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Abstract

Using Global Navigation Satellite System-Reflectometry (GNSS-R) for soil moisture (SM) retrieval has recently gained importance due to its high temporal-spatial resolution. However, the current methods, i.e., constructing a single machine learning (ML)-based model, have large model uncertainty resulting from ML networks and input schemes. Moreover, traditional Normalized Difference Vegetation Index (NDVI) cannot capture the rapid vegetation changes well. In this paper, a new SM retrieval method of constructing a hybrid model based on Bayesian model averaging (BMA) is employed to reduce the model uncertainty. Meanwhile, novel Sun-induced fluorescence (SIF) data is used as ancillary data to represent the rapid change of vegetation. We validate the proposed method at point and regional scales using in-situ data and the Global Land Data Assimilation System (GLDAS) product. The results demonstrate that our method has high accuracy and low uncertainty in SM retrieval. At the point scale, as accuracy indices, the average R () of BMA increases from 0.90 to 0.93 and the average root-mean-square-error () decreases from 0.034 to 0.029 ; as indices of uncertainty, the standard deviations of R and RMSE ( and ) decrease by 32 % and 9 % compared to the single ML-based model. For the regional scale, the increases from 0.79 to 0.81, the decreases from 0.024 to 0.023 , and the decreases by 19 %. Moreover, we take the point-scale experiment as an example for comparison to compare the performance of SIF with that of NDVI. The of BMA trained by SIF is 0.03 higher than that trained by NDVI and the decreases by 0.002 ; and decrease by 25 % and 6 %. Based on these results, the proposed method can reduce the uncertainty and the advantage of SIF has potential for improving the SM retrieval.