbody calibrator or a deep space view. The blackbody is a full aperture calibrator whose temperature may be varied from 270 to 330 K.

The optical system, consisting of a lens with three Ge elements and one ZnSe element, produces nearly diffraction-limited images at the focal plane. All but 2 of the surfaces are spherical, which simplifies fabrication. The optics are radiatively cooled to a nominal temperature of 185 K to reduce the contribution of background thermal emission to the measurement noise. Because of the fairly strong thermal dependence of the index of refraction of Ge, the focus position of the lens is a function of the optics temperature. This provides a method of adjusting focus so that, in the unlikely event that launch conditions or some other effect defocus the system, the temperature of the optics may be changed by ±5 K to refocus. That is, thermal control of the lens provides a non-mechanical focus mechanism. A +5 K change does not significantly degrade the noise performance.

A precision scene select mirror is an essential component of the TIRS instrument and it is driven by the scene select mechanism. It rotates around the optical axis on a 45º plane to provide the telescope with a view of Earth through the nadir baffle and two full aperture sources of calibration, onboard variable temperature blackbody (hot calibration target) and space view (cold calibration target). The onboard blackbody will be a NIST (National Institute of Standards and Technology) certified reference source (Figure 80).

TIRS is able to achieve a 185 km ground swath with a 15º FOV (Field of View) functioning in the pushbroom sample collection method. This method will have the benefit of being able to collect and record data without movement artifacts due to its wide instantaneous field of view. Frames will be collected at an operating cadence of 70 per second. The collected data will be stored temporarily stored on board and periodically sent to the USGS EROS facility for further storage. The instrument is designed to have an expected lifetime of at least a three years.


Figure 80: The TIRS optical sensor unit concept (image credit: NASA)


Figure 81: Schematic view of the TIRS instrument internal assembly (image credit: NASA, Ref. 75)

Legend to Figure 81: Model of the TIRS instrument showing the major components of the TIRS sensor. The scene select mechanism rotates the field of regard from the Earth view to either the space view or to the on-board calibrator. The right side provides some detail of optical system showing the 4-element lens, a cut-away view of the SM and the thermal strap connecting the FPA to the cryocooler cold tip. The MEB (Main Electronics Box) and the CCE (Cyrocooler and its associated Control Electronics), not shown, are mounted to the spacecraft.

TIRS instrument calibration:

Consistent with previous Landsat missions, LDCM TIRS will be fully calibrated prior to launch. Calibration measurements will be made at GSFC and will be done at the component, subsystem and instrument level. NIST-traceable instrument level calibration will be done using an in-chamber calibration system. 83) 84)

Among other uses, TIRS data will be used to measure evapotranspiration (evaporation from soil and transpiration from plants); to map urban heat fluxes, to monitor lake thermal plumes from power plants; to identify mosquito breeding areas and vector-borne illness potential; and to provide cloud measurements. The evapotranspiration data may be used to estimate consumptive water use on a field-by-field basis.

TIRS instrument calibration makes use of the following elements:

• Precision scene select mirror to select between calibration sources and nadir view

• Two full aperture calibration sources

- Onboard variable temperature blackbody

- Space view

- Calibration every 34 minutes

• NIST Traceable radiometric calibration


Figure 82: Schematic of inclusion of NIST standards (image credit: NASA)

TIRS calibration system:

• A 41 cm diameter source is covering full field and aperture of TIRS (Flood Source)

• Target Source Module (GeoRadSource)

- Blackbody point source w/ filter & chopper

- All reflective, off-axis parabola collimator

- Motorized target and filter wheels

- A square steering mirror system (33 cm side length) is permitting coverage of the full aperture and field

• Cooled enclosure over entire system

• External monochromator (spectral source)

• Components are mounted to common base plate.

The TIRS radiometric response is determined via the prelaunch characterization relative to the laboratory blackbody. This approach provides the highest accuracy calibration. The calibration philosophy is then to evaluate (or validate) the calibration parameters once TIRS is on orbit. If the calibration of TIRS is demonstrated to change significantly while on orbit using measurements during the checkout period, then the on-board blackbody (OBB) will be used as the primary pathway to NIST traceability.


Figure 83: Illustration of the TIRS calibration system (image credit: USGS)


Figure 84: Illustration of TIRS on the LDCM spacecraft (image credit: NASA, Ref. 3)

SSM (Scene Select Mechanism) of TIRS:

The SSM for the TI RS instrument, developed at NASA/GSFC, is a single axis, direct drive mechanism which rotates a 207 mm scene mirror from the nadir science position to the 2 calibration positions twice per orbit. It provides pointing knowledge and stability to ~10 µradians. The SSM can be driven in either direction for unlimited rotations. The rotating mirror is dynamically balanced over the spin axis, and does not require launch locking. 85)

The design of the SSM is straightforward; it is a single axis rotational mechanism. The operational cadence was to hold the scene mirror stationary for ~40 minutes staring at nadir, rotate 120º to the space view aperture and stare for 30 seconds, rotate 120º to the internal blackbody and stare for 30 seconds and then rotate the mirror to the back to nadir. Then the entire process would start again. The mechanism would be operating all of the time, or have a 100% duty cycle. Since LDCM/TI RS was to be in a highly-inclined polar orbit, the general idea was to calibrate twice per orbit while over the poles.


