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.

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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)




Spacecraft:

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)

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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).

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

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Figure 4: Block diagram of the C&DH subsystem (image credit: NASA, USGS, Ref. 80)

- 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.

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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.

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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. 80).

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

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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. 80).

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).

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

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

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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 intofour 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 2020, in addition to some of the mission milestones.

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 imagery of 2020

• March 17, 2020: One of the oldest continuously inhabited settlements in the world, Hasankeyf, has been home to more than 20 cultures over the past 12,000 years. Assyrians carved caves into the surrounding limestone cliffs. Romans built a fortress to monitor crop and livestock transportation. Travelers on the Silk Road often stopped in the area to trade during the Middle Ages. 30)

- Remnants of past cultures have been preserved for thousands of years in Hasankeyf, which was absorbed by the Ottoman Empire in the 1500s and has remained part of Turkey ever since. But those artifacts—thousands of human-made caves and hundreds of well-preserved medieval monuments—may soon be underwater. A new dam and reservoir threatens to drown the city.

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Figure 13: The 12,000-year old town of Hasankeyf could soon be underwater due to the construction of a new dam. This natural-color image of OLI on Landsat-8 shows Hasankeyf on February 22, 2019. The reservoir began filling in July 2019 (NASA Earth Observatory, images by Lauren Dauphin, using Landsat data from the U.S. Geological Survey)

- Located about 56 kilometers (35 miles) downstream of Hasankeyf, the 138-meter tall Ilisu Dam is expected to provide 1,200 megawatts of electricity (around 1.5 percent of Turkey’s total power-generating capacity). The dam is part of Turkey’s Southeastern Anatolia Project, which consists of 19 hydroelectric plants and 22 dams on the Tigris and Euphrates Rivers. The effort is designed to help promote economic growth and energy independence for the country. But there will also be a cost.

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Figure 14: This natural-color image of OLI on Landsat-8 shows Hasankeyf on 12 March 2020 (image credit: NASA Earth Observatory and data from the USGS)

- As of February 2020, water levels behind the dam were rising at a rate of about 15 cm/day. The reservoir is only about one quarter full and is expected to rise another 50 meters in upcoming months—enough to submerge thousands of nearby caves and nearly all of the Hasankeyf fortress previously occupied by the Romans, Mongols, and Seljuk Turks.

- Some historical structures (including a tomb, mosque, and ancient bath) and all residents have been relocated to a new town on a nearby hill called New Hasankeyf (or Yeni Hasankeyf). Once the reservoir is full, a ferry system will shuttle people between the new town and what remains above water in Hasankeyf.

• March 16, 2020: When NASA engineers need to communicate with distant spacecraft, the signal goes out through one of three NASA communication complexes spread around the world. Without the Deep Space Network (DSN), it would not be possible to stay in touch with missions such as Voyager 1—which launched in 1977 and is still sending back signals from interstellar space, some 22 billion kilometers (14 billion miles) away—or the Deep Space Climate Observatory (DSCOVR)—a satellite that takes full-disc images of Earth from 1.5 million kilometers (1 million miles) away. 31)

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Figure 15: On February 24, 2020, the Operational Land Imager (OLI) on Landsat-8 acquired a natural-color image of the communications site in Canberra, Australia (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)

- Each of the three DSN complexes has large antennas that are designed to enable daily radio communication between NASA spacecraft and engineers on Earth. Canberra, like the other two facilities, has at least four antennas, each with large, parabolic dishes and sensitive receiving stations. In the Landsat image of Figure 15, the dishes appear as white circles.

- The most powerful antenna at the Canberra station is Deep Space Station 43. With a diameter of 70 meters, it is the largest steerable parabolic antenna in the Southern Hemisphere. In March 2020, engineers began working on critical upgrades that will reduce the risk of unplanned outages and make the antenna more compatible with future missions, such as the Mars 2020 rover and other Moon and Mars missions.

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Figure 16: Photo of the Canberra DSN site (image credit: DSN. DSN is a service of NASA’s Space Communications and Navigation Program (SCaN) within the agency’s Human Exploration and Operations Mission Directorate)

- The other two DSN sites are in Goldstone, California, and near Madrid, Spain, putting the three stations about 120 degrees apart. The strategic placement allows for continuous communication with spacecraft even as Earth rotates. Together, the three stations make the Deep Space Network the largest and most sensitive scientific telecommunications system in the world.

- NASA’s first station was established in Goldstone, California, in 1958. When it came time to build the second station, the agency chose the Tidbinbilla Valley, 35 kilometers southwest of Canberra, due to its proximity to the city and the fact that the surrounding ridges help shield the site from unwanted radio interference. Construction of the complex began in June 1963, with communication operations beginning in December 1964, in time to support the Mariner 4 encounter with Mars.

• March 7, 2020: In late 2019, the Spanish electrical company Iberdrola completed the largest photovoltaic plant in Europe. Comprised of more than 1.4 million solar panels, the Núñez de Balboa plant has an installed capacity of 500 megawatts and is expected to supply energy to 250,000 people per year. The plant, which took less than a year to complete, is scheduled to start operating in early 2020. 32)

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Figure 17: This image shows the Núñez de Balboa photovoltaic plant, located in the town of Usagre in the western Spanish region of Extremadura. The image was acquired with OLI on Landsat-8 on 2 February 2020. The solar panels cover an area of nearly 10 km2 with a mass of than 12,000 tons (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- In addition to residential users, the plant will supply energy to local businesses from a Spanish bank to a supermarket distributor. The company projects that Núñez de Balboa can eliminate 215,000 tons of carbon dioxide emissions per year.

- The photovoltaic plant is part of Iberdrola’s plan to increase clean energy in Spain. The company is planning several projects that could provide 2,000 MW of solar and wind power in Extremadura by 2022. One of the plants—the 590 MW Francisco Pizarro project—is expected to surpass the Núñez de Balboa plant as Europe’s largest solar plant. By 2030, the company plans to commission up to 10,000 MW of solar and wind energy.

