Citation: If using this dataset please cite the following in your work: @misc{VotDasNemSri2010 , author = "Petr Votava and Kamalika Das and Rama Nemani and Ashok N. Srivastava", year = "2010", title = "MODIS surface reflectance data repository", url = "https://c3.ndc.nasa.gov/dashlink/resources/331/", institution = "NASA Ames Research Center" } Petr Votava, Kamalika Das, Rama Nemani, Ashok N. Srivastava. (2010). MODIS surface reflectance data repository. NASA Ames Research Center. Data Description: The California satellite dataset using the MODerate-resolution Imaging Spectroradiometer (MODIS) product MCD43A4 provides reflectance data adjusted using a bidirectional reflectance distribution function (BRDF) to model the values as if they were taken from nadir view. Both Terra and Aqua data are used in the generation of this product, providing the highest probability for quality input data. More information at: https://lpdaac.usgs.gov/lpdaac/products/modis_products_table/nadir_brdf_adjusted_reflectance/16_day_l3_global_500m/v5/combined Data Organization: The nine data folders correspond to three years of data.Under this top level directory structure are separate files for each band (1 - 7) and each 8-day period of the particular year. Within the period the best observations were selected for each location. File Naming Conventions: Each of the files represent a 2D dataset with the naming conventions as follows: MCD43A4.CA_1KM.005.. .flt32 where is the beginning year-day of the period that where YYYY = year and DDD = day of year (001 - 366) represents the observations in particular (spectral) band (band 1 - band 7) - since the indexing is 0-based, the range of indexes on the files is from 0 - 6 (where 0 = band 1, and 6 = band 7) The spectral band frequencies for the MODIS acquisitions are as follows: BAND1 620 - 670 nm BAND2 841 - 876 nm BAND3 459 - 479 nm BAND4 545 - 565 nm BAND5 1230 - 1250 nm BAND6 1628 - 1652 nm BAND7 2105 - 2155 nm File Specifications: Each file is a single 2D dataset. DATA TYPE: 32-bit floating point (IEEE754) with little-Endian byte ordering NUMBER OF ROWS: 1203 NUMBER OF COLUMNS: 738 FILL VALUES (observations that are either not valid or not on land, such as ocean etc.): -999.0 Overview: DATASET: MODIS 8-day Surface Reflectance BRDF-adjusted from Terra and Aqua COLLECTION: 5 DATA TYPE: IEEE754 float (32-bit float) BYTE ORDER: LITTLE ENDIAN (Intel) DIMS: 1203 rows x 738 columns FILL VALUE: -999.0 SPATIAL RESOLUTION: 1km PROJECTION: Lambert Azimuthal Equal Area

Declassified satellite images provide an important worldwide record of land-surface change. With the success of the first release of classified satellite photography in 1995, images from U.S. military intelligence satellites KH-7 and KH-9 were declassified in accordance with Executive Order 12951 in 2002. The data were originally used for cartographic information and reconnaissance for U.S. intelligence agencies. Since the images could be of historical value for global change research and were no longer critical to national security, the collection was made available to the public. Keyhole (KH) satellite systems KH-7 and KH-9 acquired photographs of the Earth’s surface with a telescopic camera system and transported the exposed film through the use of recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications. The KH-7 surveillance system was a high resolution imaging system that was operational from July 1963 to June 1967. Approximately 18,000 black-and-white images and 230 color images are available from the 38 missions flown during this program. Key features for this program were larger area of coverage and improved ground resolution. The cameras acquired imagery in continuous lengthwise sweeps of the terrain. KH-7 images are 9 inches wide, vary in length from 4 inches to 500 feet long, and have a resolution of 2 to 4 feet. The KH-9 mapping program was operational from March 1973 to October 1980 and was designed to support mapping requirements and exact positioning of geographical points for the military. This was accomplished by using image overlap for stereo coverage and by using a camera system with a reseau grid to correct image distortion. The KH-9 framing cameras produced 9 x 18 inch imagery at a resolution of 20-30 feet. Approximately 29,000 mapping images were acquired from 12 missions. The original film sources are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery.

