Remote sensing of the earth (RS) is a type of geospatial technology that collects samples of emitted and reflected electromagnetic (EM) radiation from terrestrial, atmospheric and aquatic ecosystems to detect and monitor the physical characteristics of an area without physical contact. Most commonly, this data collection method typically involves aerial (at this stage exclusively UAVs) and satellite sensors, which are categorized as passive or active sensors (detectors). Ground-based sensors (instruments) are used locally and to enhance the quality of satellite and airborne data.

In recent years, ground-based sensors have been included in the field of remote sensing, which, in conjunction with space and aviation-based sensors, allows for obtaining a new level of detail for the investigated area or object.

Passive sensors respond to external stimuli by collecting radiation that is reflected or emitted by objects or the surrounding environment. The most common source of radiation measured by passive remote sensing is reflected sunlight. Popular examples of passive remote sensors include charge-coupled devices (CCD), digital images and video cameras, radiometers, hyperspectral, and infrared sensors.

Active sensors, on the other hand, use internal stimuli to collect data by emitting energy to scan objects and areas, after which the sensor measures the energy reflected from the target.

For example, RADAR and LiDAR sensors are typical active remote sensing instruments that measure the time delay between emission and return to establish the location, direction, and speed of an object. The collected remote sensing data is then processed and analyzed using remote sensing equipment and computer software (most advanced solutions offer quasi-real-time analytical products), which are available in various applications, primarily in Geographic Information Systems (GIS).

Table №1 shows generalized characteristics of the most demanded remote sensing data sources in the market today.



Aerial imagery

Piloted Aviation

Unmanned Aerial Vehicles (UAVs)

Optimal Coverage Area

>100 km2


<100 km2















LIDAR (Laser Scanning)

LIDAR (Laser Scanning)

Spatial Resolution

0.25 m to 10 m

0.1 m to 1.5 m

0.02 m to 1 m

Image Cost per Area (USD)


1 km




5 km




25 km




50 km




100 km




1000 km




10000 km




Anyway, any remote sensing data sources represent spatial data.

Some common goals and tasks of Earth remote sensing:

  1. Environmental Study: Remote sensing is used to study various aspects of the environment, including climate, atmospheric conditions, Earth's surface, vegetation, hydrology, etc. This helps in monitoring changes in the environment and understanding its dynamics.

  2. Weather Analysis and Forecasting: Data obtained through remote sensing is used for weather analysis and forecasting. This improves weather forecasts, tracks storms, hurricanes, floods, and other weather phenomena.

  3. Cartography and Geodesy: Remote sensing allows for the creation of high-quality maps and terrain models. It is useful for urban development planning, agriculture, transportation infrastructure, land use studies, and other geodetic tasks.

  4. Natural Resource Management: Remote sensing aids in monitoring and managing natural resources such as forests, agricultural lands, water resources, and fish stocks. This optimizes resource utilization, prevents forest fires, controls water pollution, etc.

  5. Climate Change Research: Remote sensing plays a crucial role in studying and monitoring climate change. By analyzing remote sensing data, scientists can study changes in ice cover, sea level, surface temperature, and other factors related to climate change. This helps in understanding long-term trends and forecasting the consequences of climate change.

  6. Natural Disaster Monitoring: Remote sensing is used to monitor and warn of natural disasters such as earthquakes, volcanic eruptions, floods, landslides, and forest fires. Remote sensing data allows for timely responses to threats and organizing rescue operations.

  7. Ecosystem Observation: Remote sensing enables the study of changes in ecosystems, including deforestation, loss of biodiversity, changes in species distribution, and ecological restoration. This aids in developing and implementing measures for nature conservation and biodiversity preservation.

  8. Anthropogenic Impact Monitoring: Remote sensing helps in tracking anthropogenic impact on the environment, such as air and water pollution, land use changes, urban and infrastructure expansion. This helps in assessing the impact of human activities and developing measures to mitigate negative effects.

Remote sensing applications are diverse and can be applied in various fields, including science, ecology, geography, agriculture, urban planning, transportation, and other industries.

Prices for services

Consultation Free of charge
Preliminary analysis Free of charge
Aerospace imaging The cost of remote sensing materials is calculated individually for each order and may vary: minimum cost from $0.5 per 1 km2.
Execution time New imaging takes from 5 working days from the moment of prepayment. The execution time may be extended for significant areas and specific climate conditions of the area of interest. Delivery of archive data within 3 days.

