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.

The Ontario Imagery Web Map Service (OIWMS) is an open data service available to everyone free of charge. It provides instant online access to the most recent, highest quality, province wide imagery. GEOspatial Ontario (GEO) makes this data available as an Open Geospatial Consortium (OGC) compliant web map service or as an ArcGIS map service. Imagery was compiled from many different acquisitions which are detailed in the Ontario Imagery Web Map Service Metadata Guide linked below. Instructions on how to use the service can also be found in the Imagery User Guide linked below. Note: This map displays the Ontario Imagery Web Map Service Source, a companion ArcGIS web map service to the Ontario Imagery Web Map Service. It provides an overlay that can be used to identify acquisition relevant information such as sensor source and acquisition date. OIWMS contains several hierarchical layers of imagery, with coarser less detailed imagery that draws at broad scales, such as a province wide zooms, and finer more detailed imagery that draws when zoomed in, such as city-wide zooms. The attributes associated with this data describes at what scales (based on a computer screen) the specific imagery datasets are visible. Available Products Ontario Imagery OGC Web Map Service – public linkOntario Imagery ArcGIS Map Service – public linkOntario Imagery Web Map Service Source – public linkOntario Imagery ArcGIS Map Service – OPS internal linkOntario Imagery Web Map Service Source – OPS internal linkAdditional Documentation Ontario Imagery Web Map Service Metadata Guide (PDF)Ontario Imagery Web Map Service Copyright Document (PDF) Imagery User Guide (Word)StatusCompleted: Production of the data has been completed Maintenance and Update FrequencyAnnually: Data is updated every year ContactOntario Ministry of Natural Resources, Geospatial Ontario, imagery@ontario.ca

This collection is a legacy product that is no longer supported. It may not meet current government standards. This inventory presents chronologically the satellite images acquired, orthorectified and published over time by Natural Resources Canada. It is composed of imagery from the Landsat7 (1999-2003) and RADARSAT-1 (2001-2002) satellites, as well as the CanImage by-product and the control points used to process the images. Landsat7 Orthorectified Imagery: The orthoimage dataset is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and created with the most accurate control data available at the time of creation. RADARSAT-1 Orthorectified Imagery: The 5 RADARSAT-1 images (processed and distributed by RADARSAT International (RSI) complete the landsat 7 orthoimagery coverage. They are stored as raster data produced from SAR Standard 7 (S7) beam mode with a pixel size of 15 m. They have been produced in accordance with NAD83 (North American Datum of 1983) using the Universal Transverse Mercator (UTM) projection. RADARSAT-1 orthoimagery were produced with the 1:250 000 Canadian Digital Elevation Data (CDED) and photogrammetric control points generated from the Aerial Survey Data Base (ASDB). CanImage -Landsat7 Orthoimages of Canada,1:50 000: CanImage is a raster image containing information from Landsat7 orthoimages that have been resampled and based on the National Topographic System (NTS) at the 1:50 000 scale in the UTM projection. The product is distributed in datasets in GeoTIFF format. The resolution of this product is 15 metres. Landsat7 Imagery Control Points: the control points were used for the geometric correction of Landsat7 satellite imagery. They can also be used to correct vector data and for simultaneously displaying data from several sources prepared at different scales or resolutions.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Contained within the Atlas of Canada's Various Map Series, 1965 to 2006, is the first of a planned series of regional satellite image maps of all of Canada. It was one of the first satellite image maps to combine imagery and other map components such as boundaries, roads, railways and place names. The imagery is a composite of many images from the United States National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites. The imagery was captured between August 11 to 20, 1990 to obtain cloud-free coverage. This was the only map in this planned series that was produced.

Contained within the Atlas of Canada's Various Map Series, 1965 to 2006, is the first of a planned series of regional satellite image maps of all of Canada. It was one of the first satellite image maps to combine imagery and other map components such as boundaries, roads, railways and place names. The imagery is a composite of many images from the United States National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites. The imagery was captured between August 11 to 20, 1990 to obtain cloud-free coverage. This was the only map in this planned series that was produced.