• February 27, 2020: During the last Ice Age, advancing and retreating glaciers in northeastern Canada scraped the surface clean of debris to help make visible some stunning fold patterns in the basaltic rock. Those folds are still visible today and appear in these images, which show part of a geologic belt called the New Quebec Orogen (also known as the Labrador Trough). 33)

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Figure 18: The orogen stretches southeast from Ungava Bay through Quebec and Labrador, with striking geologic features throughout. These images highlight the deformation in Earth’s crust just east of the Caniapiscau River. The images were acquired on February 13, 2020, by the Operational Land Imager (OLI) on Landsat-8, and were overlaid on a digital elevation model from the Shuttle Radar Topography Mission (SRTM) to give a sense of the topography. Wintertime snow and ice blanket some of the landscape (image credit: NASA Earth Observatory images by Joshua Stevens. using topographic data from the Shuttle Radar Topography Mission (SRTM) and Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)

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Figure 19: Detail 1 image of Figure 18. The striking patterns in northeastern Canada’s flood basalts tell a story of continental collisions that played out almost two billion years ago.

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Figure 20: Detail 2 image of Figure 18

- ”The patterns shown in the images have quite a long history—from rifting to cooling to folding—during continental collision,” said Deanne van Rooyen, a geologist at Cape Breton University who has studied the region. About 2.17 billion years ago, she explained, molten rock erupted from rifts in Earth’s crust and flooded the landscape with basalt. Successive flows of this so-called “flood basalt” were laid down in nearly horizonal layers, producing the step-like pattern visible in these images. When viewed up close, most flows show spectacular columnar jointing structures.

- David Corrigan of the Geological Survey of Canada notes that the cliff face of each flow (or series of flows) represents a step, each standing about 50 to 70 meters tall. Geologists often refer to geometry like this as “traps”—the Dutch word for “step”—which can be found around the planet in places like the Deccan Traps in India or the Siberian Traps in Russia. “I affectionately name our example the ‘Labrador Traps,’” said Corrigan, who previously led a geological mapping project in the general area.

- The gentle folding of the traps came later with the collisions of cratons—ancient, stable parts of Earth’s crust—with a microcontinent known as the “Core Zone” sandwiched in between, Van Rooyen explained. The Core Zone collided first with the North Atlantic Craton around 1.87 billion years ago, and then collided (with the North Atlantic Craton attached) with the Superior Craton between about 1.80 billion years ago. This more recent collision initially occurred head on, but became oblique as the North Atlantic Craton rotated. The rotation caused Core Zone rocks to move down the side of the Superior Craton, and the dragging of layered rock alongside the solid craton gave rise to the folded patterns.

- “These types of folds are not rare,” Corrigan said. “But in this case, they are made spectacular by the nature of the rocks they fold. With a bit of erosion, they become stair-shaped. If they are folded a bit, the stairs, or steps, stand out.”

- But the story does not end there. Sometime after the folds formed, the rock became brittle and the continued motion began to produce linear breaks. The offsets on either side of the cracks indicate movement along the faults. In some places you can see where layers were dragged along the plane of the fault, with the friction causing the folds to curve back toward the fault—an effect called “drag folding.”

- Satellite images and field work have given scientists a good sense of the region’s geology, but there are still plenty of questions to be investigated. “The basic geology is well-mapped,” Van Rooyen said, “but there has been a lot of new work in the area in the past decade by the Geological Survey of Canada as part of their Geomapping for Energy and Minerals (GEM) program, the Newfoundland and Labrador Geological Survey, the Quebec Ministry of Energy and Natural Resources, and many university geologists like me.”

- For example, the flood basalts provide a hint of what the chemical composition of the underlying mantle may have been 2.17 billion years ago, providing key information on Earth’s evolution. Scientists also want to know more about the timing of the evolution of the New Quebec Orogen, such as when the different phases of collision happened. There are also questions about the pressures and temperatures when the rocks formed, as well as questions about the economic potential for the rocks to host gold, platinum, or other important metals.

• February 21, 2020: On February 6, 2020, weather stations recorded the hottest temperature on record for Antarctica. Thermometers at the Esperanza Base on the northern tip of the Antarctic Peninsula reached 18.3°C (64.9°F)—around the same temperature as Los Angeles that day. The warm spell caused widespread melting on nearby glaciers. 34)

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Figure 21: The warm temperatures arrived on February 5 and continued until February 13, 2020. The images above show melting on the ice cap of Eagle Island and were acquired by the Operational Land Imager (OLI) on Landsat-8 on February 4 and February 13, 2020 (image credit: NASA Earth Observatory, images by Joshua Stevens, using Landsat data from the U.S. Geological Survey, Story by Kasha Patel)

- Mauri Pelto, a glaciologist at Nichols College observed that during the warming event, around 1.5 square kilometers (0.9 square miles) of snowpack became saturated with meltwater (shown in blue above). According to climate models, Eagle Island experienced peak melt—30 millimeters (1 inch)—on February 6. In total, snowpack on Eagle Island melted 106 millimeters (4 inches) from February 6- February 11. About 20 percent of seasonal snow accumulation in the region melted in this one event on Eagle Island.

- “I haven’t seen melt ponds develop this quickly in Antarctica,” said Pelto. “You see these kinds of melt events in Alaska and Greenland, but not usually in Antarctica.” He also used satellite images to detect widespread surface melting nearby on Boydell Glacier.

