Satellite Monitoring

Satellite Monitoring

Satellite monitoring has become one of the most important tools for observing environmental change across the Earth. Satellites provide repeated measurements over enormous areas, allowing scientists, governments, conservation organizations, and communities to observe ecosystems that would be difficult, expensive, or impossible to monitor continuously from the ground.

Modern Earth-observation systems can measure vegetation, forest structure, land cover, water extent, ocean conditions, atmospheric pollution, soil moisture, snow, glaciers, wildfire activity, coastal change, urban development, agricultural conditions, and many other environmental variables. Increasingly powerful satellite sensors, combined with artificial intelligence and machine learning, are also making it possible to detect wildlife, ships, methane plumes, invasive plants, harmful algal blooms, landslides, and other features directly from space.

Satellite monitoring does not eliminate the need for field research. Ground observations remain important for validating satellite measurements and interpreting what remotely sensed changes actually mean. However, satellites provide something that field observations generally cannot: frequent, standardized observation of very large areas over long periods of time.

Satellite Monitoring and Biodiversity

Biodiversity monitoring has traditionally depended heavily on field surveys. These provide detailed information about species and ecosystems but can be expensive and geographically limited. Satellite observations make it possible to extend biodiversity monitoring across landscapes, countries, and continents.

Remote sensing can measure ecosystem structure, vegetation cover, productivity, fragmentation, biomass, habitat condition, and other variables associated with biodiversity. Researchers have proposed using these measurements as indicators for evaluating progress under international biodiversity agreements such as the Kunming-Montreal Global Biodiversity Framework.

Satellite information can also be integrated with ecological surveys, evolutionary research, geographic information systems, and species-distribution models. This combination allows scientists to examine relationships among habitat conditions, ecosystem function, species distributions, and environmental change.

Artificial intelligence has further expanded the usefulness of satellite imagery. Deep-learning systems can identify ecological patterns within enormous image collections, helping researchers classify habitats and recognize changes that might otherwise require extensive manual analysis.

Wildlife Monitoring from Space

Very-high-resolution satellite imagery is increasingly capable of detecting animals directly.

Researchers have demonstrated satellite-based approaches for detecting or estimating populations of large terrestrial mammals, whales, and penguins. Satellite imagery can also measure habitat conditions associated with birds, elephants, marine mammals, and other species.

Emperor penguin colonies can sometimes be identified because guano stains create visible signatures on Antarctic ice. High-resolution imagery has allowed researchers to track changes in colonies over time.

Similar methods are being investigated for whales. Satellite imagery combined with artificial intelligence has been used to detect fin whales, estimate beluga abundance, and test methods for locating critically endangered North Atlantic right whales.

Wildlife monitoring also includes satellite tracking devices attached to animals. Tracking collars can reveal migration routes, seasonal habitats, movement corridors, and interactions between wildlife and human development.

Forests, Deforestation, and Carbon

Forests are among the most extensively monitored ecosystems from space.

Landsat, Sentinel, radar satellites, and other Earth-observation systems can identify forest clearing, fires, fragmentation, degradation, plantation expansion, and changes in vegetation condition. Long satellite records make it possible to reconstruct decades of forest change.

Satellite monitoring has become particularly important in tropical regions where rapid deforestation can occur across large and difficult-to-access landscapes.

New datasets attempt to distinguish different causes of forest loss, including permanent agricultural conversion, forestry, shifting cultivation, fires, and other disturbances. This distinction matters because forest loss caused by a temporary disturbance can have very different ecological consequences from permanent land conversion.

Satellite radar provides an important advantage in tropical regions because radar can observe the Earth's surface through clouds and smoke.

New missions are also improving measurements of forest biomass. ESA's Biomass satellite uses P-band radar capable of penetrating forest canopies, providing information about woody vegetation and helping scientists estimate the amount of carbon stored in forests.

Wildfires and Vegetation Change

Satellites are central to modern wildfire monitoring.

Earth-observation systems can detect active fires, identify vegetation conditions before fires occur, estimate burned area, observe smoke and atmospheric pollution, estimate emissions, and monitor ecosystem recovery after fires.

Satellite records have also helped researchers study how drought affects vegetation recovery following wildfire.

Artificial intelligence is increasingly being combined with satellite imagery, drones, fire towers, weather observations, and sensor networks to improve fire detection and potentially forecast fire progression.

Satellite measurements can also detect less dramatic forms of vegetation disturbance. Harmonized Landsat and Sentinel datasets can reveal changes in forests, grasslands, savannas, polar vegetation, and other ecosystems.

Wetlands, Rivers, Lakes, and Water Quality

Wetlands are difficult to monitor because their boundaries and water levels can change rapidly between seasons.

Satellite observations can measure wetland extent, flooding, vegetation, surface water, river conditions, and habitat changes. Radar imagery is especially useful because it can detect water and vegetation structure even under cloudy conditions.

Long Landsat records allow researchers to reconstruct decades of wetland change.

Satellite measurements can also provide information about inland water quality. Remote sensing can estimate or detect variables such as surface-water temperature, turbidity, chlorophyll, suspended sediment, and cyanobacteria.

Programs such as the Cyanobacteria Assessment Network use satellite observations to identify potentially harmful cyanobacterial blooms across large numbers of lakes.

The SWOT satellite represents another major advance in water monitoring by measuring surface-water elevation and extent in rivers, lakes, and other water bodies.

Oceans, Coral Reefs, and Coastal Ecosystems

Satellite monitoring is essential for understanding oceans because direct sampling can cover only a small fraction of the world's marine environment.

Ocean-observation satellites measure sea-surface temperature, sea level, ocean color, currents, biological productivity, and other variables.

Satellite measurements are especially important for monitoring coral reefs during marine heatwaves. NOAA Coral Reef Watch uses satellite sea-surface-temperature observations to estimate accumulated heat stress and identify regions where coral bleaching is likely.

High-resolution imagery can also reveal changes associated with severe bleaching events.

Seagrass meadows can be monitored through repeated Sentinel and Landsat observations. Seasonal changes in these habitats can provide information about coastal ecosystem health.

Mangroves are another important application. Optical and radar satellite observations can measure mangrove distribution, fragmentation, biomass, degradation, and changes caused by development or pollution.

Harmful Algal Blooms and Ocean Color

Satellite ocean-color sensors can detect environmental conditions associated with harmful algal blooms.

MODIS, VIIRS, Sentinel-3, and other satellite instruments have been used to observe blooms in coastal waters and inland lakes. Researchers increasingly combine satellite imagery with environmental models and machine learning to improve forecasting and early-warning systems.

Satellite monitoring cannot always determine the exact species or toxicity of an algal bloom. Field sampling therefore remains important. However, satellites can reveal where blooms are occurring and how rapidly they are spreading over large areas.

Fisheries and Maritime Activity

Satellites have transformed the monitoring of fishing activity at sea.

Traditional vessel-monitoring systems frequently rely on Automatic Identification System signals transmitted by ships. Some vessels, however, do not broadcast reliable tracking information.

Satellite radar, optical imagery, nighttime-light observations, and artificial intelligence can identify vessels independently of public tracking signals.

Global Fishing Watch and related research have revealed substantial amounts of industrial fishing and maritime activity that were previously difficult to observe.

Satellite monitoring can therefore support enforcement against illegal fishing and help evaluate whether fishing vessels are operating inside marine protected areas.

Methane and Greenhouse-Gas Monitoring

Atmospheric satellites increasingly allow scientists to identify major methane concentrations and emission sources.

Sentinel-5P and other methane-monitoring satellites can detect large regional hotspots. Higher-resolution satellites can then help locate individual facilities responsible for emissions.

These techniques are being applied to oil and gas facilities, pipelines, landfills, and other methane sources.

Satellite monitoring has shown that emissions from some landfills and industrial facilities can differ significantly from conventional estimates.

UNEP's Methane Alert and Response System uses satellite observations to identify major methane plumes and provide information that governments and companies can use to investigate emissions.

Air Pollution and Dust

Geostationary satellites are creating a new generation of air-quality monitoring.

NASA's TEMPO mission provides repeated daytime measurements of pollutants over North America. Because the instrument observes the same region many times each day, scientists can track how pollution changes over periods of hours rather than relying only on occasional satellite passes.

Satellite instruments can monitor nitrogen dioxide, ozone, formaldehyde, wildfire smoke, and other atmospheric pollutants.

Artificial intelligence is also being placed directly aboard satellites. Experimental systems can process observations in orbit, potentially shortening the time between detection of an environmental hazard and delivery of an alert.

Similar onboard systems are being developed for rapid dust-storm detection.

Agriculture, Irrigation, and Drought

Agriculture has become one of the largest applications of satellite remote sensing.

Satellite imagery can identify crop types, monitor vegetation development, estimate soil moisture, evaluate drought, identify irrigation, estimate evapotranspiration, and measure changes in agricultural productivity.

Sentinel-1 radar can provide soil-moisture information even when clouds prevent optical observations.

Combining radar, optical imagery, temperature measurements, precipitation datasets, and vegetation indicators provides more comprehensive assessments of drought conditions.

Satellite drought monitoring has been applied in Kenya, Mozambique, Uruguay, the Horn of Africa, and many other regions.