Figure 85: Cutaway view of the SSM (image credit: NASA)

Instrument mass, power

15 kg, 6 W average

Pointing knowledge, stability

±9.7 µradians over 34 minutes, ±9.7 µradians over 2.5 seconds

Duty cycle


Thermal operational

0 / +20ºC stable to ±1ºC

Thermal survival range

-50 / +40ºC


3.25 years on orbit


A/B side block redundancy

Operational cadence

Stare nadir for 30-40 minutes
Rotate 120º in < 2 minutes to space view
Stare for ~30 seconds,
Rotate 120º in < 2 minutes to blackbody view
Stare for ~30 seconds
Rotate to 120º in < 2 minutes to nadir view

Table 5: SSM driving requirements


Landsat ETM+


GMES/Sentinel-2 MSI

Spectral bands









1 (blue)


B1 (blue)


1 (blue)


2 (blue)


B2 (blue)


2 (green)


3 (green)


B3 (green)




4 (red)


B4 (red)






B5 (red edge)






B6 (red edge)






B7 (red edge)


4 (NIR)




B8 (NIR)




5 (NIR)


B8a (NIR)






B9 (water vapor)




9 (cirrus)


B10 (cirrus)


5 (SWIR1)


6 (SWIR1)


B11 (SWIR1)


7 (SWIR2)


7 (SWIR2)


B12 (SWRIR2)







6 (TIR)


10 (TIR1)






11 (TIR2)




GSD at nadir

30 m VNIR
15 m Pan
60 m TIR

30 m VNIR
15 m Pan
100 m TIR

10 m (B2, B3, B4, B8)
20 m (B5, B6, B7, B8a, B11, B12)
60 m (B1, B9, B10)


8 bit

12 bit

12 bit

Onboard Calibration




Resivit time

16 days

16 days

5 days (2 satellites)

Off-axis viewing

Up to 7.5º off nadir

Up to 7.5º off nadir

Up to 10.3º off nadir (w/o pointing)

Orbit altitude

705 km

705 km

786 km

Swath width

185 km

185 km

290 km


Cross-track scanner (Whiskbroom)



Table 6: Comparison of Landsat and GMES/Sentinel-2 imager specifications 86)

Collection of imagery onboard LDCM:

The co-aligned instruments are nominally nadir pointed and sweep the ground track land surface in contiguous image data collections, also known as image intervals. Each image interval may contain from a few WRS-2 scenes for an island or coastal area up to 77 contiguous WRS-2 scenes for an extended area of interest. For each image interval, the observatory executes a pre-defined imaging and ancillary data collection sequence as shown in Figure 86. 87)

Prior to the image interval, the spacecraft configures the onboard systems for the mission data collection session. A specific number of intervals are pre-defined on the ground based upon the number of WRS-2 scenes scheduled for collection, and allocated in the SSR (Solid State Recorder). Each instrument will transmit focal plane sensor data and instrument ancillary data (voltages, temperatures, etc.), which the spacecraft will interleave with the spacecraft ancillary data (attitude, ephemeris, etc), and record to files in the SSR.


Figure 86: Data collection sequence (image credit: USGS, NASA)

If the observatory is over an IC (International Cooperatoror) or LGN (Landsat Ground Network) station, it will simultaneously transmit data in real time to the ground. In addition, each instrument performs routine on-board calibrations (blackbody, lamps, etc) before and after each image interval, and during less frequent occasions utilizing the sun and moon as external calibration sources. A representation of the global image collection and calibration opportunities within the WRS-2 grid is shown in the 16-day repeating DRC-16 (Design Reference Case-16) in Figure 87.

The DRC-16 was developed to aid the mission architects in identification of all image and calibration activities and to verify that all are consistent with spacecraft power and mission data management capabilities. Instrument solar, lunar, and internal calibrations are required by ground system processing systems for image reconstruction, and to produce finished and distributable image products.


Figure 87: Illustration of DRC-16 collections (image credit: USGS, NASA)

End to end mission data flow is represented in Figure88 . Mission data originates as instrument sensor (or “image”) data, and are collected and processed by the instrument electronics. The instrument electronics transmits the image data to the spacecraft PIE (Payload Interface Electronics) over a HSSDB (High Speed Serial Data Bus), using a serializer-deserializer integrated circuit pair. OLI image data are compressed using the USES (Universal Source Encoder for Space) ASIC (Application-Specific Integrated Circuit), which implements the Rice algorithm for lossless compression. - TIRS data are not compressed due to the low data rate. Instrument image data are interleaved with spacecraft ancillary data to create a file, which is stored and/or provided to the transmitter for downlink.

Ancillary data are collected at rates up to 50 Hz, and is comprised of GPS (Global Positioning System) data, IMU (Inertial Measurement Unit) data, star tracker data, and select instrument engineering information required by ground system algorithms for image product generation. The ancillary data are multiplexed within the mission data files every second.

Mission data files are intentionally fixed in size at 1GB. A system architecture trade study was performed early in mission definition to establish the optimum file size given the implementation of Class 1 CCSDS (Consultative Committee for Space Data Systems) CFDP (File Delivery Protocol), and a required link BER (Bit Error Rate) of < 10-12. Utilizing the 440 Mbit/s available downlink capacity, each downlinked mission data file requires 22 seconds of continuous transmission for a completed delivery to the ground system. The low BER requirement on the communication link provides the confidence that only one file over several days would require retransmission, well within the available contact time with the ground stations.


Figure 88: Overview of the mission data flow (image credit: USGS, NASA)

Simultaneous real-time and playback mission data files are transmitted to the ground through virtual channels within a single physical channel. Five data streams on the transmitter interface board may be multiplexed on to the link via an arbiter, which interleaves the data streams according to a pre-established priority scheme. The data streams priorities are:

1) Real-time for the OLI instrument

2) Real-time for the TIRS instrument

3) SSR playback channel 1

4) SSR playback channel 2

5) A virtual channel for fill frames in case no image data are available for downlink.

