Minimize WorldView-3

WorldView-3 (WV-3)

Spacecraft     Launch    Mission Status     Sensor Complement    References

WorldView-3 is a next generation commercial imaging mission of DigitalGlobe Inc., Longmont, CO, USA. With the addition of WV-3 to its satellite constellation (in addition to QuickBird, WorldView-1 and WorldView-2), DigitalGlobe will be capable of collecting ~1 billion km2 of Earth imagery per year.

In August 2010, DigitalGlobe was awarded an SLA (Service Level Agreement) with NGA (National Geospatial-Intelligence Agency) within its Enhanced View program. The contract deals with the purchase of satellite imagery and includes also an NGA cost share for the development and launch of the WorldView-3 spacecraft. DigitalGlobe plans a launch of WorldView-3 for the end of 2014. 1)

DigitalGlobe is adding a SWIR (Shortwave Infrared) sensing capability (8-band instrument) to its planned WorldView-3 satellite that will open up a host of new civil and military applications. 2) 3)

WorldView-3 provides 31 cm panchromatic resolution, 1.24 m MS (Multispectral) resolution, 3.7 m SWIR (Short-Wave Infrared) resolution, and 30 m CAVIS ( Clouds, Aerosols, Vapors, Ice, and Snow) resolution. CAVIS will monitor the atmosphere and provide correction data to improve WorldView-3's imagery when it images earth objects through haze, soot, dust or other obscurants. WorldView-3 has an average revisit time of < 1 day and is capable of collecting up to 680,000 km2 per day, further enhancing the DigitalGlobe collection capacity for more rapid and reliable collection. 4) 5)


Figure 1: Artist's rendition of the deployed WorldView-3 spacecraft in orbit (image credit: DigitalGlobe)


On August 30, 2010, DigitalGlobe awarded contracts to BATC (Ball Aerospace & Technologies Corporation) and to ITT Industries to build the WorldView-3 spacecraft and imager, respectively. 6) 7) 8) 9)

The BCP 5000 bus is being used for the WorldView-3 spacecraft. The high-performance BCP 5000 has a design life of more than seven years, and provides a platform with increased power, resolution, agility, target selection, flexibility, transmission capability and data storage. Ball provided the BCP 5000 under a fixed-price contract.

WorldView-3 builds upon WorldView-2 and WorldView-1 technology by carrying forward the satellites' advanced CMGs (Control Moment Gyroscopes). The CMGs, developed at BATC, reorient a satellite over a desired collection area in 4-5 seconds, compared to 30-45 seconds needed for traditional reaction wheels.


Figure 2: Ball Aerospace engineers install an advanced Control Moment Gyroscope into WorldView-3 (image credit: BATC)

Spacecraft size

5.7 m tall x 2.5 m across; 7.1 m across with the solar panels deployed

Spacecraft mass

2812 kg

Spacecraft power

3.1 kW solar array, 100 Ahr battery

Design life

7.25 years, estimated service life: 10 to 12 years

ADCS (Attitude Determination and Control Subsystem)

- Type: 3-axis stabilized
- Actuators: CMGs (Control Moment Gyros)
- Sensors: Star trackers (Ball CT-602), precision IRU, GPS receiver

Spacecraft pointing

- Accuracy: < 500 m at image start/stop; Knowledge: Supports geolocation accuracy
- Geolocation accuracy: Predicted < 3.5 m CE90 without ground control

Retargeting agility

Time to slew 200 km: 12 s

Onboard data storage

2.199 Tbit solid state memory with EDAC

RF communications

Image & ancillary data: 800 and 1200 Mbit/s, X-band
Housekeeping data rates: 4, 16, 32, or 64 kbit/s real-time; 524 kbit/s stored, X-band
Command data rates: 2 or 64 kbit/s, S-band

Table 1: Overview of spacecraft parameters


Figure 3: Photo of the WorldView-3 spacecraft during AIT (Assembly, Integration and Test) phase at BATC (image credit: BATC) 10)

Project development status:

• On August 1, 2014, DigitalGlobe announced its plans to accelerate the launch of WorldView-4, previously named GeoEye-2, to mid-2016 to meet demand from DigitalGlobe’s Direct Access and other commercial customers. 11)

• June 27, 2014: BATC has delivered the next-generation commercial remote sensing satellite built for DigitalGlobe, to the launch facility at Vandenberg Air Force Base, California. The WorldView-3 spacecraft passed a full suite of environmental, functional and performance tests in preparation for integration with the launch vehicle, along with thorough pre-ship reviews by Ball Aerospace and DigitalGlobe. 12)


Figure 4: Photo of the WorldView-3 spacecraft at BATC prior to being shipped to VAFB, CA (image credit: BATC)

On June 11, 2014, Digital Globe announced that it received notice from the U.S. Department of Commerce on its application to allow the company to sell its highest-quality and industry-leading commercial satellite imagery. Effective immediately, DigitalGlobe will be permitted to offer customers the highest resolution imagery available from their current constellation. Additionally, the updated approvals will permit DigitalGlobe to sell imagery to all of its customers at up to 0.25 m panchromatic and 1.0 m multispectral GSD (Ground Sample Distance) beginning six months after its next satellite WorldView-3 is operational. The launch of Worldview-3 is scheduled for August 2014. 13)

- As a result of the U.S. Government’s recent decision to allow DigitalGlobe to sell the highest quality imagery that will be available, the company has seen sufficient demand that justifies the accelerated launch of WorldView-4 (formerly GeoEye-2)providing its customers with assured access to 30 cm resolution imagery – the highest resolution imagery commercially available.

• In January 2014, BATC has completed integration of the WorldView-3 spacecraft. With the imagery sensor and associated electronics now integrated, the completed satellite bus is ready for system-level performance testing, followed by thermal vacuum and environmental testing.

• DigitalGlobe can now confirm that it plans to complete WorldView-3 on its original schedule to be ready for launch in mid-2014 in order to meet the requirements of its EnhancedView contract with the U.S. government. That contract calls for completion and launch of WorldView-3, which will offer the most spectral diversity available commercially and be the first to offer multiple Short-Wave Infrared bands that allow for accurate imaging through haze, fog, dust, smoke and other air-born particulates. DigitalGlobe's largest customer, NGA (National Geospatial-Intelligence Agency), has confirmed the requirements of DigitalGlobe's EnhancedView contract remain unchanged.