High-resolution remote sensing can also identify irrigated fields and detect uneven irrigation patterns, potentially improving water-use efficiency.

Groundwater, Snow, and Glaciers

Some satellite missions measure environmental change without directly photographing the Earth's surface.

GRACE and GRACE-FO measure changes in Earth's gravity field. These observations allow scientists to estimate changes in groundwater, soil moisture, and surface-water storage.

Satellite observations have documented major groundwater losses in several regions.

Glaciers and snow are also extensively monitored from space. Landsat, Sentinel, MODIS, radar satellites, ICESat-2, and other systems can measure glacier extent, snow cover, glacier movement, and seasonal changes.

Millions of satellite image pairs have been used to study seasonal glacier movement around the world.

Satellite observations have also documented the continuing disappearance of tropical glaciers, including those on Mount Kenya.

Volcanoes, Landslides, Floods, and Ground Deformation

Satellites increasingly support disaster monitoring and early-warning systems.

Radar interferometry can detect extremely small changes in the Earth's surface. These measurements can reveal deformation associated with volcanoes, earthquakes, subsidence, landslides, and groundwater extraction.

Satellite monitoring has been used to follow volcanic deformation in Ethiopia and thermal activity at Mount Etna.

Researchers have proposed broader satellite-based volcano-monitoring systems that would combine radar, optical, thermal, and ultraviolet observations, particularly for volcanoes that lack extensive ground instrumentation.

Sentinel-1 radar is also widely used for flood mapping because radar can detect inundation through cloud cover.

Machine-learning systems can compare imagery before and after disasters to identify landslides and flooded areas rapidly.

Coastal Change and Sea-Level Rise

Satellite imagery provides long-term records of coastal erosion, shoreline movement, and land subsidence.

Landsat records can reconstruct shoreline positions over several decades, allowing researchers to identify areas experiencing persistent erosion or accretion.

Radar satellites offer additional monitoring capabilities where clouds limit optical imagery.

Satellite altimeters such as those in the Sentinel-6 program measure global sea-surface height. These measurements extend a record of sea-level change that began in the early 1990s.

Repeated measurements allow scientists to distinguish long-term sea-level rise from shorter-term fluctuations associated with climatic conditions such as El Niño and La Niña.

Pollution, Waste, and Marine Debris

Researchers are increasingly experimenting with satellite imagery to identify environmental pollution.

Sentinel-2 imagery combined with neural networks has been used to locate terrestrial waste sites and track their expansion.

Researchers are also testing whether satellite observations can identify floating marine debris and large concentrations of plastic waste. Current satellites have significant limitations for directly detecting small plastic particles, but large floating aggregates can sometimes be observed.

Remote sensing is also being applied to coastal microplastic research, sewage pollution, oil spills, and floating Sargassum.

These applications demonstrate both the potential and the limitations of satellite monitoring. Not every pollutant can be measured directly from orbit, and satellite observations frequently need to be combined with field measurements and environmental models.

Invasive Species and Grasslands

Satellite imagery can help identify invasive plants that alter natural ecosystems.

Sentinel-2, PlanetScope, hyperspectral imagery, radar, drones, and machine-learning systems have all been evaluated for invasive-species mapping.

Repeated observations can help determine where invasive plants are spreading and whether management programs are controlling them successfully.

Grasslands can also be monitored from space. Satellite observations can estimate vegetation biomass, forage quality, grazing intensity, and seasonal productivity.

These measurements can support both biodiversity conservation and livestock management.

Ecological Restoration

Satellite monitoring can help determine whether ecosystem-restoration projects are producing measurable results.

Researchers are combining satellite imagery with drone observations and field measurements to evaluate vegetation recovery, soil conditions, hydrology, biodiversity, and carbon storage.

Open-access Landsat and Sentinel data are particularly important because they allow restoration projects to be observed repeatedly without requiring expensive proprietary imagery.

Satellite tools are also being developed to create standardized indicators that allow restoration outcomes to be compared across different landscapes.

Conservation Enforcement

One of the most significant uses of satellite monitoring is environmental enforcement.

Near-real-time forest alerts can help authorities identify illegal logging, mining, road construction, and agricultural clearing.

Satellite observations can also reveal fishing vessels operating inside marine protected areas.

However, monitoring alone does not guarantee conservation. Research from the Amazon shows that satellite alerts are most effective when governments and conservation organizations have sufficient capacity to investigate violations and enforce environmental laws.

Monitoring systems can also influence behavior. If land users understand the detection limits of a satellite system, they may alter clearing patterns to remain below known detection thresholds.

Satellite technology therefore works best as part of a broader system that includes field investigation, law enforcement, community participation, environmental regulation, and scientific monitoring.

The Growing Role of Artificial Intelligence

Artificial intelligence is becoming increasingly important throughout satellite monitoring.

Machine-learning and deep-learning systems can process enormous quantities of satellite imagery far more rapidly than human analysts.

Applications include:

• identifying forests and land-cover change;
• detecting wildlife;
• mapping invasive plants;
• classifying wetlands;
• locating fishing vessels;
• identifying methane plumes;
• detecting landslides;
• monitoring crops and irrigation;
• forecasting harmful algal blooms;
• detecting atmospheric pollution; and
• identifying environmental disturbances.

Some systems are beginning to process data directly aboard satellites. This could reduce the delay between observing an environmental event and producing an alert.

The combination of increasingly frequent satellite observations, higher spatial resolution, open satellite archives, cloud computing, and artificial intelligence is likely to make environmental monitoring faster and more comprehensive.

Limits of Satellite Monitoring

Despite its capabilities, satellite monitoring has important limitations.

Optical satellites can be blocked by clouds, smoke, or darkness. Radar can overcome many of these problems but measures different physical properties and can be more difficult to interpret.

Spatial resolution also matters. Some environmental changes or individual organisms are too small to detect reliably from available imagery.

Algorithms may produce classification errors, and monitoring accuracy can change with season, habitat type, weather, and sensor characteristics.

Satellite measurements therefore often require validation using field observations, drones, aircraft, acoustic surveys, weather stations, wildlife surveys, and other independent measurements.

Remote sensing is most powerful when these different forms of observation are combined.

Conclusion

Satellite monitoring has evolved from a specialized scientific technique into a fundamental part of modern environmental observation.

Earth-observation satellites now monitor forests, biodiversity, wildlife, oceans, wetlands, rivers, agriculture, air pollution, methane, drought, wildfires, glaciers, sea-level rise, floods, volcanoes, coastal erosion, invasive species, and many other environmental processes.

The greatest strength of satellites is their ability to observe large areas repeatedly and consistently. Long-running missions such as Landsat provide records spanning decades, while newer systems such as Sentinel, NISAR, SWOT, TEMPO, PACE, Sentinel-6, and Biomass provide increasingly specialized measurements.

Artificial intelligence is expanding these capabilities further by allowing enormous satellite datasets to be analyzed rapidly and automatically.

Satellite monitoring cannot replace scientists, field observations, conservation organizations, or environmental enforcement. Instead, it provides a global observational system that can reveal where change is occurring, how quickly it is happening, and where closer investigation or action may be needed.

As satellite resolution, frequency, computing power, and analytical methods continue to improve, Earth observation is likely to become an increasingly important foundation for biodiversity conservation, climate science, disaster response, resource management, and environmental accountability.

Satellite Monitoring: Foundations and Biodiversity

1. Remote Sensing Delivers Tropical Forest Resilience Monitoring for the Global Biodiversity Framework

| Jesús Aguirre-Gutiérrez et al. | Nature Reviews Biodiversity | 2026-07-08

Satellite observations can track tropical forest structure, functioning, disturbance, recovery, and resilience, providing scalable indicators for assessing progress under the Global Biodiversity Framework.

2. Deep Learning and Satellite Remote Sensing for Biodiversity Monitoring and Conservation

| Nathalie Pettorelli | Remote Sensing in Ecology and Conservation | 2024-06-17

Deep-learning techniques are expanding the ability to extract ecological information from satellite imagery, including habitat conditions, ecosystem change, and biodiversity-related patterns.

3. Need and Vision for Global Medium-Resolution Landsat and Sentinel-2 Data Products

| USGS researchers | U.S. Geological Survey | 2024-01-01

Combining Landsat and Sentinel-2 observations could provide global land-monitoring products every few days at resolutions suitable for ecosystem science, conservation, and management.

4. Advancing Terrestrial Biodiversity Monitoring with Satellite Remote Sensing in the Context of the Kunming-Montreal Global Biodiversity Framework

| Various authors | Ecological Indicators | 2023-10

Satellite products for ecosystem distribution, vegetation cover, biomass, fragmentation, plant traits, and productivity could substantially strengthen monitoring of global biodiversity targets.

5. Integrating Remote Sensing with Ecology and Evolution to Advance Biodiversity Conservation

| Jeannine Cavender-Bares et al. | Nature Ecology & Evolution | 2022-03-24

Integrating satellite measurements with ecological field observations can reveal biodiversity patterns and help connect ecosystem structure, function, species distributions, and evolutionary history.

6. Priority List of Biodiversity Metrics to Observe from Space

| Andrew K. Skidmore et al. | Nature Ecology & Evolution | 2021-05-13

Experts identify ecosystem structure and ecosystem function as particularly mature biodiversity variables for direct monitoring using Earth-observation satellites.