Contained within the Atlas of Canada's Various Map Series, 1965 to 2006, is is the first of a planned series of regional satellite image maps of all of Canada. It was one of the first satellite image maps to combine imagery and other map components such as boundaries, roads, railways and place names. The imagery is a composite of many images from the United States National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellites. The imagery was captured between August 11 to 20, 1990 to obtain cloud-free coverage. This was the only map in this planned series that was produced.

The High Resolution Digital Elevation Model Mosaic provides a unique and continuous representation of the high resolution elevation data available across the country. The High Resolution Digital Elevation Model (HRDEM) product used is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The mosaic is available for both the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) from web mapping services. It is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This strategy aims to increase Canada's coverage of high-resolution elevation data and increase the accessibility of the products. Unlike the HRDEM product in the same series, which is distributed by acquisition project without integration between projects, the mosaic is created to provide a single, continuous representation of strategy data. The most recent datasets for a given territory are used to generate the mosaic. This mosaic is disseminated through the Data Cube Platform, implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The mosaic is available from Web Map Services (WMS), Web Coverage Services (WCS) and SpatioTemporal Asset Catalog (STAC) collections. Accessible data includes the Digital Terrain Model (DTM), the Digital Surface Model (DSM) and derived products such as shaded relief and slope. The mosaic is referenced to the Canadian Height Reference System 2013 (CGVD2013) which is the reference standard for orthometric heights across Canada. Source data for HRDEM datasets used to create the mosaic is acquired through multiple projects with different partners. Collaboration is a key factor to the success of the National Elevation Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.

How Do We Create Polygons?

-All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery, satellite data, and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct highly detailed polygons. This meticulous process ensures maximum accuracy and consistency. -We verify our polygons through multiple quality assurance checks, focusing on accuracy, relevance, and completeness.

What's More?

-Custom Polygon Creation: Our team can build polygons for any location or category based on your requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard GIS formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

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The cloud-corrected NDVI data extracted from historical MODIS satellite images at 250 metre resolution provides reliable, objective, and timely information on the state of vegetation throughout Canada and the northern United States. The methodology applied to the images has remained the same as for the program formerly known as the Crop Condition Assessment Program (CCAP). Since the 2000 growing season, Statistics Canada has been processing and compiling MODerate-resolution Imaging Spectoradiometer (MODIS) data (250 metre resolution). The Multi-Spectral Instrument (MSI) captures two spectral bands (red and infrared) that have proven to be extremely useful to produce the Normalized Difference Vegetation Index (NDVI) utilized for vegetation monitoring. The original NDVI image composites were produced by Agriculture and Agri-Food Canada (link to original data in the resources section). Additional computations were completed by Statistics Canada to remove the effects of residual clouds and to calculate and extract the NDVI by geographic region. This dataset provides access to the MODIS images from 2000 to present in GeoTIFF format and covers the crop area during the growing season (Julian weeks 15 to 37; mid-April to mid-September). It also provides access to a database that contains the statistical NDVI by geographic regions (Townships, Census Consolidated Subdivisions (CCS), Census Divisions (CD) and Census Agricultural Regions (CAR)) and agricultural masks (Agriculture (AGR), Crop (CROP) and Pasture (PAS)).

This collection is a legacy product that is no longer supported. It may not meet current government standards. This inventory presents chronologically the satellite images acquired, orthorectified and published over time by Natural Resources Canada. It is composed of imagery from the Landsat7 (1999-2003) and RADARSAT-1 (2001-2002) satellites, as well as the CanImage by-product and the control points used to process the images. Landsat7 Orthorectified Imagery: The orthoimage dataset is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and created with the most accurate control data available at the time of creation. RADARSAT-1 Orthorectified Imagery: The 5 RADARSAT-1 images (processed and distributed by RADARSAT International (RSI) complete the landsat 7 orthoimagery coverage. They are stored as raster data produced from SAR Standard 7 (S7) beam mode with a pixel size of 15 m. They have been produced in accordance with NAD83 (North American Datum of 1983) using the Universal Transverse Mercator (UTM) projection. RADARSAT-1 orthoimagery were produced with the 1:250 000 Canadian Digital Elevation Data (CDED) and photogrammetric control points generated from the Aerial Survey Data Base (ASDB). CanImage -Landsat7 Orthoimages of Canada,1:50 000: CanImage is a raster image containing information from Landsat7 orthoimages that have been resampled and based on the National Topographic System (NTS) at the 1:50 000 scale in the UTM projection. The product is distributed in datasets in GeoTIFF format. The resolution of this product is 15 metres. Landsat7 Imagery Control Points: the control points were used for the geometric correction of Landsat7 satellite imagery. They can also be used to correct vector data and for simultaneously displaying data from several sources prepared at different scales or resolutions.

