ESTIMATING SURFACE SOIL MOISTURE FROM SATELLITE OBSERVATIONS USING MACHINE LEARNING TRAINED ON IN SITU MEASUREMENTS IN THE CONTINENTAL US

This study attempts to develop a novel solution for the estimation of regional surface soil moisture (SSM) using a machine learning model trained on in situ measurement target data. Specifically, the generalized regression neural network (GRNN) is employed to establish the relationship between in-situ measurements from Sparse Network Stations (SNSs) in the continental U.S. and passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite for April 2015 to March 2018. More importantly, to address the scale mismatch issue resulting from the small spatial support of in situ measurements, we turn to the extended triple collocation technique whereby individual SNSs' reliability at the SMAP coarse footprint is determined before fed into GRNN. The cross-validation results show that the GRNN model trained on reliable SNSs obtains a fairly good performance, with out-of-sample cross-validated R and unbiased RMSE values of 0.92 and 0.043 cm(3) cm(-3), respectively. Moreover, the comparison in space shows that the spatial patterns of GRNN retrievals is the most consistent with in situ measurements than both the SMAPL3SMP and the ERA-Interim SSM data. Furthermore, the GRNN-estimated SSM time series over stations agrees much better with in-situ measurements than the official SMAP passive SSM product. All these results indicate that the statistical GRNN modeling has shown great potential in estimating reliable regional SSM climate records using in-situ measurements as training references.