Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation

• URL: http://arxiv.org/abs/2602.18083v1
• Date: Fri, 20 Feb 2026 09:17:12 GMT
• Title: Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation
• Authors: Ioannis Kontogiorgakis, Athanasios Askitopoulos, Iason Tsardanidis, Dimitrios Bormpoudakis, Ilias Tsoumas, Fotios Balampanis, Charalampos Kontoes,
• Abstract summary: We present a high-resolution (10m) soil moisture estimation framework for vegetated areas across Europe.<n>We compare modality combinations with temporal parameterizations, using spatial cross-validation, to ensure geographic generalization.<n>We also evaluate whether foundation model embeddings from IBM-NASA's Prithvi model improve upon traditional hand-crafted spectral features.
• Score: 0.9674544640949528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate soil moisture (SM) estimation is critical for precision agriculture, water resources management and climate monitoring. Yet, existing satellite SM products are too coarse (>1km) for farm-level applications. We present a high-resolution (10m) SM estimation framework for vegetated areas across Europe, combining Sentinel-1 SAR, Sentinel-2 optical imagery and ERA-5 reanalysis data through machine learning. Using 113 International Soil Moisture Network (ISMN) stations spanning diverse vegetated areas, we compare modality combinations with temporal parameterizations, using spatial cross-validation, to ensure geographic generalization. We also evaluate whether foundation model embeddings from IBM-NASA's Prithvi model improve upon traditional hand-crafted spectral features. Results demonstrate that hybrid temporal matching - Sentinel-2 current-day acquisitions with Sentinel-1 descending orbit - achieves R^2=0.514, with 10-day ERA5 lookback window improving performance to R^2=0.518. Foundation model (Prithvi) embeddings provide negligible improvement over hand-crafted features (R^2=0.515 vs. 0.514), indicating traditional feature engineering remains highly competitive for sparse-data regression tasks. Our findings suggest that domain-specific spectral indices combined with tree-based ensemble methods offer a practical and computationally efficient solution for operational pan-European field-scale soil moisture monitoring.

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