• Accordingly, following its just completed combination with GeoEye, DigitalGlobe intends to complete the construction of GeoEye-2 in 2013 and to preserve it as a ground spare to meet customer demand or as a replacement for other on-orbit satellites. Previously, GeoEye had expected to launch GeoEye-2 in 2013 (Ref. 14).

DigitalGlobe and GeoEye merged on January 31, 2013 to become one company, DigitalGlobe. On February 4, 2013, DigitalGlobe announced that its previously planned satellite construction program related to its third WorldView-class satellite remains on track. 14)

Launch: WorldView-3 was launched on August 13, 2014 (18:30:30 UTC) on an Atlas-V 401 vehicle of LMCLS (Lockheed Martin Commercial Launch Services) from VAFB, CA. 15) 16)

Orbit: Sun-synchronous orbit, altitude = 617 km, inclination = 98º, LTDN (Local Time on Descending Node) = 13:30 hours, period = 97 minutes.

Mission status

• July 6, 2022: Scientists have used Earth observation data to reveal enormous methane plumes spilling out from an offshore oil and gas production rig in the Gulf of Mexico. 17)

- This represents a significant breakthrough in the monitoring of industrial methane emissions from space.

- The analysis – led by scientists from the Universitat Politècnica de València – drew on data from the WorldView-3 satellite, which were delivered on a free basis via ESA’s Third Party Missions (TPM) programme. It also used imagery collected by the US-led Landsat-8 mission.

- Methane is less abundant in the atmosphere than carbon dioxide, but it is a more powerful heat-trapping gas. As a result, scientists believe that addressing methane leaks from human activities could be key for the global drive to tackle climate change.

- Using satellites to monitor methane emissions from offshore oil and gas infrastructure could make important contributions to these mitigation efforts.

- However, despite the rapid development of space-based methane plume detection methods over land, it remains difficult to use remote sensing missions to monitor these emissions over sea.

- Methane detection from space involves the use of shortwave infrared wavelengths. But the high absorption of this radiation by water limits the amount of light bouncing back to the satellite’s sensor, making it problematic to distinguish methane emissions.

- As part of the analysis, the researchers overcame this challenge by using an imaging mode in which the angular configuration of the satellite is adjusted.

- The study focused on oil and gas production activities off the coast of the Mexican state of Campeche, in one of the country’s major oil fields. The initial aim of the analysis was to explore the feasibility of using WorldView-3 to monitor offshore methane plumes. 18)

- Launched in 2014, WorldView-3 is a multispectral Earth observing satellite that is owned and operated by US space technology firm Maxar. ESA added the mission to its TPM programme in 2016.

- The satellite’s instrument – called the WorldView-110 camera – captures images in a number of spectral ranges, including eight bands of the short-wave infrared range.

- These data have a high resolution of 3.7 m and a high signal-to-noise ratio, which make it a powerful mission for methane mapping.


Figure 5: This map shows the oil and gas extraction areas in and around the Gulf of Mexico. Data has been extracted from the Global Energy Monitor [image credit: ESA (Data: Global Oil and Gas Extraction Tracker, Global Energy Monitor, January 2022)]


Figure 6: Methane plume from the Zaap-C platform. This image shows a methane plume from an offshore platform as detected by the WorldView-3 satellite on 18 December 2021 [image credit: ESA (Data: WorldView-3)]


Figure 7: This Copernicus Sentinel-2 image, captured on 28 December 2021, shows the location of the Zaap-C offshore platform with many other offshore platforms visible flaring in the area. Please note that the water vapour columns are very typical on days when flaring is active. It is not the case for the days when the methane fluxes occur - on these days, there is neither flaring nor water vapour (image credit: ESA, the image contains modified Copernicus Sentinel data (2021), processed by ESA, CC BY-SA 3.0 IGO, CC BY-SA 3.0 IGO]

• October 19, 2020: Scientists from NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and international collaborators demonstrated a new method for mapping the location and size of trees growing outside of forests, discovering billions of trees in arid and semi-arid regions and laying the groundwork for more accurate global measurement of carbon storage on land. 19)

Figure 8: Scientists from NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and international collaborators demonstrated a new method for mapping the location and size of trees growing outside of forests, discovering surprisingly high numbers of trees in semi-arid regions and laying the groundwork for more accurate global measurement of carbon storage on land (video credit: NASA/GSFC, Scientific Visualization Studio)

- Using powerful supercomputers and machine learning algorithms, the team mapped the crown diameter – the width of a tree when viewed from above – of more than 1.8 billion trees across an area of more than 500,000 square miles, or 1,300,000 km2. The team mapped how tree crown diameter, coverage, and density varied depending on rainfall and land use.

- Mapping non-forest trees at this level of detail would take months or years with traditional analysis methods, the team said, compared to a few weeks for this study. The use of very high-resolution imagery and powerful artificial intelligence represents a technology breakthrough for mapping and measuring these trees. This study is intended to be the first in a series of papers whose goal is not only to map non-forest trees across a wide area, but also to calculate how much carbon they store – vital information for understanding the Earth’s carbon cycle and how it is changing over time. 20)

Measuring carbon in trees

- Carbon is one of the primary building blocks for all life on Earth, and this element circulates among the land, atmosphere, and oceans via the carbon cycle. Some natural processes and human activities release carbon into the atmosphere, while other processes draw it out of the atmosphere and store it on land or in the ocean. Trees and other green vegetation are carbon “sinks,” meaning they use carbon for growth and store it out of the atmosphere in their trunks, branches, leaves and roots. Human activities, like burning trees and fossil fuels or clearing forested land, release carbon into the atmosphere as carbon dioxide, and rising concentrations of atmospheric carbon dioxide are a main cause of climate change.

- Conservation experts working to mitigate climate change and other environmental threats have targeted deforestation for years, but these efforts do not always include trees that grow outside forests, said Compton Tucker, senior biospheric scientist in the Earth Sciences Division at NASA Goddard. Not only could these trees be significant carbon sinks, but they also contribute to the ecosystems and economies of nearby human, animal and plant populations. However, many current methods for studying trees’ carbon content only include forests, not trees that grow individually or in small clusters.

