Simulation of Soil Roughness Impact in GNSS-R Soil Moisture Retrieval

This paper presents two dual antenna GNSS-R(global navigation satellite system reflectometry) soil moisture retrieval models with soil roughness compensation-an analytic model and an artificial neural network (ANN) model. Then a simulator for GNSS-R soil moisture retrieval is built in consideration of GPS L1 C/A code modulation. After that the impact of soil roughness is elaborated. The simulation results show that the roughness compensation is necessary for the analytic model when the RMSH(root mean square high) is larger than 0.010 m. The roughness compensation works well for small roughness, but there are some limitations for large roughness. Under the situation where RMSH is greater than 0.025 m, the accuracy of ANN model is 36.83%-72.36% higher than the analytic model without roughness compensation, and the accuracy of ANN model is 42.86%-54.40% higher than the analytic model with roughness compensation. The ANN model achieves similar accuracy regardless of roughness compensation, and the accuracy of ANN model without compensation is still 35.83%-53.48% higher than the analytic model with compensation.