Abstract
Water is crucial for rice growth, particularly in semi-arid Australia, where climate change threatens water allocation for rice growers and challenges the sustainability of the rice industry. Estimating water depth using remote sensing can be beneficial, as it can help growers transition from traditional continuous flooding (CF) to irrigation practices such as Delayed Permanent Water (DPW) and Aerobic Rice (AR), which are promising pathways to ensure industry sustainability. Synthetic aperture radar (SAR) has been widely evaluated for water depth prediction in rice crops. To date, a significant gap remains in the performance of the proposed models, with authors suggesting the use of optical and SAR data to improve their estimates. To this end, this research evaluated three machine learning (ML) models to estimate water depth in rice under different irrigation practices in the Australian semi-arid region. Three different ML approaches (Tree-based, Neural Networks, and pretrained foundational model) were evaluated with three data configurations: Set 1 with SAR only, set 2 with Optical Only, and Set 3 with multimodal (SAR and Optical) data. The pre-trained foundational model achieved the best results, with RMSE of 14.19 cm, MAE of 10.01 cm, R2 of 0.63, KGE of 0.73, and CCC of 0.78. Interestingly, the SHAP analysis reveals that optical remote sensing is more significant for the model than SAR. Specifically, the mean GVMI and VAWIlog standard deviation.