The cost of satellite imaging depends on the area of the site, quality requirements, and the type of final material - orthophoto, DTM, DSM, 3D model, the need for thematic processing, etc., and is calculated individually for each customer.

The cost of execution is calculated on an individual basis, taking into account a specific of task.

After receiving the task description, we calculate the cost and send you a commercial offer.

Period of execution

Alignment of requirements for remote sensing materials: from 1 to 5 days*
Contract conclusion: from 1 to 5 days*
Ordering of the imaging (task assignment to the spacecraft operator): from 5 days**
Obtaining archive remote sensing materials: from 3 days**
Thematic processing of remote sensing materials (if necessary): from 15 days*
TOTAL TIME: from 15 days

* working days
** from the date of receiving 100% advance payment

The deadlines for satellite imaging depend on the total area of the territory, imaging parameters, the final product required, and are calculated individually for each customer.

How to place an order:

  1. STEP №1: Submit an application on the website with the following details:
    • Description of the task requiring the use of land monitoring or object monitoring materials obtained through satellite or aerial/Unmanned Aerial Vehicle (UAV) imaging;
    • Location of the area of interest (coordinates, district name, region, shapefile, etc.);
    • Requirements for the frequency of imaging;
    • Requirements for the imaging period (period for which archive data can be used or new imaging is required);
    • Quality requirements for the imaging (inclination angles of images, resolution on the ground, cloud cover, sun angle, panchromatic, multispectral, hyperspectral, laser imaging, etc.);
    • Deadline for the delivery of the final materials.
  2. STEP №2: Agreement on technical specifications and cost:
    • Used source of remote sensing data and imaging schedule;
    • Formats for presenting the results;
    • Technical requirements for remote sensing materials;
    • Additional requirements for the output data (if necessary);
    • Final cost of work and completion time.
  3. STEP №3: Signing the contract and starting the work
    • Timeframe of 5 working days from the date of receiving 100% advance payment - payment is accepted only through bank transfer.

We work with individuals, legal entities, individual entrepreneurs, government and municipal authorities, foreign customers, etc.

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Stages of service provision

Stage № 0 (PRE-Contract):

  • Agreement on the task requiring the use of imaging materials.
  • Evaluation of the technical feasibility of solving the Client's task through the application of remote sensing methods.
  • Determination of the monitoring area, parameters, and frequency of imaging;
  • Selection of the source of remote sensing data;
  • Determination of deadlines and types of work to be performed.

RESULT: possibility (YES/NO) of providing the service

Stage № 1 (PRE-Contract):

  • Agreement with the customer on the source of remote sensing data;
  • Agreement with the customer on additional requirements for monitoring results;
  • Agreement with the customer on requirements for additional sources of geospatial data for LULC (Land Use and Land Cover) mapping;
  • Agreement with the customer on the format of the transmitted data;
  • Final determination of labor and material costs, agreement on completion times and cost of work.

RESULT: signed contract

Stage № 2 (contract execution):

  1. Receipt of advance payment (100%) for the order and purchase of remote sensing data;
  2. Execution of aerospace imaging, adhering to the temporal parameters and requirements for remote sensing materials;
  3. Thematic processing of data (if necessary);
  4. Delivery of materials to the customer.

RESULT: a set of data obtained as a result of space imaging.

The result of the provision of services

Creation of the final product based on imaging materials:

  • Archived images of various types: black and white, color, multi-zone, synthesized for the monitoring period, according to the Client's Technical Task.
  • Materials of new imaging of various types.
  • Thematic LULC maps (Land Use and Land Cover).
  • Results of LULC analysis in agreed indices and formats.

GEO INNOTER delivers to the Customer who requested the imaging materials, the finished product according to the Technical Task on electronic media or via the Internet through FTP servers.

Requirements for Source Data

Precise coordinates of the area of interest, precise requirements for remote sensing materials (resolution on the ground, type of imaging, maximum angle of image tilt, minimum angle of the sun, imaging period), additional requirements for the final product (if necessary), output data formats.

If it is not possible to provide the specified information, provide information about the purposes for which the remote sensing materials will be used, and GEO INNOTER specialists will analyze the requirements and propose an optimal solution.

