Probabilistic soil moisture predictions at field scale using in situ data in a Bayesian inverse modelling framework SWIM² and the potential of remote sensing

The SWIM² framework (Hendrickx et al., 2025) integrates a soil water balance model with in situ sensor data and soil moisture samples through Bayesian inverse modelling. The calibrated model then generates probabilistic 10-day soil moisture forecasts, enabling real-time, site-specific irrigation advice. SWIM² was validated in a real-time setup for 18 vegetable cropping cycles on agricultural fields in Flanders, Belgium, with reliable precipitation data. Although using minimal prior knowledge and despite sensor bias, SWIM² achieves robust soil moisture predictions for a 7-day horizon, with accuracies comparable to sensor measurements. We also assessed the impact of model parameter and weather forecast uncertainty on SM prediction uncertainty, water stress prediction and irrigation advice by integrating the calibrated model ensemble with ensemble-based probabilistic weather forecasts, resulting in high detection rate and accuracy in predicting water stress triggering the irrigation threshold.Time series of vegetation indices such as NDVI and LAI from Sentinel-2 optical remote sensing as well as LST from Sentinel-3 contain much information on crop growth and crop evapotranspiration. Additionally, the new NISAR mission is promising for high-resolution surface soil moisture observations. We assess relations between in situ measurements and model outputs (crop growth curve, actual ET and SWC), and the remote sensing data, and we discuss opportunities of these data to improve soil moisture and ETa predictions.Reference: Hendrickx, M.G.A., Vanderborght, J., Janssens, P., Laloy, E., Bombeke, S., Matthyssen, E., Waverijn, A., Diels, J. (2025). Field-scale soil moisture predictions in real time using in situ sensor measurements in an inverse modeling framework: SWIM². Authorea Preprints, doi:10.22541/ESSOAR.175103915.57413983/V1.