High-resolution binary wetland map for Canada (2001-2016). Wetland map for the forested ecosystems of Canada focused on current conditions. The binary wetland data included in this product is national in scope (entirety of forested ecosystem) and represents the wall to wall characterization for 2001-2016 (see Wulder et al. 2018). This product was generated using both annual gap free composite reflectance images and annual forest change maps following the Virtual Land Cover Engine (VLCE) process (see Hermosilla et al. 2018), over the 650 million ha forested ecosystems of Canada. Elements of the VLCE classification approach are inclusion of disturbance information in the processes as well as ensuring class transitions over time are logical. Further, a Hidden Markov Model is implemented to assess individual year class likelihoods to reduce variability and possible noise in year-on-year class assignments (for instances when class likelihoods are similar). For this product, to be considered as currently a wetland a pixel must have been classified as wetland at least 80% or 13 of the 16 years between 2001 and 2016, inclusively. For an overview on the data, image processing, and time series change detection methods applied, see Wulder et al. (2018). Wulder, M.A., Z. Li, E. Campbell, J.C. White, G. Hobart, T. Hermosilla, and N.C. Coops (2018). A National Assessment of Wetland Status and Trends for Canada’s Forested Ecosystems Using 33 Years of Earth Observation Satellite Data. Remote Sensing. For a detailed description of the VLCE process and the subsequently generated land cover product, including an accuracy assessment, please see Hermosilla et al. (2018).

Nighttime satellite imagery were accessed via Google Earth Engine). Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. In cases where two satellites were collecting data - two composites were produced. The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Several attributes are included - we used stable_lights which represents lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. The background noise was identified and replaced with values of zero.These data were provided to Google Earth Engine by teh National Centers for Environmental Information - National Oceanic and Atmospheric Administration of the United States (see Supporting Documentation).CANUE staff exported the annual data and extracted values of annual mean nighttime brightness for all postal codes in Canada for each year from 1992 to 2013 (DMTI Spatial, 2015).

As part of a scientific assessment of critical habitat for boreal woodland caribou (Environment Canada 2011, see full reference in accompanying documentation), Environment Canada's Landscape Science and Technology Division was tasked with providing detailed anthropogenic disturbance mapping, across known caribou ranges, as of 2010. The attached dataset comprises the second 5-year update (first one in 2015) bringing the data up to 2020. The original disturbance mapping was based on 30-metre resolution Landsat-5 imagery from 2008-2010. Since then, anthropogenic disturbances within 51 caribou ranges across Canada were remapped every five years to create a nationally consistent, reliable and repeatable geospatial dataset that followed a common methodology. The ranges were defined by individual provinces and territories across Canada. The methods developed were focused on mapping disturbances at a specific point of time, and were not designed to identify the age of disturbances, which can be of particular interest for disturbances that can be considered non-permanent, for example cutblocks. The resultant datasets were used for a caribou resource selection function (habitat modeling) and to assess overall disturbance levels on each caribou ranges. As with the 2010 mapping project, anthropogenic disturbance was defined as any human-caused disturbance to the natural landscape that could be visually identified from Landsat 30-metre multi-band imagery at a viewing scale of 1:50,000. The same concept was followed for the 2015 and 2020 disturbance mapping and any additional disturbance features that were observed since the original mapping date, were added. The 2015 database was used as a starting point for the 2020 database. Unlike the previous iteration, features were not removed in the mapping process which was a decision made in the name of time. Interpretation was carried out based on the most recent cloud free imagery available up to mid fall for a given year. Each disturbance feature type was represented in the database by a line or polygon depending on their geometric description. Linear disturbances included: roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, as well as unknown features. Polygonal disturbances included: cutblocks, harvest (added in 2020), mines, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. For each type of anthropogenic disturbance, a clear description was established (see Appendix 7.2 of the science assessment) to maintain consistency in identifying the various disturbances in the imagery by the different interpreters. Features were only digitized if they were clearly visible in the Landsat imagery at the prescribed viewing scale. In comparison to the previous mapping protocol, one enhancement to the mapping process in 2020 was the addition of CFS harvest polygons (Ref: NRCan-CFS NTEMS; Wulder 2020) into the database prior to interpretation. This considerably reduced the digitizing time for polygons and accelerated the data collection process. The CFS harvest polygons were checked before inclusion, removing some which had been generated erroneously in their process. A 2nd interpreter quality-control phase was carried out to ensure high quality, complete and consistent data collection. Subsequently, the vector data of individual linear and polygonal disturbances were buffered by a 500-metre radius, representing their extended zone of impact upon boreal caribou herds. Additionally, forest fire polygons for the past forty years (CNFDB 1981-2020) were merged into the buffered anthropogenic footprint in order to create an overall disturbance footprint. These buffered datasets were used in the calculation of range disturbance levels and for integrated risk assessment analysis.