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Spatial data is any type of data that relates directly or indirectly to a specific geographic area or location. Now more commonly referred to as geospatial data or geospatial information, spatial data can also numerically represent a physical object in a geographic coordinate system. However, the concept of geospatial data is much more than a spatial component such as maps. One should realize that there is raw data (80% of it is imagery) obtained from remote sensing sensors, processed data (remote sensing products, usually in a GIS shell) and analytical data - information, clues about a particular industry, task or military system. There is a huge difference between data and information. Data is raw, voluminous, and often useless. On the other hand, information is power. Information is useful to the end user and helps make informed decisions that can be the difference between success and failure. Speaking from the perspective of the geospatial industry, remote sensing analytics companies represent the difference between data and information. Using powerful algorithms, they can transform large amounts of data into information that is used by organizations of all sizes and shapes to make decisions. Users can store spatial data in a variety of formats, as it can contain more than just location data. Analyzing this data allows for a better understanding of how each variable affects individuals, communities, populations, territories, facilities, etc. There are several types of spatial data, but the two main types of spatial data are geometric data and geographic data.

Geometric data is a type of spatial data displayed on a flat two-dimensional surface. An example of this could be the geometric data found in floor plans. Google Maps is an application that utilizes geometric data to determine precise directions. In fact, this is one of the simplest examples of spatial data usage in action.

Geographic Referencing and Geocoding

Similar processes, geographic referencing, and geocoding are important aspects of geospatial analysis. Both geocoding and geographic referencing involve aligning data to the real world using appropriate coordinates, but the similarity ends there.

Geographic referencing focuses on assigning coordinates to vector or raster data, helping accurately model the Earth's surface.

Photogrammetry uses visualization rather than collecting data on the wavelength of light. It involves determining the spatial properties and dimensions of objects captured in digital photographs.

Vector and raster (Fig. 1) are common data formats used for storing geospatial data.

Vectors are a graphic representation of the real world. There are three main types of vector data: points, lines, and polygons. Points help create lines, and connecting lines form closed areas or polygons. Vectors often represent the generalization of features or objects on the Earth's surface. For example, vector data (used by over 78% of users) is stored in shapefiles, sometimes referred to as shp files (used in ArcGIS software).

Raster represents information presented in a grid of pixels. Each pixel stored in the raster has a value. This value can be anything from a measurement unit, color, or information about a specific element. Typically, raster refers to images, but in spatial analysis, it often refers to orthoimages or satellite images taken and preprocessed from aerial devices or satellites.

рис 1.jpg

Fig. 1.

There is also something called an attribute. Whenever spatial data contains additional information or non-spatial data, they are referred to as attributes. Spatial data can have any number of location attributes. For example, this could be a map, photographs, historical references, or anything else deemed necessary.

The field of spatial data technology focuses on extracting a deeper understanding from data using a complete set of spatial algorithms and analytical methods. Modern methods include the use of machine learning and deep learning to uncover hidden patterns in data, improving predictive models.

Spatial data can also include attributes that provide additional information about the object they represent. This helps users understand where things are happening and why. Geographic Information Systems (GIS) and other specialized software applications help access, visualize, manipulate, and engage in spatial analysis.

Experts expect spatial data science to become more important as government agencies and businesses seek to make more informed decisions based on data.

Other aspects of spatial data science include spatial data analytics and data visualization.

Spatial data analytics involves the process of discovering hidden patterns in large spatial datasets. As a key factor in GIS application development, spatial data analytics allows users and geospatial professionals to extract valuable data about neighboring regions and explore spatial patterns. In this scenario, spatial variables such as distance and direction are taken into account.

Data visualization software allows various spatial data files to be connected in geospatial technology, such as Esri File geodatabases in ArcGIS, GeoJSON files, Keyhole Markup Language (KML) files, MapInfo tables, shapefiles, TopoJSON files, etc.

After connecting, GIS specialists or users can create maps of points, lines, and polygons using information from spatial data files, LiDAR data files, and geospatial data files.

Over the past 20 years, spatial data has been used not only for cartography (the share of map creation based on remote sensing data was 60% in 2021, compared to 95% in 2000) but also for new areas such as BIM, SMART, IoT, OutDoor/InDoor.

Navigational and mobile technologies form the foundational basis for spatial data. For example, popular mobile applications allow data developers to create complex integrated applications using sets of geospatial and temporal data from IoT data, maps, weather data, UAVs, satellites, etc.

Today, spatial data plays a leading role in managing and selecting the necessary information components in the BIG DATA environment, machine reading ("artificial intelligence"), neural network analysis, and other promising fields of science and IT technologies.