7. The Role of Satellite Remote Sensing in Structured Ecosystem Risk Assessments

| Nicholas J. Murray et al. | Science of the Total Environment | 2018-04-01

Long-term satellite observations can measure ecosystem area, structure, and function and provide quantitative evidence for assessing the risk of ecosystem collapse.

8. Satellite Remote Sensing, Biodiversity Research and Conservation of the Future

| Nathalie Pettorelli et al. | Philosophical Transactions of the Royal Society B | 2014

Satellite remote sensing can improve understanding of biodiversity change while giving conservation scientists consistent observations across large and inaccessible landscapes.

9. Remote Sensing for Conservation Monitoring: Assessing Protected Areas, Habitat Extent, Habitat Condition, Species Diversity, and Threats

| Harini Nagendra et al. | Ecological Indicators | 2013-10-01

Landsat, high-resolution optical imagery, LiDAR, and radar can monitor protected areas, habitat loss, fragmentation, vegetation structure, and threats to biodiversity.

10. Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

| Various authors | Sensors | 2010

This review examines high-resolution, hyperspectral, thermal, LiDAR, satellite-constellation, image-classification, data-fusion, and GIS techniques for ecological monitoring.

Biodiversity, Habitats, and Wildlife

11. Improving Marine Protected Area Zoning Through Species-Oriented Analysis Using Wildlife Remote Sensing

| Various authors | Frontiers in Marine Science | 2026

Very-high-resolution satellite imagery is increasingly capable of detecting animals directly, providing new information for marine protected-area planning and wildlife management.

12. Monitoring Biodiversity and Ecosystem Services Using L-Band Synthetic Aperture Radar Satellite Data

| Brian Alan Johnson et al. | Remote Sensing | 2025-10-20

L-band radar is increasingly used to map habitats, forest biomass, land-cover change, species richness, and other indicators of biodiversity and ecosystem services.

13. Systematic Review of Satellite-Based Earth Observation Applications for Wildlife Ecology Research in Terrestrial Polar and Mountain Regions

| Helena Wehner et al. | Remote Sensing | 2025-08-11

Earth-observation satellites offer growing opportunities to connect rapidly changing habitats with wildlife ecology in polar and mountain environments.

14. Satellite Imagery Can Predict Bird Species Occupancy and Inform Multispecies Management in Pine Savannas

| Various authors | Ornithological Applications | 2025-04-25

Sentinel-2 habitat measurements can predict occupancy patterns for multiple bird species and potentially reduce dependence on labor-intensive vegetation surveys.

15. Using Geographic Information Systems and Remote Sensing Technique to Classify Land Cover Types and Predict Grassland Bird Abundance and Distribution in Nairobi National Park, Kenya

| Various authors | International Journal of Geoheritage and Parks | 2025-03

Sentinel-2 imagery can classify grassland, woodland, forest, shrubland, water, and bare ground and support models of bird abundance and habitat distribution.

16. Early Detection of Vegetation Stress in Nairobi National Park: Structural Change Analysis from 2005 to 2025

| Isaac Kipkemoi | Frontiers in Environmental Science | 2025

MODIS vegetation time series reveal abrupt changes in Nairobi National Park that may indicate habitat stress from development, climate variability, and other pressures.

17. Remote Sensing for Urban Biodiversity: A Review and Meta-Analysis

| Michele Finizio et al. | Remote Sensing | 2024-11-29

Satellite and airborne remote sensing can measure urban vegetation, habitat structure, connectivity, and other features relevant to conserving biodiversity in cities.

18. Deep Learning Enables Satellite-Based Monitoring of Large Populations of Terrestrial Mammals Across Heterogeneous Landscape

| Zijing Wu et al. | Nature Communications | 2023-05-27

Very-high-resolution satellite imagery combined with artificial intelligence can detect and count large mammals across extensive and complex landscapes.

19. Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity—A Review

| Patrick Kacic and Claudia Kuenzer | Remote Sensing | 2022-10-26

Differences in spectral responses detected by satellites can act as proxies for plant diversity and forest habitat heterogeneity across large areas.

20. Satellite Data Helps Migrating Birds

| NASA Earth Observatory | NASA | 2015-09-25

Landsat imagery combined with bird observations helped conservationists identify California rice fields that could temporarily be flooded to provide migratory shorebird habitat.

Forests, Deforestation, and Carbon Storage

21. New Data Shows What’s Driving Forest Loss Around the World

| Michelle Sims et al. | World Resources Institute | 2026-06-04

New satellite-based datasets classify forest-loss drivers, helping distinguish permanent agricultural conversion from fires, forestry, shifting cultivation, and other disturbances.

22. Tropical Rainforest Loss Slowed in 2025, but Fire Is a Growing Threat to Forests Worldwide

| Elizabeth Goldman et al. | World Resources Institute | 2026-04-29

Annual satellite monitoring shows how tropical primary-forest loss changes from year to year and distinguishes fire-driven losses from other forms of clearing.

23. Biomass Satellite Returns Striking First Images of Forests and More

| European Space Agency | ESA | 2025-06-23

The first images from ESA's Biomass satellite demonstrate the potential of P-band radar for observing forest structure and ultimately quantifying carbon stored in forests.

24. Global Forest Loss Shatters Records in 2024, Fueled by Massive Fires

| World Resources Institute | WRI | 2025-05-20

Satellite-derived Global Forest Watch data showed exceptionally high forest loss in 2024, with fires becoming a major driver of tropical primary-forest destruction.

25. A New Space Age for Forests – but Groundwork Still Matters

| European Space Agency | ESA | 2025-04-24

ESA's Biomass mission introduces P-band radar capable of penetrating dense forest canopies to estimate woody biomass and improve measurements of forest carbon.

26. Natural Forests of the World – A 2020 Baseline for Deforestation and Degradation Monitoring

| Various authors | Scientific Data | 2025

Deep learning applied to satellite imagery produced a global baseline designed to distinguish natural forests and improve future monitoring of deforestation and degradation.

27. Is the Change Deforestation? Using Time-Series Analysis of Satellite Data to Disentangle Deforestation from Other Forest Degradation Causes

| Ignacio Fuentes et al. | Remote Sensing Applications: Society and Environment | 2024-08

Landsat time series and change-detection algorithms can help distinguish deliberate deforestation from fires, drought damage, and other causes of vegetation change.

28. Human Degradation of Tropical Moist Forests Is Greater Than Previously Estimated

| Various authors | Nature | 2024

Satellite observations of forest-cover change, canopy height, and biomass reveal widespread degradation caused by logging, fires, fragmentation, and forest-edge effects.

29. Using a Data Cube to Monitor Forest Loss in the Amazon

| European Space Agency | ESA | 2023

Sentinel-1 radar allows frequent forest monitoring even through clouds, smoke, and darkness, making it especially useful for rapid detection of Amazon forest disturbance.

30. Satellite Remote Sensing of Deforestation for Oil Palm

| Matthew Payne | Nature Reviews Earth & Environment | 2021-03-17

Repeated satellite surveys can distinguish plantation expansion and associated deforestation, allowing large industrial plantations and smallholder development to be evaluated over time.

Wildfires, Vegetation Change, and Land Degradation

31. Wildfires, Drought and Extreme Heat, 2026

| European Space Agency | ESA | 2026-08-18

Sentinel and other Earth-observation missions monitor active fires, heat, vegetation conditions, smoke, atmospheric pollution, and drought during extreme-weather events.

32. Amazon Wildfire Emissions Up to Three Times Higher Than Estimated

| European Space Agency | ESA | 2026-03-25

Satellite measurements of carbon monoxide combined with artificial intelligence suggest that emissions from severe 2024 Amazon fires were substantially underestimated.

33. Satellite Remote Sensing for Monitoring Cork Oak Woodlands—A Comprehensive Literature Review

| Various authors | Diversity | 2025-06-14

Landsat and other satellite datasets have been used to monitor cork-oak mortality, forest disturbance, land-cover change, vegetation condition, and woodland management.

34. From Spark to Suppression: An Overview of Wildfire Monitoring, Progression Prediction, and Extinguishing Techniques

| Guangqing Zhai et al. | International Journal of Applied Earth Observation and Geoinformation | 2025-06

Satellites, drones, fire towers, sensor networks, and artificial intelligence can be integrated to detect fires and forecast how they may spread.

35. Advancing Sparse Vegetation Monitoring in the Arctic and Antarctic

| Various authors | Remote Sensing | 2025-04-24

Satellite imagery, drones, machine learning, and sensor fusion are improving the ability to detect sparse and climate-sensitive vegetation in polar environments.

36. Spotting Disruptions to Earth’s Vegetation

| NASA Earth Observatory | NASA | 2025-03-11

Harmonized Landsat and Sentinel data can identify disturbances not only in forests but also in grasslands, savannas, and other frequently overlooked ecosystems.

37. Satellite-Derived Productivity Outputs for Land Degradation Assessment Vary With Biome and Rainfall

| Colleen L. Seymour et al. | Land Degradation & Development | 2025-03-09

Satellite-derived vegetation productivity can support land-degradation assessments, but relationships between productivity, degradation, rainfall, and ecosystem type require careful interpretation.

