Explore the top 10 free satellite datasets in Google Earth Engine for land cover, climate, NDVI, water monitoring, and more—ideal for remote sensing
🌍 Google Earth Engine (GEE) is a cloud-based geospatial platform that provides free access to petabytes of satellite imagery and geospatial datasets. It enables users to analyze environmental changes at scale — without needing powerful hardware or downloading large datasets.
Whether you're working on land cover classification, NDVI analysis, climate monitoring, or urban expansion, GEE offers dozens of curated datasets ready for instant analysis.
In this article, we highlight the top 10 free satellite datasets available in Google Earth Engine — with their descriptions, ideal use cases, and example code.
1. Landsat Collection (USGS/NASA)
The Landsat program is one of the longest-running Earth observation missions, continuously collecting imagery since 1972. It includes satellites such as Landsat 5, 7, 8, and the recently launched Landsat 9. The dataset provides a range of products, including surface reflectance, top-of-atmosphere (TOA) imagery, and Level-2 science-ready data. Landsat imagery is widely used for land cover classification, monitoring vegetation health through NDVI, and tracking urban expansion and deforestation over time.
Code Sample:
var image = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
.filterDate('2022-01-01', '2022-12-31')
.filterBounds(ee.Geometry.Point(-75, 40))
.median();
Map.addLayer(image, {
bands: ['SR_B4', 'SR_B7', 'SR_B2'],
min: 8000,
max: 17000
}, 'Landsat 8 By GeoJamal');
Map.setCenter(-75, 40, 11)
2. Sentinel-2 MSI (ESA)
Sentinel-2 is a high-resolution multispectral dataset acquired from the Sentinel-2A and Sentinel-2B satellites. It features 13 spectral bands designed for land surface monitoring, with a 5-day revisit time and global coverage. This makes it ideal for capturing changes in vegetation, water bodies, and built environments with consistent temporal detail.
Sentinel-2 imagery is widely used in crop monitoring and precision agriculture, assessing coastal and inland water quality, and detecting urban expansion and land use changes.A high-resolution multispectral dataset with 13 bands from Sentinel-2A and 2B satellites. It has a 5-day revisit period and global coverage.
Code Sample:
var image = ee.ImageCollection("COPERNICUS/S2_SR")
.filterDate('2022-01-01', '2022-12-31')
.filterBounds(ee.Geometry.Point(-75, 40))
.median();
Map.addLayer(image, {
bands: ['B4', 'B8', 'B2'],
min: 1000,
max: 4500
}, 'Sentinel-2 By GeoJamal');
Map.setCenter(-75, 40, 11)
3. MODIS (NASA/USGS)
🛰️ Description:
The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global observations across a wide range of environmental variables. Available from both the Terra and Aqua satellites, MODIS products include vegetation indices like NDVI and EVI, land surface temperature (LST), and other climate-related datasets. Its moderate spatial resolution and high temporal frequency make it a vital tool for global-scale analysis.
MODIS is commonly used for monitoring vegetation trends and ecosystem health, conducting phenology studies, and supporting climate change and environmental monitoring research at regional to global scales.
Sample Collection:
MODIS/006/MOD13Q1 – NDVI every 16 days at 250m resolution
var dataset = ee.ImageCollection('MODIS/061/MOD13Q1')
.filter(ee.Filter.date('2018-01-01', '2018-05-01'));
var ndvi = dataset.select('NDVI');
var ndviVis = {
min: 0,
max: 8000,
palette: [
'ffffff', 'ce7e45', 'df923d', 'f1b555', 'fcd163', '99b718', '74a901',
'66a000', '529400', '3e8601', '207401', '056201', '004c00', '023b01',
'012e01', '011d01', '011301'
],
};
Map.setCenter(6.746, 46.529, 2);
Map.addLayer(ndvi, ndviVis, 'NDVI');
4. NOAA VIIRS (Visible Infrared Imaging Radiometer Suite)
The NOAA VIIRS (Visible Infrared Imaging Radiometer Suite) sensor, aboard the Suomi NPP and NOAA-20 satellites, provides moderate-resolution Earth observation imagery with excellent radiometric accuracy and advanced cloud screening capabilities. With spatial resolutions of 375 meters for its imagery bands and 750 meters for its moderate bands, VIIRS is not classified as a high-resolution sensor like Sentinel-2 or Landsat, but it offers a valuable balance of spatial detail and temporal frequency suitable for large-scale environmental monitoring.
One of the standout features of VIIRS is its Day/Night Band (DNB), which enables the monitoring of nighttime light emissions from cities, roads, ships, and wildfires. This makes VIIRS an important tool for analyzing human activity, urban expansion, disaster impacts, and energy consumption patterns. Additionally, VIIRS provides surface reflectance products that are essential for tracking vegetation health, land cover change, and surface energy balance over time.