In practice, the ecosystem of human habitation and development is connected with spatial data - land, water, ocean, atmosphere, nature, resources, cities, roads, any infrastructure, social, political, and military interactions.

The quality and technical characteristics of sensors, as well as the conditions for Earth remote sensing, are determined based on their spatial, spectral, radiometric, and temporal resolution (sampling frequencies), which are defined by the task of processing data and obtaining the final product or service of remote sensing (RS). The Client's tasks determine the choice of sensor data characteristics.

Spatial Resolution

The size of a pixel recorded in a raster image. Usually, pixels can correspond to square areas with sides ranging from 0.15 to 1000 meters.

The ability to capture the Earth's surface in one pixel, for example, of satellite sensors, is called spatial resolution (commonly referred to as geometric resolution on the Earth's surface). In some cases, spatial resolution depends on the orbit on which the spacecraft flies.

Currently, great attention is paid to increasing the spatial resolution of satellite sensors. It reaches 15-25 cm. Increasing spatial resolution improves the quality of images.

Aerial and UAVs have a resolution of up to 3-5 cm, while ground lidar surveys have a resolution of up to 3-5 mm.

Visual comparative images to understand spatial resolution are shown in figures 2, 3, 4, 5.

Рис.2 Спутниковые сенсоры.jpg

Figure 2 Satellite Sensors

Рис.3 Спутниковые сенсоры.jpg

Figure 3 Satellite Sensors 

Рис.4 Сенсоры БПЛА (разрешение на местности 3-5 см).jpg

Figure 4 UAV Sensors (Resolution on the Ground 3-5 cm)

Рис.5 Сравнительные изображение от спутника и БПЛА.png

Figure 5 Comparative Image from Satellite and UAV

Spectral Resolution

The wavelength of various recorded frequency ranges - usually associated with the number of frequency ranges of the electromagnetic spectrum recorded by the sensor platform (Figure 6).


Figure 6

Spectral resolution is a characteristic of a sensor that captures images in different wavelengths of the spectrum. Currently, in modern remote sensing, panchromatic, multispectral (Figure 7), hyperspectral, and super-spectral images based on this characteristic are used. Different objects reflect rays in different wavelengths. Therefore, to determine any characteristic of an object, its reflective properties in different wavelengths of the spectrum must be studied. For example, to use an image as a background, it is sufficient to have its image taken in three spectral ranges: green, blue, and red (RGB).

However, if we want to obtain more data from it, such as expressing impermeable surfaces or vegetation classification, we need to use the near-infrared (NIR) or thermal infrared (IR) range. In this case, the possibilities of the MODIS scanner are not limited . It takes images in 36 spectra in the wavelength range from 0.4 µm to 14.4 µm and transmits them to Earth. The more ranges, the easier it is to identify an object. In this regard, the Landsat 8 and 9 satellite scanner takes second place. Its spectral image is taken in 10 ranges (coastal, blue, green, red, NIR, SWIR1, SWIR2, Pan, TIR1, TIR2). If we consider the water vapor Cirus identifier as one band, the number of Landsat 8 OLI TIRS bands will reach 11 bands. The thing is, the Cirus band does not capture images in the visible wavelength range but operates according to the function of light-returning oncoming aerosols. The last place is occupied by the WorldView2 and WorldView3 satellites. The sensors of these satellites provide us with images in 9 spectral wavelength ranges. These ranges are Coastal, Blue, Green, Yellow, Red, RedEdge, NIR1, NIR2, and Pan. In this regard, MODIS satellite scanners received high ratings. However, due to poor spatial resolution, it is not widely used.

Therefore, currently, the highest demand is for data from the WV3 satellite. Especially its extended red range.

Thematic solutions for space data create the majority of RS products based precisely on WV3 images.

The conditions for the normal operation of sensors, i.e., obtaining high-quality images, are determined by the atmospheric transparency windows that allow passing a specified portion of the sensor's electromagnetic spectrum.

Spectral characteristics and transparency windows for RS sensors are presented in figure 8.


Figure 8

Radiometric Resolution

The number of different radiation intensities that the sensor can distinguish. Usually, this ranges from 8 to 14 bits, which corresponds to 256 levels of gray scale and up to 16,384 intensities or "shades" of color in each band. This also depends on the sensor's "noise."

The difference in radiometric resolution can be illustrated easily using Figure 9.


