Conference Agenda
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Land Applications I Location: Red Hall Session Chair: Felix Isundwa, University of Stirling Session Chair: Krištof Oštir, University of Ljubljana | |
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2:30pm - 2:50pm
Estimating Soil Moisture Anomalies via Temporal-SKP Decomposition Politecnico di Milano, Italy This paper introduces Adaptive sum of Kronecker products for QUAntitative- Soil Moisture Anomalies retrieval (AKQUA-SMA), a novel polarimetric framework for SMA estimation. The core innovation lies firstly in the use of Temporal-SKP (T-SKP) [1], which exploits the temporal-polarimetric domain to isolate two scattering components: the moisture-related contribution, namely the Latent contribution, from the other scattering mechanism, namely Ground, i.e., above-ground scattering components. To do so, the SKP solution is chosen through exhaustive search as the one that minimizes the l1-norm of the error between the decomposed coherence values in the Latent structure matrix and the theoretical expected moisture related complex coherences. In particular, the search grid originates from the model proposed in [2], which has been slightly modified to account for a double-scattering mechanism. Then, the Ground component is chosen as the one that minimizes the phase residues, i.e., the most triangular scattering mechanism, and used for phase calibration similarly to what is done for the BIOMASS tomography. Finally, absolute N (zero-mean) SMA values are regressed by LS estimation from the estimated variations in the N(N-1)/2 InSAR pairs. The AKQUA-SMA algorithm was applied to the Hydrosoil dataset [3], which was collected over a 20m × 58m agricultural field using a C-Band ground-based PolSAR (GB-PolSAR). The campaign aimed to simulate the frequent monitoring capability of the HydroTerra mission [3] for soil moisture and vegetation parameter retrieval. The data comprises two phases: the Barley Crop (March–June 2020), a Dual-Pol dataset (18,055 acquisitions), and the Corn Crop (July–November 2020), a Quad-Pol dataset (12,945 acquisitions), both acquired every 10 minutes. This SAR data is supplemented with essential ancillary information, including probe-based volumetric moisture, plant density, and crop height. Due to the limited field size, the entire area was treated as a single resolution cell. Processing utilized a full overlapping sliding window approach, scanning the dataset in steps of six acquisitions with five temporal samples overlapping between adjacent windows. The first results reveal good estimation accuracy, with a mode RMSE of 0.2% across the entire Corn dataset. Experiments involving the estimation of SMA through exhaustive search using the single polarization channels after phase linking are currently running, in order to assess the benefit of the multi-polarimetric approach w.r.t. the single-polarization one. References [1] Tebaldini, Stefano. ”Algebraic synthesis of forest scenarios from multibaseline PolInSAR data.” IEEE Transactions on Geoscience and Remote Sensing 47.12 (2009): 4132-4142. [2] De Zan, Francesco, et al. ”A SAR interferometric model for soil moisture.” IEEE Trans actions on Geoscience and Remote Sensing 52.1 (2013): 418-425. [3] Aguasca, Albert, et al. ”Hydrosoil, soil moisture and vegetation parameters retrieval with a C-band GB-SAR: Campaign implementation and first results.” 2021 IEEE Inter national Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021 | |