The five virtual channels are arbitrated in priority order on a frame-by-frame basis. The OLI instrument has the highest priority followed by the TIRS instrument and, since the combined mission data rates are less than the total downlink bandwidth available, there is always residual bandwidth available for mission data playback. This enables maximum utilization of the downlink bandwidth. SSR playback 1 has priority over SSR playback 2. SSR playback 2 is controlled by an autonomous on-board spacecraft flight software task that queues files for playback by a predefined algorithm (i.e. oldest to newest priority files first, oldest to newest non-priority files next).

SSR playback 1 is specifically for ground system intervention, as required to supersede the onboard autonomous SSR playback 1, to downlink files which are a higher priority than originally categorized. SSR playback 2 will resume automatically upon the completion of SSR playback 1 ground commands, and there is no need to stop and restart the autonomous SSR playback 2 queue. As a frame finishes transmission, the priority arbiter selects the highest priority channel that has a frame buffer ready for transmission for the next frame.

To ensure the bandwidth of the space to ground data link is at least twice the bandwidth of the real-time mission data, the spacecraft compresses OLI image data in near real time using CCSDS compression. During early hardware development, using simulated data derived from the ALI (Advanced Land Imager) sensor aboard the EO-1 (Earth Observing-1) satellite (the precursor to the OLI instrument), data sets were constructed and flowed through the compression chip and achieved a nominal 1.55:1 compression. As compression varies on the OLI real-time virtual channel, the playback capacity also varies to use the bandwidth that is available.

The multiplexed virtual channels of mission data are provided to the RF X-band subsystem, where the transmitter adds CCSDS layers and LDPC (Low Parity Density Check) 7/8-rate forward error correction to the 384 Mbit/s data stream, resulting in a 440 Mbit/s stream from the X-band subsystem. The X-band stream is modulated, amplified and down-linked from the spacecraft antenna to the ground station antenna/receivers.

The ground station antenna system receives the 8200.5 MHz OQPSK (Offset-keyed Quadrature Phase Shift Keying) X-band signal from the observatory and forwards the down converted 1.2 GHz or 720 MHz intermediate frequency (IF) signal to a programmable telemetry receiver. The IF signals are routed through a matrix switch, providing signal distribution or routing to redundant equipment as needed.

Within the programmable telemetry receiver, the IF signal from the switching matrix is subject to low-pass filtering to prevent subsequent aliasing followed by an AGC (Automatic Gain Control) action. The AGC action is the last analog handling of the signal prior to the digitizer. The entire ground processing that remains is accomplished in the digital domain. The signal is immediately digitally demodulated within a specially designed modified Costas loop and the resultant baseband signal, now a softbit stream, is sent to the bit synchronizer. The bit stream has ambiguity resolved and is then frame synchronized. The frame synchronizer parses the data stream into equal length frames; queuing on a predefined frame synchronization pattern. The data are de-randomized using the conventional CCSDS algorithm and then stripped of parity and bit-corrected by the LDPC 7/8-rate FEC (Forward Error Correction) decoder.

The frame synchronization processor routes the framealigned data stream to the VCDU (Virtual Channel Data Unit) processor. The VCDU processor identifies the unique virtual channels within the frames and outputs these VCDU into individual data streams for packet processing.

The mission data stream from the VCDU processor is processed through the CCSDS packet processor to separate APID (Application Process Identifiers). The packet processor outputs the resulting mission stream to the CFDP processor.

Landsat-8 / LDCM ground system:

The LDCM ground system includes all of the ground-based assets needed to operate the LDCM observatory. The primary components of the ground segment are : 88) 89) 90)

- MOE (Mission Operations Element)

- CAPE (Collection Activity Planning Element)

- GNE (Ground Network Element)

- DPAS (Data Processing and Archive System).

The USGS (United States Geological Survey) -and their associated support and development contractors - will:

- Develop the Ground System (comprised of the Flight Operations and Data Processing and Archive Segments), excluding procurement of the MOE

- Provide ground system functional area expertise across all mission segments

- Lead, fund, and manage the Landsat Science Team

- Acquire the FOT (Flight Operations Team) and produce the FOT products 91)

- Lead the LDCM mission operations, after the completion of the on-orbit checkout period

- Accept and execute all responsibilities associated with the transfer of the LDCM OLI (Operational Land Imager) instrument, TIRS (Thermal Infrared Sensor) instrument, spacecraft bus and Mission Operations Element contracts from NASA following on-orbit acceptance of the LDCM system including assuming contract management”

- Provide system engineering for the USGS-managed segments and elements.

The MOE is being provided by the Hammer Corporation. The MOE contract was awarded in September 2008. The MOE provides capability for command and control, mission planning and scheduling, long-term trending and analysis, and flight dynamics analysis. The overall activity planning for the mission is divided between the MOE and CAPE. The MOE hardware and software systems reside in the LDCM MOC (Mission Operations Center).

The CAPE develops a set of image collection and imaging sensor(s) calibration activities to be performed by the observatory. The CAPE schedules activities on a path-row scene basis. The MOE converts CAPE-generated path-row scenes to observatory activities, schedules these and any other detailed observatory activities, and generates commands necessary to collect the identified scenes and operate the observatory.

The GNE is comprised of two nodes located at Fairbanks, Alaska and Sioux Falls, SD. Each node in the GNE includes a ground station that will be capable of receiving LDCM X-band data. Additionally, each station provides complete S-band uplink and downlink capabilities. The GNE will route mission data and observatory housekeeping telemetry to the DPAS.

