Agricultural Sandbox NL: A national-scale database of parcel-level, processed Sentinel-1 SAR data

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Vineet Kumar, Manuel Huber, Bjorn Rommen, Susan C. Steele-Dunne


Synthetic Aperture Radar (SAR) data handling, processing, and interpretation are barriers preventing a rapid uptake of SAR data by application specialists and non-expert domain users in the field of agricultural monitoring. To improve the accessibility of Sentinel-1 data, we have generated a reduced-volume, multi-year Sentinel-1 SAR database. It includes mean and standard deviation of VV, VH and VH/VV backscatter, pixel counts, geometry, crop type, local incidence angle and azimuth angle at parcel-level. The database uses around 3100 Sentinel-1 images (5 TB) to produce a 12 GB time series database for approximately 770,000 crop parcels over the Netherlands for a period of three years. The database can be queried by Sentinel-1 system parameters (e.g. relative orbit) or user application-specific parameters (e.g. crop type, spatial extent, time period) for parcel level assessment. The database can be used to accelerate the development of new tools, applications and methodologies for agricultural and water related applications, such as parcel-level crop bio-geophysical parameter estimation, inter-annual variability analysis, drought monitoring, grassland monitoring and agricultural management decision-support.



Databases and Information Systems, Geographic Information Sciences, Remote Sensing, Spatial Science


Sentinel-1, SAR, Google Earth Engine, Parcel


Published: 2021-07-15 15:20


CC BY Attribution 4.0 International

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