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Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Daily Overview

Session

Land Applications I
Location: Red Hall
Session Chair: Felix Isundwa, University of Stirling
Session Chair: Krištof Oštir, University of Ljubljana

Presentation 5

3:30pm - 3:50pm

AgriROSE-L 2025 Case Study: Soil Moisture Estimation from L-Band PolSAR Time Series

Nikita Basargin^1,2,3, Alberto Alonso-González⁴, Irena Hajnsek^1,5

1: Microwaves and Radar Institute, German Aerospace Center (DLR), Weßling, Germany; 2: School of Life Sciences, Technical University of Munich (TUM), Freising, Germany; 3: Munich School for Data Science (MUDS), Munich, Germany; 4: Signal Theory and Communications Department, Universitat Politécnica de Catalunya (UPC), Barcelona, Spain; 5: Institute of Environmental Engineering, ETH Zürich, Zürich, Switzerland

Recently, the German Aerospace Center (DLR) conducted the AgriROSE-L 2025 (CROPEX 2025) airborne F-SAR campaign in preparation for the upcoming ESA ROSE-L mission. Multiple flights acquired a long and dense (6-day revisit) time series of fully polarimetric SAR acquisitions over agricultural areas between April and July 2025. Ground teams accompanied each flight to collect in situ measurements and record the soil and vegetation conditions. Agricultural areas are subject to rapid changes caused by crop growth, transitions of phenological stages, and alterations in soil conditions, including changes in soil moisture and roughness. The campaign provides a valuable dataset for analyzing the dynamics of the polarimetric signal over time and developing new methods for geophysical parameter retrieval.

In this study, we provide an early evaluation of the new dataset, focusing on soil moisture estimation, an essential climate variable (ECV) important for hydrology and agriculture. A significant challenge for SAR-based high-resolution soil moisture estimation is the presence of vegetation, which interferes with the ground signal. Here, the use of longer wavelengths (L-band) with deeper penetration is beneficial in minimizing the influence of vegetation. Additionally, using fully polarimetric data enables a certain degree of separation between the ground and vegetation contributions.

We apply a model-based tensor decomposition [1] to separate the signal into surface, dihedral, and volume contributions. The method jointly exploits polarimetric and temporal information, analyzes a time series of acquisitions, and provides estimates for different geophysical parameters, including soil moisture. We validate the accuracy across different crop types and growth stages using the in situ measurements acquired during the campaign.

References

[1] N. Basargin, A. Alonso-González, and I. Hajnsek, "Model-based tensor decompositions for geophysical parameter retrieval from multidimensional SAR data," Submitted to IEEE Transactions on Geoscience and Remote Sensing, under review.