A temporal gap filling approach for GNSS-IR soil moisture inversion based on generalized extension

Abstract

Soil moisture monitoring is a research hotspot in the application of navigation satellite reflected signals. In order to retrieve soil moisture in any period with high precision and solve the problem that soil moisture cannot be retrieved when satellite observation data is partially missing, the principle of generalized extension in engineering science is applied to GNSS-IR technology in the paper. Firstly, the generalized extension approximation method is used to interpolate the precise ephemeris to obtain the altitude angle information of each satellite in each epoch. Then, the generalized extendedinterpolation and extrapolation models were used to supplement the initial phase of the reflected signal corresponding to the missing data as the input of the inversion model to predict soil moisture. The soil moisture inversion experiment was completed according to the observation data provided by PBO. The results show that the generalized extension method can cope with the missing data of different lengths and distributions in GNSS-IR soil moisture retrieval, When the data were confirmed for 15 days, the correlation coefficient of inversion results was 0.916, RSME was 0.03358 cm3/cm3, and MAE was 0.02912 cm3/cm3, and the correlation coefficient of more than 0.85 can be maintained when the amount of missing data reaches 60 % of the test set.