Abstract
This paper proposes a spaceborne GNSS-R soil moisture retrieval method based on CYGNSS data. Firstly, the theoretical model of soil moisture retrieval is constructed by combining the surface reflectance parameters extracted from CYGNSS data and the auxiliary information of vegetation optical depth, surface roughness and temperature extracted from SMAP data. The fine mathematical model of soil moisture retrieval is determined by using the neural network model. Then, the soil moisture obtained by the proposed model is processed at an interval of 0.35, and the stage model proposed in this paper is used to improve the soil moisture retrieval accuracy, and the spaceborne GNSS-R soil moisture is obtained globally by using the CYGNSS data from October 2018 to May 2019. Finally, the effectiveness of the spaceborne GNSS-R soil moisture retrieval method proposed in this paper is evaluated through comparing with the soil moisture data provided by SMAP, and the time series of spaceborne GNSS-R soil moisture is analyzed. The results show that the soil moisture obtained by the method proposed in this paper is in good agreement with the soil moisture obtained by SMAP, and the trend of variation with time is also consistent with the actual situation, which provides a new idea for high-precision soil moisture retrieval.
| Translated title of the contribution | Spaceborne GNSS-R for retrieving soil moisture based on the correction of stage model |
|---|---|
| Original language | Chinese (Simplified) |
| Pages (from-to) | 1942-1950 |
| Number of pages | 9 |
| Journal | Acta Geodaetica et Cartographica Sinica |
| Volume | 51 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2022 |