Enhanced Generalized Regression Neural Network for Soil Moisture Estimation Over the Qinghai-Tibet Plateau

Abstract

Soil moisture (SM) is a critical parameter in maintaining the balance of water cycle and energy budgets between climate system and the Earth's environment. Generalized regression neural network (GRNN) has been substantially verified as a powerful model for SM estimation due to the ability of capturing complex, non-linear relationships between predictors and responses. However, GRNN builds a full adjacency matrix using Gaussian kernel, which is computationally expensive and may ignore the local structure. In addition, it is laborious to optimize the “spread” parameter. To overcome the above issues, we propose an enhanced generalized regression neural network (EGRNN) for SM estimation, where two main adaptations are made. On the one hand, the City block distance instead of the Euclidean distance is used for building Gaussian kernel. On the other hand, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbors ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN) is adopted to yield an empirically sparse adjacency matrix. As the key advantage, the proposed EGRNN weakens the sensitivity to outliers since large differences are weighted more heavily by using Euclidean distance than City block distance. Another advantage is that EGRNN models more local and discriminant information in the pattern layer since only the data points within neighbors are connected by using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN. Experiments conducted in the Qinghai-Tibet Plateau (QTP) demonstrate that; 1) EGRNN outperforms the other four neural network models, with R = 0.9485 and RMSE = 0.0325 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ; 2) It can well capture spatial-temporal dynamics and has higher consistent with the in-situ measurements; 3) It adapts well to different in-situ networks and has better generalization performance.