Air quality researchers also benefit from VIIRS data, particularly for detecting and analyzing aerosols, smoke, and dust events on a regional and global scale. With daily global coverage and continuity from the MODIS legacy, VIIRS plays a key role in supporting Earth system science, environmental health monitoring, and operational decision-making.
Dataset ID: NOAA/VIIRS/001/VNP09GA
var dataset = ee.ImageCollection('NASA/VIIRS/002/VNP09GA')
.filter(ee.Filter.date('2014-05-01', '2014-05-31'));
var rgb = dataset.select(['M5', 'M4', 'M3']);
var rgbVis = {
min: 0.0,
max: 0.3
};
Map.setCenter(17.93, 7.71, 2);
Map.addLayer(rgb, rgbVis, 'RGB');
5. Global Surface Water (JRC)
Based on Landsat imagery (1984–present), this dataset shows monthly and yearly water occurrence, seasonality, and change dynamics.
Use Cases:
- Wetland mapping
- Flood risk modeling
- Tracking water body dynamics
Dataset ID: JRC/GSW1_3/GlobalSurfaceWater
var dataset = ee.Image('JRC/GSW1_4/GlobalSurfaceWater');
var visualization = {
bands: ['occurrence'],
min: 0.0,
max: 100.0,
palette: ['ffffff', 'ffbbbb', '0000ff']
};
Map.setCenter(59.414, 45.182, 6);
Map.addLayer(dataset, visualization, 'Occurrence');
6. CHIRPS (Climate Hazards Center)
The CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset offers a gridded precipitation product at a spatial resolution of 0.05°—approximately 5 kilometers. Designed for climate monitoring and environmental research, it integrates high-quality satellite rainfall estimates with in-situ station observations to produce consistent and reliable rainfall data from 1981 to near-present. This long-term, quasi-global dataset is widely used for tracking precipitation trends, detecting anomalies, and supporting decision-making in weather-sensitive sectors.
Its applications are particularly relevant to drought monitoring and early warning systems, where timely and spatially detailed rainfall data are critical. CHIRPS is also instrumental in analyzing rainfall variability, assessing climate change impacts, and planning agricultural activities. By enabling researchers and policymakers to understand and respond to hydrometeorological patterns with greater precision, CHIRPS supports both scientific studies and practical field-level interventions.
Dataset ID: UCSB-CHG/CHIRPS/DAILY
var dataset = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY')
.filter(ee.Filter.date('2018-05-01', '2018-05-03'));
var precipitation = dataset.select('precipitation');
var precipitationVis = {
min: 1,
max: 17,
palette: ['001137', '0aab1e', 'e7eb05', 'ff4a2d', 'e90000'],
};
Map.setCenter(17.93, 7.71, 2);
Map.addLayer(precipitation, precipitationVis, 'Precipitation');
7. SRTM: Shuttle Radar Topography Mission
The Shuttle Radar Topography Mission (SRTM) dataset provides a global elevation model with a spatial resolution of 30 meters, covering most of the Earth’s land surfaces. Originally captured in 2000 by NASA, this dataset has since been corrected and void-filled to ensure greater accuracy and completeness, making it one of the most widely used digital elevation models (DEMs) for geospatial analysis. Its uniform resolution and global availability have made SRTM a cornerstone resource in terrain and topographic studies across a wide range of disciplines.
SRTM is extensively used in hydrological modeling and watershed delineation, helping researchers and engineers understand water flow and drainage patterns across landscapes. It also serves as a foundation for terrain analysis, including slope, aspect, and elevation profiling—key parameters for applications in agriculture, land planning, infrastructure design, and environmental management. Furthermore, SRTM data is frequently applied in floodplain mapping and simulation models, supporting disaster preparedness and risk assessment efforts.
Dataset ID: USGS/SRTMGL1_003
var dataset = ee.Image('USGS/SRTMGL1_003');
var elevation = dataset.select('elevation');
var slope = ee.Terrain.slope(elevation);
Map.setCenter(-112.8598, 36.2841, 10);
Map.addLayer(slope, {min: 0, max: 60}, 'slope');
Map.addLayer(elevation, {min: 500, max: 2500}, 'Elevation')
8. Copernicus Global Land Cover (CGLS-LC100)
The Copernicus Global Land Cover (CGLS-LC100) dataset provides annual global land cover maps from 2015 to the present at an impressive 100-meter spatial resolution. Developed as part of the Copernicus Land Monitoring Service, this dataset offers detailed, harmonized, and thematically consistent land cover classifications, making it ideal for monitoring land use change and modeling environmental processes on a global scale. The data is derived from satellite observations, primarily using Sentinel-2 imagery, and undergoes rigorous validation to ensure high accuracy.