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Figure 22: The heat is apparent on this map, which shows temperatures across the Antarctic Peninsula on February 9, 2020. The map was derived from the Goddard Earth Observing System (GEOS) model, and represents air temperatures at 2 meters above the ground. The darkest red areas are where the model shows temperatures surpassing 10ºC (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey and GEOS-5 data from the Global Modeling and Assimilation Office at NASA GSFC. Story by Kasha Patel)

- Pelto noted that such rapid melting is caused by sustained high temperatures significantly above freezing. Such persistent warmth was not typical in Antarctica until the 21st century, but it has become more common in recent years.

- The warm temperatures of February 2020 were caused by a combination of meteorological elements. A ridge of high pressure was centered over Cape Horn at the beginning of the month, and it allowed warm temperatures to build. Typically, the peninsula is shielded from warm air masses by the Southern Hemisphere westerlies, a band of strong winds that circle the continent. However, the westerlies were in a weakened state, which allowed the extra-tropical warm air to cross the Southern Ocean and reach the ice sheet. Sea surface temperatures in the area were also higher than average by about 2-3°C.

- Dry, warm foehn winds also could have played a part. Foehn winds are strong, gusty winds that cause downslope windstorms on mountains, often bringing warm air with them. In February 2020, westerly winds ran into the Antarctic Peninsula Cordillera. As such winds travel up the mountains, the air typically cools and condenses to form rain or snow clouds. As that water vapor condenses into liquid water or ice, heat is released into the surrounding air. This warm, dry air travels downslope on the other side of the mountains, bringing blasts of heat to parts of the peninsula. The drier air also means fewer clouds and more direct sunlight.

- “Two things that can make a foehn-induced melt event stronger are stronger winds and higher temperatures,” said Rajashree Tri Datta, an atmospheric researcher at NASA’s Goddard Space Flight Center. With warmer air in the surrounding atmosphere and ocean, the conditions were conducive this month for a foehn wind event.

- This February heatwave was the third major melt event of the 2019-2020 summer, following warm spells in November 2019 and January 2020. “If you think about this one event in February, it isn’t that significant,” said Pelto. “It’s more significant that these events are coming more frequently.“

• February 17, 2020: About 7,000 years ago, a vast lake spread hundreds of km2 across north-central Africa. Known to scientists as Lake Mega Chad, it covered more than 400,000 km2 (150,000 square miles) at its peak, making it slightly larger than the Caspian Sea, the biggest lake on Earth today. 35)

- Modern Lake Chad has shrunk to just a fraction of its former size, but evidence of the lake’s ancient shorelines is still etched into desert landscapes — hundreds of kilometers from the shores of the modern lake.

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Figure 23: Detail image of the Space Shuttle SRTM mission in February 2000 showing the ancient short line of Lake Chad. The spits etched into a desert in Chad were actually formed thousands of years ago along the shores of a vast lake (image credit: NASA Earth Observatory, image by Joshua Stevens, using topographic data from the Shuttle Radar Topography Mission (SRTM))

- Sand spits generally form along coves and estuaries as prevailing winds drive currents that transport sand and other sediments along the shore. In the era of Lake Mega Chad (and today), winds blew from the northeast, which caused the spit to grow toward the southwest.

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Figure 24: Superimposed images of SRTM (2000) and Landsat-8 data (2020). Elevation data from the Shuttle Radar Topography Mission (SRTM) reveals the former shorelines of the lake. In the map above, lower-elevation areas appear darker. An image from the Operational Land Imager (OLI) on Landsat-8 marks the location of the present-day lake. The elevation data highlights sand spits and beach ridges that formed along Lake Mega Chad’s northeastern shores [image credit: NASA Earth Observatory images by Joshua Stevens. using topographic data from the Shuttle Radar Topography Mission (SRTM) and Landsat data from the U.S. Geological Survey. Story by Adam Voiland]

• February 14, 2020: In a waiting game that spanned several months of 2019 and 2020, scientists watched cracks grow across the tongue of Antarctica’s Pine Island Glacier. It was always a matter of when, not if, the glacier would spawn a new iceberg. In this case, it spawned many. 36)

- The waiting ended on February 9, 2020, when radar images from the Sentinel-1 satellites showed numerous icebergs detaching from the glacier and floating in Pine Island Bay. The largest piece, Iceberg B-49, is about twice the size of Washington D.C. It is the only piece large enough to be named and tracked by the U.S. National Ice Center.

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Figure 25: Through patchy cloud cover satellites captured natural-color imagery, including the detailed view, which was acquired on February 11, 2020, with the Operational Land Imager (OLI) on Landsat-8. Infrared data are superimposed on natural-color wavelengths to emphasize detail through areas of thin clouds (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)

- In the past, large icebergs would break from Antarctica’s Pine Island Glacier every four to six years. Calving now occurs almost annually, and the bergs tend to more easily break up into smaller pieces. The fracturing indicates just how weak the thinning ice shelf has become, as relatively warm water in Pine Island Bay is partly melting it from below. Thinning of the floating ice tongue destabilizes the overall shelf by reducing the ice’s contact with underwater “pinning points” that slow ice flow and influence how the shelf calves.

- The last major iceberg to break from Pine Island Glacier was B-46 in October 2018. Around that time, the main flow of the glacier lost contact with thicker ice from a tributary flowing from the southwest. As a result, Pine Island Glacier (PIG) lost ice at the front of its southwest shear margin—visible in these images as the crumbled ice between the edge of the fast flow of the glacier and the South Ice Shelf.

- “When Pine Island Glacier lost contact with the thicker ice of the southwest tributary it was like the loss of protection on the glacier’s flank,” said NASA/UMBC glaciologist Christopher Shuman. “The growing indentation into the shear margin was likely a factor in the formation of all the rifts that then caused PIG’s ice to fall apart in the last calving. This area is likely to continue to breakdown as Pine Island Glacier pushes past the thinner South Ice Shelf, further exposing that flank.”

