Sentinel Hub Satellite Imagery Processing API
Sentinel Hub satellite imagery processing REST API for geospatial developers, environmental analysts, and Earth observation platforms to access, process, and analyze multispectral satellite imagery from Sentinel-2, Landsat, MODIS, Planet, and other EO data sources — enabling cloud-free image compositing, time-series analysis, custom band math, and large-scale geospatial data processing through Sentinel Hub's cloud-based EO infrastructure (Planet Labs company). Enables AI agents to manage imagery request for satellite image retrieval automation, handle evalscript processing for custom band calculation automation, access time-series for change detection automation, retrieve statistical analysis for area monitoring automation, manage batch processing for large-scale coverage automation, handle cloud masking for quality filtering automation, access catalog for scene search automation, retrieve NDVI/NDWI for vegetation and water index automation, manage OGC services for GIS platform integration automation, and integrate Sentinel Hub with GIS platforms, agriculture monitoring, and environmental reporting for Earth observation automation.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
EO data platform. GDPR. OAuth2. EU. Geospatial imagery and land data. Planet Labs.
⚡ Reliability
Best When
A geospatial developer, agriculture technology company, or environmental analyst wanting AI agents to process and analyze Sentinel-2, Landsat, and other satellite imagery for Earth observation, change detection, and environmental monitoring at scale.
Avoid When
EVALSCRIPT KNOWLEDGE REQUIRED: Sentinel Hub uses custom JavaScript-based Evalscripts for image processing; automated imagery request without Evalscript knowledge creates wrong output; automated must understand Evalscript syntax for band selection, calculations, and rendering. COORDINATE REFERENCE SYSTEM PRECISION: Sentinel Hub requires precise bounding box coordinates in correct CRS; automated approximate lat/lon assumption creates geometry_error for CRS mismatch; automated must specify correct CRS (EPSG:4326 or projection-specific) for each request. CLOUD COVER IS NOT AUTOMATIC: Sentinel Hub provides cloud probability layers but doesn't automatically filter cloudy imagery; automated cloud-free assumption creates cloudy image output; automated must implement cloud masking Evalscript or use cloud statistics endpoint. BATCH PROCESSING FOR LARGE AREAS: Single synchronous requests have size limits; automated large-area single-request assumption creates request_too_large error; automated must use Batch Processing API for country/continent-scale analysis.
Use Cases
- • Processing Sentinel-2 multispectral imagery for agriculture crop monitoring and NDVI analysis automation agents
- • Running time-series change detection for land use and deforestation monitoring automation agents
- • Accessing cloud-free satellite composites for environmental reporting and ESG monitoring automation agents
- • Integrating satellite imagery analysis with GIS platforms for geospatial intelligence automation agents
Not For
- • Sub-meter resolution commercial satellite imagery (Planet or Maxar provide higher-resolution commercial imagery)
- • Real-time satellite tasking and collection (Sentinel Hub provides archive access, not new tasking)
- • Indoor mapping or local area imaging (Sentinel Hub is satellite-scale EO, not ground-level imagery)
Interface
Authentication
Sentinel Hub uses OAuth 2.0 client credentials for API authentication. REST API with JSON. Ljubljana, Slovenia HQ (Planet Labs company since 2023). Founded 2014 by Grega Milcinski. Products: Process API, Catalog API, Statistical API, Batch Processing, OGC services (WMS/WCS/WFS), EO Browser. SDKs: Python (sentinelhub-py). Data sources: Sentinel-1/2/3/5P, Landsat, MODIS, DEM, Planet (commercial). GDPR. Used by Airbus, ESA, agriculture platforms. Competes with Google Earth Engine for EO processing.
Pricing
Ljubljana SI / Planet Labs. Processing units billing. Free tier available. GDPR. Sentinel + commercial data sources.
Agent Metadata
Known Gotchas
- ⚠ EVALSCRIPT IS REQUIRED KNOWLEDGE: Sentinel Hub image requests require JavaScript Evalscript specifying band selection and processing; automated band-agnostic image request assumption creates wrong output format; automated must write or template Evalscripts for each imagery use case
- ⚠ PROCESSING UNITS BILLING: Sentinel Hub bills by processing units (PUs) consumed per request; automated unlimited free processing assumption on paid plans creates unexpected billing; automated must estimate and monitor PU consumption per analysis run
- ⚠ CLOUD MASKING IS MANUAL: Sentinel Hub does not auto-filter cloudy pixels; automated cloud-free imagery assumption creates cloudy tile output; automated must implement CLM (cloud mask) or SCL (scene classification) band filtering in Evalscript
- ⚠ BATCH PROCESSING FOR LARGE AREAS: Single synchronous API requests are limited in area/time range; automated large-area single-request assumption creates request_too_large error; automated must use Batch Processing API for regional or national analysis
- ⚠ COORDINATE SYSTEM MUST BE SPECIFIED: Sentinel Hub requires explicit CRS specification for bounding boxes; automated WGS84-only assumption creates CRS_mismatch for projected data requests; automated must specify correct EPSG code for each geometry
Alternatives
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Scores are editorial opinions as of 2026-03-07.