The purpose of the SNF Study was to develop the techniques to make the link from biophysical measurements made on the ground to aircraft radiometric measurements and then to scale up to satellite observations. Therefore, satellite image data were acquired for the Superior National Forest study site. These data were selected from all the scenes available from Landsat 1 through 5 and SPOT platforms. Image data substantially contaminated by cloud cover or of poor radiometric quality was not acquired. Of the Landsat scenes, only one Thematic Mapper (TM) scene was acquired, the remainder were Multispectral Scanner (MSS) images. Some of the acquired image data had cloud cover in portions of the scene or other problems with the data. These problems and other comments about the images are summarized in the data set. This data set contains a listing of the scenes that passed inspection and were acquired and archived by Goddard Space Flight Center. Though these image data are no longer available from either the Goddard Space Flight Center or the ORNL DAAC, this data set has been included in the Superior National Forest data collection in order to document which satellite images were used during the project.

The satellite image of Canada is a composite of several individual satellite images form the Advanced Very High Resolution Radiometre (AVHRR) sensor on board various NOAA Satellites. The colours reflect differences in the density of vegetation cover: bright green for dense vegetation in humid southern regions; yellow for semi-arid and for mountainous regions; brown for the north where vegetation cover is very sparse; and white for snow and ice. An inset map shows a satellite image mosaic of North America with 35 land cover classes, based on data from the SPOT satellite VGT (vegetation) sensor.

anomalies

The cost of acquiring a satellite data was highest for the images from the GeoEye-1 satellite, at ** U.S. dollars per square kilometer of the image. Most of the satellite data have a minimum order quantities based on the company and the cost depends mostly on the spatial resolution of the satellite image. Most of the satellites are commercially owned and provide users with data as an end product based on the requirement. Processing smaller patches of the raw images obtained from a satellite to an end product are not profitable. Hence, there is a minimum order limit of ** to ** square kilometers based on the requested product.

The Defense Meteorological Satellite Program (DMSP) satellites collect visible and infrared cloud imagery as well as monitoring the atmospheric, oceanographic, hydrologic, cryospheric and near-Earth space environments. The DMSP program maintains a constellation of sun-synchronous, near-polar orbiting satellites. The orbital period is 101 minutes and inclination is 99 degrees. The atmospheric and oceanographic sensors record radiances at visible, infrared and microwave wavelengths. The solar geophysical sensors measure ionospheric plasma fluxes, densities, temperatures and velocities. DMSP visible and infrared imagery of clouds covers a 3,000 km swath, thus each satellite provides global coverage of both day night time conditions each day. The field view of the microwave imagers and sounders is only 1,500 km thus approximately 3 days data are required for one instrument to provide global coverage at equatorial latitudes. The solar geophysical instruments make in-situ measurements of ionospheric parameters, some of which vary very rapidly. The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive the complete DMSP data stream from the Air Force Weather Agency (AFWA), Offutt Air Force Base, Omaha, Nebraska. Data are currently transmitted in near realtime from AFWA directly to the archive via a designated T1 line. Archive processing prepares orbital data sets of calibrated, quality assessed data organized as a time-series, restores data lost during transmission,and accurately computes satellite positions. NCEI maintains an archive of all data recorded on DMSP satellites as relayed to The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) by the Air Force Weather Agency. Data from March 1992 to March 1994, are considered to be experimental. After March 1994, the system was fully operational. NCEI archives contain data that are post process reconstructed, positioned and geolocated using the same software.

NEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete.

NEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the GOES-R ABI Reprocess User Guide.


NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket.

GOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. SUVI products available in both NetCDF and FITS.

STRAT_Satellite_Data is the supplementary satellite data collected during the Stratospheric Tracers of Atmospheric Transport (STRAT) campaign. Satellite images from the GOES-7 and GOES-9 satellites are featured in this collection. Data collection for this product is complete.The STRAT campaign was a field campaign conducted by NASA from May 1995 to February 1996. The primary goal of STRAT was to collect measurements of the change of long-lived tracers and functions of altitude, latitude, and season. These measurements were taken to aid with determining rates for global-scale transport and future distributions of high-speed civil transport (HSCT) exhaust that was emitted into the lower atmosphere. STRAT had four main objectives: defining the rate of transport of trace gases from the stratosphere and troposphere (i.e., HSCT exhaust emissions), improving the understanding of dynamical coupling rates for transport of trace gases between tropical regions and higher latitudes and lower altitudes (between tropical regions, higher latitudes, and lower altitudes are where most ozone resides), improving understanding of chemistry in the upper troposphere and lower stratosphere, and finally, providing data sets for testing two-dimensional and three-dimensional models used in assessments of impacts from stratospheric aviation. To accomplish these objectives, the STRAT Science Team conducted various surface-based remote sensing and in-situ measurements. NASA flew the ER-2 aircraft along with balloons such as ozonesondes and radiosondes just below the tropopause in the Northern Hemisphere to collect data. Along with the ER-2 and balloons, NASA also utilized satellite imagery, theoretical models, and ground sites. The ER-2 collected data on HOx, NOy, CO2, ozone, water vapor, and temperature. The ER-2 also collected in-situ stratospheric measurements of N2O, CH4, CO, HCL, and NO using the Aircraft Laser Infrared Absorption Spectrometer (ALIAS). Ozonesondes and radiosondes were also deployed to collect data on CO2, NO/NOy, air temperature, pressure, and 3D wind. These balloons also took in-situ measurements of N2O, CFC-11, CH4, CO, HCL, and NO2 using the ALIAS. Ground stations were responsible for taking measurements of O3, ozone mixing ratio, pressure, and temperature. Satellites took infrared images of the atmosphere with the goal of aiding in completing STRAT objectives. Pressure and temperature models were created to help plan the mission.