Figure 26: Retreat at Pine Island Glacier. Pine Island Glacier is one of the fastest-retreating glaciers in Antarctica. Watch the glacier’s ice front as it retreats and sheds some notable icebergs over the past two decades. Images were acquired by the MODIS instrument on NASA’s Terra and Aqua satellites from 2000 to 2020. Notice that there are times when the front appears to stay in the same place or even advance, though the overall trend is toward retreat (video credit: NASA Earth Observatory)

- Pine Island Glacier, along with neighboring Thwaites Glacier, is one of the main pathways for ice entering the Amundsen Sea from the West Antarctic Ice Sheet. Pine Island is also one the fastest-retreating glaciers in Antarctica. It is a normal part of life for the floating ice from huge glaciers to fracture near the seaward edge and calve off as icebergs. If the icebergs break off at a rate that matches the glacier’s forward flow, the ice front stays in place. But the calving rate at Pine Island has increased more than the glacier been able to move inland ice forward into Pine Island Bay.

• February 6, 2020: Antarctica’s Thwaites Glacier has been in the spotlight in recent years, as scientists have undertaken a multi-part international project to study the vast glacier from all angles. The urgency stems from observations and analyses showing that the amount of ice flowing from Thwaites—and contributing to sea level rise—has doubled in the span of three decades. Scientists think the glacier could undergo even more dramatic changes in the near future. 37)

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Figure 27: This Thwaites Glacier image was observed with ETM+ (Enhanced Thematic Mapper Plus) on Landsat-7 on 2 December 2001 [image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen, with image interpretation by Christopher Shuman (NASA/UMBC) and Ted Scambos (University of Colorado)].

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Figure 28: This Thwaites Glacier image was observed with OLI (Operational Land Imager) on Landsat-8 on 28 December 2019 [image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen, with image interpretation by Christopher Shuman (NASA/UMBC) and Ted Scambos (University of Colorado)].

- This image pair of Figures 27 and 28 demonstrates the changes that have occurred since the start of this century. Both images show the glacier where it exits the land in West Antarctica and stretches over the Amundsen Sea as thick floating ice. Ice that originates on land can raise sea level if it is delivered to the ocean at a faster rate than it is being replaced inland by snowfall. Indeed, Thwaites Glacier is one of the largest contributors to global sea level rise from the West Antarctic Ice Sheet. The flow speed of Thwaites has been increasing, while inland snowfall has not changed significantly.

- Notice the size of the glacier’s main ice tongue in 2001, when the glacier was advancing by about 4 kilometers per year. The large rift across the glacier eventually spawned Iceberg B-22 in 2002.

- In the past ten years, the tongue has continued to fracture and separate from the Thwaites Eastern Ice Shelf. By the time the 2019 image was acquired, the main tongue had retreated substantially, and the ocean in front of Thwaites had become filled with mélange, a mixture of icebergs and sea ice.

- Unlike Pine Island Glacier—which tends to shed large icebergs every few years (now almost annually)—the icebergs that now break from Thwaites are generally not large enough to be named and tracked by the U.S. National Ice Center. Instead, the glacier is constantly producing many small broken bits.

- The melting of floating ice as it makes contact with the ocean is a key reason why the glacier is coming unglued. Seawater that is a few degrees above freezing is melting the ice shelf from below. Warm water has recently been recorded near the Thwaites Glacier grounding line—the location where the glacial ice rests on the seafloor.

- “What the satellites are showing us is a glacier coming apart at the seams,” said Ted Scambos, a senior scientist at the University of Colorado. “Every few years a new area seems to be letting go and accelerating. Like taffy being stretched out, this glacier is being drawn into the ocean.”

• February 1, 2020: Natural-color satellite images can capture art-like beauty when sediments trace water currents and eddies. Other kinds of data can make that art intersect with scientific understanding. 38)

- See, for example, the colorful details in the Mediterranean Sea (Figure 29). When paired with a false-color observation of temperature, scientists can say more about the likely source of the color.

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Figure 29: Aida Alvera-Azcárate, an ocean scientist at University of Liège, noticed colorful swirls off the coast of western Italy starting in late December 2019, as observed by the European Space Agency’s Sentinel-3 and Sentinel-2 satellites. The Operational Land Imager (OLI) on Landsat 8 acquired a similar scene on December 26 (top) showing colorful waters between the island of Elba and the Italian mainland (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)

- The colors are primarily the result of suspended sediments that were carried by several rivers into the sea. (There might be some phytoplankton contributing as well). Water with more sediment appears green-brown, and water with less sediment is light blue.

- When there is ample sediment in a body of water, it becomes easy to see otherwise invisible motions. Along the west coast of Italy, a strong current flows south. As the water is channeled between the island and mainland, the current encounters a shallow bay where it becomes unstable. It starts to swirl and produce numerous small-scale eddies.

- Small features like these—less than 10 km diameter—are common in the oceans. According to Alvera-Azcárate, they can contribute to the total movement of water, nutrients, and heat around the planet. “But their precise role in this is very difficult to assess because they are not easy to model or measure,” she said.

- Sediments and phytoplankton are not always present to act as tracers, and the features can be short-lived, quickly erased by stronger currents. Still, satellites are helping scientists compile the observations needed to better understand how the role of small-scale eddies compares to larger and more long-lived flows.

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Figure 30: False-color images offer a different but equally compelling view. The Thermal Infrared Sensor (TIRS) on Landsat-8 acquired the second image at the same time that the natural-color image was acquired. TIRS measures the water’s relative warmth (yellows and oranges) and coolness (blues and white). The spatial resolution is lower, but you can still see some of the same swirling patterns (image credit: NASA Earth Observatory, image by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)

- Temperature is useful for revealing the origin of the waters. River water is cooler than the seawater here, so the white plume —where water from the Corina River enters the bay—stands out. Interestingly, that same plume is not apparent in the natural-color image, which means the river might not be contributing much sediment. Alvera-Azcárate thinks the main sources of sediment come from larger rivers to the north along the Italian coast. She notes: “Having the two variables—color and temperature—helps to better understand what is going on.”

