A GNSS-IR soil moisture retrieval method via multi-layer perceptron with consideration of precipitation and environmental factors.

Soil moisture monitoring is a significant aspect of environmental and agricultural studies, and Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as a promising technology for this purpose. Traditionally, GNSS-IR is mainly employed for bare soil experiments, and the effectiveness of bare soil retrieval algorithm will be reduced due to the influence of vegetation and meteorological, etc. To address the limitations of the bare-soil retrieval algorithm, a multi-feature soil moisture retrieval approach was proposed. This approach integrated multiple factors including GNSS signals, cumulative precipitation, effective reflection height, and Normalized Microwave Reflection Index (NMRI), and then multi-layer perceptron (MLP) was employed to build retrieval models. In this study, measurements from the Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China were used for experiments and validation, and the geographical environment of stations are various. The experimental results demonstrated several key findings: (1) The delay phase is not sensitive to the variations in soil moisture before and after precipitation, but by integrating the cumulative precipitation data, the accuracy of the model could be improved. (2) The introduction of NMRI and reflection height can help remove the influence of vegetation and penetration depth. (3) Compared between three retrieval models (i.e., unary linear regression, multiple linear regression, and MLP), the decrease in the mean absolute error (MAE) of MLP is up to 96% most and the mean coefficient of determination (R²) is all above 0.98. Meanwhile, this study proved that the proposed method could fully utilize satellite reflection signals from all directions and better reflect the fluctuation of soil moisture. [ABSTRACT FROM AUTHOR]