Development of a deep learning method for soil moisture estimation at high spatial and temporal resolution using satellite data

Sammanfattning: Soil moisture (SM) is an essential climate variable that controls fundamental hydrological and climatic processes. Soil moisture products derived from microwave remote sensing often provide measurements at low spatial resolution and incomplete temporal records. This thesis presents a novel method for estimation soil moisture at both high spatial and temporal resolution by using a deep learning recurrent neural network model. The model relies primarily on Sentinel-1 synthetic aperture radar data but includes additional ancillary data, such as Sentinel-2 vegetation indices, land cover, and weather variables. The model is calibrated and validated on four SM probe networks within continental Europe with data from the International Soil Moisture Network (ISMN) and the Integrated Carbon Observation System (ICOS). The model has been compared with existing SM products and has shown comparable or better results with a mean absolute error of 9.33% and a correlation of r=0.49 with observed measurements. It performs best over agricultural land covers in temperate regions, where satellite observations are most frequent, and poorer over vegetated land surfaces like forests due to the attenuation of microwave signals. The temporal predictions show high accuracy and precision, while the spatial predictions retain a high accuracy but with lower precision. The predictions show satisfactory results overall but warrant further research to test the feasibility of this architecture over larger areas and different climate types.