• January 30, 2020: For several decades, scientists and astronauts observing Lake Baikal have noticed giant rings in the spring ice on one of the world’s oldest and deepest lakes. Russian researchers first spotted them in satellite images in the early 2000s, but it was after astronauts on the International Space Station photographed two ice rings in April 2009 that the phenomenon become a topic of international study and fascination. 39)

- While the rings have attracted speculation and a few conspiracy theories, decades of satellite data and field-based studies have shed light on why they form. “Results of our field surveys show that before and during ice ring manifestation, there are warm eddies that circulate in a clockwise direction under the ice cover,” explained Alexei Kouraev, a hydrologist at the University of Toulouse. “In the eddy center, the ice does not melt — even though the water is warm — because the currents are weak. But on the eddy boundary, the currents are strong and warmer water leads to rapid melting.”

- During field work, Kouraev and his colleagues from France, Russia, and Mongolia drilled holes near ice rings and deployed sensors capable of measuring the temperature and salinity of the water column to a depth of 200 meters. Typically the water in the eddies was 1 to 2 degrees Celsius warmer than the surrounding water.

- The research team is still investigating what causes the eddies, but an analysis of meteorological and hydrological data suggests that they typically get going in autumn, before ice has covered the lake. They likely form because of persistent wind patterns and the inflow of water from certain rivers. The shape of the coastline and lake bottom also play a role in determining where the eddies form and move.

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Figure 31: The puzzling features are most easily seen from above, but they pose real risks at the surface. The Landsat-8 image shows an ice ring in the central part of the lake on April 1, 2016. That ring was particularly well-studied because Kouraev and colleagues were nearby, taking measurements of the ice and underlying water. The thin ice of the ring appears darker and more transparent than the whiter, thicker ice surrounding it (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Adam Voiland)

- This particular ring was more than just a scientific oddity; it posed a serious hazard because Russians often drive over the ice to get across the lake in the winter. In fact, a few weeks before the satellite image was captured, a van broke through and sank along the edge of this ice ring; the driver and passengers escaped and were rescued. A few days later, a second van (Figure 32) broke through and got stuck along the eastern boundary of the ice ring.

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Figure 32: Photo of the van which broke into an ice ring (photo credit: Alexander Beketov)

- To better understand where and how often giant ice rings form, scientists mined all the available satellite imagery of Lake Baikal back to 1969 and identified dozens of rings. Most appeared in March or April and had diameters of about 5 to 7 kilometers (3 to 4 miles) — too big to recognize from the ground but easily seen from above. Some rings were ephemeral, lasting a day or two. Others persisted for weeks or months.

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Figure 33: On April 25, 2019, the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on NASA’s Terra satellite acquired an image of the most recent Baikal ice rings detected by satellites (image credit: NASA Earth Observatory, image by Lauren Dauphin, using MODIS data from NASA EOSDIS/LANCE and GIBS/Worldview)

- The ring in the image of Figure 31 formed off the Nizhneye Izgolovye Cape, one of the most common places for rings to occur. Of the 57 rings detected on Baikal, about 13 formed in this area. According to Kouraev, that is likely because a sharp slope on the lake bed tends to “trap” eddies in this area. “People often drive a direct line between Nizhneye Izgolovye Cape and Khoboy Cape,” he said, “but we strongly advise that they take a more southerly route to avoid the frequent ice rings in this dangerous region.”

- For a number of years, one of the most discussed theories in both the scientific community and the news media was that gas hydrates — an ice-like form of methane found at the bottom of the lake — may play a role. Work by Kouraev and colleagues suggests otherwise. When combing through Landsat and MODIS satellite image archives for evidence of rings, scientists identified several over shallow parts of the lake where conditions are not right for gas hydrates. They have also discovered giant rings in satellite imagery of Lake Hovsgol in Mongolia and Lake Teletskoye in Russia’s Altai Republic, both of which are shallower than Lake Baikal and are not known to have gas emissions.

• January 24, 2020: Fifty years ago, Cancún was virtually unknown to the world. With a population of roughly 100 people, the town was located in one of the poorest regions of Mexico. It had odd-shaped sand dunes and a coast occupied by marshes, mangroves, and a snake-infested jungle. Over the past five decades, though, Cancún has been transformed into one of Mexico’s top tourist attractions. The growth didn’t happen by chance. 40)

- In the late 1960s, the Mexican government took an interest in developing the country’s tourism sector to boost the economy. To determine the perfect place, government officials analyzed statistics from several successful resort locations such as Miami Beach and Acapulco. They compiled information on the number of tourists, number of hotel rooms, average temperatures, average rainfall, and hurricane events and fed them into a computer program. The computer selected several candidates for a new resort town. Officials then visited each site along Mexico’s approximately 10,000 km of coastline to personally inspect the beaches, swimming, and living conditions.

- In the end, they selected Cancún because it had good weather year-round, blue seas, and white sand beaches. It was also located near great archeological treasures, such as the Mayan ruins at Chichen Itza and Tulum. It also had a high level of poverty and no existing industry.

- In January 1970, technicians arrived and began building the resort town. By September 1974, Cancún’s first hotel opened its doors. Within a year, Cancún added more hotels and welcomed around 100,000 tourists. Today, Cancún accommodates around two million visitors annually and generates around one-fourth of the country’s tourism revenue.

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Figure 34: Image of Cancún acquired by the TM instrument of Landsat-5 on 28 March 1985 (image credit: NASA, image by Allison Nussbaum, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- The image pair of Figures 34 and 35 show the growth of Cancún between 28 March 1985, and 11 April 2019. The images were acquired by the Thematic Mapper (TM) on Landsat 5 and the Operational Land Imager (OLI) on Landsat-8, respectively. In the late 1980s, Cancún’s population registered around 120,000. A census report in 2015 conducted by the National Institute of Statistics and Geography (INEGI) reported around 740,000 people. Most of the hotels are located on a 27 km stretch of beach known as the Hotel Zone.