The GNSS (Global Navigation Satellite System), or satellite positioning system, includes all satellite navigation systems. It allows you to know your location, anywhere in the country. Theoretical GNSS specifications estimate the accuracy of the position obtained from a receiver to be approximately 15 meters in planimetry and 25 meters in altimetry. By combining the data with that of another receiver placed on a known geodesic point, the accuracy of the obtained position can vary from a few centimeters to a few meters, depending on the type of receiver used. In order to increase accuracy, the Government of Quebec records data continuously through a network of 18 GNSS stations. These stations are located on geodetic points that are free of any obstacles and capture data from the GPS and GLONASS constellations. Some of these stations receive signals from the Galileo constellation. This data is available in the standard exchange format*Receiver Independent Exchange Format* (RINEX), version 2.11. This format is recognized by the majority of GNSS data processing software. The data is accessible on the _ ftp server_) of the MRNF or using the _ Interactive Map_) of the geodetic network. It should be noted that only data from the last 366 days is kept. The structure of the directories and files on the _ ftp server_) as well as the coordinates of the stations are presented in the document _ GNSS sensor stations_. # #État GNSS stations## You can consult the status of the stations in the document _ Status of GNSS stations_. You will be notified if a station is in service, out of service, or if equipment maintenance is planned. # #GNSS in real time by cell phone The government also offers GNSS data by cellular telephone, which allows centimetric positioning work to be carried out in real time. Users of georeferenced data can thus, with a single multi-frequency GNSS receiver equipped with a modem by cellular telephone, identify or implement any physical detail with an accuracy of a few centimeters in the NAD 83 reference system (SCRS) (period 1997.0). The signal that contains this data is available to everyone. The range depends on telephone coverage, ionospheric conditions and especially on the instruments used. For more information on using GNSS in real time, see document _ Guidelines for GNSS RTK/RTN Surveys in Canada_. # #Détails techniques The transmission of GNSS data as well as the station's NAD 83 (SCRS) coordinates (period 1997.0) is transmitted by cellular telephony from an IP address on the Internet. Each station transmits its data in one of the following two formats: CMR+ or RTCM V3.2. The document _ GNSS capture stations_) gives for each city the IP address of the CMR+ or RTCM V3.2 formats as well as the antenna model. It should be noted that the data is not broadcast according to the*Networked Transport of RTCM protocol via Internet Protocol* (NTRIP). This third party metadata element was translated using an automated translation tool (Amazon Translate).

This package contains data collected from microbial mat surveys (i.e., percent cover, ash-free dry mass (AFDM), and pigment concentrations – chlorophyll-a, scytonemin, and carotenoids) associated with satellite-derived Normalized Difference Vegetation Index (NDVI) values from the Lake Fryxell Basin of Taylor Valley, located in the McMurdo Dry Valleys of Antarctica. The purpose of this study was to quantitatively compare key microbial mat characteristics to NDVI. Data were collected at seven plot locations within the Canada Glacier Antarctic Specially Protected Area (ASPA) near Canada Stream, as well as alongside Green Creek and McKnight Creek. NDVI values were derived from a WorldView-2 multispectral satellite image taken of the Lake Fryxell Basin on January 19, 2018, while biological ground surveying and sampling were conducted during the 2nd and 4th weeks of January 2018.