- Tucker and his NASA colleagues, together with an international team, used commercial satellite images from DigitalGlobe, which were high-resolution enough to spot individual trees and measure their crown size. The images came from the commercial QuickBird-2, GeoEye-1, WorldView-2, and WorldView-3 satellites. The team focused on the dryland regions – areas that receive less precipitation than what evaporates from plants each year – including the arid south side of the Sahara Desert, that stretches through the semi-arid Sahel Zone and into the humid sub-tropics of West Africa. By studying a variety of landscapes from few trees to nearly forested conditions, the team trained their computing algorithms to recognize trees across diverse terrain types, from deserts in the north to tree savannas in the south.


Figure 9: The team focused on the dryland regions of West Africa, including the arid south side of the Sahara Desert, stretching through the semi-arid Sahel Zone and into the humid sub-tropics. By studying a variety of landscapes from few trees to nearly forested conditions, the team trained their computing algorithms to recognize trees across diverse terrain types, from deserts in the north to tree savannas in the south [image credits: NASA's Scientific Visualization Studio; Blue Marble data is courtesy of Reto Stockli (NASA/GSFC)]

Learning on the job

- The team ran a powerful computing algorithm called a fully convolutional neural network (“deep learning”) on the University of Illinois’ Blue Waters, one of the world’s fastest supercomputers. The team trained the model by manually marking nearly 90,000 individual trees across a variety of terrain, then allowing it to “learn” which shapes and shadows indicated the presence of trees.

- The process of coding the training data took more than a year, said Martin Brandt, an assistant professor of geography at the University of Copenhagen and the study’s lead author. Brandt marked all 89,899 trees by himself and helped supervise training and running the model. Ankit Kariryaa of the University of Bremen led the development of the deep learning computer processing.

- “In one kilometer of terrain, say it’s a desert, many times there are no trees, but the program wants to find a tree,” Brandt said. “It will find a stone, and think it’s a tree. Further south, it will find houses that look like trees. It sounds easy, you’d think – there’s a tree, why shouldn’t the model know it’s a tree? But the challenges come with this level of detail. The more detail there is, the more challenges come.”

- Establishing an accurate count of trees in this area provides vital information for researchers, policymakers and conservationists. Additionally, measuring how tree size and density vary by rainfall – with wetter and more populated regions supporting more and larger trees – provides important data for on-the-ground conservation efforts.

- “There are important ecological processes, not only inside, but outside forests too,” said Jesse Meyer, a programmer at NASA Goddard who led the processing on Blue Waters. “For preservation, restoration, climate change, and other purposes, data like these are very important to establish a baseline. In a year or two or ten, the study could be repeated with new data and compared to data from today, to see if efforts to revitalize and reduce deforestation are effective or not. It has quite practical implications.”

- After gauging the program’s accuracy by comparing it to both manually coded data and field data from the region, the team ran the program across the full study area. The neural network identified more than 1.8 billion trees – surprising numbers for a region often assumed to support little vegetation, said Meyer and Tucker.

- “Future papers in the series will build on the foundation of counting trees, extend the areas studied, and look ways to calculate their carbon content,” said Tucker. NASA missions like GEDI (Global Ecosystem Dynamics Investigation), and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2), are already collecting data that will be used to measure the height and biomass of forests. In the future, combining these data sources with the power of artificial intelligence could open up new research possibilities.

- “Our objective is to see how much carbon is in isolated trees in the vast arid and semi-arid portions of the world,” Tucker said. “Then we need to understand the mechanism which drives carbon storage in arid and semi-arid areas. Perhaps this information can be utilized to store more carbon in vegetation by taking more carbon dioxide out of the atmosphere.”

- “From a carbon cycle perspective, these dry areas are not well mapped, in terms of what density of trees and carbon is there,” Brandt said. “It’s a white area on maps. These dry areas are basically masked out. This is because normal satellites just don’t see the trees – they see a forest, but if the tree is isolated, they can’t see it. Now we’re on the way to filling these white spots on the maps. And that’s quite exciting.”


Figure 10: An astronaut aboard the International Space Station (ISS) took this oblique photograph that shows most of the West African country of Guinea-Bissau, along with neighboring Guinea, The Gambia and Senegal, and the southern part of Mauritania. This scene stretches from the green forest vegetation and wet climates of the Atlantic coast to the almost vegetation-less landscapes of the Sahara Desert (image credit: NASA)

• August 5, 2020: All three of Maxar’s WorldView-class satellites (WorldView-1, WorldView-2 and WorldView-3) collected high resolution images of Beirut on August 5th, following the horrific explosion at the city’s port. 21)


Figure 11: WorldView satellite capture of the port of Beirut prior to (left) and post (right) explosion (image credit: ©2020 Maxar Technologies)

• August 27, 2019: Maxar Technologies today announced that it has been awarded a new, four-year contract with the U.S. National Geospatial-Intelligence Agency (NGA) for the Global EGD ( Enhanced GEOINT Delivery) program. The contract, which begins September 1, 2019, is valued at $44 million for the base year and includes three option years at the same value that would extend the contract through August 2023. 22)

- The new contract will allow Maxar to continue providing more than a quarter million U.S. Government users with online and offline, on-demand access to the world’s highest resolution commercial imagery. Since 2011, the Global EGD program has allowed warfighters, first responders, intelligence analysts and civil government users to tap into Maxar’s 100-petabyte historical imagery library and daily imagery collections for time sensitive, mission-critical planning and operations.

- “The Global EGD program has proven to be an essential capability for NGA and a broad array of U.S. Government users,” said Dan Jablonsky, Maxar CEO. “With this contract, Maxar extends its decades-long standing as a trusted partner to the U.S. Government. We are proud to continue providing American troops, intelligence analysts and first responders with the information and insight to make decisions with confidence.”

- The operations of DigitalGlobe, SSL and Radiant Solutions were unified under the Maxar brand in February; MDA continues to operate as an independent business unit within the Maxar organization.

• October 11, 2017: DigitalGlobe has released high-resolution satellite images of the wildfires burning in Northern California. These wildfires have killed at least 21 people, destroyed at least 3,500 structures and have burned more than 115,000 acres (46,540 hectares).This imagery shows the fires in Santa Rosa, California, area, taken on October 10 and 11, 2017. 23)

- The October 10 images were collected using the SWIR (Shortwave Infrared) sensor on DigitalGlobe’s WorldView-3 satellite, which is uniquely able to pierce through the wildfire smoke to see where the fires are burning on the ground. For comparison, the ground and the fire line are completely obstructed by smoke in the natural color image of the same area.