The DPAS includes those functions related to ingesting, archiving, calibration, processing, and distribution of LDCM data and data products. It also includes the portal to the user community. The ground system, other than the MOE, is developed by USGS largely through their support service contract. The DAPS will be located at the USGS EROS (Earth Resources Observation and Science) Center in Sioux Falls, SD.

Data access policy: All Landsat data are freely available over the Internet.


Figure 89: Illustration of the Landsat-8 mission elements and communication architecture (image credit: NASA) 92) 93)

LGN (LDCM Ground Network) stations: The LGN is a collection of ground stations with state of the art electronics and sophisticated ground software, each providing similar mission services. The configuration of LGN uses the ground stations located at the EROS Center campus in Sioux Falls, South Dakota, the GLC (Gilmore Creek) ground station in Fairbanks, Alaska, and the SvalSat (Svalbard Satellite Station) ground station in Svalbard (Spitsbergen), Norway.

Each LGN ground station consists of a tracking antenna, S-band and X-band communication equipment, mission data storage and a file routing DCRS (Data Collection and Routing Subsystem). The LGN antenna receives X-band mission data files (autonomous playback or commanded) from the observatory, while simultaneously performing file management and subsequent image collection operations over S-band. The S-band and X-band systems of each LGN station interfaces with the MOE and DPAS in a closed loop fashion.

The USGS maintains agreements with several foreign governments referred to as the Landsat ICs (International Cooperators). The ICs are a special user community that has the ability to receive LDCM mission data from the observatory real-time X-band downlink stream. Real-time imaging sensor and ancillary data (including spacecraft and calibration data) required to process the science data are contained within the real-time stream downlink.

The ICs will be capable of receiving real-time X-band imaging sensor data downlinks and sending metadata to the DPAS. The ICs will submit imaging sensor data collection and downlink requests to the CAPE (via the DPAS user portal). ICs participate in a bilateral DV&E (Data Validation & Exchange) program with the DPAS. This program includes exchange of archive data upon request, and validation of IC processed level 1 data products by the USGS.


Figure 90: Overview of the LDCM system architecture (image credit: USGS)



Total size


Daily volume of 400 scenes

390 GByte


C&DH data rate

260.92 Mbit/s

Space to ground communication

Downlink data rate
LDPC ⅞ rate packet

384 Mbit/s data, 441 Msample/s symbol
8160 bit

Ground station

Minutes per day (14 contacts)

98 minutes

Science archive

5 year archive

~ 400 TB

Table 7: Overview of data volumes for processing and archiving functions

IC (International Cooperator) Ground Stations of the Landsat Program:

• In 41 years, 39 IC stations in 23 countries

• Most still collect and/or distribute Landsat products, reducing the load on U.S. Systems

• More than 215,000 products distributed in 2012

- Represents a nearly 10% off-loading of network bandwidth

- Enhanced regional exploitation of Landsat data


Figure 91: Overview of the IC (International Cooperator) network (image credit: USGS)

IAS (Image Assessment System):

Once the LDCM spacecraft is in orbit, the radiometric, geometric and spatial performance of OLI and TIRS sensors will be continually monitored, characterized and calibrated using the IAS (Ref. 73).

Background: The IAS was originally developed to monitor radiometric and geometric performance of the Landsat-7 ETM+ sensor and the quality of the image data in the Landsat-7 archive. The operational performance monitoring is achieved by processing a number of randomly selected Level 0R (raw reformatted) images to Level 1R (radiometrically corrected) and Level 1G (geometrically corrected) products. In that process, image statistics at different processing levels, calibration data, and telemetry data are extracted and stored in the IAS database for automatic and off-line assessment. The IAS also processes and analyzes the pre-selected geometric and radiometric calibration sites and special calibration acquisitions, e.g. solar diffuser or night data needed for radiometric calibration or noise and stability studies. The final and most important product of the IAS trending and processing is the CPF (Calibration Parameter File), the file that contains parameters needed for artifact corrections and radiometric and geometric processing of raw image data. To maintain the accuracy of the dynamic parameters, the CPF is updated at least once every three months.

The purpose of the LDCM IAS is to maintain accurate spectral, radiometric, spatial and geometric characterization and calibration of LDCM data products and sensors, ensuring compliance with the OLI and TIRS data quality requirements. The IAS will trend results of processing standard Earth images and nonstandard products, such as lunar, solar, dark Earth or stellar images, evaluate image statistics and calculate and store image characteristics for further analysis.


Figure 92: The LDCM ground system concept (image credit: USGS)

The IAS will automatically generate calibration parameters, which will be evaluated by the calibration analysts. In addition to standard operations within the IAS, the CVT (Calibration and Validation Team) will use a ‘toolkit’ module containing instrument vendor developed code and routines developed by the CVT, as a research and development environment for improvements of algorithm functionality and anomaly investigations.

Compared to the previous IAS versions, the LDCM IAS system will have to handle a significantly larger and more complex database that will include characterization data from all normally acquired images (~ 400 scenes per day, with special calibration acquisitions, e.g solar and lunar) processed through the product generation system. OLI’s pushbroom design (~ 75000 detectors), as opposed to an ETM+ whiskbroom design, requires characterization and calibration of about 550 times more detectors than in case of ETM+ (136 detectors) and represents a major challenge for the LDCM IAS. An additional challenge is that the LDCM IAS must handle data from two sensors, as the LDCM products will contain both the OLI and TIRS data.

For radiometric and geometric processing, see Ref. 73).