38. Remote Sensing for Wildfire Monitoring: Insights into Burned Area, Emissions, and Fire Dynamics

| Various authors | One Earth | 2024-06-21

Satellite observations can assess pre-fire vegetation, locate active fires, estimate emissions, map burned areas, and track ecosystem recovery following fire.

39. Disentangling Linkages Between Satellite-Derived Indicators of Forest Structure and Productivity for Ecosystem Monitoring

| Evan R. Muise et al. | Scientific Reports | 2024-06-14

Satellite measures of vegetation productivity and forest structure can provide complementary information for tracking ecosystem condition and Essential Biodiversity Variables.

40. Satellites Show How Drought Changes Wildfire Recovery in the West

| NASA Earth Science Division | NASA | 2024-03-27

Analysis of more than 1,500 fires using satellite data shows that drought conditions influence how western ecosystems recover after wildfire.

Wetlands, Freshwater, and Drought

41. Machine and Deep Learning for Wetland Mapping and Bird-Habitat Monitoring

| Various authors | Remote Sensing | 2025-10-31

A systematic review finds strong potential for machine learning and combined Sentinel-1 and Sentinel-2 observations in wetland mapping and bird-habitat monitoring.

42. NASA-ISRO Radar Satellite Captures First Image of North Dakota Wetlands, Farmlands

| NASA/JPL-Caltech | NASA | 2025-09-25

Early NISAR imagery demonstrates how radar can distinguish wetlands, agricultural fields, vegetation structures, and other landscape features regardless of cloud cover.

43. Temporal Variability in Remote Sensing Accuracy for Wetland Mapping

| Various authors | International Journal of Digital Earth | 2025-07-28

Combining Sentinel-1 radar and Sentinel-2 optical imagery improves wetland classification while showing that mapping accuracy can vary substantially through time.

44. Trends in Remote Sensing of Water Quality Parameters in Inland Water Bodies

| Sinesipho Ngamile et al. | Frontiers in Environmental Science | 2025-03-03

A review of 142 studies finds Landsat, Sentinel-2, MODIS, and machine learning increasingly important for monitoring chlorophyll, turbidity, and other water-quality indicators.

45. Wetland Vegetation Mapping Improved by Phenological Leveraging of Multitemporal Nanosatellite Images

| Various authors | Geocarto International | 2025-01-21

Repeated nanosatellite imagery can exploit seasonal plant-growth differences to distinguish wetland vegetation types that may otherwise appear spectrally similar.

46. Wetlands Mapping and Monitoring with Long-Term Time Series Satellite Data

| Jian Zhang et al. | Land | 2024-09-20

Landsat time series, Google Earth Engine, feature optimization, and Random Forest classification were used to document long-term wetland changes in Gansu Province, China.

47. Bridging the Divide Between Inland Water Quantity and Quality with Satellite Remote Sensing

| Emily A. Ellis et al. | WIREs Water | 2024-03-10

Satellites can monitor both water quantity and quality, including water extent, elevation, discharge, temperature, turbidity, chlorophyll, and suspended sediment.

48. NASA-ISRO Radar Mission to Provide Dynamic View of Forests, Wetlands

| NASA Earth Science Division | NASA | 2023-10-27

NISAR's radar instruments are designed to monitor changes in forests and wetland flooding and improve understanding of their role in the global carbon cycle.

49. Earth Observation Vital in Monitoring Wetland Waters

| European Space Agency | ESA | 2021-02-02

Satellite data can measure wetland water extent, river flows, suspended sediment, chlorophyll, and water-quality changes across large and difficult-to-monitor regions.

50. Space Key to Wetland Conservation

| European Space Agency | ESA | 2020-02-05

ESA's GlobWetland Africa project combines Sentinel and Landsat observations to map wetland extent, seasonal dynamics, habitat conditions, and human pressures.

Oceans, Coasts, Coral Reefs, and Algal Blooms

51. NOAA Coral Reef Watch Current Global Bleaching: Status Update

| NOAA Coral Reef Watch | NOAA | 2026-06-02

Satellite sea-surface-temperature monitoring provides global maps of accumulated heat stress and helps identify reef regions exposed to coral-bleaching conditions.

52. Behind the Data: Observing California’s Toxic Algae from Space

| EUMETSAT | EUMETSAT | 2026-02-24

Sentinel-3 ocean-colour observations help scientists investigate environmental conditions associated with a major harmful algal bloom along the California coast.

53. Remote Sensing Technologies for Monitoring Coral Reef Health Under Climate Change

| Ricky Anak Kemarau et al. | Marine Environmental Research | 2025-10-09

A review compares Sentinel-2, Landsat, MODIS, hyperspectral imagery, and drones for detecting coral bleaching and monitoring reef health.

54. Monitoring Coral Reefs by Remote Sensing Techniques and Acoustic Data as a Ground Truth Reference in Hurghada, Red Sea, Egypt

| Various authors | Egyptian Journal of Aquatic Research | 2025-09

Sentinel-2 imagery combined with sonar and underwater observations can map reef habitats, estimate depth, and detect bleaching following marine heatwaves.

55. Remote Sensing Tools for Monitoring Marine Phanerogams: A Review of Sentinel and Landsat Applications

| Various authors | Journal of Marine Science and Engineering | 2025-02-04

Landsat and Sentinel imagery provide accessible tools for mapping and repeatedly monitoring seagrass and other submerged marine vegetation.

56. Examining Global Trends of Satellite-Derived Water Quality Variables in Shallow Lakes

| Various authors | Remote Sensing Applications: Society and Environment | 2025

ESA satellite products allow long-term comparisons of chlorophyll, turbidity, and surface-water temperature across more than 2,000 lakes.

57. Impacts of the 2023 Marine Heatwave in the Florida Keys

| Mariam Ayad et al. | Environmental Science & Technology | 2025

High-resolution Planet SuperDove satellite imagery detected changes associated with severe coral bleaching during the 2023 Florida Keys marine heatwave.

58. A Review on Monitoring, Forecasting, and Early Warning of Harmful Algal Bloom

| Various authors | Aquaculture | 2024-12-15

Satellite imagery, environmental models, and machine learning can be combined to improve detection and forecasting of harmful algal blooms.

59. Sentinel-2 Unveils the Seasonal Rhythm of Intertidal Seagrass

| European Space Agency | ESA | 2024-10-03

Sentinel-2 time series reveal seasonal changes in seagrass meadows, an important habitat and biodiversity indicator in European and North African coastal ecosystems.

60. Detection of Harmful Algal Blooms from Satellite-Based Inherent Optical Properties of the Ocean in Paracas Bay – Peru

| Carlos Paulino et al. | Marine Pollution Bulletin | 2024-04

MODIS and VIIRS observations were used to identify harmful algal blooms and estimate their spatial extent in coastal waters of southern Peru.

Fisheries and Direct Monitoring of Animals

61. Satellite Monitoring of North Atlantic Right Whales

| NOAA Fisheries | NOAA | 2026

NOAA is testing targeted very-high-resolution satellite imagery to locate critically endangered North Atlantic right whales in important feeding and migration areas.

62. 2024 Annual Data: Vessel Detections from Sentinel-2

| Global Fishing Watch | Zenodo | 2025-07-23

A satellite dataset maps vessels using Sentinel-2 imagery and deep-learning models, including estimates of vessel size, speed, orientation, and activity.

63. Improving Detectability of Illegal Fishing Activities Across Supply Chains

| Rodrigo Oyanedel et al. | npj Ocean Sustainability | 2025-06-21

Research on illegal fishing enforcement highlights how satellite monitoring can complement inspections and improve the probability of detecting prohibited activity.

64. Deep Learning-Based Detection and Tracking of Fin Whales Using High-Resolution Space-Borne Remote Sensing Data

| Various authors | Remote Sensing Applications: Society and Environment | 2025-04

High-resolution imagery and deep-learning algorithms show potential for automatically locating and tracking fin whales across remote marine environments.

65. Visualization of Humpback Whale Tracking on Edge Device Using Space-Borne Remote Sensing Data for Indian Ocean

| Various authors | Egyptian Journal of Remote Sensing and Space Sciences | 2024-12

High-resolution satellite observations combined with deep learning provide a potential approach for detecting humpback whales over remote portions of the Indian Ocean.

66. Estimating Beluga Whale Abundance from Space

| Various authors | Remote Sensing in Ecology and Conservation | 2024-05-08

Very-high-resolution satellite imagery validated with drone observations demonstrates a method for estimating beluga whale abundance in remote Arctic environments.

67. Satellite Mapping Reveals Extensive Industrial Activity at Sea

| Various authors | Nature | 2024

Satellite radar, optical imagery, vessel tracking data, and deep learning revealed large amounts of industrial fishing and vessel activity missing from public tracking systems.

68. Global Fishing Watch Annual Report 2024

| Global Fishing Watch | Global Fishing Watch | 2024

Global Fishing Watch describes using Sentinel-1 radar and artificial intelligence to detect vessels that are absent from conventional AIS tracking systems.

69. Harnessing AI to Map Global Fishing Vessel Activity

| Various authors | One Earth | 2024

Computer vision applied to radar, optical imagery, and nighttime lights can locate fishing vessels and identify vessels missing from public tracking datasets.

