Exploration of Machine Learning Techniques in Emulating a Coupled Soil–Canopy–Atmosphere Radiative Transfer Model for Multi-Parameter Estimation From Satellite Observations

Abstract

The time-consuming modeling of physical remote sensing models restricts their application to parameter estimation from satellite observations. Machine learning techniques have become highly developed in recent years and show good capacity for model fitting. Based on our previously developed coupled soil–canopy–atmosphere radiative transfer model (RTM) and a multiple parameters estimation scheme, this paper evaluates the performance of four machine learning algorithms [Gaussian process regression (GPR), back-propagation neural networks (NNs), random forest regression, and general regression NN] on emulating the coupled RTM, where the traditional lookup table (LUT) algorithm is also compared. The results show that the GPR algorithm can emulate complex RTMs with excellent accuracy and efficiency. GPR emulators of photosynthetically active radiation (PAR), fraction of absorbed PAR, and incident shortwave radiation were applied to the multi-parameter estimation scheme to replace the traditional LUT algorithm, which avoids the need to integrate over the spectra while achieving an acceleration ratio of 16. A test of the updated multi-parameter estimation scheme at the Bondville site using 18 years of clear-sky observations demonstrates that replacing the computationally expensive integration processes with GPR emulators is practical. The emulators can also be used to simulate the corresponding parameters independently, and this GPR acceleration method for complex models is universal and can be easily applied to other time-consuming models.