This project involves loading, resampling, and reprojecting multi-source remote sensing data, including Sentinel-1 (VV, VH Radar Backscatter), Sentinel-2 (NDVI), ERA5 Temperature, and Elevation Data. The final goal is to integrate them into a unified dataset.
- Define the Area of Interest (AOI) & Time Range
- Focuses on the region experiencing land use and climate changes.
- Use bounding box coordinates (min/max lat/lon).
- Query the STAC API for Required Datasets - done
- Fetches cloud-hosted remote sensing data efficiently.
- Uses pystac-client to search for:
- Sentinel-1 (VV, VH - Radar Backscatter)
- Sentinel-2 (NDVI - Vegetation Health)
- ERA5 (Temperature - Climatic Variations)
- DEM (Elevation - Terrain Impact)
- Load Data as Raster Arrays - need help to modify my code here
- Converts remote files into numerical arrays for analysis.
- Uses rioxarray to open raster files.
- Reproject Data to a Common Spatial Reference System (CRS) **
- Ensures datasets align spatially before integration.
- Uses rioxarray.rio.reproject() to match the highest resolution dataset’s CRS.
- Integrate Datasets into a Datacube **
- Creates a structured, multi-dimensional dataset for analysis.
- Uses xarray.Dataset() to merge layers (VV, VH, NDVI, Elevation).
- Save the Datacube in a Portable Format (NetCDF)
- datacube.to_netcdf("datacube.nc")