70. How Global Fishing Watch Used AI to Map Human Activity at Sea

| Google | Google | 2024

Artificial intelligence and satellite imagery reveal previously unmapped fishing vessels, offshore infrastructure, and other human activity across the world's oceans.

Penguins, Methane, and Atmospheric Monitoring

71. Methane – Earth Indicator

| NASA Science | NASA | 2026-08-14

NASA combines satellite, aircraft, ground measurements, and atmospheric modeling to monitor methane concentrations and locate major natural and human-caused emission sources.

72. Satellite Mapping of Emperor Penguin Habitat Dispersal Under Climate Extremes

| Hong Lin et al. | Remote Sensing of Environment | 2025-12-01

Satellite observations of guano signatures enable long-term tracking of emperor penguin colonies and reveal changing habitat use during extreme climate conditions.

73. Better Data Driving Action on Methane Emissions, but More Work Needed

| UN Environment Programme | UNEP | 2025-10-22

UNEP's satellite-based Methane Alert and Response System sends information about major emission plumes to governments and companies so leaks can be investigated and reduced.

74. A Tapestry of Tales: 10th Anniversary Reflections from NASA’s OCO-2 Mission

| NASA Earth Observer Staff | NASA | 2025-08-12

A decade of OCO-2 observations has advanced carbon monitoring while providing unexpected applications for plant health, drought warning, crops, forests, and rangelands.

75. Top 10 Persistent Methane Sources

| European Space Agency | ESA | 2025-02-03

Sentinel-5P observations were used to identify hundreds of persistent methane-emitting locations and rank major regional sources.

76. Satellites Help Tackle Landfill Methane Leaks

| European Space Agency | ESA | 2025

Sentinel-5P and higher-resolution methane satellites can be combined with aircraft and ground surveys to identify and quantify emissions from individual landfill sites.

77. Monitoring Penguins from Space

| Copernicus Data Space Ecosystem | Copernicus | 2024-09-28

Sentinel-2 imagery can reveal emperor penguin colonies because guano staining creates a visible spectral signature on otherwise bright Antarctic ice.

78. High Resolution Imagery Advances the Ability to Monitor Decadal Changes in Emperor Penguin Populations

| Woods Hole Oceanographic Institution | WHOI | 2024-03-13

Very-high-resolution satellite imagery combined with field surveys provides a multi-year record of global emperor penguin population changes.

79. The 2024 Global Methane Budget Reveals Alarming Trends

| European Space Agency | ESA | 2024

Sentinel-5P provides daily global measurements that help identify methane hotspots, evaluate emission trends, and connect regional concentrations with individual super-emitters.

80. Monitoring Methane from Space

| European Space Agency | ESA | 2023-11-13

The Tropomi instrument aboard Sentinel-5P maps global methane concentrations every day and helps scientists locate large emission hotspots.

Satellite Missions and Monitoring Technologies

81. First Image from Sentinel-6B Extends Sea-Level Legacy

| European Space Agency | ESA | 2025-12-16

Initial Sentinel-6B altimeter observations demonstrate continuity with earlier satellite measurements of sea-surface height and major ocean-current systems.

82. Sentinel-6B Launched to Extend Record of Sea-Level Rise

| European Space Agency | ESA | 2025-11-17

Radar altimetry aboard Sentinel-6B continues a satellite record of global sea-surface height that began in the early 1990s.

83. Sentinel-6B: Monitoring Earth’s Sea-Levels

| Elyna Niles-Carnes | NASA | 2025-11-16

Sentinel-6B maps most of Earth's ice-free ocean every ten days, extending observations of sea-level rise, ocean circulation, and atmospheric conditions.

84. Sea-Level Monitoring Satellite Sentinel-6B Sets Sail

| European Space Agency | ESA | 2025-07-23

The Sentinel-6 program is designed to continue precise radar-altimetry measurements needed to monitor long-term global sea-level change.

85. Sentinel-1C Demonstrates Power to Map Land Deformation

| European Space Agency | ESA | 2025-02-13

Radar interferometry from Sentinel-1C can detect subtle ground movements associated with subsidence, earthquakes, landslides, glacier flow, and volcanic processes.

86. The Earth Observer: Offering Perspectives from Space Through Time

| NASA Science | NASA | 2025

NASA reviews Earth-observation missions including NISAR, whose dual-frequency radar can monitor ecosystem disturbance, ice, groundwater, land deformation, forests, and wetlands.

87. 10 Ways Sentinel-1 Data Lets Us ‘See’ Our World

| European Space Agency | ESA | 2024-11-27

Sentinel-1 radar supports forest mapping, crop monitoring, flood detection, soil-moisture assessment, sea-ice mapping, oil-spill detection, ship tracking, and illegal-fishing surveillance.

88. Sentinel-2C Delivers Stunning First Images

| European Space Agency | ESA | 2024-09-17

Sentinel-2C continues high-resolution multispectral monitoring of land, vegetation, inland waters, islands, and coastal environments across 13 spectral bands.

89. Satellites Size Up Bubbles of Methane in Lake Ice

| NASA Earth Observatory | NASA | 2020

L-band synthetic-aperture radar can detect methane-related features beneath Arctic lake ice even during darkness, snow cover, and cloudy conditions.

90. Methane Matters

| NASA Earth Observatory | NASA | 2016

Satellite measurements reveal regional methane hotspots that may be difficult to detect with sparse ground-monitoring networks alone.

Agriculture, Drought, Water, Ice, and Urban Monitoring

91. Seasonal Drought Dynamics in Kenya: Remote Sensing and Combined Indices for Climate Risk Planning

| Various authors | Climate | 2026-01-07

Satellite-derived precipitation, vegetation, soil-moisture, and productivity indicators reveal persistent drought hotspots and rising agricultural exposure across Kenya.

92. NASA Data, Trainings Help Uruguay Navigate Drought

| Melody Pederson and Rachel Jiang | NASA | 2025-09-10

Landsat observations helped Uruguay develop an operational system for monitoring reservoir water and supporting management decisions during severe drought.

93. High-Resolution Drought Monitoring with Sentinel-1 and ASCAT: A Case Study over Mozambique

| Various authors | Agricultural Water Management | 2025-09-01

Sentinel-1 radar soil-moisture measurements can produce high-resolution drought indicators useful for agricultural early-warning systems in Mozambique.

94. Regional Drought Monitoring Using Satellite-Based Precipitation and Standardized Palmer Drought Index

| Mingwei Ma et al. | Water | 2025-04-09

Satellite precipitation records can supplement sparse ground observations and improve regional drought assessment and water-management planning.

95. Remote Sensing-Derived Time Series of Transient Glacier Snowline Altitudes for High Mountain Asia, 1985–2021

| David Loibl et al. | Scientific Data | 2025-01-17

Decades of satellite imagery provide a regional record of glacier snowline changes that can be used to monitor glacier condition and climate response.

96. Monitoring Coastal Water Turbidity Using Sentinel-2—A Case Study in Los Angeles

| Various authors | Remote Sensing | 2025-01-08

Sentinel-2 observations combined with machine learning can complement field measurements of coastal turbidity and reveal seasonal impacts from stormwater runoff.

97. Spatiotemporal Analysis of Agricultural Drought and Its Relationship with Climate Variabilities in the Growing Season of the Horn of Africa

| Various authors | Frontiers in Climate | 2025

MODIS, GIMMS, soil-moisture, rainfall, and temperature datasets reveal changing drought severity and persistent agricultural vulnerability across the Horn of Africa.

98. Agricultural Drought Monitoring and Early Warning at the Regional Scale Using a Remote Sensing-Based Combined Index

| Trupti Satapathy et al. | Environmental Monitoring and Assessment | 2024-10-30

Satellite vegetation, temperature, crop-water-stress, and soil-moisture indicators were combined to develop a regional agricultural drought early-warning index.

99. Climatic Comparison of Surface Urban Heat Island Using Satellite Remote Sensing in Tehran and Suburbs

| Motahhareh Zargari et al. | Scientific Reports | 2024-01-05

MODIS, Sentinel-3, and Landsat temperature observations reveal spatial and seasonal patterns of urban heat across Tehran and surrounding suburban landscapes.

100. Microwave Remote Sensing for Agricultural Drought Monitoring: Recent Developments and Challenges

| Mariette Vreugdenhil et al. | Frontiers in Water | 2022

Microwave satellites provide frequent observations of soil moisture and vegetation conditions that can supplement rainfall and temperature data in agricultural drought monitoring.

Volcanoes, Earthquakes, Landslides, and Ground Deformation

101. Steps Toward a Satellite-Based Global Volcano Monitoring and Early Warning System

| Michael P. Poland et al. | U.S. Geological Survey | 2026-06-23

A proposed global satellite volcano-warning system would combine thermal, ultraviolet, optical, and synthetic-aperture radar observations to monitor active volcanoes that currently lack adequate ground-based instrumentation.

102. A Spatio-Temporal Dataset for Satellite-Based Landslide Detection

| Paul Höhn et al. | Scientific Data | 2025-11-11

A large satellite-image dataset provides paired before-and-after observations of landslides that can be used to develop and test automated landslide-detection systems.