CGLS-LC100 plays a vital role in supporting a wide range of geospatial and environmental applications. It enables the tracking of urban expansion over time, helping planners and researchers assess the growth of built-up areas. The dataset is also valuable for carbon stock estimation, land degradation assessments, and sustainable land management practices. In biodiversity and conservation contexts, it supports habitat fragmentation analysis, allowing users to evaluate the impacts of land conversion on ecosystems and wildlife corridors. With annual updates and consistent global coverage, CGLS-LC100 is a foundational dataset for land cover dynamics and sustainable development monitoring.
Dataset ID:
COPERNICUS/Landcover/100m/Proba-V/Global
var dataset = ee.Image('COPERNICUS/Landcover/100m/Proba-V-C3/Global/2019')
.select('discrete_classification');
Map.setCenter(-88.6, 26.4, 4);
Map.addLayer(dataset, {}, 'Land Cover');
9. Global Forest Change (Hansen)
The Global Forest Change dataset, commonly known as the Hansen dataset, provides comprehensive, high-resolution global data on forest extent, loss, and gain from the year 2000 to the present. Developed by the University of Maryland (UMD) in collaboration with Google and other partners, the dataset is based on time-series analysis of Landsat satellite imagery at 30-meter resolution. It offers annual updates that allow researchers, governments, and environmental organizations to monitor changes in forest cover with remarkable spatial detail and temporal consistency.
This dataset is a critical tool for detecting deforestation and forest degradation across diverse ecosystems, from tropical rainforests to boreal woodlands. It supports efforts to quantify forest carbon dynamics and contributes significantly to climate change mitigation strategies, particularly in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) initiatives. By providing transparent, globally consistent data, the Hansen dataset enables robust environmental reporting, conservation planning, and forest governance at national and international levels.
Dataset ID:
UMD/hansen/global_forest_change_2022_v1_10
var dataset = ee.Image('UMD/hansen/global_forest_change_2023_v1_11');
var treeCoverVisParam = {
bands: ['treecover2000'],
min: 0,
max: 100,
palette: ['black', 'green']
};
Map.addLayer(dataset, treeCoverVisParam, 'tree cover 2000');
var treeLossVisParam = {
bands: ['lossyear'],
min: 0,
max: 23,
palette: ['yellow', 'red']
};
Map.addLayer(dataset, treeLossVisParam, 'tree loss year');
10. ERA5-Land Climate Reanalysis (ECMWF)
The ERA5-Land Climate Reanalysis dataset, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), offers high-resolution gridded climate data from 1981 to near real-time. With a spatial resolution of approximately 9 kilometers, ERA5-Land provides hourly estimates of key atmospheric and land-surface variables, including temperature, precipitation, soil moisture, snow cover, and evapotranspiration. The dataset is generated using advanced data assimilation systems and modeling techniques, integrating observations from satellites, ground stations, and other sources into a consistent global framework.
ERA5-Land serves as a powerful resource for long-term climate modeling, allowing researchers to explore historical trends and simulate future climate impacts. Its fine temporal and spatial resolution make it especially useful in hydrological modeling, including applications such as runoff estimation, drought risk assessment, and flood forecasting. Additionally, the dataset supports environmental change analyses, helping scientists and policymakers track ecosystem responses to climate variability, land-use shifts, and extreme weather events. With its consistency, accuracy, and near-real-time availability, ERA5-Land has become a cornerstone for Earth system science and sustainable development planning.
Dataset ID: ECMWF/ERA5_LAND/DAILY_AGGR
var dataset = ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR').first();
var visualization = {
bands: ['temperature_2m'],
min: 250,
max: 320,
palette: [
'000080', '0000d9', '4000ff', '8000ff', '0080ff', '00ffff',
'00ff80', '80ff00', 'daff00', 'ffff00', 'fff500', 'ffda00',
'ffb000', 'ffa400', 'ff4f00', 'ff2500', 'ff0a00', 'ff00ff',
]
};
Map.setCenter(-45, 45, 3);
Map.addLayer(
dataset, visualization, 'Air temperature (K) at 2m height', true, 0.8);
Where to Explore All Google Earth Engine Datasets
Looking for satellite imagery, climate data, land cover, or environmental indices? 🌍 The full Earth Engine Data Catalog is your gateway to hundreds of geospatial datasets from NASA, USGS, ESA, and more. Start exploring here:
👉 Browse the Earth Engine Data Catalog
Conclusion
Google Earth Engine offers one of the richest collections of environmental data available today. These 10 free satellite datasets are among the most useful for research, conservation, and development applications across the globe.
Whether you're working on agriculture, forestry, climate change, or disaster risk reduction — mastering these datasets will take your Earth Engine projects to the next level.
📚 Related Articles:
- Calculating Enhanced Vegetation Index EVI using Google Earth Engine GEE
- Illuminating terrain : visualizing Hillshade in Google Earth Engine
- Calculating NDVI using MODIS in GEE
✍️ Written by Jamal Chaaouan for GEE Academy
COMMENTS