Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data

Abstract

Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and all-weather operation. GNSS signal reflected at L-band also has significant advantages for SM estimation. Usually, SM is estimated based on the sensitivity of GNSS-R reflectivity to SM, but the noise in observations can significantly impact SM estimation results. A new SM retrieval method based on robust regression is proposed to address this issue in this work, and the effects of roughness and vegetation on the effective reflectivity of the Cyclone Global Navigation Satellite System (CyGNSS) are reconsidered. Ancillary data are provided by the SM Active Passive (SMAP) mission. The retrieved results from the training sets and test sets agree well with the referenced SMAP SM data. The correlation coefficient R is 0.93, the root mean square error (RMSE) is 0.058 cm3cm−3, the unbiased RMSE (ubRMSE) is 0.042 cm3cm−3, and the mean absolute error (MAE) is 0.040 cm3cm−3 in the training sets. For the test, the correlation coefficient is 0.91, the RMSE is 0.067 cm3cm−3, the ubRMSE is 0.051 cm3cm−3, and the MAE is 0.044 cm3cm−3. The proposed method has been evaluated using in situ measurements from the SMAP/in situ core validation site; in situ measurements and retrieval results exhibit good consistency with the ubRMSE value below 0.35 cm3cm−3. Moreover, the SM retrieval results using robust regression methods show better performance than CyGNSS official SM products that use linear regression. In addition, the land cover types significantly affect the accuracy of SM retrieval, and the incoherent scattering in densely vegetated areas (tropical forests) usually leads to more errors