103. Flood Susceptibility Mapping Using Machine Learning and Geospatial-Sentinel-1 SAR Integration

| Various authors | Remote Sensing | 2025-10-17

Multi-year Sentinel-1 radar observations were combined with machine learning to identify repeatedly flooded terrain and improve flood-susceptibility mapping.

104. Real-Time Satellite Monitoring of the 2024–2025 Dyke Intrusion Sequence at Fentale-Dofen Volcanoes, Ethiopia

| Lin Way et al. | Bulletin of Volcanology | 2025-10-12

Satellite radar observations allowed scientists to follow rapid ground deformation during the Fentale-Dofen volcanic unrest in Ethiopia, demonstrating the importance of near-real-time satellite monitoring in regions with limited ground instrumentation.

105. The Contribution of Meteosat Third Generation Observations to Monitoring Thermal Volcanic Activity

| Carolina Filizzola et al. | Remote Sensing | 2025-06-19

Meteosat Third Generation imagery acquired every ten minutes provided detailed observations of thermal activity during Mount Etna's February–March 2025 eruption.

106. V-STAR: A Cloud-Based Tool for Satellite Detection and Mapping of Volcanic Thermal Anomalies

| Simona Cariello et al. | GeoHazards | 2025-05-27

V-STAR uses satellite thermal observations and cloud-based processing to identify volcanic heat anomalies and rapidly map evolving volcanic activity.

107. Rapid Probabilistic Inundation Mapping Using Local Thresholds and Sentinel-1 SAR Data

| Various authors | Remote Sensing | 2025-05-16

A probabilistic flood-mapping approach uses Sentinel-1 radar to indicate both likely inundation and the uncertainty associated with flood classifications.

108. Detecting Flooded Areas Using Sentinel-1 SAR Imagery

| Francisco Alonso-Sarria et al. | Remote Sensing | 2025-04-11

Sentinel-1 radar enables flooded areas to be mapped even when clouds prevent optical satellites from observing the Earth's surface.

109. Rapid Landslide Detection from Free Optical Satellite Imagery Using a Robust Change Detection Technique

| Rosa Coluzzi et al. | Scientific Reports | 2025-02-08

Free optical satellite imagery and automated change detection can rapidly identify landslide scars after major rainfall and other triggering events.

110. Floods Modeling and Analysis for Dubai Using HEC-HMS Model and Remote Sensing Using GIS

| Ihsanullah R. Khan et al. | Scientific Reports | 2024-10-23

Satellite remote sensing, GIS, elevation data, and hydrological modeling were combined to evaluate flood hazards in the rapidly urbanizing landscape of Dubai.

Floods, Subsidence, and Coastal Change

111. Satellite Monitoring and Disaster Assessment of Rainfall-Induced Floods in Indonesia

| Central South University | Central South University | 2025-12-11

Sentinel-1, Sentinel-2, Landsat, and China's GF-3 satellite were used for rapid mapping of extensive rainfall-induced flooding and infrastructure exposure in Indonesia.

112. Tracking Coastal Changes Through Integrated NDWI-Derived Shorelines from Multi-Sensor Satellite Time Series

| Various authors | Remote Sensing Applications: Society and Environment | 2025-08

Four decades of Landsat and Sentinel imagery reconstructed shoreline change and identified persistent coastal erosion along portions of Italy's Tyrrhenian coast.

113. Land Subsidence Near Hanford and Corcoran, California, from Cryosat-2 Altimetry and Sentinel-1A SAR Imagery

| Shiang-Hung Wei and Cheinway Hwang | Terrestrial, Atmospheric and Oceanic Sciences | 2025-03-13

CryoSat-2 and Sentinel-1 measurements demonstrate how satellite altimetry and radar interferometry can monitor groundwater-related land subsidence in California's Central Valley.

114. Satellite Monitoring for Post-Clearance Accountability: Lessons from Rushikonda Hill

| Various authors | Discover Cities | 2025

High-resolution satellite images documented land disturbance extending substantially beyond an approved development footprint, demonstrating the potential of remote sensing for environmental compliance audits.

115. On the Shoreline Monitoring via Earth Observation: An Isoradiometric Method

| F. Caldareri et al. | Remote Sensing of Environment | 2024-09-01

A subpixel satellite technique improves shoreline extraction by analyzing radiometric transitions between water and land across images of different spatial resolutions.

116. A Benchmarking Framework for Shoreline Monitoring Accuracy

| U.S. Geological Survey | USGS | 2024-05-06

Researchers developed standardized benchmarks for determining how accurately satellite algorithms reproduce real shoreline positions.

117. Monitoring Coastal Change via Satellite Imagery at Regional Scale in the Pacific Northwest

| U.S. Geological Survey | USGS | 2024-03-21

Landsat-derived shoreline positions provide a scalable method for measuring erosion and accretion along hundreds of kilometers of coastline.

118. Satellite Remote Sensing Can Provide Semi-Automated Monitoring to Aid Coastal Decision-Making

| Various authors | Estuarine, Coastal and Shelf Science | 2024-03

Landsat imagery and Random Forest classification reconstructed several decades of salt-marsh change, showing how automated satellite analysis can inform coastal policy.

119. Assessment of Shoreline Change from SAR Satellite Imagery in Three Tidally Controlled Coastal Environments

| Salvatore Savastano et al. | Journal of Marine Science and Engineering | 2024-01-15

Synthetic-aperture radar provides an alternative means of identifying shorelines when clouds or other conditions limit conventional optical imagery.

120. Sentinel-1 and Satellite Radar for Changing Lands

| European Space Agency | ESA | 2024

Repeated radar observations can measure millimeter-scale land deformation associated with subsidence, landslides, earthquakes, volcanoes, and other geologic processes.

Glaciers, Snow, and Frozen Landscapes

121. Tracking Glacial Change with Landsat and Radar

| NASA Science | NASA | 2026

More than 36 million satellite image pairs were analyzed to create a global view of how glacier flow varies between seasons.

122. 2025 Among the Worst Years on Record for Global Glacier Loss

| University of Edinburgh | School of GeoSciences | 2026

Satellite and field observations indicate that glaciers outside Greenland and Antarctica lost hundreds of billions of tonnes of ice during the 2025 hydrological year.

123. Deep Learning-Based Remote Sensing Monitoring of Rock Glaciers

| Yidan Liu et al. | Remote Sensing | 2025-12-05

Sentinel-2 imagery and deep-learning segmentation were used to identify and map rock glaciers in Pakistan's Hunza River Basin.

124. Satellite-Based Observations for Snow Cover in the Northwest Himalaya

| Praveen Thakur and Sakshi Tripathi | IIRS Science Portal | 2025-03-27

AWiFS, MODIS, and other satellite records reveal seasonal and long-term changes in Himalayan snow cover important for downstream water resources.

125. A New Method for Automatic Glacier Extraction by Building Decision Trees Based on Pixel Statistics

| Xiao Liu et al. | Remote Sensing | 2025-02-19

Automated analysis of satellite pixels improves glacier mapping, including difficult areas containing shadows and debris-covered ice.

126. Monitoring Global Groundwater from Space

| NASA Scientific Visualization Studio | NASA | 2025-02-10

GRACE and GRACE-FO satellite gravity measurements are combined with models to map changing groundwater, root-zone moisture, and surface-water storage worldwide.

127. Satellites Detect Seasonal Pulses in Earth's Glaciers

| NASA Earth Observatory | NASA | 2025

Millions of optical and radar satellite images reveal seasonal acceleration and slowing in glaciers across the world.

128. Recent Advances in Snow Monitoring from Local to Global Scales

| Various authors | Current Climate Change Reports | 2025

Satellite systems including MODIS, ICESat-2, and radar missions are expanding measurements of snow cover, depth, and snow-water equivalent.

129. Efficient and Regionally Transferable Snow Water Equivalent Estimation Using a Long Short-Term Memory Network

| Various authors | Journal of Geophysical Research: Machine Learning and Computation | 2025

MODIS snow-cover observations and machine learning were combined to estimate snow-water equivalent across landscapes where direct measurements are sparse.

130. Mapping the Loss of Mt. Kenya's Glaciers

| Various authors | Geosciences | 2018-05

Pléiades, Sentinel-2, TanDEM-X, and other satellite observations document the rapid disappearance of the exceptionally small tropical glaciers of Mount Kenya.

Water, Soil Moisture, and Irrigation

131. A Review of the Use of Remote Sensing Techniques in Assessing Irrigation Water Use

| Various authors | Agricultural Water Management | 2025-10-01

Satellite observations provide increasingly practical methods for measuring irrigated area, evapotranspiration, soil moisture, and agricultural water consumption.

132. Value of Microwave Soil Moisture and Thermal-Infrared Evapotranspiration Retrievals for Mapping Irrigation Coverage

| Wade T. Crow et al. | International Journal of Applied Earth Observation and Geoinformation | 2025-09

Satellite estimates of evapotranspiration and soil moisture provide complementary signals for identifying irrigated agriculture from space.

133. Precision Identification of Irrigated Areas Using Optical-Radar Time-Series Features

| Various authors | Hydrology | 2025-08-14

Optical and radar satellite time series combined with machine learning improve identification of irrigated farmland in semi-arid regions.