• Processing latency for real-time downlinks

• Average latency is ~ 5 hours from acquisition to product availability

• Closed loop between ground and space for data management

• The system requirement calls for 85% data availability to the user community through EROS Portal within 48 hours. The actual performance for Landsat-8 averages within 5 hours.

Table 8: Landsat 8 operational characteristics

Landsat-8 reprocessing:

• All Landsat 8 data is being reprocessed to make corrections based on first year data analysis.

• Corrections to both OLI (Operational Land Imager) and the TIRS (Thermal Infrared Sensor) data are being made including:

- all calibration parameter file updates since launch

- improved OLI reflectance conversion coefficients for the cirrus band

- improved OLI radiance conversion coefficients for all bands

- refined OLI detector linearization to decrease striping

- a radiometric offset correction for both TIRS bands

- a slight improvement to the geolocation of the TIRS data

• Approximately 90% of reprocessing is completed with estimated completion by March 30, 2014.

Landsat: Continuing the Legacy

April 1, 2021: Five decades ago, NASA and the U.S. Geological Society launched a satellite to monitor Earth’s landmasses. The Apollo era had given us our first look at Earth from space and inspired scientists to regularly collect images of our planet. The first Landsat — originally known as the Earth Resources Technology Satellite (ERTS) — rocketed into space in 1972. Today we are preparing to launch the ninth satellite in the series. 94)

Each Landsat has improved our view of Earth, while providing a continuous record of how our home has evolved. We decided to examine the legacy of the Landsat program in a four-part series of videos narrated by actor Marc Evan Jackson (who played a Landsat scientist in the movie Kong: Skull Island). The series moves from the birth of the program to preparations for launching Landsat 9 and even into the future of these satellites.

Figure 93: Episode 1: Getting Off the Ground (video credit: NASA Earth Observatory)

The soon-to-be-launched, Landsat-9 is the intellectual and technical successor to eight generations of Landsat missions. Episode 1 answers the “why?” questions. Why did space exploration between 1962 and 1972 lead to such a mission? Why did the leadership of several U.S. government agencies commit to it? Why did scientists come to see satellites as important to advancing earth science? In this episode, we are introduced to William Pecora and Stewart Udall, two men who propelled the project forward, as well as Virginia Norwood, who breathed life into new technology.

Figure 94: Episode 2: Designing for the Future (video credit: NASA Earth Observatory)

The early Landsat satellites carried a sensor that could “see” visible light, plus a little bit of near-infrared light. Newer Landsats, including the coming Landsat 9 mission, have two sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Together they observe in visible, near-infrared, shortwave-infrared, and thermal infrared wavelengths. By comparing observations of different wavelengths, scientists can identify algal blooms, storm damage, fire burn scars, the health of plants, and more.

Episode 2 takes us inside the spacecraft, showing how Landsat instruments collect carefully calibrated data. We are introduced to Matt Bromley, who studies water usage in the western United States, as well as Phil Dabney and Melody Djam, who have worked on designing and building Landsat-9. Together, they are making sure that Landsat continues to deliver data to help manage Earth’s precious resources.

Figure 95: Episode 3: More Than Just a Pretty Picture (video credit: NASA Earth Observatory)

The Landsat legacy includes five decades of observations, one of the longest continuous Earth data records in existence. The length of that record is crucial for studying change over time, from the growth of cities to the extension of irrigation in the desert, from insect damage to forests to plant regrowth after a volcanic eruption. Since 2008, that data has been free to the public. Anyone can download and use Landsat imagery for everything from scientific papers to crop maps to beautiful art.

Episode 3 explores the efforts of USGS to downlink and archive five decades of Landsat data. We introduce Mike O’Brien, who is on the receiving end of daily satellite downloads, as well as Kristi Kline, who works to make Landsat data available to users. Jeff Masek, the Landsat 9 project scientist at NASA, describes how free access to data has revolutionized what we are learning about our home planet.

Figure 96: Episode 4: Plays Well With Others (video credit: NASA Earth Observatory)

For the past 50 years, Landsat satellites have shown us Earth in unprecedented ways, but they haven’t operated in isolation. Landsat works in conjunction with other satellites from NASA, NOAA, and the European Space Agency, as well as private companies. It takes a combination of datasets to get a full picture of what’s happening on the surface of Earth.

In Episode 4, we are introduced to Danielle Rappaport, who combines audio recordings with Landsat data to measure biodiversity in rainforests. Jeff Masek also describes using Landsat and other data to understand depleted groundwater.

1) Bill Ochs, “Status of the Landsat Data Continuity Mission,” Landsat Science Team Meeting, Boise, Idaho, June 15-17, 2010, URL:

2) Brian L. Markham, Philip W. Dabney, James C. Storey, Ron Morfitt, Edward J. Knight, Geir Kvaran, Kenton Lee, “Landsat Data Continuity Mission Calibration and Validation,” Proceedings of the Pecora 17 Memorial Remote Sensing Symposium, Denver, CO, USA, Nov. 18-20, 2008

3) Brian Markham, “Landsat Data Continuity Mission: Overview and Status,” 10th Annual JACIE ( Joint Agency Commercial Imagery Evaluation) Workshop, March 29-31, 2011, Boulder CO, USA, URL:

4) J. R. Irons, J. G. Masek, “Requirements for a Landsat Data Continuity Mission,” PE&RS, Vol. 72, No 10, Oct. 2006, pp. 1102-1108

