Thesis title: Machine Learning Driven Multi-Sensor and Cross-Frequency SAR Fusion for High-Resolution Soil Moisture Retrieval: Integrating LSTM Networks, Cross-Sensor Calibration, and Multi-Source Data Synergy
Surface soil moisture (SSM) is significant for hydrological, agricultural, and climate system , yet retrieval from remote sensing is constrained by vegetation-induced signal saturation, neglect of temporal persistence, and the absence of firm cross-frequency calibration for non-coincident Synthetic Aperture Radar (SAR) missions. This thesis develops and evaluates a machine-learning- driven framework which integrates multi-frequency SAR (SAOCOM L-band, Sentinel-1, RCM C-band), optical (Sentinel-2 NDVI), and meteorological measurements to address these constraints and enable high-resolution, scalable SSM retrieval.
The methodology is structured around three pillars. First, feature-level fusion of Sentinel-1/2 and meteorological data in Italian croplands established a baseline (Random Forest, R² ≈ 0.86) while exposing limitations of static models. Secondly, Bayesian-optimized LSTMs applied in Argentina highlighted the significance of temporal memory and synergy at multi-frequencies, achieving R²
≈ 0.84 (RMSE ≈ 0.022 m³/m³) and estimating non-SAR contribution (precipitation 11.9%, temperature 9.5%, NDVI 3.6%). Third, cross-frequency calibration between non-coincident SAOCOM and RCM observations in Canada (ELR, R ≈ 0.72) and kernel-based domain adaptation in Kenya (ensemble R² ≈ 0.44) demonstrated that asynchronous SAR missions can be harmonized and transferred across agro-ecological zones.
Results confirmed that dual-frequency SAR fusion improves retrieval accuracy by ~0.20 R relative to single-frequency models, LSTMs were able to capture hysteresis in soil moisture development, and ancillary optical–meteorological inputs reduced errors by >11.7%. Aside from enhanced performance, the approach delineated a mission-agnostic pipeline that can generate 10–30 m soil moisture maps for irrigation planning, flood/drought monitoring, and crop stress analysis. By bridging physical scattering physics with interpretable machine learning (ablation) and cross- validation across Italy, Argentina, Canada, and Kenya, this thesis transposes SSM retrieval from sensor-domain science to operational and transferable solutions.