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ERS-2 Radar image of the Elbe flood
© ESA
Logo of the FDR4LDYN project showing ERS, Metop and Metop-SG satellitesFDR4LDYN / Data / Use Cases

Use Cases

In this section, we showcase the potential of FDR4LDYN for monitoring long-term land surface dynamics through a series of scientific use cases. Each thematic area provides access to interactive Jupyter Notebooks that guide you through the exploration of soil moisture, vegetation, and flood dynamics using FDR4LDYN data.

Plant sprouting from soil
© Pixabay/M_wie_Moehre

Soil Moisture

Soil moisture will be retrieved from ESCAT using the Water Retrieval Package developed at TU Wien, based on the TU Wien change detection method. The resulting product will demonstrate interoperability with Metop ASCAT and will be validated against ERA5-Land modelled soil moisture and in situ observations from the International Soil Moisture Network (ISMN). To further assess consistency across missions, the use case example will be extended to include ASCAT data, and trends or discontinuities during sensor transitions will be evaluated across various land cover types and climatic regions.
Meadow with fruit trees
© Pixabay/Tom

Vegetation Monitoring

This use case explores the potential of the FDR4LDYN dataset for vegetation monitoring by comparing FDR4LDYN-derived slope with long-term Vegetation Optical Depth (VOD) from the VODCA dataset and MODIS Leaf Area Index (LAI). We analyse backscatter dynamics over key sites such as the Amazon rainforest and North American grasslands, applying explainable machine learning to relate observed signals to geophysical drivers like soil moisture and LAI. Additionally, we investigate the influence of microwave frequency by comparing C-band FDR4LDYN data with Ku-band QuikSCAT observations to assess frequency-dependent sensitivity to vegetation changes.
Woman riding bicycle through flooded area
© Pixabay/Tri Le

Surface Water and Flood Dynamics

The third use case focuses on the sensitivity of the FDR4LDYN dataset to surface water dynamics, particularly in tropical wetlands. Preliminary analyses using Metop ASCAT have shown that backscatter, slope, and curvature are sensitive to variations in the extent of the Sudd wetlands in South Sudan. Here, we explore the spatiotemporal patterns and seasonal dynamics in FDR4LDYN data, evaluating them against ESA CCI annual land cover datasets and the Tropical Wetland Mapping Tool (TropWet6).

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