INTEX-NA is a two phase experiment that aims to understand the transport and transformation of gases and aerosols on transcontinental/intercontinental scales and assess their impact on air quality and climate. The primary constituents of interest are ozone and precursors, aerosols and precursors, and the long-lived greenhouse gases. The first phase (INTEX-A) was completed in the summer of 2004 and the second phase (INTEX-B) is to be performed in the spring of 2006. This document is intended to provide an update on the goals of INTEX-B and define its implementation strategy. The scientific goals envisioned here are based on the joint implementation of INTEX-B, MIRAGE-Mex and DLR/IMPACT studies and their coordination with satellite observations. In collaboration with these partners, the main goals of INTEX-B are to:- Quantify the transpacific transport and evolution of Asian pollution to North America and assess its implications for regional air quality and climate; - Quantify the outflow and evolution of gases and aerosols from the Mexico City Megaplex; - Investigate the transport of Asian and North America pollution to the eastern Atlantic and assess its implications for European air quality; - Validate and refine satellite observations of tropospheric composition; - Map emissions of trace gases and aerosols and relate atmospheric composition to sources and sinks.The INTEX-B field study is to be performed during an approximate 8-week period from March 1 to April 30, 2006.

Satellite Data Services Market size was valued at USD 9.75 Billion in 2023 and is projected to reach USD 34.06 Billion by 2030, growing at a CAGR of 19.6% during the forecast period 2024-2030.

Global Satellite Data Services Market Drivers

The market drivers for the Satellite Data Services Market can be influenced by various factors. These may include:

Growing Need for Earth Observation Data: As worries about natural disasters, urban planning, agriculture, climate change, and environmental monitoring grow, so does the need for satellite data used for earth observation. Technological developments in satellites: The possibilities for satellite data uses are growing thanks to developments in miniaturisation, resolution, and data transmission capabilities. This influences the market's supply and demand. Government Initiatives and Regulations: For the sake of infrastructure development, defence, surveillance, and disaster management, governments throughout the world are funding satellite programmes. The market is also shaped by regulations, especially those that deal with data security, privacy, and licencing. Emergence of New Players: Government organisations are no longer the only ones controlling the satellite market. With big plans for satellite constellations, private companies like SpaceX, OneWeb, and Amazon are joining the market, bringing competition and new options. Growing Use of Satellite Communication Services: In situations when traditional communication infrastructure is inadequate or unreliable, such as in distant locations, maritime environments, aviation environments, or disaster recovery scenarios, satellite communication services are essential. The need for satellite data services is fueled by this. Increase in IoT and M2M Connectivity: Particularly in remote or mobile contexts, the growth of Internet of Things (IoT) devices and Machine-to-Machine (M2M) communication necessitates dependable connectivity, which is frequently offered by satellite networks. Demand for Location-Based Services: A number of location-based services (LBS), including fleet management, asset tracking, and navigation, are dependent on satellite data, mostly from Global Navigation Satellite Systems (GNSS) like GPS, GLONASS, and Galileo. Growing Uses in Many Industries: The demand for satellite imagery and analytics is diversified and is driven by the expanding use of satellite data services in industries such as agriculture, forestry, energy, mining, transportation, urban planning, insurance, and finance. Growing Interest and Investment in Space Exploration: The need for satellite data services for communication, navigation, and scientific research is fueled by the growing interest in and funding for space exploration, which includes moon missions, asteroid mining, and Mars colonisation. Global Connectivity programmes: In order to close the digital gap, there is a growing need for satellite data services. Examples of these programmes are the Broadband Commission for Sustainable Development and the Sustainable Development Goals (SDGs) of the United Nations.

