Mapping near real-time soil moisture dynamics over Tasmania with transfer learning

Abstract

Abstract. Soil moisture, an essential parameter for hydroclimatic studies, exhibits significant spatial and temporal variability, making it challenging to map at fine spatiotemporal resolutions. Although current remote sensing products provide global soil moisture estimate at a fine temporal resolution, they are mostly at a coarse spatial resolution. In recent years, deep learning (DL) has been applied to generate high-resolution maps of various soil properties, but DL requires a large amount of training data. This study aimed to map daily soil moisture across Tasmania, Australia at 80 meters resolution based on a limited set of training data. We assessed three modelling strategies: DL models calibrated using an Australian dataset (51,411 observation points), models calibrated using the Tasmanian dataset (9,825 observation points), and a transfer learning technique that transferred information from Australian models to Tasmania. We also evaluated two DL approaches, i.e. Multilayer perceptron (MLP) and Long Short-Term Memory (LSTM). Our models included data of Soil Moisture Active Passive (SMAP) dataset, weather data, elevation map, land cover and multilevel soil properties maps as inputs to generate soil moisture at the surface (0–30 cm) and subsurface (30–60 cm) layers. Results showed that (1) models calibrated from the Australia dataset performed worse than Tasmanian models regardless of the type of DL approaches; (2) Tasmanian models, calibrated solely using Tasmanian data, resulted in shortcomings in predicting soil moisture; and (3) Transfer learning exhibited remarkable performance improvements (error reductions of up to 45 % and a 50 % increase in correlation) and resolved the drawbacks of the Tasmanian models. The LSTM models with transfer learning had the highest overall performance with an average mean absolute error (MAE) of 0.07 m³m^-3 and a correlation coefficient (r) of 0.77 across stations for surface layer and MAE = 0.07 m³m^-3, and r = 0.69 for subsurface layer. The fine-resolution soil moisture maps captured the detailed landscape variation as well as temporal variation according to four distinct seasons in Tasmania. The best performance of soil moisture models were made available live to predict near-real-time daily soil moisture of Tasmania, assisting agricultural decision making.