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Figure 35: Image of Cancún acquired by the OLI instrument on Landsat-8 on 11 April 2019 (image credit: NASA, image by Allison Nussbaum, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- While creating a large source of revenue, Cancún’s tourism also has had major impacts on the environment. One of the biggest issues is water pollution due to sewage from hotels (about 95 percent of all sewage from the area)—significantly more than the local treatment plants can handle. Untreated sewage ends up in the sea and becomes a threat to aquatic ecosystems, sometimes introducing pathogens that affect coral growth. The resort has also significantly increased the amount of garbage produced, a share of which is sent to illegal garbage dumps. Hotel construction and human presence have also eroded beaches, threatening local reef and coral systems.

• January 18, 2020: Approximately 4,000 years ago, a volcano in the South Ocean launched massive amounts of rock and magma—between 30 and 60 km3—into the sky. The eruption had the same severity as the cataclysmic 1991 eruption of Mount Pinatubo. It was the biggest eruption around Antarctica in the past 12,000 years. 41)

- As the volcano’s magma chamber emptied, the sudden drop in pressure inside the volcano caused the top to collapse and form a caldera. The caldera had a diameter of eight to ten kilometers (five to six miles). A collapse at this magnitude is large enough to induce multiple, intense high-magnitude earthquakes, according to researchers.

- These two natural-color images show Deception Island in early autumn (March 23, 2018, Figure 36) and early spring (September 21,2017, Figure 37), as observed by OLI (Operational Land Imager) on Landsat-8.

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Figure 36: This image shows the island on March 23, 2018, when the top of the volcano was visible (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- Deception Island is one of two active volcanoes around Antarctica, and it has erupted more than twenty times since the 19th century. The most recent eruptions occurred between 1967 and 1970, while seismic activity occurred as recently as 2014-2015. Deception Island remains the one of the only places in the world where ships can sail directly into the center of a restless volcano.

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Figure 37: This image, taken on September 21, 2017, shows the volcano and caldera covered in snow and ice (image credit: NASA Earth Observatory, image by Lauren Dauphin, using Landsat data from the U.S. Geological Survey. Story by Kasha Patel)

- Despite the island’s eruptive history, its harbor—Port Foster—is considered one of the safest in Antarctica due to the absence of large glaciers. At the beginning of the 19th century, people began visiting the island to hunt seals, a popular commercial frenzy at the time. When the seals were nearly hunted to extinction by the early 1900s, seafarers switched to whaling and set up operations at Whalers Bay on the east side of the port.

- Today, Deception Island is home to scientific research stations, although some have been wiped away by past volcanic activity. The island is also a popular place for tourists, who can haul out on the beach and sit in geothermal baths. Visitors can also see one of the world’s largest rookeries of chinstrap penguins located on the island.

• January 2, 2020: Mountains of sand, some as tall as 300 meters (1000 feet), reach from the floor of Africa’s Namib Desert toward the sky. Driven by wind, these dunes march across the desert, bordered to the west by the Atlantic Ocean and in other directions by solid, rocky land. 42)

- The images show a region along the northern boundary of the Namib Sand Sea, which generally traces the path of the Kuiseb River. The sporadic flow of the Kuiseb River depends on flooding rains that can occur during the rainy season, typically from November to March. Floods, such as one in April 2011, wash away accumulated sand and temporarily halt the northward progression of the dunes. The occasional floodwaters also bring life to the desert, sustaining vegetation along the banks. Analysis of satellite data has shown that extreme floods lead to fast plant growth and a “green-up” that can last for up to two years.

- Without much water, vegetation elsewhere in the sand sea is limited--though not completely absent, thanks to fog from the ocean.

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Figure 38: Namibia’s sea of sand is bounded on its northern side by the impermanent Kuiseb River

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Figure 39: The abrupt transition from sand to land is visible in these images (Figures 38 & 39), acquired on November 13, 2019, by the Operational Land Imager (OLI) on Landsat-8. They show the northern extent of the Namib Sand Sea—a field of sand dunes spanning more than 3 million hectares (more than 10,000 square miles) within the Namib-Naukluft Park, which was named a UNESCO World Heritage site in 2013. Sand appears red, painted by a layer of iron oxide (image credit: NASA Earth Observatory, images by Joshua Stevens, using Landsat data from the U.S. Geological Survey. Story by Kathryn Hansen)




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. 43)

• 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. 44)

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 40 and 41 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 41 are simply not visible in the natural color image of Figure 40. This new analysis feature will give scientists a better handle to study the changing environment.

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Figure 40: Natural color image of the Aral Sea region observed on March 24, 2013 (image credit: NASA)

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Figure 41: 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. 45) 46) 47) 48)

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. 49) 50)

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Figure 42: 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 43).

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Figure 43: This thermal image was taken by the TIRS instrument on April 29, 2013 (image credit: USGS, NASA)

Legend to Figure 43: 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. 51)

• 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. 52) 53)

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

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Figure 44: 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. 53)

Legend to Figure 44: 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. 54)

• 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. 55)

- 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.

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Figure 45: First image of LDCM released in March 2013 (image credit: NASA) 56)

Legend to Figure 45: 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. 53).

• 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. 57)

• 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).




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). 58) 59)

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.

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Figure 46: 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: 60)

• 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

GSD

Radiance (W/m2 sr μm), typical

SNR
(typical)

1

New Deep Blue

433-453

Aerosol/coastal zone

30 m

40

130

2

Blue

450-515

Pigments/scatter/coastal

 

 

30 m
(TM heritage bands)

40

130

3

Green

525-600

Pigments/coastal

30

100

4

Red

630-680

Pigments/coastal

22

90

5

NIR

845-885

Foliage/coastal

14

90

6

SWIR 2

1560-1660

Foliage

4.0

100

7

SWIR 3

2100-2300

Minerals/litter/no scatter

1.7

100

8

PAN

500-680

Image sharpening

15 m

23

80

9

SWIR

1360-1390

Cirrus cloud detection

30 m

6.0

130

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).

