Sentinel Hub - ESA
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Introduction
Sentinel Hub is a cloud API platform for satellite imagery operated by the European Space Agency (ESA). It supports the Sentinel series of satellites (such as Sentinel-1, Sentinel-2, Sentinel-3, etc.), as well as other data sources such as Landsat and MODIS. The platform uses APIs to enhance access to satellite data. Users can process data of interest through its APIs and access these data in the required format to obtain analysis-ready data (ARD) without additional cleanup. Sentinel Hub provides functions such as EO Browser (interactive visualization tool) and Custom Script (JavaScript-based API, which can customize algorithms for batch processing of data), supports OGC services (such as WMS/WMTS/WCS protocols), and can be integrated into GIS software (such as QGIS).
File Structure
The data file structure of Sentinel Hub usually contains the following parts:
- Image data file
- GeoTIFF/JPEG2000: stores raster data, divided by bands (such as 13 bands of Sentinel-2).
- File naming convention: usually contains information such as satellite name, product level, date, orbit number, etc. (such as S2A_MSIL1C_20230101T100000_N0209_R001_T32TQP_20230101T120000.SAFE).
- Metadata file (XML)
- Contains detailed description of the data, such as imaging time, solar altitude angle, sensor configuration, pixel coordinates, etc.
- Example: MTD_MSIL1C.xml (Sentinel-2 L1C product metadata).
- Quality assessment file (QI)
- Stores information such as cloud coverage and atmospheric correction quality for data screening and preprocessing.
- Example: Cloud mask file in the QI_DATA folder.
- Index file (optional)
Used to quickly locate and access data blocks, suitable for efficient processing of large-scale data sets.
- Auxiliary data file (optional)
Such as atmospheric correction parameters, terrain data, etc., to support advanced analysis.
Pros
- Rich and comprehensive data: Supports Sentinel series satellites and multi-source data such as Landsat, MODIS, etc., covering multiple bands from visible light to microwaves, and provides high-resolution, multi-spectral, all-weather earth observation data worldwide.
- Efficient and convenient processing: Provides analysis-ready data, covering functions such as orthorectification, data conversion, rescaling, and reprojection to reduce the user’s data processing burden.
- Flexible and powerful functions: Interactive visualization and time series analysis are achieved through EO Browser, and custom algorithm batch processing is supported through Custom Script to meet the needs of different scenarios.
- Strong integration and compatibility: Supports OGC service protocols and can be seamlessly integrated into GIS software such as QGIS. It also provides Python libraries (such as sentinelhub packages) and RESTful APIs to facilitate developers to integrate into project processes.
Cons
- **Free account limitations: **Free accounts can process a limited area per month (about 100 km²). If the quota is exceeded, you need to purchase quotas, which may affect large-scale data processing needs.
- **Data delay issues: **Sentinel-2 data is usually available online 24 to 48 hours after transit, and there may be delays in scenarios with high real-time requirements.
- **High learning cost: **You need to master skills such as API calls and data processing algorithms, which may be a certain threshold for beginners.
Application Scenario
Sentinel Hub is suitable for scenarios that require efficient acquisition, processing and analysis of multi-source satellite data, such as environmental monitoring (forest cover, vegetation health, water body changes, etc.), agricultural management (crop growth status assessment, yield prediction), urban planning (urban expansion analysis, traffic flow monitoring), disaster response (real-time tracking of floods, wildfires, earthquakes, etc.) and education research (remote sensing data analysis teaching and scientific research).
Example
- ESA - Sentinel Hub service.