Figure 12: Northwest Santa Rosa and Coffee Park fires captured on October 10 by DigitalGlobe's WorldView-3 satellite (image credit: DigitalGlobe) 24)

October 5, 2017: MDA (MacDonald, Dettwiler and Associates Ltd.) of Richmond, BC, Canada, a global communications and information company providing technology solutions to commercial and government organizations worldwide, today announced it has completed its acquisition of DigitalGlobe, Inc. ("DigitalGlobe"), the global leader in high resolution Earth imagery and information about our changing planet. The merger creates the leading integrated commercial provider of satellites, imagery and geospatial solutions to commercial and government customers worldwide. The newly combined company will offer a broader set of space-based solutions, increased scale and a more diversified revenue base.

New Corporate Name: MDA also announced today that it will become Maxar Technologies Ltd., and its U.S.-headquartered operating company, SSL MDA Holdings, Inc., will become Maxar Technologies Holdings Inc. The new Maxar Technologies launches a distinctive group of leading space brands, technologies and capabilities.

Canada-based MDA and U.S.-based DigitalGlobe overcame an extended review by the Committee on Foreign Investment in the United States (CFIUS) after refiling merger paperwork in July. The inter-agency committee, which reviews potential national security risks from foreign buyers of American businesses, ultimately found no issue with the merger.

MDA Corp.’s president and chief executive Howard Lance stated: ”Maxar Technologies encompasses four of the leading commercial space technology brands—SSL, MDA, DigitalGlobe and Radiant—and represents the expanded benefits and value we will offer to our customers, shareholders, partners and employees.”

With the addition of DigitalGlobe and Radiant Solutions, the Maxar Technologies portfolio of best-in-class brands offers a full suite of end-to-end solutions capabilities with deep domain expertise and space heritage.

- SSL: the leading commercial provider of communications and Earth observation satellites and scientific mission spacecraft for commercial and government markets

- MDA: an internationally recognized leader in space robotics, satellite antennas and subsystems, surveillance and intelligence systems, defense and maritime systems and geospatial radar imagery

- DigitalGlobe: the global leader in high resolution optical satellite imagery and information about our changing planet; and

- Radiant: a highly specialized provider of geospatial data, analytics, software and services to facilitate insights and intelligence where and when it matters most.

Table 2: Acquisition of DigitalGlobe by MDA 25) 26)

• May 4, 2017: Scientists have started counting individual birds from space. They are using the highest-resolution satellite images available to gauge the numbers of Northern Royal albatrosses. This endangered animal nests almost exclusively on some rocky sea-stacks close to New Zealand’s Chatham Islands. The audit, led by experts at the BAS (British Antarctic Survey), represents the first time any species on Earth has had its entire global population assessed from orbit. The scientists report the satellite technique in Ibis, a journal of the British Ornithologists' Union. 27) 28)

- Ordinarily, these birds are very difficult to appraise because their nesting sites are so inaccessible. Not only are the sea-stacks far from NZ (680km), but their vertical cliffs mean that any visiting scientist might also have to be adept at rock climbing. "Getting the people, ships or planes to these islands to count the birds is expensive, but it can be very dangerous as well," explained Peter Fretwell from BAS.

- This makes the DigitalGlobe WorldView-3 satellite something of a breakthrough. In March 2015, the U.S. Congress relaxed restrictions on the spatial resolution of commercial satellite imagery from 50 to 30 cm, ushering in a new era of super-high resolution optical satellite imagery for scientific and other applications. The threshold size of objects that can be seen from space is now much smaller, and the definition and the reliability with which they can be discriminated are much improved. This more than doubles the potential density of pixels from 4 pixels/m2 (for a 50 cm resolution image) to 10.4 pixels/m2 (31 cm resolution). For wildlife applications, there are therefore now more species for which individual animals are potentially visible, or can be visualized at considerably higher definition by satellite.


Figure 13: WorldView-3 can see the nesting birds as they sit on eggs to incubate them or as they guard newly hatched chicks (image credit: BBC, DigitalGlobe)

- The study is the first to utilize 30 cm resolution imagery from the WorldView-3 satellite of DigitalGlobe to count wildlife directly. We test the accuracy of the satellite method for directly counting individuals at a well-studied colony of Wandering Albatross Diomedea exulans at South Georgia, and then apply it to the closely related Northern Royal Albatross Diomedea sanfordi, which is near-endemic to the Chatham Islands and of unknown recent population status due to the remoteness and limited accessibility of the colonies.

- Study areas: Bird Island (54°00' S, 38°03' W) is a small island (~4.5 km2) to the west of mainland South Georgia (Figure 14). It held 61% of the breeding population of Wandering Albatross at South Georgia in the austral summer of 2003/2004, equivalent to around 10% of the global population. The birds nest in relatively flat areas of the tussock grass Poa flabellata, which they used to construct nest mounds.

- The Chatham Islands (44°23' S, 176°17' W) group lies 680 km east of New Zealand and consists of one large island, 10 smaller islands and other sea stacks. Ninety-nine percent of the global population of Northern Royal Albatross breed on three of the smaller islands: Big and Little Sister (usually termed the Sisters) and, further to the east, the Forty-Fours (Figure 15). The three small islands in the Chatham Group are precipitous and have no easy access, ground visits are extremely difficult, and the distance of the islands from mainland New Zealand means that aerial surveys are expensive. The other 1% of the global population breed at Taiaroa Head on the South Island, New Zealand, which is accessible and is monitored regularly.


Figure 14: The location of the two study areas. (a) Bird Island, South Georgia. The pink area depicts the area covered by the WorldView-3 satellite image taken on 10 January 2016. (b) The location of The Sisters and Forty-Fours in the Chatham Islands. The red areas depict the areas covered by the WorldView-3 satellite images; these images are in (c) and (d). Cloud-free satellite imagery covered the full extent of the study area (image credit: BAS)


Figure 15: Note: Figure 14 of Ref. 28) was split into Figures (5 and 6) for a better presentation (image credit: BAS)

- Detectability of individual great albatrosses: Great albatrosses breed on elevated, flat or gently sloping terrain, and tend to prefer areas of tussock or other grassy vegetation that provides the material for their nests. The head, back and upper tail of adult Wandering or Northern Royal Albatrosses are largely white, although with dark vermiculation on some feathers, and they have a body length of 107–135 cm. Individual birds are therefore likely to show as several white pixels in the WorldView-3 satellite imagery, given the 31 cm cell size (Figure 16). The upper-wing surface also includes dark feathers, and so the size of the white dot is not necessarily much bigger in a bird with extended wings that is displaying on the ground, or a bird in flight.