OLI (LDCM)

ETM+ (Landsat-7)

Band Nr

Wavelength (µm)

GSD (m)

Band No.

Wavelength (µm)

GSD (m)

8 (PAN)

0.500 - 0.680

15

8 (PAN)

0.52 - 0.90

15

1

0.433 - 0.453

30

 

 

 

2

0.450 - 0.515

30

1

0.45 - 0.52

30

3

0.525 - 0.600

30

2

0.53 - 0.61

30

4

0.630 - 0.680

30

3

0.63 - 0.69

30

 

 

 

4

0.78 - 0.90

30

5

0.845 - 0.885

30

 

 

 

9

1.360 - 1.390

30

 

 

 

6

1.560 - 1.660

30

5

1.55 - 1.75

30

7

2.100 - 2.300

30

7

2.09 - 2.35

30

OLI does not include thermal imaging capabilities

6 (TIR)

10.40 - 12.50

60

Figure 47: Spectral parameter comparison of OLI and ETM+ instruments

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Figure 48: OLI and ETM spectral bands (image credit: NASA)

OLI instrument:

The OLI design features a multispectral imager with a pushbroom architecture (Figure 49) 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.. 61) 62) 63) 64) 65)

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Figure 49: 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 50). 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 51) 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.

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Figure 50: Schematic view of the FPM layout concept (image credit: BATC, USGS)

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Figure 51: 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

Telescope

- 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

Calibration

- 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. 66)

OLI calibration:

The OLI calibration subsystem (Figures 52 and 53) 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). 67) 68)

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. 67):

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.

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Figure 52: OLI block diagram illustrating the calibration subsystem in front of the telescope (image credit: NASA, BATC)

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Figure 53: Blow-up of the calibration subsystem illustrating the solar diffuser and shutter assemblies (image credit: NASA, BATC)

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Figure 54: Illustration of the OLI instrument (image credit: NASA, BATC)

In Nov. 2008, the OLI instrument passed the ICDR (Instrument Critical Design Review). 69)

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Figure 55: 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. 70) 71)

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.

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Figure 56: Functional block diagram of TIRS (image credit: NASA, Ref. 68)

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). 72) 73) 74)

Instrument type

Pushbroom imager

Two channel thermal imaging instrument

10.8 and 12.0 µm band centers

Bandwidths

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

Detector

- 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

Telescope

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

Telescope f number

f/1.64

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. 73).

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. 75)

Advantages of QWIP technology:

- Large lattice-matched substrates

- Mature materials technology

- No unstable mid-gap traps

- Inherently, radiation hard.

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Figure 57: QWIP quantum state diagram (image credit: NASA/JPL)

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Figure 58: TIRS 10-13 µm QWIP spectral response requirement (image credit: NASA)

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Figure 59: Overview of the TIRS focal plane layout (image credit: NASA, Ref. 68)

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.

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Figure 60: Schematic view of the FPA (Focal Plane Assembly), image credit: NASA

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Figure 61: Photos of the FPA (image credit: NASA)

Legend to Figure 61: 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 62).

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.

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Figure 62: The TIRS optical sensor unit concept (image credit: NASA)

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Figure 63: Schematic view of the TIRS instrument internal assembly (image credit: NASA, Ref. 68)

Legend to Figure 63: 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. 76) 77)

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

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Figure 64: 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.

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Figure 65: Illustration of the TIRS calibration system (image credit: USGS)

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Figure 66: 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. 78)

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.

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Figure 67: 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

100%

Thermal operational

0 / +20ºC stable to ±1ºC

Thermal survival range

-50 / +40ºC

Lifetime

3.25 years on orbit

Redundancy

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


Parameter

Landsat ETM+

LDCM OLI

GMES/Sentinel-2 MSI

Spectral bands

Band

µm

Band

µm

Band

µm

 

 

1 (blue)

0.43-0.45

B1 (blue)

0.43-0.45

1 (blue)

0.45–0.52

2 (blue)

0.45–0.52

B2 (blue)

0.46–0.52

2 (green)

0.52–0.60

3 (green)

0.52–0.60

B3 (green)

0.54–0.58

3(red)

0.63–0.69

4 (red)

0.63–0.68

B4 (red)

0.65-0.68

 

 

 

 

B5 (red edge)

0.70-0.71

 

 

 

 

B6 (red edge)

0.73-0.75

 

 

 

 

B7 (red edge)

0.77-0.79

4 (NIR)

0.76–0.90

 

 

B8 (NIR)

0.78-0.90

 

 

5 (NIR)

0.84-0.88

B8a (NIR)

0.86-0.88

 

 

 

 

B9 (water vapor)

0.93-0.95

 

 

9 (cirrus)

1.36-1.39

B10 (cirrus)

1.37-1.39

5 (SWIR1)

1.55–1.75

6 (SWIR1)

1.56-1.66

B11 (SWIR1)

1.57-1.66

7 (SWIR2)

2.08–2.35

7 (SWIR2)

2.10-2.30

B12 (SWRIR2)

2.10-2.28

 

 

LDCM TIRS

 

 

6 (TIR)

10.4–12.5

10 (TIR1)

10.3-11.3

 

 

 

 

11 (TIR2)

11.5-12.5

 

 

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)

Quantization

8 bit

12 bit

12 bit

Onboard Calibration

Yes

Yes

Yes

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

Architecture

Cross-track scanner (Whiskbroom)

Pushbroom

Pushbroom

Table 6: Comparison of Landsat and GMES/Sentinel-2 imager specifications 79)


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 68. 80)

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.