Figure 9

Temporal Resolution

The frequency of satellite or aircraft (UAV) passes and imaging over the same territory or observation target. It is of great importance for temporal data series research in RS when monitoring processes (agriculture, construction, military affairs, water resources, emergencies, etc.). For example, the temporal resolution of RS satellites today in military affairs reaches up to 15 minutes, and in civilian applications, it can be 1-2 hours, and seasonal resolution can be annual (archive data). Archive data for in-depth research of territory changes can be ten years old (Figure 10, 11).


Figure 10

Рис.11 Изменения состояния сх территории в течение года (технология LULC).png

Figure 11 Changes in Cropland State During the Year (LULC Technology)

For most systems, such as space remote sensing, it is assumed to extrapolate sensor data with respect to a ground control point, including distances between known points on the Earth. This is called the reference, which is used to subsequently correct images, maps, or charts.

Therefore, image processing technology - photogrammetry - is of great importance.

In addition, radiometric and atmospheric corrections of images may be required.

Radiometric Correction

It allows avoiding radiometric errors and distortions. The illumination of objects on the Earth's surface is uneven due to different relief properties. This factor is taken into account in the radiometric distortion correction method. Radiometric correction sets the scale of pixel values, for example, a monochromatic scale from 0 to 255 will be transformed into actual brightness values.

Topographic Correction (also called terrain correction)

In rugged mountains, effective pixel illumination varies significantly due to terrain relief. In remote sensing images, a pixel on a shaded slope receives weak illumination and has a low brightness value, unlike a pixel on a sunny slope that receives strong illumination and has a high brightness value. For the same object, the brightness value of a pixel on a shaded slope will differ from that on a sunny slope. Moreover, different objects may have the same brightness values. These ambiguities seriously affected the accuracy of extracting information from remote sensing images in mountainous regions. It became the main obstacle to further application of remote images. The goal of topographic correction is to eliminate this effect and restore the true reflectance or brightness of objects under horizontal conditions.

Atmospheric Correction

It removes atmospheric haze by rescaling each frequency band so that its minimum value corresponds to the value of pixel 0. Digitizing data also allows manipulating data by changing gray scale values (Figure 11).


Figure 11

It is a common practice to provide meteorological conditions for the date and time of RS imaging.

Levels of Earth remote sensing data processing were first defined by NASA in 1986. Their definition and image processing technology are used in global remote sensing standardization and undergo changes and approvals at the United Nations (UN).

The primary processing of remote sensing data (from raw images to their georeferencing) is categorized from levels 0 to 3. Following that, there is what is called thematic processing. Its classification encompasses over 250 directions, and the thematic scope expands each year. Thematic technology is tailored to the customer's specific needs. Currently, the main product of Earth remote sensing is analytics.

The most requested applications of remote sensing data are in the fields of military, agriculture, and construction.

Storage of Earth Remote Sensing Data

Data archiving is done using computer-readable ultraphishes, usually with fonts like OCR-B, or in the form of scanned halftone images. Ultraphishes are well-preserved in standard libraries with a lifespan of several centuries. They can be created, copied, stored, and retrieved by automated systems.

When planning and implementing remote sensing projects, it is important to consider several key parameters:

  • Area of interest
  • Accessibility of the survey
  • Survey period
  • Archival or new survey
  • Spectral channels
  • Project budget

Efficiency and scalability

1) Speed and ease of data collection: Satellites can cover thousands of square kilometers in a matter of minutes. This allows gathering a large volume of information in a short period without the complexities of flight planning, obtaining permissions, and selecting take-off and landing points for aircraft. Thus, satellites offer high speed and simplicity of data collection.

2) Absence of logistical issues: Satellites do not face problems related to airspace restrictions, complex route planning, or constantly changing limitations in survey areas. They can collect data from isolated, conflicting, or transboundary locations without restrictions, which is particularly valuable for large-scale cartographic projects.

3) Independence from weather conditions: Unlike aviation methods, which are constrained by weather conditions, satellites can operate regardless of weather, except for cloud cover. This ensures the continuity of data collection and reduces the risks of delays or cancellations due to weather conditions.

Tasks and processing

1) Resolution and spectral characteristics of sensors: Customers set requirements for spatial resolution and spectral ranges that match their specific needs. This allows obtaining data with the necessary level of detail and spectral information for the studied phenomena or objects. As of May 2023, there is a huge selection of Earth observation satellites to choose from.

