Fusion of Machine Learning and Semi-Empirical Models for Cooperative Retrieval of Soil Moisture With Optical and SAR Remote Sensing: Cyclic or Parallel?

Semi-empirical models and machine learning (ML) models have been widely used in remote sensing studies. In order to explore the feasible joint mode integrating semi-empirical model and ML algorithm, two distinct joint modes, cycle and series mode and deep and parallel mode, were designed and evaluated to synthesize the respective advantages of the two models to complete high-resolution soil moisture retrieval. Cycle and series mode improved the generalization ability of the retrieval models in the case of less labeled data and enhanced its physical interpretability. The deep and parallel mode improved the accuracy of the retrieval models in a wider range of applications with an estimation accuracy of 0.754 and 0.071 m
$^{3}\cdot $
m3 in terms of coefficient of determination and unbiased root-mean-square error in site-based validation. The joint modes constructed in this study provide ideas for subsequent studies on model fusion and improving the physical interpretability of ML models.