Satellite images are essentially the eyes in the sky. Some of the recent satellites, such as WorldView-3, provide images with a spatial resolution of 0.3 meters. This satellite with a revisit time of under 24 hours can scan a new image of the exact location with every revisit.

Spatial resolution explained Spatial resolution is the size of the physical dimension that can be represented on a pixel of the image. Or in other words, spatial resolution is a measure of the smallest object that the sensor can resolve measured in meters. Generally, spatial resolution can be divided into three categories:

– Low resolution: over 60m/pixel. (useful for regional perspectives such as monitoring larger forest areas)

– Medium resolution: 10‒30m/pixel. (Useful for monitoring crop fields or smaller forest patches)

– High to very high resolution: 0.30‒5m/pixel. (Useful for monitoring smaller objects like buildings, narrow streets, or vehicles)

Based on the application of the imagery for the final product, a choice can be made on the resolution, as labor intensity from person-hours to computing power required increases with the resolution of the imagery.

The cost of acquiring a satellite data was highest for the images from the GeoEye-1 satellite at 25 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 25 to 50 square kilometers based on the requested product.

There has been a tremendous increase in the volume of Earth Science data over the last decade from modern satellites, in-situ sensors and different climate models. All these datasets need to be co-analyzed for finding interesting patterns or for searching for extremes or outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets are physically stored at different geographical locations. Moving these petabytes of data over the network to a single location may waste a lot of bandwidth, and can take days to finish. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the global data without moving all the data to one location. The algorithm is highly accurate (close to 99%) and requires centralizing less than 5% of the entire dataset. We demonstrate the performance of the algorithm using data obtained from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images.

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.

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.

Inventory of various satellite image data acquired for the Superior National Forest, MN study including MSS, TM, SPOT, and HRV1-HRV2 over a period from 03JUL1983 to 16AUG1990

The National Centers for Environmental Information in partnership with the Cooperative Institute for Climate and Satellites - North Carolina is reprocessing the GOES (Geostationary Operational Environmental Satellite) Variable (GVAR) period of record: 1994-2015. GridSat GOES represents a reformatted, remapped and calibrated GOES brightness temperatures and reflectance provided in Climate and Forecasting (CF)-compliant netCDF format. This is similar to the current GridSat-B1 CDR, but at a higher spatial and temporal resolution. The data are provided near the original spatial resolution of the infrared channels (4 km) on an equal angle grid (0.04 degrees). Data are mapped to a region spanning the view of GOES East and West (150 deg East to 5 deg East). The data are provided hourly, with all data mapping to the nearest hour. Currently, the data are limited to variables including the observations from the GOES satellites: 5 total channels. However, future efforts are planned to include some basic cloud information (cloud probability, temperature, etc.). Other possible updates include: improved coverage by expanding the GOES inventory (currently, gaps exist in the CLASS archive) and expand to the predecessor to the GOES Imager: GOES VISSR, which would expand coverage back to the 1980s.

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

QuickBird high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, QuickBird offers archive panchromatic products up to 0.60 m GSD resolution and 4-Bands Multispectral products up to 2.4 m GSD resolution. Band Combination Data Processing Level Resolution Panchromatic and 4-bands Standard(2A)/View Ready Standard (OR2A) 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map-Ready (Ortho) 1:12,000 Orthorectified 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm 4-Bands being an option from: 4-Band Multispectral (BLUE, GREEN, RED, NIR1) 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) Natural Colour / Coloured Infrared (3-Band pan-sharpened) Native 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique intelligently increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details.

Caeli can provide this data through an API, dashboard, real-time geo map, or via datasets(.csv). In addition, all this data is available in daily, monthly and annual formats. The data can be delivered in various spatial resolutions starting from 0.001 degrees latitude and longitude (WSG 84), which roughly converts to 100X100 meter.

The Caeli datasets are often used for creating and validating various models and for training machine learning algorithms. We’ll allow you to specify your state or country, your preferred timeframe, resolution, and pollutant. Based on this information we’ll compile a reliable dataset. The measurements in de dataset can be used in determining the air quality of a region for a specific period of time. Additionally, your composite dataset can also serve for strategy and reporting purposes, such as ESG strategy, TCDF, SFDR, and sustainable decision making. The price of the dataset is based on the size of the area, the resolution chosen, and the number of years.

Are you interested in one of these pollutants or would you like to gather more information about our opportunities? Please, do not hesitate to contact us. www.caeli.space

Sector coverage: Financial | Energy | Government | Agricultural | Health | Shipping.

Australia has been receiving Earth Observations from Space (EOS) for over 50 years. Meteorological imagery dates from 1960 and Earth observation imagery from 1979. Australia has developed world-class scientific, environmental and emergency management EOS applications.