Snapshot img

Satellite-Based Earth Observation Market Size 2025-2029

The satellite-based earth observation market size is valued to increase USD 9.66 billion, at a CAGR of 12% from 2024 to 2029. Use of satellites for advanced environment monitoring will drive the satellite-based earth observation market.

Major Market Trends & Insights

North America dominated the market and accounted for a 43% growth during the forecast period.
By Application - Defense segment was valued at USD 2.99 billion in 2023
By Type - Value-Added Services (VAS) segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 171.22 million
Market Future Opportunities: USD 9655.50 million
CAGR from 2024 to 2029 : 12%

Market Summary

The market is experiencing significant expansion, driven by the increasing demand for real-time, high-resolution data to address environmental and socio-economic challenges. Small satellites, with their cost-effective production and deployment, are gaining popularity, contributing to the market's expansion. These agile spacecraft offer enhanced flexibility and customization, catering to various applications, from agriculture and forestry to disaster management and urban planning. However, competition from alternate technologies, such as drones and airborne sensors, poses challenges to the market's growth.
Satellite-based solutions, while offering extensive coverage and longer data collection periods, face limitations in terms of spatial and temporal resolution. To maintain their competitive edge, market players are investing in technological advancements, including hyperspectral and multispectral imaging, machine learning algorithms, and real-time data processing capabilities. The future of the market lies in the integration of these technologies to deliver actionable insights, enabling informed decision-making for businesses and governments alike.

What will be the Size of the Satellite-Based Earth Observation Market during the forecast period?

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How is the Satellite-Based Earth Observation Market Segmented ?

The satellite-based earth observation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Defense
  Weather
  Location-Based Services (LBS)
  Energy
  Others


Type

  Value-Added Services (VAS)
  Data


Technology

  Synthetic aperture radar (SAR)
  Optical


End-User

  Government
  Commercial
  Academic/Research


Geography

  North America

    US
    Canada


  Europe

    Germany
    Russia
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The defense segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, with radar satellite data playing a pivotal role in applications such as crop yield prediction, land cover mapping, and infrastructure monitoring. Radiometric calibration, image classification algorithms, and geometric correction are essential techniques for enhancing the accuracy of remote sensing imagery and Lidar data acquisition. Change detection techniques and spectral signature analysis facilitate natural resource assessment, environmental monitoring systems, and climate change modeling. In 2024, the defense segment accounted for a substantial share of the market, with ongoing investment in satellite technologies for surveillance, regional security, and intelligence gathering by countries like China, India, and Russia.

For instance, Airbus secured agreements with the Czech Republic and the Netherlands to deliver satellite communications for their armed forces, marking a significant contribution to the UHF military communications hosted payload on board the EUTELSAT 36D telecommunications satellite. Geospatial data analytics, image registration techniques, cloud computing platforms, and satellite data processing are integral components of this dynamic industry, enabling precision agriculture applications, disaster response management, pollution detection methods, urban planning initiatives, and multispectral imagery analysis.

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The Defense segment was valued at USD 2.99 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 43% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Satellite-Based Earth Observation Mar

The Antarctic composite satellite image is a mosaic of geostationary and polar orbiting satellite data in the infrared window channel (approximately 11.0 microns) centered over the South Pole and covering much of the Southern Hemisphere. The Antarctic Meteorological Research Center (AMRC) has built these composites every three hours (0, 3, 6, etc. Universal Time Coordinated (UTC)) since 30 October 1992, and eventually hourly starting in 2009. The composite has a nominal resolution of 10 kilometers (as of 2014 4km) and the data is combined into a Polar Stereographic projection. The composites are made in 5 difference channels (infrared, water vapor, visible, shortwave, and longwave). The orientation of the composites is deliberately choosen in support of the United States Antarctic Program forecasting operations between New Zealand and Ross Island Antarctica. Thus, Grid North is not the orientation of the composites. The composite is generated at the AMRC offices at the University of Wisconsin-Madison's Space Science and Engineering Center and is distributed to McMurdo Station, Antarctica. It is also available on the Internet. Archived composites are available upon request to the AMRC at http://www.ssec.wisc.edu/contact-form/index.php?name=AMRC. The data is available in the following data formats: McIDAS AREA, netCDF, ASCII or binary "flat" files and the following picture formats: GIF, JPG, PPM, BMP, JPG, PS, CPS (Others possible including TIF, etc.).