Figure 16: (a) Part of the WorldView-3 satellite image of Bird Island showing the distribution of white dots. (b) Photograph of Bird Island for comparison (photo credit: R. A. Phillips). (c) Close-up of a representative white dot in (a), indicating pixel composition (image credit: BAS)

- Satellite image counts: As albatrosses were clearly evident as white dots in the satellite imagery, these were counted by eye on screen directly from the WorldView-3 image, in separate polygons of 200 x 200 m (roughly the area that fits within a single screen at the scale the birds were counted). The dots on the image were digitized manually in ArcMap 10.1 (Environmental Systems Resource Institute, Redlands, CA, USA). Due to the positional errors associated with a handheld GPS, and the distortion inherent in the orthorectified image, matching of individual nest locations recorded on the ground with those in the satellite imagery was not possible on a one-to-one basis, and therefore our main comparisons were of total counts of Wandering Albatrosses on Bird Island.

- Analysis of the oblique digital photography for non-breeding birds: The number of Wandering Albatrosses counted in hand-held digital photographs exceeded those in the ground counts of nests in the same areas at Bird Island by 0.4% to 58.3%, or 11.1% overall, based on mean values in areas counted twice. The greatest discrepancy (58.3%) was for the area with the fewest nests and the highest proportion of non-breeders. We assumed counts of AOSs (Apparently Occupied Sites) from a satellite image to over-estimate the overall number of nests by 11.1%.

- Accuracy of satellite remote-sensing of great albatrosses: The count of apparently occupied sites from the WorldView-3 satellite imagery of Bird Island provided a reasonable match with the number of Wandering Albatross nests in which eggs were laid, based on ground counts; the 20.1% over-estimate in the difference between satellite counts and ground observations could be explained largely by the presence of non-breeders. Based on the oblique digital photographs, the expected number of individuals in the satellite image was 754 x 1.111 = 837.7 birds, which is 6.5% lower than the number of AOSs counted from the satellite image.

- At Bird Island, counts of AOSs from satellite images and photographs were higher than the number of nesting adults. Nesting Wandering Albatrosses are very conspicuous to ground counters and in oblique photographs, and by those dates all areas of the island had been visited several times; moreover, the topography of the island is such that few, if any, active nests would have been missed. We consider that the ground counts were accurate and that the satellite count did not include undiscovered nests at this site. Based on data from surveys in an area that is monitored daily, all pairs had laid by the time the satellite image was taken. Hence, we conclude that the discrepancy between the counts is related to the proportion of non-breeding birds visiting the island and a smaller number of errors of commission that could be rocks or flying birds. Non-breeders are mainly pre-breeding or deferring breeders, as few nests (nine of 754; 1.2%) had failed by the date of the satellite image, and members of those pairs are as likely to have been at sea as they were to have been present on the island. This level of nest failure is well within the variance of the satellite-based counts and hence adds only marginal error to the population estimate. From the analysis of the oblique photographs taken in early to mid-incubation, the percentage of non-breeding birds varied among areas, but determining whether this was due to time of day, date, habitat type or other factors (e.g. attractiveness of each area to pre-breeders) would require collection and analysis of a larger dataset. Hence, we would urge caution before using the ratio of non-breeders to breeders recorded in this study to correct counts of Wandering Albatross or other species of great albatrosses at other sites.

• Sept. 30, 2016: DigitalGlobe reports that its online Basemap Suite has reached a significant milestone, with the availability of more than 250 million km2 of the world's highest-resolution 30 cm commercial satellite imagery. This includes over 500 population centers in the Basemap +Metro product now available at 30 cm resolution and 1,700 cities available at 50 cm or better resolution, a level of detail that is unrivaled in the industry. In total, the Basemap Suite now features 1.5 billion km2 of high-accuracy, high-resolution imagery - 10 times the land surface area of the Earth. 29)

- With the initial launch of the Basemap product line five years ago, DigitalGlobe created the industry standard for online, on-demand access to the world's highest-quality commercial satellite imagery. Today the DigitalGlobe Basemap Suite features a global imagery base layer with a powerful time-lapse image browsing tool and near-seamless satellite imagery mosaics.

• ESA 3rd Party missions, June 23, 2016: The WorldView-1/-2/-3, GeoEye-1 and QuickBird mission archive and new acquisitions are now available for research and application development. ESA will support as many high-quality and innovative European and Canadian projects as possible within the available budget. In the frame of ESA cooperation activities, users outside Europe can also be served. The products can be made available for free upon project proposal submission via the WorldView, GeoEye-1, and QuickBird information areas on ESA's Earth Online Portal. 30)

• WorldView-3 geolocation accuracy: A study of NGA was presented at JACIE 2016 describing the WorldView-3 geolocation accuracy. Metric Design and Internal Geometric Calibration: 31) 32)

- GPS receiver on-board used to determine orbital position

- Inertial Reference Units and Star Trackers used to determine pointing direction

- Sensor and its relationship to GPS and star trackers are highly calibrated.

Sample size of 27 WorldView-3 Basic 1B Mono Images (Physical Model)



Sample Mono HE90 (m)

Confidence Statements

Nominal Nadir Pixels



>94% confidence that True CE90 <3.8 m


8-Band MSI


>94% confidence that True CE90 <5.0 m


8-Band SWIR


>94% confidence that True CE90 <7.6 m



Sample size of 27 WorldView-3 Basic 1B Mono Images, RPC(Rational Polynomial Coefficients)



Sample Mono HE90 (m)

Confidence Statements

Nominal Nadir Pixels



>94% confidence that True CE90 <3.7 m


8-Band MSI


>94% confidence that True CE90 <4.5 m


8-Band SWIR


>94% confidence that True CE90 <6.1 m


Table 3: WV03 absolute geolocation accuracy results using no ground control points

Same 217 points measured in Pan, MSI, and SWIR images. Images related using Basic 1B photogrammetric sensor models.