2) Angles of capture: Customers specify the angles of capture to obtain images from optimal perspectives. This is especially useful for analyzing three-dimensional objects or terrain where specific angles can provide a more comprehensive understanding and analysis.

3) Real-time weather updates: Regular real-time weather updates help avoid capturing data in unfavorable weather conditions and cloud cover. This maximizes the use of available windows for data collection.

4) Fast data delivery: After the satellite captures images, they are uploaded via a ground station and can be delivered to users within four hours. This provides operational access to fresh data and allows for quick analysis and processing.

5) Data processing and delivery options: Users can choose different data processing options according to their requirements.

Imaging capabilities

Remote sensing satellite operators offer a wide range of imaging capabilities, providing various spectral ranges and ultra-high resolution.

1) Spectral ranges: Spacecraft operators offer images in various spectral ranges. This allows analyzing objects and phenomena at different wavelengths and obtaining specific spectral information. Hyperspectral imaging can cover hundreds of spectral ranges, expanding research possibilities.

2) Stereoscopic imaging: Satellites can also perform stereoscopic imaging, providing stereo images from different angles. Stereoscopic images provide reliable data for creating digital elevation models (DEMs) and virtual 3D models, which are useful for analyzing relief and three-dimensional objects.

3) Ultra-high resolution: Satellites provide high-resolution images. For example, the Spacewill company's Superview NEO satellite can offer satellite images with a resolution of up to 30 centimeters. This allows identifying various objects and features on the ground with high accuracy and detail.

Survey accessibility

1) Accessibility and predictability: Satellites can reach areas that may be difficult or inaccessible for other surveying methods, such as remote or geographically isolated locations. Thanks to the availability of remote sensing satellite clusters, survey plans become more accessible and predictable for clients.

2) Frequency of updates: A high frequency of satellite image updates allows obtaining fresh data at regular intervals. This is particularly important for automated analysis, where constant data updates are required. Users can confidently rely on constant data availability for their workflow. As of May 2023, GEO Innoteс offers its customers satellite surveys from 100+ remote sensing satellites with a resolution better than 1 meter.

3) Integration with artificial intelligence programs: Satellite images can be integrated into programs that use artificial intelligence (AI) for automatic extraction and classification of objects and features in the image. This helps optimize workflows and automate data analysis.

4) Extended training data: Thanks to the large number of images collected by satellites over time, users have access to extended training data for machine learning programs. This improves the quality and accuracy of machine learning models used for analyzing satellite data.

5) Historical data and modeling: Satellite data provides access to historical data that can be used for modeling and forecasting. This is particularly important for analyzing trends, detecting anomalies, and assessing profitability. The use of historical data helps understand long-term changes.

These satellite imaging capabilities provide high accuracy and data quality, making them an effective alternative to aviation imaging. As of May 2023, there is a global trend of unmanned aerial vehicles (UAVs) displacing manned aviation at a rapid pace.

GEO Innotech offers its customers integrated solutions of space + UAVs.

Remote sensing of the Earth (RS) is a method of studying the Earth and its environment in which information is collected by satellites or other remote sensing vehicles at an altitude above the Earth's surface. RS provides a wealth of data about objects on the Earth's surface, including geologic structures, landscapes, climatic conditions, and other parameters. This method is used in various fields including geology, geography, agronomy, ecology, meteorology, hydrology, etc. In geology, Earth remote sensing can be used to search for mineral deposits including oil and gas, and to study geologic structures like geologic folds, fractures, etc. Remote sensing of the Earth can also be used to study changes in the Earth's surface caused by various factors such as climatic changes, natural disasters or anthropogenic influences. In general, remote sensing of the Earth is a powerful tool for studying the Earth's surface and its environment, and finds wide application in various scientific and practical fields.
There are many satellites in space that are used to acquire Earth remote sensing data. They may differ in purpose, orbit characteristics, resolution, spectral characteristics and other parameters. Let's consider the most common types of satellites for remote sensing:

  1. Optical satellites - used to acquire information in the visible and infrared bands. Examples of such satellites are Landsat, Sentinel-2, SPOT, MODIS.
  2. Radar satellites - use radio wave radiation to acquire data. They can operate in all weather conditions and daylight hours. Examples of such satellites are RADARSAT, Sentinel-1, TerraSAR-X.
  3. Gravity satellites are used to measure the Earth's gravity field and the mass of objects on its surface. Examples of such satellites are GRACE, GRACE-FO.
  4. Geodetic satellites - used for precise determination of coordinates and heights of points on the Earth's surface. Examples of such satellites are GPS, GLONASS, Galileo.
  5. Atmospheric satellites - used to study atmospheric phenomena such as clouds, atmospheric gases, meteorological conditions. Examples of such satellites are Aqua, Terra, MetOp.
  6. Space telescopes - used to study space objects and phenomena such as stars, galaxies, space dust. Examples of these satellites are Hubble Space Telescope, Chandra X-Ray Observatory, Spitzer Space Telescope.