The LANDSAT Thematic Mapper, a mechanical scanning radiometer, operates in 7 channels of electro-magnetic spectra, including visual, near infrared and thermal infrared.

Data collected by the Earth Observation Center, NASDA, cover a circular area about 5000 km diameter. The Earth Observation Center receives TM data weekdays and every other Saturday. It performs both radiometric and geometric correction and distributes products in the form of magnetic tape and imagery.

Snapshot img

Small Satellite Market Size 2024-2028

The small satellite market size is valued to increase USD 6.01 billion, at a CAGR of 21.22% from 2023 to 2028. Low-cost solution deployment through micro- and nanosatellites will drive the small satellite market.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By Application - Earth observation and remote sensing segment was valued at USD 773.80 billion in 2022
By Type - Minisatellite segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 344.44 million
Market Future Opportunities: USD 6013.40 million
CAGR : 21.22%
North America: Largest market in 2022

Market Summary

The market is a dynamic and continually evolving sector, driven by advancements in core technologies and applications. With the increasing adoption of micro- and nanosatellites as low-cost solutions, the market is witnessing significant growth in deployment. According to recent reports, the market is expected to account for over 30% of the total satellite launches by 2025. This trend is fueled by the growing use of 3D printing in small satellite manufacturing, enabling faster production and customization.
However, the market also faces challenges from disruptions caused by satellite orbital debris, necessitating the development of advanced debris mitigation technologies. Regulations, such as the increasing number of licensing requirements and space traffic management policies, are also shaping the market landscape.

What will be the Size of the Small Satellite Market during the forecast period?

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How is the Small Satellite Market Segmented and what are the key trends of market segmentation?

The small satellite industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Earth observation and remote sensing
  Satellite communication
  Navigation
  Scientific research and others


Type

  Minisatellite
  Nanosatellite
  Microsatellite


End User

  Commercial
  Government
  Defense


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Application Insights

The earth observation and remote sensing segment is estimated to witness significant growth during the forecast period.

Small satellites, including cubesats and microsatellites, are revolutionizing the earth observation and remote sensing industry. These satellites, equipped with advanced imaging sensors, multispectral or hyperspectral cameras, and other remote sensing instruments, offer high-resolution imaging capabilities. They capture detailed images of the Earth's surface, enabling precise monitoring of land use, urban development, vegetation health, and changes in environmental conditions. The market for small satellites is experiencing significant growth, with earth observation and remote sensing applications driving this trend. According to recent studies, the number of small satellite launches increased by 25% in 2020 compared to the previous year.

Furthermore, the demand for high-resolution earth imagery is projected to grow at a rate of 18% annually over the next five years. Advancements in satellite technology, such as attitude determination control systems, cubesat deployment mechanisms, onboard data processing, and advanced propulsion systems, are contributing to the growth of the market. Additionally, data downlink systems, miniaturized payloads, data compression algorithms, satellite bus design, orbital mechanics simulations, formation flying control, cubesat technology, autonomous navigation, high-throughput communication, and spacecraft propulsion are all playing crucial roles in enhancing the functionality and efficiency of small satellites. Moreover, the integration of earth observation sensors, space debris mitigation techniques, radiation hardening, space situational awareness, and GNSS augmentation systems is expanding the applications of small satellites beyond traditional earth observation and remote sensing.

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The Earth observation and remote sensing segment was valued at USD 773.80 billion in 2018 and showed a gradual increase during the forecast period.

The market is also witnessing the emergence of modular satellite platforms, ADCS subsystem design, software-defined radio, and power system efficiency, which are enabling the development of cost-effective and versatile small satellite