Physical Model RMSE (Root Mean Square Error)

RPC (Rational Polynomial Coefficients) RMSE

MSI- Panchromatic

1.6 m (1.3 pixels)

1.2 m (0.9 pixels)

SWIR - Panchromatic

4.3 m (1.2 pixels)

3.3 m (0.9 pixels)


3.3 m (0.9 pixels)

2.7 m (0.7 pixels)

Table 4: WV03 Sensor Co-Registration

217 measured points on 27 Basic 1B images

• Absolute geolocation accuracy (no ground control points)

- Panchromatic: 2.8-2.9 meters HE90 (Horizontal Error 90%)

- 8-Band MSI: 3.2-3.5 meters HE90

- 8-Band SWIR: 4.6-5.9 meters HE90

• Error propagation and sensor co-registration results are reasonable

RPC (Rational Polynomial Coefficients) fits Physical Model within 1 to 1.5 pixels

Band-to-Band co-registration

• SWIR bands within 0.35 pixels

• CAVIS bands within 0.25 pixels

Table 5: Summary of accuracy study

• August 13, 2015: One year ago, WorldView-3 successfully launched into orbit, bolstering DigitalGlobe’s world-leading constellation with the first super-spectral, high-resolution commercial satellite. WorldView-3’s unmatched capabilities have expanded the bounds of what is possible in remote sensing. With 30 cm panchromatic resolution that collects five times more data than 70 cm imagery, SWIR (Shortwave Infrared) bands that allow for accurate imaging through haze, fog, dust, smoke and other airborne particles; and eight multispectral bands, WorldView-3 has helped the customers of DigitalGlobe to see the Earth clearly and in new ways. 33)

WorldView-3 enabled DigitalGlobe to contribute to disaster and humanitarian relief efforts, push the boundaries of technology and support commercial applications in ways never seen before. Here are a few highlights.

1) Contributions to disaster and humanitarian efforts:

- In response to the devastating 7.8 magnitude earthquake that struck central Nepal on April 25, 2015, WorldView-3 did what others could not, offering images sooner than any competitors due to the satellite's ability to maneuver and collect imagery despite poor weather conditions. The multispectral imagery captured by WorldView-3 was made freely available online along with pre-event imagery to aid in the global crisis response.

- DigitalGlobe is collaborating with WRI ( World Resources Institute) to map burn scars from the rash of fires that began May 29, 2015 in Indonesia’s Tesso Nilo National Park. WorldView-3’s 30 cm resolution and SWIR technology have enabled WRI to more accurately determine the extent of land affected by forest and bush fires as well as the degree of illegal encroachment of the protected park.

2) Pushing the boundaries of technology:

- WorldView-3’s CAVIS sensor offers the capability to improve imagery by compensating for conditions – clouds, aerosol, vapor, ice and snow. When CAVIS data is collected, it can boost the accuracy of WorldView-3’s surface reflectance, improving image quality with better colors and more consistency.

- A research paper published in the Journal of Applied Remote Sensing in May 2015 confirmed WorldView-3’s eight SWIR bands provide extensive new mineral mapping capabilities not available from other spaceborne multispectral systems. With the help of the SWIR sensor, WorldView-3 offers more accurate information for geology, mining, agriculture and many other applications. 34)

3) Opening new commercial opportunities:

- Feeding subscription-based solutions like GBDX (Geospatial Big Data platform) with higher quality information from WorldView-3 is leading to new innovative applications for commercial customers. New uses include monitoring pipelines, big data commodity forecasting and automated feature extraction.

- Using WorldView-3’s various spectral bands and 30 cm imagery, civil governments are able to collect information to better understand crop inventory and assess crop health in a given region. Information on crop inventory and health can help government officials with decisions on policy and food security.


Figure 17: Illustration of the DigitalGlobe commercial imaging constellation in the summer of 2015 (image credit: DigitalGlobe, EUSI) 35)

• May 2015: WorldView-3 Absolute Geolocation Accuracy Evaluation. 36)

- The PM (Physical Sensor Model) relates ground positions to image pixels by modeling geometry of imaging

- The RPC (Rational Polynomial Coefficient Model) relates image pixels to ground positions, but using ratio of 3rd order polynomial equations.

Sample size of 33 WorldView-3 Basic 1B Stereo Pairs PM (Physical Sensor Model)

Sample Mono HE90 (Horizontal Error 90%)

3.8 m

> 96% confidence that True CE90 < 4.1 m

Sample Stereo HE90

3.7 m

> 96% confidence that True CE90 < 4.3 m

Sample Stereo VE90 (Vertical Error 90%)

2.7 m

> 96% confidence that True LE90 < 5.0 m


Sample size of 33 WorldView-3 Basic 1B Stereo Pairs RPC (Rational Polynominal Coefficient Model)

Sample Mono HE90

3.9 m

> 96% confidence that True CE90 < 4.1 m

Sample Stereo HE90

3.8 m

> 96% confidence that True CE90 < 3.9 m

Sample Stereo VE90

2.7 m

> 96% confidence that True LE90 < 6.3 m

Table 6: WV03 absolute geolocation accuracy results

• April 26, 2015: In response to the devastating 7.8 magnitude earthquake that struck central Nepal on April 25, DigitalGlobe has made high resolution satellite imagery of the affected areas freely available online to all groups involved in the response and recovery effort. This imagery can be accessed via User name: nepal; Password: forcrisis. 37)

- Specifically, DigitalGlobe activated FirstLook, the subscription service that provides emergency management and humanitarian workers with fast, web-based access to pre- and post-event images of the impacted area. DigitalGlobe captured imagery of the area yesterday through heavy cloud cover with its WorldView-1, and WorldView-3, and GeoEye-1 satellites. WorldView-2 and WorldView-3 have been tasked to image the area again tomorrow morning. Pre-event imagery dating back to April 1, 2015, is also available to aid understanding and coordination for on-the-ground missions.

- In addition, DigitalGlobe has activated Tomnod, the crowdsourcing platform that allows web-connected volunteers around the globe to help disaster response teams by mapping damage from this earthquake. While satellite imagery on its own is useful, greater benefit comes from extracting meaningful information that can be used by first responder and recovery agencies.