5) “Landsat Data Continuity Mission,” NASA/GSFC,


7) J. D. McCuistion, C. D. Wende, J. R. Irons, “Landsat Data Continuity Mission: Creating a Unique Government-Industry Partnership for Global Research,” Proceedings of IGARSS 2003, Toulouse, France, July 21-25, 2003

8) J. R. Irons, N. J. Speciale, et al., “Data Specifications for the Landsat Data Continuity Mission,” Proceedings of IGARSS 2003, Toulouse, France, July 21-25, 2003

9) Landsat Data Continuity Mission (LDCM) Implementation Phase - Data Specification,” Jan. 6, 2003, URL:

10) James Verdin, “The requirement for high quality data and information for Science,” 10th Annual JACIE ( Joint Agency Commercial Imagery Evaluation) Workshop, March 29-31, 2011, Boulder CO, USA, URL:

11) “Landsat Data Continuity Mission,” USGS, URL:

12) J. Irons, “New Landsat Data Continuity Mission (LDCM) Memorandum from OSTP,” The Earth Observer, NASA/GSFC, Vol. 18, Issue 1, January-February 2006, pp. 4-5

13) “A Landsat Timeline,” NASA/GSFC, URL:

14) “NASA, USGS Begin Work on Landsat 9 to Continue Land Imaging Legacy,” NASA, April 16, 2015, URL:

15) Cynthia M. O'Carroll, ”NASA Awards Letter Contract for Landsat 9 Imager-2,” NASA, Dec. 31, 2015, URL:

16) “NASA selects contractor for Landsat Data Continuity Mission spacecraft,” April 22, 2008, URL:

17) B. Berger, “Price the Deciding Factor in General Dynamics' LDCM Win,” Space News, May 5, 2008, p. 6

18) Bill Anselm, “LDCM Spacecraft Status,” June 23, 2009, URL:

19) Mike Wulder, Landsat Science Team, “Landsat Data Continuity Mission and Beyond,” 4th Global Vegetation Workshop, Missoula, MT, USA, June 16-19, 2009


21) James R. Irons, John L. Dwyer, “An overview of the Landsat Data Continuity Mission (LDCM),” Proceedings of the SPIE, Volume 7695, 2010, pp. 769508-769508-7, 'Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI,' edited by Sylvia S. Shen, Paul E. Lewis, Paul, April 5-9, 2010, Orlando, FLA, USA,

22) Brian Markham, James Irons, Philip Dabney, “Landsat Data Continuity Mission,” Proceedings of IGARSS (International Geoscience and Remote Sensing Symposium), Vancouver, Canada, July 24-29, 2011

23) Kate Ramsayer, “Landsat Looks to the Moon,” NASA, July 11, 2014, URL:

24) USGS, March 2, 2012, URL:

25) Steve Cole, George Diller, Rani Gran, “NASA Launches New Earth Observation Satellite to Continue 40-Year Legacy ,” NASA News Release 13-040, Feb. 11, 2013, URL:

26) NASA Selects Launch Services Provider for Earth Imagery Satellite,” Oct. 3, 2007, URL:

27) Laurie M Mann, Susan M. Good, Ann M. Nicholson, Mark A. Woodard, “Landsat Data Continuity Mission (LDCM) Safe Operations Ascent Design,” Proceedings of SpaceOps 2012, The 12th International Conference on Space Operations, Stockholm, Sweden, June 11-15, 2012

28) ”Anatomy of Landsat 8, USGS satellite,” USGS, NASA, 13 June 2017, URL:

29) ”Landsat Data Continuity Mission Overview,” NASA, 22 May 2013, URL:

30) ”Eruption at La Soufrière,” NASA Earth Observatory, Image of the Day for 14 April 2021, URL:

31) ”A Thousand Islands in South Korea,” NASA Earth Observatory, Image of the Day for 3 April 2021, URL:

32) ”Historic Floods in New South Wales,” NASA Earth Observatory, Image of the Day for 25 March 2021, URL:

33) ”A Fast-Changing Delta in China,” NASA Earth Observatory, Image of the Day for 24 March 2021, URL:

34) Kate Ramsayer, ”Landsat Satellite Data Warns of Harmful Algal Blooms,” NASA Feature, 22 March 2021, URL:

35) ”Dry Country of Turquoise,” NASA Earth Observatory, Image of the Day for 17 March 2021, URL:

36) ”Deforestation in Papua,” NASA Earth Observatory, Image of the Day for9 March 2021, URL:

37) ”Blue-green Algae at Lake Burrinjuck,” NASA Earth Observatory, Image of the Day for 8 March 2021, URL:

38) ”Breakup at Brunt,” NASA Earth Observatory, Image of the Day for 3 March 2021, URL:

39) ”A Deadly Debris Flow in India,” NASA Earth Observatory, Image of the Day for 25 February 2021, URL:

40) ”From Russia with Questions,” NASA Earth Observatory, Image of the day for 23 February 2021, URL:

41) ”A Watery Day for Lake Lefroy,” NASA Earth Observatory, Image of the Day for 15 February 2021, URL:

42) ”A Short Journey to the Center of the Earth,” NASA Earth Observatory, Image of the Day for 12 February 2021, URL:

43) ”Trading Surfboards for Snowboards,” NASA Earth Observatory, Image of the Day for 9 February 2021, URL:

44) ”Eloise Floods Mozambique,” NASA Earth Observatory, Image of the Day for 3 February 2021, URL:

45) ”Gold Mining in Russia ’s Central Aldan Ore District,” NASA Earth Observatory, Image of the Day for 30 January 2021, URL:

46) ”Brunt Breaking Up with Antarctica this Year?,” NASA Earth Observatory, Image of the Day for 20 January 2021, URL:

