SEN12TP - Sentinel-1 and -2 images, timely paired

The SEN12TP dataset (Sentinel-1 and -2 imagery, timely paired) contains 2319 scenes of Sentinel-1 radar and Sentinel-2 optical imagery together with elevation and land cover information of 1236 distinct ROIs taken between 28 March 2017 and 31 December 2020. Each scene has a size of 20km x 20km at 10m pixel spacing. The time difference between optical and radar images is at most 12h, but for almost all scenes it is around 6h since the orbits of Sentinel-1 and -2 are shifted like that. Next to the \(\sigma^\circ\) radar backscatter also the radiometric terrain corrected \(\gamma^\circ\) radar backscatter is calculated and included. \(\gamma^\circ\) values are calculated using the volumetric model presented by Vollrath et. al 2020. The uncompressed dataset has a size of 222 GB and is split spatially into a train (~90%) and a test set (~10%). For easier download the train set is split into four separate zip archives. Please cite the following paper when using the dataset, in which the design and creation is detailed: T. Roßberg and M. Schmitt. A globally applicable method for NDVI estimation from Sentinel-1 SAR backscatter using a deep neural network and the SEN12TP dataset. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2023. https://doi.org/10.1007/s41064-023-00238-y. The file sen12tp-metadata.json includes metadata of the selected scenes. It includes for each scene the geometry, an ID for the ROI and the scene, the climate and land cover information used when sampling the central point, the timestamps (in ms) when the Sentinel-1 and -2 image was taken, the month of the year, and the EPSG code of the local UTM Grid (e.g. EPSG:32643 - WGS 84 / UTM zone 43N). Naming scheme: The images are contained in directories called {roi_id}_{scene_id}, as for some unique regions image pairs of multiple dates are included. In each directory are six files for the different modalities with the naming {scene_id}_{modality}.tif. Multiple modalities are included: radar backscatter and multispectral optical images, the elevation as DSM (digital surface model) and different land cover maps. Data modalities name Modality GEE collection s1 Sentinel-1 radar backscatter COPERNICUS/S1_GRD s2 Sentinel-2 Level-2A (Bottom of atmosphere, BOA) multispectral optical data with added cloud probability band COPERNICUS/S2_SR COPERNICUS/S2_CLOUD_PROBABILITY dsm 30m digital surface model JAXA/ALOS/AW3D30/V3_2 worldcover land cover, 10m resolution ESA/WorldCover/v100 The following bands are included in the tif files, for an further explanation see the documentation on GEE. All bands are resampled to 10m resolution and reprojected to the coordinate reference system of the Sentinel-2 image. Modality Bands Modality Band count Band names in tif file Notes s1 5 VV_sigma0, VH_sigma0, VV_gamma0flat, VH_gamma0flat, incAngle VV/VH_sigma0 are the \(\sigma^\circ\) values, VV/VH_gamma0flat are the radiometric terrain corrected \(\gamma^\circ\) backscatter values incAngle is the incident angle s2 13 B1, B2, B3, B4, B5, B7, B7, B8, B8A, B9, B11, B12, cloud_probability multispectral optical bands and the probability that a pixel is cloudy, calculated with the sentinel2-cloud-detector library optical reflectances are bottom of atmosphere (BOA) reflectances calculated using sen2cor dsm 1 DSM Height above sea level. Signed 16 bits. Elevation (in meter) converted from the ellipsoidal height based on ITRF97 and GRS80, using EGM96†1 geoid model. worldcover 1 Map Landcover class Checking the file integrity After downloading and decompression the file integrity can be checked using the provided file of md5 checksum. Under Linux: md5sum --check --quiet md5sums.txt References: Vollrath, Andreas, Adugna Mullissa, Johannes Reiche (2020). "Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine". In: Remote Sensing 12.1, Art no. 1867. https://doi.org/10.3390/rs12111867.This work was supported by the German Federal Ministry for Economic Affairs and Energy in the project "DESTSAM - Dense Satellite Time Series for Agricultural Monitoring" (FKZ 50EE2018A)