However, in the top fifty economies of the world, Australia is one of only three nations which does not have a space program. The satellites on which Australia depends are supplied by other countries which is a potential problem due to Australia having limited control over data continuity and data access.

The National Remote Sensing Technical Reference Group (NRSTRG) was established by Geoscience Australia as an advisory panel in 2004. It represents a cross-section of the remote sensing community and is made up of representatives from government, universities and private companies. Through the NRSTRG these parties provide Geoscience Australia with advice on technical and policy matters related to remote sensing.

In February 2009 the NRSTRG met for a day specifically to discuss Australia's reliance on EOS, with a view to informing the development of space policy. This report is the outcome of that meeting. Australia has some 92 programs dependent on EOS data. These programs are concerned with environmental issues, natural resource management, water, agriculture, meteorology, forestry, emergency management, border security, mapping and planning. Approximately half these programs have a high dependency on EOS data. While these programs are quite diverse there is considerable overlap in the technology and data.

Of Australia's EOS dependent programs 71 (77%) are valued between $100,000 and $10 million and 82 (89%) of all these programs have a medium or high dependency on EOS data demonstrating Australia's dependency on space based imaging.

Earth observation dependencies within currently active Federal and state government programs are calculated to be worth just over $949 million, calculated by weighting the level of dependency on EOS for each program. This includes two programs greater than $100 million in scale and one program greater than a billion dollars in scale.

This document is intended as a summary of Australia's current space and Earth observation dependencies, compiled by the NRSTRG, to be presented to the Federal Government's Space Policy Unit, a section of the Department of Innovation, Industry, Science and Research, as an aid to space policy formation.

This dataset consists of ground-based Global Navigation Satellite System (GNSS) Observation Data (30-second sampling, hourly files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. GNSS data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Since 2011, the CDDIS GNSS archive includes data from other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs), which are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The hourly GNSS observation files (un-compacted) contain one hour of GPS or multi-GNSS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per hour per site. More information about these data is available on the CDDIS website at https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/hourly_30second_data.html.

This data release contains field sampling data collected on a farm located in Talbot County. Maryland, roadside survey data from the area surrounding the farm, and WorldView-3 satellite data of the study area. Datasets include: 1) CropResidueDataset.csv: Tabular data for 174 photo sampling locations with crop residue cover ranging from 0% to 98%, as well as line-point transect residue cover measurements and lat-long geolocations 2) Roadside_Survey_May14th2015.zip: Zipfile containing roadside survey data for 63 fields documenting percent crop residue cover, including shapefile of field boundaries 3) GroundCoverPhotographs.zip: Zipfile containing 174 nadir photographs that were the basis for ground cover calculations 4) WorldView-3 satellite imagery collected May 14, 2015 and converted to surface reflectance using MODTRAN. The data support a manuscript published in Remote Sensing journal: Hively, W.D; Lamb, B.T. Daughtry, C.S.T. Shermeyer, J. McCarty, G.W., and Quemada, M., 2018, Mapping Crop Residue and Tillage Intensity Using WorldView-3 Satellite Shortwave Infrared Residue Indices: Remote Sensing, vol. 10, p. 1657. https://doi.org/10.3390/rs10101657

Satellite data services market was valued at USD 5.97 billion in 2020 and is expected to grow at a CAGR of 20.9% during 2021 - 2028

The CEOP satellite datasets are geo-coded data resampled to regular latitude longitude grid on three type scales: reference site scale, monsoon scale, and global scale. It is easy to compare satellite data with in-situ measurement and or model output. Also, the satellite datasets include radiance data and geo-physical parameter such as soil moisture, vegetation index, water vapor, precipitation, sea surface temperature and so on, which are retrieved from data of MODIS, AMSR-E, SSM/I, PR, TMI, AVNIR2, PALSAR and PRISM.

The CEOP-MODIS datasets are generated on 35 reference sites, 5 monsoon regions and global during EOP-3 date period.