47) ”Curious Clouds in the Transantarctic Mountains,” NASA Earth Observatory, Image of the Day for 19 January 2021, URL:

48) ”Rising Seas in Charleston,” NASA Earth Observatory, Image of the Day for 11 January 2021, URL:

49) ”An Outburst from Popocatépetl,” NASA Earth Observatory, Image of the Day for 7 January 2021, URL:

50) Steve Cole, Kate Ramsayer, Jon Campbell, “Landsat 8 Satellite Begins Watch,” NASA Release 13-160, May 30, 2013, URL:

51) Kate Ramsayer, “NASA's Landsat Satellite Looks for a Cloud-Free View,” NASA, May 22, 2013, URL:

52) Jon Campbell, “Landsat Images Provide the Gold Standard for New Earth Applications,” USGS News rooms, May 9, 2013, URL:

53) Rebecca Moore, “A picture of Earth through time,” Google Official Blog, May 9, 2013, URL:

54) Steve Cole, “New Public Application of Landsat Images Released,” NASA, May 9, 2013, URL:

55) “LDCM Mission Updates,” NASA, URL:

56) “Landsat Thermal Sensor Lights Up from Volcano's Heat,” NASA, May 6, 2013, URL:

57) “Landsat Data Continuity Mission,” NASA, News and Features, URL:

58) “LDCM Status Update for May 2, 2013,” NASA, May 15, 2013, URL:

59) Matt Radcliff, Rob Simmon, Jesse Allen, Holli Riebeek, Paul Przyborski, “Come Fly With the Newest Landsat,” NASA Earth Observatory, URL:

60) Tom Holm, “Landsat: Building a Future on 40 Years of Success - April 14, 2013, 705 km Orbit,” 12th Annual JACIE (Joint Agency Commercial Imagery Evaluation) Workshop , St. Louis, MO, USA, April 16-18, 2013, URL:

61) “LDCM Underfly with Landsat 7,” USGS, March 29, 2013, URL:

62) Steve Cole, Jon Campbell, “First Images Released From Newest Earth Observation Satellite,” NASA, USGS, March 21, 2013, URL:

63) “A Closer Look at LDCM's First Scene,” NASA, March 21, 2013, URL:

64) “LDCM Status Update for Feb. 21,” NASA, Feb. 21, 2013, URL:

65) “NASA Completes Critical Design Review Of One Landsat Instrument,” Space Daily, May 28, 2010, URL:

66) Bryant Cramer, “USGS Perspectives on LDCM and Landsat,” Landsat Science Team Meeting,” Jan. 19-21, 2010, Mountain View, CA, USA, URL:

67) Tom Loveland, “Landsat and LDCM Status,” 2008 NASA Carbon Cycle & Ecosystems Joint Science Workshop, April 28-May 2, 2008, University of Maryland, Adelphi, MD, USA

68) James Storey, Michael Choate, Kenton Lee, “Geometric performance comparison between the OLI and ETM+,” Proceedings of the Pecora 17 Memorial Remote Sensing Symposium, Denver, Co, USA, Nov. 16-20, 2008

69) Jeanine Murphy-Morris, “Operational Land Imager ,” Landsat Science Team Meeting, Sioux Falls, SD, Jan. 8, 2008, URL:

70) Edward J. Knight, “OLI Overview and Status,” Landsat Science Team Meeting, July 15, 2008, Reston, VA, URL:

71) Bill Ochs, “Status of the Landsat Data Continuity Mission,” Landsat Science Team Meeting, July 15, 2008, Reston, VA, URL:

72) Edward J. Knight, Brent Canova, Eric Donley, Geir Kvaran, Kenton Lee, “The Operational Land Imager:Overview and Performance,” 10th Annual JACIE ( Joint Agency Commercial Imagery Evaluation) Workshop, March 29-31, 2011, Boulder CO, USA, URL:

73) Esad Micijevic, Ron Morfitt, “Operational Calibration and Validation of Landsat Data Continuity Mission (LDCM) Sensors using the Image Assembly System (IAS),” Proceedings of IGARSS (IEEE International Geoscience and Remote Sensing Symposium) 2010, Honolulu, HI, USA, July 25-30, 2010

74) Brian L. Markham, Philip W. Dabney, Edward J. Knight, Geir Kvaran, Julia A. Barsi, Jeanine E. Murphy-Morris, Jeffrey A. Pedelty, “The Landsat Data Continuity Mission Operational Land Imager (OLI) Radiometric Calibration,” Proceedings of IGARSS (IEEE International Geoscience and Remote Sensing Symposium) 2010, Honolulu, HI, USA, July 25-30, 2010, URL:

75) Brian Markham, “LDCM On-Orbit Cal/Val Considerations,” Proceedings of the Landsat Science Team Meeting, Mesa, AZ, USA, March 1-3, 2011, URL:

76) “Ball Aerospace Completes CDR For Landsat's Operational Land Imager,” Nov. 26, 2008, Spacemart, URL:

77) “NASA Completes Critical Design Review of Landsat Data Continuity Mission,” Science Daily, June 1, 2010, URL:

78) Dennis Reuter, Cathy Richardson, James Irons, Rick Allen, Martha Anderson, Jason Budinoff, Gordon Casto, Craig Coltharp, Paul Finneran, Betsy Forsbacka, Taylor Hale, Tom Jennings, Murzy Jhabvala, Allen Lunsford, Greg Magnuson, Rick Mills, Tony Morse, Veronica Otero, Scott Rohrbach, Ramsey Smith, Terry Sullivan, Zelalem Tesfaye, Kurtis Thome, Glenn Unger, Paul Whitehouse, “The Thermal Infrared Sensor on the Landsat Data Continuity Mission,” Proceedings of IGARSS (International Geoscience and Remote Sensing Symposium), Honolulu, Hawaii, USA, July 25-30, 2010, URL:

