Sentinel-derived soil moisture retrievals during winter and summer cropping: Potential and limitations of S²MP

Field-scale soil moisture retrieval from Sentinel-1 synthetic aperture radar (SAR) is rather well established for bare soils, winter crops, and grasslands, but its applicability during summer cropping periods remains uncertain due to dense vegetation and complex vegetation structure. This study evaluates the potential and limitations of the Sentinel-derived soil moisture product (S²MP, El Hajj et al. (2017)), based on a neural network that uses Sentinel-1 VV backscatter and Sentinel-2 NDVI, for surface soil moisture estimation during summer cropping.

The first part of this study evaluates S²MP against in situ measurements at 10 cm depth over several winter and summer crops in a Mediterranean context (Bazzi et al., 2023). Results show that Sentinel-1 mainly senses the top few centimetres of soil, leading to strong underestimation in dry conditions (up to ~20 vol.%) and smaller biases under moderately wet conditions, while performance degrades again in very wet soils. Reliable soil moisture retrievals are limited to low–moderate vegetation cover (NDVI < 0.7), with crop-dependent biases under dense canopies, and accuracy improves at lower radar incidence angles (< 35°).

The second part analyses summer vegetable case studies in Flanders, comparing S²MP with in situ observations across irrigated and rainfed fields. S²MP successfully captures rainfall and irrigation signals during early growth stages and differentiates irrigated from non-irrigated areas, but performance under dense canopies strongly depends on crop type. Crops with complex canopy structures (e.g. beans podding stage, pumpkins) show reduced or inconsistent sensitivity, while onions and carrots retain detectable soil moisture dynamics even at high NDVI. These results demonstrate that NDVI alone is insufficient to characterise vegetation effects on SAR soil moisture retrievals and highlight the need for crop-specific parameterisation and complementary longer-wavelength SAR observations.

References:
El Hajj, M., Baghdadi, N., Zribi, M., & Bazzi, H. (2017). Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sensing 2017, Vol. 9, Page 1292, 9(12), 1292. https://doi.org/10.3390/RS9121292

Bazzi, H., Baghdadi, N., Nino, P., Napoli, R., Najem, S., Zribi, M., & Vaudour, E. (2023). Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer. Water 2024, Vol. 16, Page 40, 16(1), 40. https://doi.org/10.3390/W16010040