Each of these types of satellites has its own characteristics and applications, and the choice of a particular satellite depends on the problem to be solved.
Remote Sensing of the Earth (RS) provides a wide range of capabilities to obtain information about the Earth and its surface from space. Some of the major capabilities of remote sensing include:

  1. Obtaining information about the geographical position of objects on Earth. Using satellite remote sensing, it is possible to obtain the exact coordinates and elevations of geographical features such as mountains, rivers, lakes, settlements, etc.
  2. Study of changes in the natural environment. With the help of remote sensing it is possible to observe changes in forest areas, pastures, as well as other zones of natural environment, such as deserts, tundras, etc. This allows tracking the processes of erosion, forestation, droughts and other natural phenomena.
  3. Study of climatic processes. RS data are used to study climate changes on the planet, such as global warming, glacier spreading, etc.
  4. Monitoring and forecasting of natural disasters. Earth remote sensing can be used to monitor processes related to earthquakes, volcanic eruptions, floods and other natural disasters. This makes it possible to predict the danger to the population and take measures to prevent natural disasters.
  5. Mineral prospecting and mining. Remote sensing allows finding mineral deposits such as oil, gas, gold, silver, etc. This is especially useful in areas that are difficult to access and poorly explored.
  6. Environmental pollution monitoring. Remote sensing data is used to track water and air pollution, and to monitor soil quality, etc.
  7. Exploration of oceans and seas. Remote sensing provides information on temperature, salinity, currents, waves and other parameters of oceans and seas. This makes it possible to forecast conditions for fishing, track the movement of icebergs, predict sea level changes and other parameters important for studying marine ecosystems.
  8. Monitoring of transportation and communications. Remote sensing can be used to track the movement of vehicles such as cars, trains, ships and airplanes. This can be useful for controlling traffic flows and optimizing routes. Also, remote sensing data can be used to find oil or gas leaks in pipelines and other communications.
  9. Defense and Security Support. Remote sensing is used to support national security by monitoring borders, controlling the surface of the earth and the air. In addition, remote sensing can help detect sources of terrorist threats and prevent possible attacks.
  10. Space Object Surveillance. Remote sensing data is used to study space objects such as planets, galaxies, stars, etc. With the help of remote sensing it is possible to study their properties, physical characteristics and motion.

In general, remote sensing provides information about the Earth and its environment that can be used for decision-making in various fields of activity, from economics to ecology and science.
Remote sensing data refers to information collected about the Earth's surface without direct physical contact. It is obtained through sensors on satellites, aircraft, or other platforms that capture electromagnetic radiation, including visible, infrared, and microwave wavelengths. This data is crucial for applications such as environmental monitoring, agriculture, urban planning, and disaster management.
The spatial resolution of remote sensing data determines the level of detail it can provide. Common categories of spatial resolution include coarse (above 30 meters), medium (10 to 30 meters), high (1 to 10 meters), and very high (below 1 meter). Higher spatial resolution allows for more detailed observations, suitable for applications such as land cover mapping and change detection.
Key spectral bands in remote sensing data include visible, near-infrared, shortwave infrared, and thermal infrared. These bands capture different wavelengths of electromagnetic radiation, enabling the identification of various surface features. For example, vegetation reflects strongly in the near-infrared, aiding in vegetation health assessments, while thermal infrared reveals temperature variations.
Temporal resolution refers to how often a satellite revisits a specific location. It impacts the effectiveness of monitoring dynamic changes over time. Applications benefiting from frequent revisit times include agriculture, where crop health changes rapidly, and disaster management, where real-time monitoring is crucial for assessing and responding to events such as wildfires or floods.
The integration of remote sensing data with GIS technology enhances its utility by providing a spatial context for analysis. GIS allows for the creation of maps, spatial modeling, and overlaying diverse datasets, facilitating informed decision-making. This integration is vital for applications such as urban planning, natural resource management, and environmental monitoring.


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