79) Ramsey L. Smith, Kurtis Thome, Cathleen Richardson, James Irons, Dennis Reuter, “Terrestrial Applications of the Thermal Infrared Sensor, TIRS,” URL:

80) M. Jhabvala, D. Reuter, K. Choi, C. Jhabvala, M. Sundaram, “QWIP-Based Thermal Infrared Sensor for the Landsat Data Continuity Mission,” Proceedings of the QSIP (Quantum Structure Infrared Photodetector) 2009 International Conference, January 18-23, 2009, Yosemite, CA

81) M. Jhabvala, D. Reuter, K. Choi, M. Sundaram, C. Jhabvala, A. La, A. Waczynski, J. Bundas, “The QWIP focal plane assembly for NASA's Landsat Data Continuity Mission,” Proceedings of the SPIE, 'Infrared Technology and Applications XXXVI,' edited by Bjørn F. Andresen, Gabor F. Fulop, Paul R. Norton, Volume 7660, April 5-9, 2010, Orlando, FLA, USA, pp. 76603J-76603J-13, doi:10.1117/12.862277

82) M. Jhabvala, K. K. Choi, C. Monroy, A. La, “Development of a 1 k × 1 k, 8–12 µm QWIP array,” Infrared Physics & Technology, Volume 50, Issues 2-3, April 2007, pp. 234-239

83) K. Thome, D. Reuter, A. Lunsford, M. Montanaro, R. Smith, Z. Tesfaye, B. Wenny, “Calibration overview for the Thermal Infrared Sensor (TIRS) on the LandsatData Continuity Mission,” 10th Annual JACIE ( Joint Agency Commercial Imagery Evaluation) Workshop, March 29-31, 2011, Boulder CO, USA, URL:

84) K. Thome, D. Reuter, A. Lunsford, M. Montanaro, R. Smith, Z. Tesfaye, B. Wenny, “Calibration of ther Thermal Infrared Sensor on the Landsat Data Continuity Mission,” Proceedings of IGARSS (International Geoscience and Remote Sensing Symposium), Vancouver, Canada, July 24-29, 2011

85) Jason Budinoff, Konrad Bergandy, Joseph Schepis, Adam Matuszeski, Richard Barclay, “Development of the Scene Select Mechanism for the Thermal Infrared Sensor Instrument,” Proceedings of the 14th European Space Mechanisms & Tribology Symposium – ESMATS 2011, Constance, Germany, Sept. 28–30 2011 (ESA SP-698)

86) Mary Pagnutti, Robert E. Ryan, Kara Holekamp, “Landsat Data Continuity Mission and Sentinel-2 Multi-Spectral Instrument Image Product Simulations for Sensor Comparisons and Data Fusion Research,” Proceedings of the 11th Annual JACIE (Joint Agency Commercial Imagery Evaluation ) Workshop, Fairfax, VA, USA, April 17-19, 2012, URL:

87) James Nelson, Robert Patschke, Howard Garon, Alan Ames, Claire Mott, Grant Mah, Jason Williams, James Joseph, “Landsat Data Continuity Mission (LDCM) Space to Ground Mission Data Architecture,” Proceedings of the 2012 IEEE Aerospace Conference, Big Sky, Montana, USA, March 3-10, 2012

88) Landsat Data Continuity Mission (LDCM), Ground System (GS) Integration and Test Plan,” USGS, LDCM-I&T-001, Version 1.1, September 2009, URL:
/LDCM-I&T-001_GS%20Integration% 20&%20Test%20Plan_v1.1.pdf

89) Jonathan Gal-Edd, “LDCM Ground System - Network Lessons Learned,” SOSTC GSFC May 24-25, 2010, URL:

90) Dave Hair , Doug Daniels, “Landsat Data Continuity Mission (LDCM) USGS Project Status Report,” Proceedings of the Landsat Science Team Meeting, Mesa, AZ, USA, March 1-3, 2011, , URL:

91) Susan M. Good, Ann M. Nicholson, Mark A. Woodard, “Landsat Data Continuity Mission (LDCM) Flight Dynamics System (FDS),” Proceedings of SpaceOps 2012, The 12th International Conference on Space Operations, Stockholm, Sweden, June 11-15, 2012

92) G. R. Mah, H. Garon, C. Mott, M. O'Brien, “Ground System Architectures Workshop 2014, Landsat 8 Test as You Fly, Fly as You Test,” Proceedings of GSAW 2014 (Ground System Architectures Workshop), Los Angeles, CA, USA, Feb. 24-27, 2014, URL:

93) Del Jenstrom, “Status of the Landsat Data Continuity Mission,” Proceedings of the Landsat Science Team Meeting, Mesa, AZ, USA, March 1-3, 2011

94) Matthew Radcliff, Mike Carlowicz,”Landsat: Continuing the Legacy,” NASA Earth Observatory, 1April 2021, URL:

The information compiled and edited in this article was provided by Herbert J. Kramer from his documentation of: ”Observation of the Earth and Its Environment: Survey of Missions and Sensors” (Springer Verlag) as well as many other sources after the publication of the 4th edition in 2002. - Comments and corrections to this article are always welcome for further updates (

Spacecraft    Launch    Mission Status    Sensor Complement    Ground Segment    References    Back to top