► Enhanced Observing Period　EOP-3: 01/10/2002 - 30/09/2003 EOP-4: 01/10/2003 - 31/12/2004

► Reference Site Locations http://www.eol.ucar.edu/projects/ceop/dm/documents/ref_site.html

► Definition of Monsoon Regions Asia EQ-50N 60E-160E North American EQ-35N 120W-60W South American EQ-35S 80W-30W Australian EQ-25S 90E-160E North African EQ-25N 30W-40E

► Product Code MOD07_L2 for Terra/MODIS MYD07_L2 for Aqua/MODIS

► Product Name L2 Atmosphere Profile Product

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). Methods

1. INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limited human intervention. Sloan et al. (2023) present a map indicating the various areas of Equatorial Asia from which these images were sourced.
IMAGE NAMING CONVENTION A common naming convention applies to satellite images’ file names: XX##.png where:

XX – denotes the geographical region / major island of Equatorial Asia of the image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

1. INTERPRETING ROAD FEATURES IN THE IMAGES For each of the 200 input satellite images, its road was visually interpreted and manually digitized to create a reference image dataset by which to train, validate, and test AI road-mapping models, as detailed in Sloan et al. (2023). The reference dataset of road features was digitized using the ‘pen tool’ in Adobe Photoshop. The pen’s ‘width’ was held constant over varying scales of observation (i.e., image ‘zoom’) during digitization. Consequently, at relatively small scales at least, digitized road features likely incorporate vegetation immediately bordering roads. The resultant binary (Road / Not Road) reference images were saved as PNG images with the same image dimensions as the original 200 images.

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

The 200 satellite images and the corresponding 200 road-reference images were both subdivided (aka ‘sliced’) into thousands of smaller image ‘tiles’ of 256x256 pixels each. Subsequent to image subdivision, subdivided images were also rotated by 90, 180, or 270 degrees to create additional, complementary image tiles for model development. In total, 8904 image tiles resulted from image subdivision and rotation. These 8904 image tiles are the main data of interest disseminated here. Each image tile entails the true-colour satellite image (256x256 pixels) and a corresponding binary road reference image (Road / Not Road).
Of these 8904 image tiles, Sloan et al. (2023) randomly selected 80% for model training (during which a model ‘learns’ to recognize road features in the input imagery), 10% for model validation (during which model parameters are iteratively refined), and 10% for final model testing (during which the final accuracy of the output road map is assessed). Here we present these data in two folders accordingly:

'Training’ – contains 7124 image tiles used for model training in Sloan et al. (2023), i.e., 80% of the original pool of 8904 image tiles. ‘Testing’– contains 1780 image tiles used for model validation and model testing in Sloan et al. (2023), i.e., 20% of the original pool of 8904 image tiles, being the combined set of image tiles for model validation and testing in Sloan et al. (2023).

IMAGE TILE NAMING CONVENTION A common naming convention applies to image tiles’ directories and file names, in both the ‘training’ and ‘testing’ folders: XX##_A_B_C_DrotDDD where

XX – denotes the geographical region / major island of Equatorial Asia of the original input 1920x886 pixel image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

A, B, C and D – can all be ignored. These values, which are one of 0, 256, 512, 768, 1024, 1280, 1536, and 1792, are effectively ‘pixel coordinates’ in the corresponding original 1920x886-pixel input image. They were recorded within the names of image tiles’ sub-directories and file names merely to ensure that names/directory were uniquely named)

rot – implies an image rotation. Not all image tiles are rotated, so ‘rot’ will appear only occasionally.

DDD – denotes the degree of image-tile rotation, e.g., 90, 180, 270. Not all image tiles are rotated, so ‘DD’ will appear only occasionally.

Note that the designator ‘XX##’ is directly equivalent to the filenames of the corresponding 1920x886-pixel input satellite images, detailed above. Therefore, each image tiles can be ‘matched’ with its parent full-scale satellite image. For example, in the ‘training’ folder, the subdirectory ‘Bo12_0_0_256_256’ indicates that its image tile therein (also named ‘Bo12_0_0_256_256’) would have been sourced from the full-scale image ‘Bo12.png’.