validation

The first generation of U.S. photo intelligence satellites collected more than 860,000 images of the Earth’s surface between 1960 and 1972. The classified military satellite systems code-named CORONA, ARGON, and LANYARD acquired photographic images from space and returned the film to Earth for processing and analysis. The images were originally used for reconnaissance and to produce maps for U.S. intelligence agencies. In 1992, an Environmental Task Force evaluated the application of early satellite data for environmental studies. Since the CORONA, ARGON, and LANYARD data were no longer critical to national security and could be of historical value for global change research, the images were declassified by Executive Order 12951 in 1995. The first successful CORONA mission was launched from Vandenberg Air Force Base in 1960. The satellite acquired photographs with a telescopic camera system and loaded the exposed film into recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications. The intelligence community used Keyhole (KH) designators to describe system characteristics and accomplishments. The CORONA systems were designated KH-1, KH-2, KH-3, KH-4, KH-4A, and KH-4B. The ARGON systems used the designator KH-5 and the LANYARD systems used KH-6. Mission numbers were a means for indexing the imagery and associated collateral data. A variety of camera systems were used with the satellites. Early systems (KH-1, KH-2, KH-3, and KH-6) carried a single panoramic camera or a single frame camera (KH-5). The later systems (KH-4, KH-4A, and KH-4B) carried two panoramic cameras with a separation angle of 30° with one camera looking forward and the other looking aft. The original film and technical mission-related documents are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery. Mathematical calculations based on camera operation and satellite path were used to approximate image coordinates. Since the accuracy of the coordinates varies according to the precision of information used for the derivation, users should inspect the preview image to verify that the area of interest is contained in the selected frame. Users should also note that the images have not been georeferenced.

ARCTAS_Satellite_Data is the supplementary satellite data for the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites sub-orbital campaign. Data from TES, MOPITT and OMI are featured in this data product and data collection is complete.The Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA’s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.ARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.ARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.During ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.The ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth’s environment and climate.

GEOSatDB is a semantic representation of Earth observation satellites and sensors that can be used to easily discover available Earth observation resources for specific research objectives.BackgroundThe widespread availability of coordinated and publicly accessible Earth observation (EO) data empowers decision-makers worldwide to comprehend global challenges and develop more effective policies. Space-based satellite remote sensing, which serves as the primary tool for EO, provides essential information about the Earth and its environment by measuring various geophysical variables. This contributes significantly to our understanding of the fundamental Earth system and the impact of human activities.Over the past few decades, many countries and organizations have markedly improved their regional and global EO capabilities by deploying a variety of advanced remote sensing satellites. The rapid growth of EO satellites and advances in on-board sensors have significantly enhanced remote sensing data quality by expanding spectral bands and increasing spatio-temporal resolutions. However, users face challenges in accessing available EO resources, which are often maintained independently by various nations, organizations, or companies. As a result, a substantial portion of archived EO satellite resources remains underutilized. Enhancing the discoverability of EO satellites and sensors can effectively utilize the vast amount of EO resources that continue to accumulate at a rapid pace, thereby better supporting data for global change research.MethodologyThis study introduces GEOSatDB, a comprehensive semantic database specifically tailored for civil Earth observation satellites. The foundation of the database is an ontology model conforming to standards set by the International Organization for Standardization (ISO) and the World Wide Web Consortium (W3C). This conformity enables data integration and promotes the reuse of accumulated knowledge. Our approach advocates a novel method for integrating Earth observation satellite information from diverse sources. It notably incorporates a structured prompt strategy utilizing a large language model to derive detailed sensor information from vast volumes of unstructured text.Dataset InformationThe GEOSatDB portal(https://www.geosatdb.cn) has been developed to provide an interactive interface that facilitates the efficient retrieval of information on Earth observation satellites and sensors.The downloadable files in RDF Turtle format are located in the data directory and contain a total of 132,681 statements:- GEOSatDB_ontology.ttl: Ontology modeling of concepts, relations, and properties.- satellite.ttl: 2,453 Earth observation satellites and their associated entities.- sensor.ttl: 1,035 Earth observation sensors and their associated entities.- sensor2satellite.ttl: relations between Earth observation satellites and sensors.GEOSatDB undergoes quarterly updates, involving the addition of new satellites and sensors, revisions based on expert feedback, and the implementation of additional enhancements.