134. Irrigation Monitoring from Satellite at Hyper-High Resolution

| Various authors | Agricultural Water Management | 2025-08-01

Multi-resolution Earth-observation data can identify irrigation and estimate applied water at approximately 10-meter spatial resolution.

135. An Object-Based Crop Classification Using Remotely Sensed Phenological and Multi-Spectral Data

| Wasif Yousaf et al. | Remote Sensing in Earth Systems Sciences | 2025-05-31

Landsat observations taken at key growth stages distinguish wheat, rice, maize, cotton, sugarcane, orchards, and other agricultural land-cover types.

136. Advancing Irrigation Uniformity Monitoring Through Remote Sensing

| Various authors | Agricultural Water Management | 2025-04-01

Deep learning applied to satellite imagery can identify non-uniform irrigation patterns and diagnose possible problems within center-pivot systems.

137. Two Decades of Soil Moisture from Space

| NASA Scientific Visualization Studio | NASA | 2025-02-10

GRACE satellite observations help reconstruct long-term changes in shallow groundwater, root-zone moisture, and surface soil moisture.

138. Satellite Monitoring of Agricultural Drought

| Institute of Geodesy and Cartography | IGiK | 2025

Operational satellite products classify crop moisture conditions and estimate potential yield losses caused by agricultural drought.

139. Automatic Classification of Agricultural Crops Using Sentinel-2 Data in Southern Kazakhstan

| Various authors | Agronomy | 2025

Sentinel-2 vegetation time series and crop phenology provide an automated approach for mapping crop types in rainfed agricultural regions.

140. NASA Satellites Find Snow Didn't Offset Southwest US Groundwater Loss

| NASA | NASA | 2024

Two decades of GRACE observations show substantial groundwater losses in the Great Basin despite occasional high-snowfall winters.

Air Pollution, Dust, and Methane

141. Satellite On-Orbit Chip-Level Deep Learning Model for Real-Time Dust Storm Monitoring

| Various authors | Environmental Science & Technology | 2026-03-19

An onboard deep-learning system analyzes geostationary satellite observations directly in orbit, dramatically reducing delays in identifying and quantifying dust storms.

142. CELNet: Deep Learning for Atmospheric Methane Plume Identification

| Fang Chen et al. | Remote Sensing of Environment | 2025-10-01

A deep neural network automatically delineates methane plumes in satellite-derived concentration imagery while reducing noise and false detections.

143. Mitigating Bias Induced by Missing Data in Geostationary Satellite Monitoring of Ground-Level NO2

| Various authors | Environmental Pollution | 2025-09-15

Machine learning can fill gaps in hourly geostationary satellite measurements and improve estimates of ground-level nitrogen dioxide.

144. NASA Mission Monitoring Air Quality from Space Extended

| Charles G. Hatfield | NASA | 2025-07-03

TEMPO's successful initial mission led NASA to extend its high-frequency monitoring of nitrogen dioxide, ozone, formaldehyde, and other pollutants.

145. TEMPO Air Quality Monitoring: Three Example Cases

| NASA Scientific Visualization Studio | NASA | 2025-07-03

TEMPO observations demonstrate how pollution associated with traffic, power generation, wildfire smoke, and other sources changes hour by hour.

146. Revolutionizing Satellite Real-Time Air Pollution Alerts Through New On-Orbit System-on-Chip Technology

| Various authors | Environmental Science & Technology | 2025-06-24

Artificial intelligence running directly aboard satellites could detect unusual particulate matter and ozone conditions without waiting for extensive ground-based processing.

147. STAQS Release Announcement

| NASA Atmospheric Science Data Center | NASA | 2024-08-14

Aircraft, ground sensors, and TEMPO observations were combined to validate and improve satellite air-quality measurements over major U.S. metropolitan areas.

148. NASA Releases New High-Quality, Near Real-Time Air Quality Data

| Charles G. Hatfield | NASA | 2024-05-30

NASA's TEMPO instrument provides hourly daytime observations of atmospheric pollution at spatial scales approaching individual neighborhoods.

149. A New Era of Air Quality Monitoring from Space over North America with TEMPO

| Xiong Liu et al. | NASA Technical Reports Server | 2024-05

Early TEMPO results demonstrate the scientific potential of hourly geostationary measurements of atmospheric pollution across North America.

150. Upping the TEMPO on Air Pollution Observations from Space for Enhanced Science Applications

| Aaron Naeger et al. | NASA Technical Reports Server | 2024-02-05

TEMPO's high temporal resolution offers new opportunities for tracking pollution episodes and incorporating satellite observations into operational air-quality applications.

Methane, Waste, and Pollution Detection

151. Improved Monitoring of Methane Emissions for the Oil and Gas Sector with Sentinel-2

| Various authors | Atmospheric Environment | 2025-12-15

A new Sentinel-2 processing method detects and estimates methane releases from oil-and-gas facilities and pipelines.

152. Application of Remote Sensing for Detection and Monitoring of Microplastics in the Colombian Caribbean

| Various authors | Microplastics | 2025-10-21

Sentinel-2 multispectral imagery and machine learning were tested as tools for identifying spatial patterns associated with coastal microplastic contamination.

153. A High-Resolution Satellite Survey of Methane Emissions from Sixty North American Landfills

| Various authors | Environmental Science & Technology | 2025-07-29

A constellation of high-resolution satellites recorded hundreds of methane plumes from municipal landfills across North America.

154. Are Sewage Spills and Coastal Winds Contributing to Airborne Microplastics?

| Plymouth Marine Laboratory | PML | 2025-07-09

Satellite, weather, and sewage-discharge data were combined to investigate conditions capable of transferring microplastic pollution from coastal waters into the atmosphere.

155. Understanding the Sargassum Phenomenon in the Tropical Atlantic Ocean

| Marianne Debue et al. | Marine Pollution Bulletin | 2025-07

Satellite spectral indices are central to detecting floating Sargassum and are increasingly combined with ocean-current models to predict coastal strandings.

156. Satellite Monitoring of Annual US Landfill Methane Emissions and Trends

| Nicholas Balasus et al. | Environmental Research Letters | 2025-01-16

TROPOMI observations of major U.S. landfills revealed methane emission levels and trends that differed substantially from some conventional inventory estimates.

157. Results of a Preliminary Satellite Monitoring Survey of Marine Debris in European Seas

| Achille Ciappa and Giorgio Budillon | Marine Pollution Bulletin | 2025

Sentinel-2 imagery demonstrates that large aggregates of floating material can sometimes be identified from orbit, although reliably quantifying marine plastic remains difficult.

158. Advancing Marine Debris Monitoring Through Artificial Intelligence and Remote Sensing

| R. Wisnu Adjie Pramudito et al. | Indonesian Journal of Educational Research and Technology | 2025

A systematic review examines how satellite imagery, drones, and artificial intelligence can improve scalable monitoring of marine debris.

159. Satellite Monitoring of Terrestrial Plastic Waste

| Caleb Kruse et al. | PLOS ONE | 2023

Neural networks analyzing Sentinel-2 images identified hundreds of previously undocumented waste sites across Southeast Asia and tracked their expansion through time.

160. Cleaning Our Oceans with Observations and Models: Copernicus Keeps Track of Plastic Pollution

| Copernicus | European Union | 2020

Copernicus explains both the possibilities and major limitations of attempting to detect floating plastic debris using existing Earth-observation satellites.

Oceans, Lakes, and Water Quality

161. SWOT Monitoring of Water Surface Elevation and Extent on French Lakes

| Various authors | Earth and Space Science | 2026-06-23

SWOT observations accurately reproduce lake area and, under appropriate conditions, can track water-level changes in inland lakes.

162. NASA Analysis Shows La Niña Limited Sea Level Rise in 2025

| NASA | NASA | 2026-03-20

Sentinel-6 and GRACE-FO observations show how short-term climate variability changes annual sea-level trends superimposed on the long-term rise of the global ocean.

163. Cyanobacteria Assessment Network

| U.S. Environmental Protection Agency | EPA | 2026

CyAN uses satellite observations to provide consistent detection and quantification of potentially harmful cyanobacteria across thousands of U.S. lakes.

164. CyAN Satellite Application for Cyanobacterial Harmful Algal Blooms

| U.S. Environmental Protection Agency | EPA | 2026

Sentinel-3 observations support regularly updated maps and experimental forecasts of cyanobacteria abundance in freshwater bodies.

165. International Training Advances Ocean Color Satellite Validation

| Carina Poulin | NASA PACE | 2025-07-29

International calibration and validation programs help ensure that PACE and other ocean-color satellites accurately measure biological and optical properties of the ocean.

166. Monitoring, Simulation and Early Warning of Cyanobacterial Harmful Algal Blooms

| Various authors | Environmental Research | 2025-01-01

Satellite monitoring and forecasting models can be integrated to provide higher-frequency warnings of harmful cyanobacterial blooms in eutrophic lakes.

167. Southern Ocean 3D Eddy Diagnostics Derived from SWOT

| Various authors | Journal of Geophysical Research: Oceans | 2025

SWOT detects fine-scale ocean eddies and current structures that were poorly resolved by earlier generations of satellite altimeters.