- On April 29, 2015, DigitalGlobe released the initial results from the Tomnod crowdsourcing campaign in response to the earthquake that struck Nepal on April 25. The damage mapping campaign will continue, and volunteers are encouraged to visit the Tomnod website to contribute their time and efforts. 38)

• March 3, 2015: Last week DigitalGlobe announced that they are now selling the new 30 cm imagery to customers. Until recently it was actually illegal for US companies to sell satellite imagery at this resolution. As we have noted in the past, aerial imagery is typically of similar or better resolution and is not subject to that restriction, but for global coverage and bulk image capturing satellites work out much more cost effective. 39)

• February 25, 2015: DigitalGlobe today announced the full availability of 30 cm satellite imagery products, an industry first that builds upon the company’s gold standard for image quality and resolution. Access to the world’s highest resolution commercial satellite imagery captured by DigitalGlobe’s WorldView-3 satellite will improve decision making, enable more efficient operations, and enhance a variety of applications for customers in the civil government, defense and intelligence, energy, mining, and global development sectors. 40) 41)

- 30 cm imagery brings new value to a variety of use cases and market segments including mining, oil and gas, civil government, social/mobile/location services, and even global development organizations. This new level of value means better operational efficiency and cost management, more effective disaster planning and recovery, a better customer experience in consumer-facing, map-centric market segments, and more efficient humanitarian assistance.


Figure 18: A sample image of a 30 cm resolution city scene of Shanhai, China, acquired with WorldView-3 (image credit: DigitalGlobe)

• January 9, 2015: DigitalGlobe’s fourth annual Top Satellite Image of the Year contest began December 8, 2014, and ran through the end of month. The selection of images comprised more than just stunning shots of the globe. The images demonstrated the wide range of capabilities and diverse industries DigitalGlobe supports. The top five images alone were relevant to four diverse customer segments: global development organizations, location-based services, civil governments, and the mining industry. The Rainbow Range in Canada took the top spot during final round of voting. The SWIR bands of the WV-3 imager are particularly valuable for geologists in analyzing their imagery; these bands can differentiate between specific minerals. 42)


Figure 19: WorldView-3 image of the Rainbow Range in Canada (image credit: DigitalGlobe)

• October 30, 2014: DigitalGlobe’s latest WorldView-3 satellite provides imagery with unprecedented quality that allows our customers to see the Earth clearly and in new ways resulting in valuable information to save lives, resources and time. WorldView-3’s super-spectral 30 cm imagery allows for fast and precise mapping of various features anywhere in the world.


Figure 20: WorldView-3 sample image (31 cm imagery) of the Madrid airport released by DigitalGlobe in October 2014 (image credit: DigitalGlobe) 43)

• Sept. 3, 2014: The integration of the eight SWIR (Shortwave Infrared) bands into the super-spectral WV-3 imager provides a new observation quality to DigitalGlobe’s WorldView-3 spacecraft. For instance, it permits the capture of high-resolution imagery through a thick cloud of smoke of an active forest fire, marking the first time this capability has been commercially available from a satellite platform. Taken above the Happy Camp complex in California’s Klamath National Forest, the imagery (Figure 21) shows an active fire beneath a thick cloud of smoke. Hot spots are clearly visible even without being shown at full resolution. 44) 45)


Figure 21: WorldView-3 satellite image capture of the fire at the Happy Camp complex in California’s Klamath National Forest (image credit: DigitalGlobe)

Legend to Figure 21: The SWIR bands penetrate smoke to differing degrees. SWIR band 8 has the best smoke penetration; here is a zoomed in shot of the fire line in which no smoke is visible.

• On August 21, 2014, the DigitalGlobe team completed the focusing and achieved IOC (Initial Operational Capability) on the entire suite of WorldView-3’s super-spectral bands. 46)

• On August 19, 2014, six days after launch, the DigitalGlobe team completed commissioning of the satellite bus and opened the door on the main telescope to begin observing the changing planet.


Figure 22: Sample image of Madrid, Spain, acquired on August 21, 2014 (image credit: DigitalGlobe)

Note: Due to regulatory restrictions, the company is unable to display the 30 cm native resolution data, so they are sharing imagery that has been re-sampled to 40 cm, with the compressed “jpg" images available at

DigitalGlobe formally notified NOAA of WorldView-3’s IOC, which means that beginning on February 21, 2015,the company will be able to deliver 30 cm imagery to all its customers. In the meantime, DigitalGlobe will make 40 cm panchromatic and 1.6 m multispectral data available to its customers when WorldView-3 completes its validation and testing.

Sensor complement: (WV-3 Imager, CAVIS)

The WV-3 Imager, including the SWIR sensor and optics, was designed and built by ITT Exelis. The telescope primary mirror features an aperture diameter of 110 cm. In total, the WV-3 imager has 29 spectral bands including a panchromatic band, eight multispectral bands, 8 shortwave infrared bands and 12 CAVIS bands (Table 7). Scanning technique: pushbroom with a 35,000 pixel dectector array for PAN and a 9,300 pixel detector arra for multispectral bands. Note: The WV-3 Imager is also referred to as WV110 (same camera as in WorldView-2).

- In September 2010, Exelis was selected to build the imaging system, which will include a sensor subsystem and an optical telescope unit, for DigitalGlobe's WorldView-3 spacecraft.

- The CDR (Critical Design Review) for the imaging payload of WorldView-3 was completed on April 14, 2011.

- In Sept. 2013, Exelis delivered an integrated, super-spectral payload consisting of a telescope, sensor and shortwave infrared system for the WorldView-3 satellite. 47)

Note: On October 31, 2011, ITT Corporation spun off its defense and water technology businesses to form three separate, publicly-traded companies: 48)

1) Xylem Inc., a water technology and services company headquartered in White Plains, NY.

2) Exelis Inc. (or ITT Exelis), a defense technology business headquartered in Tysons Corner, VA. ITT Exelis Geospatial Systems of Rochester, N.Y., is supplying the sensor complement of WorldView-3.

3) ITT Corporation, a global manufacturing company headquartered in White Plains, NY.

Note: In May 2015, Harris Corp. of Melbourne, FL. acquired Exelis Inc. of Fort Wayne, IN. 49)

Exelis has designed and built imaging systems for each of DigitalGlobe’s current satellite constellation, including WorldView-1, WorldView-2, Ikonos-2, GeoEye-1, GeoEye-2 and QuickBird.