The global satellite data services market is experiencing robust growth, driven by increasing demand across various sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. Firstly, advancements in satellite technology are leading to higher-resolution imagery, improved data accuracy, and increased data accessibility. Secondly, the growing adoption of satellite data in diverse applications, including precision agriculture, urban planning, environmental monitoring, disaster management, and defense & security, is significantly contributing to market growth. Furthermore, the decreasing cost of satellite data acquisition and processing is making it more accessible to a wider range of users, including small and medium-sized enterprises (SMEs). However, the market faces certain restraints. Data security and privacy concerns, along with the regulatory complexities surrounding data usage and ownership, could hinder market growth. Competition among established players and emerging startups is also intense, requiring continuous innovation and strategic partnerships to maintain a competitive edge. The market is segmented by data type (optical, radar, hyperspectral), application (agriculture, defense, mapping), and region (North America, Europe, Asia-Pacific, etc.). Key players like Maxar Technologies, Airbus, Planet Labs, and others are investing heavily in research and development, focusing on developing advanced data analytics capabilities and expanding their global reach to capitalize on the market's significant potential. The continued miniaturization of satellites and the rise of constellations are further disrupting and accelerating the market's expansion, making satellite data more readily available and cost-effective.

Satellite Data Services Market touched USD 6.5 billion in 2021 and expected to reach USD 30.5 billion in 2029, growing at a significant CAGR of 21.4% till 2029

The WorldView-4 Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the SpaceView-110 camera across the global land surface from December 2016 to January 2019. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The multispectral imagery has a spatial resolution of 1.24m at nadir and has a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program.

Satellite Data Services Market Outlook

As per our latest research, the global satellite data services market size reached USD 8.7 billion in 2024, driven by increasing demand for real-time geospatial intelligence and advanced analytics across multiple industries. The market is poised for robust expansion, registering a CAGR of 18.2% from 2025 to 2033. By 2033, the satellite data services market is forecasted to attain a value of USD 44.1 billion, propelled by technological advancements, the proliferation of small satellite constellations, and growing integration of satellite data into commercial applications. This growth trajectory underscores the transformative impact of satellite data on decision-making processes and operational efficiency across global sectors.

One of the principal growth factors for the satellite data services market is the surge in demand for high-resolution imagery and geospatial analytics across sectors such as agriculture, energy, defense, and environmental monitoring. The rapid digitization of industries and the need for precise, real-time data to support critical operations have fueled investments in satellite data services. Additionally, the increasing frequency of natural disasters and the growing importance of climate change monitoring have necessitated the use of satellite-based solutions for timely and accurate information. The integration of artificial intelligence and machine learning with satellite data analytics has further amplified the value proposition of these services, enabling predictive insights and automated anomaly detection for enhanced decision-making.

Another significant driver is the expansion of small satellite constellations and the decreasing cost of satellite launches, which have democratized access to satellite data. The advent of low Earth orbit (LEO) satellites has revolutionized data acquisition, offering improved revisit rates and cost-effective solutions for commercial and governmental clients. The proliferation of private players and public-private partnerships has accelerated innovation in satellite data services, resulting in enhanced data quality, faster delivery times, and a wider range of value-added services. This democratization has opened new avenues for start-ups and SMEs, fostering a competitive environment that stimulates continuous technological advancement and market expansion.

The satellite data services market is also benefiting from increased government initiatives and policy support for space-based infrastructure and data utilization. Governments worldwide are investing in satellite programs to bolster national security, disaster management, and socio-economic development. These initiatives have led to greater collaboration between governmental agencies and private enterprises, promoting the adoption of satellite data for urban planning, resource management, and infrastructure development. Moreover, international efforts to standardize satellite data formats and improve interoperability are facilitating cross-border data sharing, thereby expanding the global reach and utility of satellite data services.

Regionally, North America remains the largest market for satellite data services, accounting for over 37% of global revenue in 2024, driven by the presence of leading satellite operators, advanced technological infrastructure, and substantial government funding. Europe follows closely, supported by strong investments in space programs and a burgeoning commercial sector. The Asia Pacific region is witnessing the fastest growth, with a projected CAGR of 21.5% during the forecast period, fueled by increasing adoption of satellite technologies in emerging economies such as China and India. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as governments and enterprises in these regions recognize the strategic value of satellite data for development and security.

Service Type Analysis

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This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.

Area Covered: World

What can I do with this layer?
The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.


It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.

Attribute Information

• Latitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?
• Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.
• Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?
• Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.
  
• Acquisition Date: Derived Date/Time field combining Date and Time attributes.
• Satellite: Whether the detection was picked up by the Terra or Aqua satellite.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.
• Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.
• Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

This map is provided for informational purposes and is not monitored 24/7 for accuracy and