Field‐Scale Soil Moisture Predictions in Real Time Using In Situ Sensor Measurements in an Inverse Modeling Framework: SWIM ²

Abstract Affordable autonomous soil sensors and IoT technology enable real‐time soil moisture monitoring, which offers opportunities for real‐time model calibration and irrigation optimization. We introduce an irrigation decision support system SWIM 2 (Sensor Wielded Inverse Modeling of a Soil Water Irrigation Model), a digital twin that integrates continuous sensor data and unbiased, periodic soil samples with an FAO‐based soil water balance model using a Bayesian inverse modeling algorithm, DREAM (ZS) (DiffeRential Evolution Adaptive Metropolis). SWIM 2 estimates 12 soil and crop parameters and their associated probability distributions and correlations, providing soil moisture predictions with uncertainty estimates. The SWIM 2 framework is illustrated and validated in a real‐time setup for 18 vegetable cropping cycles on agricultural fields in Flanders, Belgium, with in situ precipitation data. Although using minimal prior knowledge and despite sensor bias, SWIM 2 achieves robust soil moisture predictions for a 7‐day horizon, with accuracies comparable to sensor measurements. Predictions improve substantially in precision within the first 20 calibration days and maintain high predictive power throughout the growing season. The impact of in situ measurements and temporal covariance of the observational errors (“error covariance”) was assessed, indicating that good knowledge of the error covariance and independent soil moisture samples are essential to correct for sensor bias and ensure accurate model calibration, while continuous sensor data ensure accurate and precise estimates of the dynamics. This study demonstrates the use of soil moisture sensor data in a Bayesian inverse modeling framework, offering practical solutions for real‐time soil moisture prediction and irrigation decision‐making, enhancing water management across agricultural fields.