2026/07/01 by Oleg Antropov, Alireza Hamedianfar, Matthieu Molinier +5 · 1 voice
Engineering · #eess.IV #eess.SP
This paper examines several model based approaches for retrieving surface soil moisture from ALOS-2 PALSAR-2 quad-pol imagery, over a lime stone quarry in southeastern Finland. The study primarily targets physically interpretable semi-empirical modeling approaches, with generic ML modeling used as a benchmark. Along with common polarimetric observables, we propose a generalization of the SAR time series based TU Wien soil moisture index (SMI) retrievals examined across several representational spaces derived from polarimetric coherency matrix [T3]. This study was conducted over a closed tailing storage facility and a landfill, with a set of 9 repeat pass PALSAR-2 images. The best semi-empirical configuration combining temporal context SMI and current observation PolSAR parameters achieved R2=0.67 and RMSE =5.65 volumetric % units. The strongest SMI[T3] approach with sediment-specific calibration, achieved R2=0.66 and RMSE =5.67 vol. %, which was considerably better than using SMIHH or SMIVV. The proposed approach was sensitive to representations: dB-based projection outperformed linear or trace-normalized [T3] representation. Factoring in sediment information dramatically improved retrieval performance compared to using global model fitting. Machine learning results closely approached but not outperformed semi-empirical model based methodologies. Similarly, they highlighted the need for sediment-specific modeling as well as the importance of including time-series/temporal backscatter dynamics during SSM retrieval. Our study demonstrated the utility of physics based SSM retrieval approaches in the complex multi-sediment mine environment under relatively scarce reference data conditions.