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Figure 68: 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 69.

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.

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Figure 69: Illustration of DRC-16 collections (image credit: USGS, NASA)

End to end mission data flow is represented in Figure70 . 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.

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Figure 70: 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 : 81) 82) 83)

- 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 84)

- 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.

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Figure 71: Illustration of the Landsat-8 mission elements and communication architecture (image credit: NASA) 85) 86)

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.

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Figure 72: Overview of the LDCM system architecture (image credit: USGS)

Item

Parameter

Total size

System

Daily volume of 400 scenes

390 GByte

Spacecraft

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

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Figure 73: 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. 66).

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.

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Figure 74: 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. 66).

• 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.


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47) Steve Cole, “New Public Application of Landsat Images Released,” NASA, May 9, 2013, URL: http://www.nasa.gov/mission_pages/landsat/news/google-engine.html

48) “LDCM Mission Updates,” NASA, URL: http://www.nasa.gov
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49) “Landsat Thermal Sensor Lights Up from Volcano's Heat,” NASA, May 6, 2013, URL: http://www.nasa.gov/mission_pages/landsat/news/indonesia-volcano.html

50) “Landsat Data Continuity Mission,” NASA, News and Features, URL: http://www.nasa.gov/mission_pages/landsat/news/index.html

51) “LDCM Status Update for May 2, 2013,” NASA, May 15, 2013, URL: http://www.nasa.gov/mission_pages/landsat/main/mission-updates.html

52) Matt Radcliff, Rob Simmon, Jesse Allen, Holli Riebeek, Paul Przyborski, “Come Fly With the Newest Landsat,” NASA Earth Observatory, URL: http://earthobservatory.nasa.gov
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53) 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: http://calval.cr.usgs.gov/wordpress
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54) “LDCM Underfly with Landsat 7,” USGS, March 29, 2013, URL: http://landsat.usgs.gov/LDCM_Underfly_with_Landsat_7.php

55) Steve Cole, Jon Campbell, “First Images Released From Newest Earth Observation Satellite,” NASA, USGS, March 21, 2013, URL: http://www.nasa.gov
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56) “A Closer Look at LDCM's First Scene,” NASA, March 21, 2013, URL: http://www.nasa.gov/mission_pages/landsat/news/first-images-feature.html

57) “LDCM Status Update for Feb. 21,” NASA, Feb. 21, 2013, URL: http://www.nasa.gov/mission_pages/landsat/main/index.html

58) “NASA Completes Critical Design Review Of One Landsat Instrument,” Space Daily, May 28, 2010, URL: http://www.spacedaily.com/reports
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59) Bryant Cramer, “USGS Perspectives on LDCM and Landsat,” Landsat Science Team Meeting,” Jan. 19-21, 2010, Mountain View, CA, USA, URL: http://landsat.usgs.gov
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60) 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

61) 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

62) Jeanine Murphy-Morris, “Operational Land Imager ,” Landsat Science Team Meeting, Sioux Falls, SD, Jan. 8, 2008, URL: http://landsat.usgs.gov/documents/Murphy_Morris_Science_Team_OLI_chart.ppt

63) Edward J. Knight, “OLI Overview and Status,” Landsat Science Team Meeting, July 15, 2008, Reston, VA, URL: http://landsat.usgs.gov/documents/Knight_OLI.pdf

64) Bill Ochs, “Status of the Landsat Data Continuity Mission,” Landsat Science Team Meeting, July 15, 2008, Reston, VA, URL: http://landsat.usgs.gov/documents/Ochs_LDCM_Status.pdf

65) 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: http://calval.cr.usgs.gov
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66) 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

67) 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: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20100026050_2010028396.pdf

68) Brian Markham, “LDCM On-Orbit Cal/Val Considerations,” Proceedings of the Landsat Science Team Meeting, Mesa, AZ, USA, March 1-3, 2011, URL: http://landsat.usgs.gov/documents/LDCM_Cal_Val_Considerations.pdf

69) “Ball Aerospace Completes CDR For Landsat's Operational Land Imager,” Nov. 26, 2008, Spacemart, URL: http://www.spacemart.com/reports
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70) “NASA Completes Critical Design Review of Landsat Data Continuity Mission,” Science Daily, June 1, 2010, URL: http://www.sciencedaily.com/releases/2010/06/100601171850.htm

71) 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: http://landsat.gsfc.nasa.gov/pdf_archive/Reuter_etal-IGARSS2010.pdf

72) Ramsey L. Smith, Kurtis Thome, Cathleen Richardson, James Irons, Dennis Reuter, “Terrestrial Applications of the Thermal Infrared Sensor, TIRS,” URL: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20100003053_2010002398.pdf

73) 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

74) 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

75) 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

76) 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: http://calval.cr.usgs.gov/JACIE_files/JACIE11/Presentations/TuePM/340_Thome_JACIE_11.145.pdf

77) 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

78) 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)

79) 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: http://calval.cr.usgs.gov
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80) 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

81) Landsat Data Continuity Mission (LDCM), Ground System (GS) Integration and Test Plan,” USGS, LDCM-I&T-001, Version 1.1, September 2009, URL: http://www.usgs.gov/contracts
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82) Jonathan Gal-Edd, “LDCM Ground System - Network Lessons Learned,” SOSTC GSFC May 24-25, 2010, URL: https://info.aiaa.org/tac/SMG/SOSTC
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83) 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: http://landsat.usgs.gov/documents/LandsatScienceTeamLDCMGroundSystemsOverviewv4-1.pdf

84) 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

85) 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: http://gsaw.org
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86) Del Jenstrom, “Status of the Landsat Data Continuity Mission,” Proceedings of the Landsat Science Team Meeting, Mesa, AZ, USA, March 1-3, 2011


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 (herb.kramer@gmx.net).

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