168. Toward an Integrated Pantropical Ocean Observing System

| Various authors | Frontiers in Marine Science | 2025

Future tropical ocean observing systems will depend on sustained satellite monitoring of sea level, currents, temperature, productivity, and coastal conditions combined with in-situ measurements.

169. Satellite and In Situ Cyanobacteria Monitoring: Impact of Monitoring Frequency on Management Decisions

| Various authors | Journal of Hydrology | 2023

Satellite observations can fill temporal and spatial gaps between field samples and improve management decisions during rapidly changing cyanobacterial blooms.

170. Satellite Monitoring of Asian Dust Storms from SeaWiFS and MODIS

| Various authors | NASA Technical Reports Server | 2010

SeaWiFS and MODIS observations demonstrate how satellites can identify dust sources and follow long-distance atmospheric transport across East Asia and the Pacific.

Mangroves, Peatlands, and Ecological Restoration

171. Integrated Monitoring of Ecological Restoration by Sky-Ground-Air Observation

| Various authors | Measurement | 2026

Satellite imagery, drones, and field measurements were combined to evaluate vegetation recovery, biodiversity, water quality, soil conditions, and carbon storage following landscape restoration.

172. Landsat and Random Forest Modeling Reveal Sediment Fining in the Yellow River

| Zhiqiang Qiu et al. | Remote Sensing of Environment | 2025-12-01

Four decades of Landsat imagery reveal long-term changes in Yellow River sediment associated partly with ecological restoration of China's Loess Plateau.

173. Fine Monitoring Method for Mangrove Wetland Ecosystems Based on Sentinel Data Fusion

| Various authors | Systems and Soft Computing | 2025-12

Sentinel imagery and Random Forest machine learning provide detailed classification and biomass estimates for mangrove ecosystems.

174. Open-Access Satellite Data for Peatland Condition and Restoration Monitoring in the UK

| Nicole Reynolds et al. | Frontiers in Environmental Science | 2025-11-14

Sentinel, Landsat, radar, and other open satellite datasets can monitor hydrology, vegetation, terrain change, and restoration outcomes in peatland ecosystems.

175. Satellite-Based Mapping and Modeling of Mangrove Loss: A Systematic Review

| Various authors | Remote Sensing Applications: Society and Environment | 2025-08

A meta-analysis identifies vegetation indices, radar observations, temporal change, and surrounding development as key variables for satellite detection of mangrove loss.

176. Mapping Mangrove Multi-Trait Functional Diversity from Satellite Observations

| Nguyen An Binh and Leon T. Hauser | Scientific Reports | 2025-07-01

Satellite-derived spectral and biophysical indicators can map multiple functional traits across both dense and fragmented mangrove stands.

177. Can Synthetic Aperture Radar Enhance Satellite-Based Mangrove Detection?

| Various authors | Remote Sensing | 2025-05-22

Sentinel radar improves mangrove mapping where persistent tropical cloud cover reduces the availability of optical satellite imagery.

178. Monitoring Mangrove Degradation Caused by Oil Spills Using Multispectral and SAR Imagery

| Luiz Henrique Joca Leite et al. | Marine Pollution Bulletin | 2025

Combined radar and optical observations distinguish oil-related mangrove stress from normal environmental variation and track long-term ecosystem effects.

179. RESTORE-IT: Global Satellite-Based Impact Monitoring Tool for Restoration Initiatives

| European Space Agency | ESA Earth Observation Science for Society | 2024

Satellite measurements of vegetation, soil moisture, land cover, and temperature are being developed into standardized indicators for evaluating landscape-restoration projects.

180. Satellite Monitoring for Forest Management in Tropical Dry Forests

| NIRAS | NIRAS | 2021

Sentinel and other satellite datasets were converted into practical tools for monitoring dry-forest biomass, deforestation, degradation, and possible drivers of forest change.

Invasive Species and Grasslands

181. Application of Remote Sensing for Identification of Invasive Plant Species in Natural Ecosystems

| Saeedeh Eskandari | Physics and Chemistry of the Earth | 2026

A review of roughly 100 studies finds high-resolution, hyperspectral, multi-temporal, radar, and machine-learning methods increasingly capable of mapping invasive plants.

182. Distribution Mapping of Major Invasive Plant Species of India

| Various authors | Discover Forests | 2025

A systematic review identifies invasive plants affecting Indian ecosystems and evaluates remote sensing as a tool for mapping their distribution and ecological effects.

183. Satellite Monitoring of Grasslands for Crop Statistics

| Natural Resources Institute Finland | Luke | 2025

Earth-observation data and machine learning are being used to improve estimates of grassland area and forage production for agricultural statistics.

184. Satellite Monitoring to Improve Livestock Management

| Global Research Alliance | Global Research Alliance | 2025

Satellite estimates of grassland biomass and forage quality can improve grazing decisions while supporting climate adaptation and greenhouse-gas management in pastoral systems.

185. A Review of Spaceborne Synthetic Aperture Radar for Invasive Alien Plant Research

| Various authors | Remote Sensing Applications: Society and Environment | 2024-11

Radar satellites provide all-weather observations and potentially valuable structural information for invasive-plant monitoring, especially when combined with optical data.

186. Advances in Remote Sensing and Machine Learning Methods for Invasive Plants

| Various authors | Remote Sensing | 2024-10-11

Machine learning applied to satellite, hyperspectral, aerial, and drone imagery is improving detection of invasive species and measurement of their ecological impacts.

187. Sentinel-2 Versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping

| Various authors | Remote Sensing | 2024-02-08

Sentinel-2 and PlanetScope imagery were compared for mapping invasive goldenrod populations that displace native vegetation and alter habitat conditions.

188. Biodiversity from Space: Optimized Grazing and Biodiversity Conservation with Satellite Monitoring

| Peter Olsson et al. | Lund University | 2024

Optical and radar satellite imagery can measure grazing intensity in semi-natural grasslands and connect management patterns with biodiversity indicators such as pollinator resources.

189. Advancements in Satellite Remote Sensing for Mapping Alien Invasive Plant Species

| Various authors | Physics and Chemistry of the Earth | 2019

New generations of freely available multispectral satellite data and machine-learning algorithms make large-scale invasive-plant monitoring increasingly feasible.

190. Managing Plant Invasions Through the Lens of Remote Sensing

| Various authors | Science of the Total Environment | 2018

Remote sensing can support invasive-species management by identifying infestations, forecasting spread, measuring ecological impacts, and monitoring the success of control programs.

Conservation Enforcement, Forests, and Wildlife

191. Monitoring Without Capacity: Enforcement and Deforestation in the Amazon

| Alipio Ferreira | SSRN | 2025-11-01

High-resolution maps and real-time satellite alerts improve enforcement targeting in the Brazilian Amazon, but the study finds that monitoring technology alone cannot substitute for enforcement capacity.

192. Beyond the Canopy: How Satellite Detection Thresholds Influence Policy Evaluation and Deforestation Behavior

| Various authors | Journal of Environmental Economics and Management | 2025-11

Satellite monitoring can influence behavior itself, as land users may adapt clearing patterns to remain below known detection thresholds.

193. How Much Industrial Fishing Occurs in Marine Protected Areas?

| UC Santa Barbara Bren School | UCSB | 2025-09-23

Global satellite observations and AI were used to evaluate industrial fishing pressure inside some of the world's most strictly protected marine areas.

194. Elephant-Human Conflict Mitigation with Satellite Monitoring and Citizen Science

| Sayani Saha | Rufford Foundation | 2025-07-30

Satellite monitoring combined with community observations can identify elephant movement corridors and conflict hotspots where habitat fragmentation brings wildlife into agricultural landscapes.

195. Satellite Monitoring Confirms Compliance in Marine Protected Areas

| UC Santa Barbara Marine Science Institute | UCSB | 2025-07-24

Satellite imagery and artificial intelligence can reveal industrial fishing vessels operating inside or near marine protected areas, including vessels not visible in conventional tracking systems.

196. Brazil Anti-Deforestation Operation Blacklists More Than 500 Farms in the Amazon

| Shanna Hanbury | Mongabay | 2025-05-15

Brazilian authorities used satellite deforestation alerts to impose coordinated restrictions on hundreds of rural properties linked with unauthorized forest clearing.

197. Satellite Monitoring of Elephants

| Elephants Without Borders | Elephants Without Borders | 2025

Satellite tracking collars on elephants, zebra, wildebeest, giraffe, buffalo, and other large mammals reveal migration routes, habitat requirements, and obstacles to wildlife movement across southern Africa.

198. Estimating Suitable Habitat for African Elephants in Hwange National Park

| Various authors | Frontiers in Conservation Science | 2025

Remote sensing and spatial modeling identify changing seasonal elephant habitat and potential climate refugia in and around Zimbabwe's Hwange National Park.

199. Project Lifecycle: Satellite Monitoring to Track Threats and Sustainability

| Rainforest Trust | Rainforest Trust | 2025

Long-term Landsat-derived tree-cover-loss data allow conservation organizations to determine whether forests within protected project areas remain intact and where new threats require intervention.

200. Innovating for Conservation: Stopping Illegal Deforestation

| Amazon Conservation Association | Amazon Conservation | 2024-12-19

Near-real-time satellite monitoring helps local communities and conservation organizations identify illegal mining, logging, road building, and agricultural clearing throughout the Amazon.