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Estimation of surface soil moisture by combining a structural equation model and an artificial neural network (SEM-ANN) by Sinan Wang; Ruiping Li; Yingjie Wu; Wenjun Wang is a Environmental Science article available to read on EtoBox.

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Soil moisture is an important variable of the environment that directly affects hydrological, ecological, and climatic processes. However, owing to the influence of soil type, soil structure, topography, vegetation, and human activities, the distribution of soil water content is spatially heterogeneous. It is difficult to accurately monitor the distribution of soil moisture over large areas. To investigate the direct or indirect influence of various factors on soil moisture and obtain accurate soil moisture inversion results, we used structural equation models (SEMs) to determine the structural relationships between these factors and the degree of their influence on soil moisture. These models were subsequently transformed into the topology of artificial neural networks (ANN). Finally, a structural equation model coupled with an artificial neural network was constructed (SEM-ANN) for soil moisture inversion. The results showed the following: (1) The most important predictor of the spatial variability of soil moisture in the April was the temperature–vegetation dryness index, while land surface temperature was the most important predictor in the August; (2) After the ANN model was i

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Author

Sinan Wang; Ruiping Li; Yingjie Wu; Wenjun Wang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)