TRACE-P_Satellite_Data is the supplementary satellite data collected during the Transport and Chemical Evolution over the Pacific (TRACE-P) suborbital campaign. Data from the Multi-Angle Imaging SpectroRadiometer (MISR) and the Measurements of Pollution in the Troposphere (MOPITT) satellite instruments are featured in this collection. Data collection for this product is complete. The NASA TRACE-P mission was a part of NASA’s Global Tropospheric Experiment (GTE) – an assemblage of missions conducted from 1983-2001 with various research goals and objectives. TRACE-P was a multi-organizational campaign with NASA, the National Center for Atmospheric Research (NCAR), and several US universities. TRACE-P deployed its payloads in the Pacific between the months of March and April 2001 with the goal of studying the air chemistry emerging from Asia to the western Pacific. Along with this, TRACE-P had the objective studying the chemical evolution of the air as it moved away from Asia. In order to accomplish its goals, the NASA DC-8 aircraft and NASA P-3B aircraft were deployed, each equipped with various instrumentation. TRACE-P also relied on ground sites, and satellites to collect data. The DC-8 aircraft was equipped with 19 instruments in total while the P-3B boasted 21 total instruments. Some instruments on the DC-8 include the Nephelometer, the GCMS, the Nitric Oxide Chemiluminescence, the Differential Absorption Lidar (DIAL), and the Dual Channel Collectors and Fluorometers, HPLC. The Nephelometer was utilized to gather data on various wavelengths including aerosol scattering (450, 550, 700nm), aerosol absorption (565nm), equivalent BC mass, and air density ratio. The GCMS was responsible for capturing a multitude of compounds in the atmosphere, some of which include CH4, CH3CHO, CH3Br, CH3Cl, CHBr3, and C2H6O. DIAL was used for a variety of measurements, some of which include aerosol wavelength dependence (1064/587nm), IR aerosol scattering ratio (1064nm), tropopause heights and ozone columns, visible aerosol scattering ratio, composite tropospheric ozone cross-sections, and visible aerosol depolarization. Finally, the Dual Channel Collectors and Fluorometers, HPLC collected data on H2O2, CH3OOH, and CH2O in the atmosphere. The P-3B aircraft was equipped with various instruments for TRACE-P, some of which include the MSA/CIMS, the Non-dispersive IR Spectrometer, the PILS-Ion Chromatograph, and the Condensation particle counter and Pulse Height Analysis (PHA). The MSA/CIMS measured OH, H2SO4, MSA, and HNO3. The Non-dispersive IR Spectrometer took measurements on CO2 in the atmosphere. The PILS-Ion Chromatograph recorded measurements of compounds and elements in the atmosphere, including sodium, calcium, potassium, magnesium, chloride, NH4, NO3, and SO4. Finally, the Condensation particle counter and PHA was used to gather data on total UCN, UCN 3-8nm, and UCN 3-4nm. Along with the aircrafts, ground stations measured air quality from China along with C2H2, C2H6, CO, and HCN. Finally, satellites imagery was used to collect a multitude of data, some of the uses were to observe the history of lightning flashes, SeaWiFS cloud imagery, 8-day exposure to TOMS aerosols, and SeaWiFS aerosol optical thickness. The imagery was used to best aid in planning for the aircraft deployment.

Global Satellite Data Services Market size was valued at USD 6.58 billion in 2021 and is poised to grow from USD 7.86 billion in 2022 to USD 27.36 billion by 2030, at a CAGR of 19.50% during the forecast period (2023-2030).

SENTINEL-2 is a wide-swath, high-resolution, multi-spectral imaging mission, supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas.

The SENTINEL-2 Multispectral Instrument (MSI) samples 13 spectral bands: four bands at 10 metres, six bands at 20 metres and three bands at 60 metres spatial resolution.

The acquired data, mission coverage and high revisit frequency provides for the generation of geoinformation at local, regional, national and international scales. The data is designed to be modified and adapted by users interested in thematic areas such as: • spatial planning • agro-environmental monitoring • water monitoring • forest and vegetation monitoring • land carbon, natural resource monitoring • global crop monitoring

10 to 20m resolution panchromatic imagery is available for the UK and Ireland from 1986 to 1995 (from SPOT 1, 2 and 3 satellites). They are isolated scenes captured over an extended time period. The data were acquired by the Landmap project from Infoterra. The SPOT satellite Earth Observation System was designed by the Centre National d'Etudes Spatiales (CNES), in France. There have been 7 SPOT (Satellite Pour l'Observation de la Terre) satellites launched since 1986 (as of August 2014), providing medium to high resolution of the Earth's surface. SPOT 1, 2 and 3 carried a multi-spectral and panchromatic sensor on board. SPOT 4 was successfully launched in March 1998. The first three SPOT satellites carry twin HRVs (High-Resolution Visible Imaging instruments) that operate in a number of viewing configurations and in different spectral modes. Some of those viewing configurations and spectral modes include one HRV only operating in a dual spectral mode (i.e. in both panchromatic mode and multispectral mode); two HRVs operating in the twin-viewing configuration (i.e. one HRV in panchromatic mode and one HRV in multispectral mode); and two HRVs operating independently of each other (i.e. not in twin-viewing configuration). The position of each HRV entrance mirror can be commanded by ground control to observe a region of interest. Operating independently of each other, the two HRVs acquire imagery in either multispectral (XS) and/or panchromatic (P) modes at any viewing angle within plus or minus 27 degrees. This off-nadir viewing enables the acquisition of stereoscopic imagery. To make sure the satellite covers every point on the earth's surface, the HRV imaging instruments offer a field of view that is wider than the greatest distance between two adjacent tracks. The Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC. When using these data please also include the following copyright statement on any reproduced SPOT images: CNES (year of reproduction of the data from the satellite), reproduced by................................................. under licence from SPOT IMAGE