WorldView-3 combines the most productive high resolution commercial sensor subsystem available with a highly accurate and stable optical telescope unit. In addition of offering 0.31 m resolution panchromatic and 8-band MS imagery, WorldView -3 was licensed by NOAA to collect 8-band SWIR (Shortwave Infrared) imagery. This will make DigitalGlobe the only company with multiband SWIR capabilities, greatly expanding the range of customer applications enabled by the DigitalGlobe constellation. 50) 51) 52)

The WV-3 imager achieves a ground resolution of 31 cm in the panchromatic band at nadir, 1.24 m in the multispectral bands, 3.7 m in the SWIR range and 30 m for CAVIS. Off-nadir resolutions (20°) are 0.34 m for PAN, 1.38 m for MS and 4.1 m for SWIR.

WorldView-3 covers a ground swath of 13.1 km, supporting multiple swath imaging for mosaic image creation and stereo imaging. The satellite can acquire five strips to create an image of an area of 66.5 km x 112 km in a single pass. For stereo imaging, two pairs of images, measuring 26.6 km x by 112 km, can be acquired in one pass. With its high agility, WorldView-3 delivers a revisit time of under one day for any given location on Earth with a 1 m ground resolution or better. Revisit times for an off-nadir angle of 20º or less is on the order of 4.5 days.

Spectral range

Band name

Spectral band

GSD (Ground Sample Distance)

Panchromatic band (1)

450 - 800 nm

Nadir: 0.31 m, 20º off-nadir: 0.34 m

MS (Multispectral) bands (8)
in VNIR (Visible Near Infrared)

Coastal Blue

400 - 450 nm

Nadir: 1.24 m
20º off-nadir: 1.38 m


450 - 510 nm


510 - 580 nm


585 - 625 nm


630 - 690 nm

Red edge

705 - 745 nm


770 - 895 nm


860 - 1040 nm

Multiband (8 bands) in SWIR
(Shortwave Infrared) spectral range


1195 - 1225 nm

Nadir: 3.70 m
20º off-nadir: 4.10 m


1550 - 1590 nm


1640 - 1680 nm


1710 - 1750 nm


2145 - 2185 nm


2185 - 2225 nm


2235 - 2285 nm


2295 - 2365 nm





CAVIS bands (12)
CAVIS (Clouds, Aerosols, Vapors, Ice, & Snow)

Desert clouds

405 - 420 nm

Nadir: 30 m


459 - 509 nm


525 - 585 nm


620 - 670 nm


845 - 885 nm


897 - 927 nm


930 - 965 nm


1220 - 1252 nm


1350 - 1410 nm


1620 - 1680 nm


2105 - 2245 nm


2105 - 2245 nm

Data quantization

11 bit/pixel Pan and MS; 14 bit/pixel SWIR

Swath width

13.1 km

Revisit frequency
(at 40º N latitude)

1 m GSD: < 1.0 day
4.5 days at 20° off-nadir or less

Geolocation accuracy

< 3.0 m CE90 (Circular Error of 90%)

Table 7: Specification of the WorldView-3 imagers (Ref. 4)

The WV-3 imager allows to observe a much wider range of the electromagnetic spectra than most other commercial satellites, and will allow the data user to start looking for the individual spectral signatures of materials. Figure 23 shows the spectral signatures of three minerals. The light blue regions on the plot denote the VNIR bands covered by WV-2and WV-3. The light red regions denote the SWIR bands covered by the 8 new WV-3 bands. This shows just how much further the new bands penetrate into the electromagnetic spectrum. The spectral profiles of each pixel in an image can be compared to a spectral library (such as the reflectance spectra shown) to classify what material is contained within that pixel. 53)

This type of automated spectral classification is commonly carried out in ENVI (Environmental Monitoring) with hyperspectral data (from satellites containing hundreds of spectral bands) which can sometimes be costly to acquire. However, the ability to remotely monitor materials is invaluable to a great number of industries for example in forestry applications where we see users wanting to monitor tree health and pest infestation in remote regions. WorldView-3 brings a limited version of this capability to users at a reduced price, so one still can't expect the accuracy of a hyperspectral satellite, but it really is a step beyond the features we could extract from multispectral data. Digital Globe’s marketing refers to this observation scheme as ‘super-spectral’.


Figure 23: Reference spectra of three minerals (image credit: Exelis)


Figure 24: WorldView-3 spectral bands (image credit: Digital Globe) 54)

CAVIS ( Clouds, Aerosols, Vapors, Ice, and Snow):

The CAVIS imager is provided by BATC (Ball Aerospace and Technologies Corporation). The objective of CAVIS is to monitor the atmosphere and provide correction data to improve WorldView-3's high-resolution imagery when it images Earth objects through haze, soot, dust or other obscurants. The CAVIS imager has standalone optics and a focal-plane package, it features a resolution of 30 m.

The CAVIS instrument brings an unprecedented level of consistency in data, paving the way to standardization of satellite imagery. CAVIS corrects for the inconsistencies caused by certain conditions, offering standardized imagery no matter where or when the data was captured. This standardization will introduce a new age of automated information extraction and feature detection.

CAVIS performance parameters: 55)

• Image simultaneous with main instrument

• 7 VNIR + 5 SWIR bands

• 30 m resolution

• Swath is slightly wider than main instrument.


Figure 25: WorldView-3 sensor locations (image credit: DigitalGlobe, Ref. 54)


Figure 26: The spectral window of WorldView-3 (image credit: DigitalGlobe)

DigitalGlobe developed a fully automated framework for atmospherically compensating very high spatial resolution imagery from QB (QuickBird), WV1 (WorldView-1), and WV2 (WorldView-2).

This technology [DG-AComp (DigitalGlobe-Automatic Compensation)] has several advantages:

• enables the extraction of information using physical quantities, not just scene statistics (“surface reflectance” vs. “DN counts”)

• enables the extraction of information using physical quantities, not just scene statistics (“surface reflectance” vs. “DN counts”)

• facilitates cross-sensor processing

• fast processing via HPC (High Performance Computing).


- DG-AComp results are compared to in-situ measurements, and to two commercially available techniques, such as QUAC (Quick Atmospheric Correction) and FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes).

- QUAC is a fully automated method (as DG-AComp), and this represents the baseline to compare DG-AComp with.

- FLAASH requires the knowledge of atmospheric components (aerosol, water vapor, etc..), and it represents one of the most accurate method currently available. In this presentation, the atmospheric values automatically retrieved by DG-AComp are used as inputs to drive FLAASH.

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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 (

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