基于结构方程模型与人工神经网络（SEM-ANN）组合的表层土壤湿度估算。

Estimation of surface soil moisture by combining a structural equation model and an artificial neural network (SEM-ANN).

机构信息

Yinshanbeilu National Field Research Station of Desert Steppe Eco-hydrological System, China Institute of Water Resources and Hydropower Research, Beijing 100038, China; College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China; Institute of Water Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China.

College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China.

出版信息

Sci Total Environ. 2023 Jun 10;876:162558. doi: 10.1016/j.scitotenv.2023.162558. Epub 2023 Mar 7.

DOI:10.1016/j.scitotenv.2023.162558

PMID:36894100

Abstract

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 improved, the inversion accuracy of surface soil moisture by SEM-ANN model was improved, and the R of verification set was increased by 0.01 and 0.02 in April and August, respectively, and the relative analysis error was reduced by 0.5 % and 1.13 %. (3) There were no significant differences in soil moisture distribution trends between the April and August.

摘要

土壤湿度是影响水文、生态和气候过程的重要环境变量。然而，由于土壤类型、土壤结构、地形、植被和人类活动的影响，土壤含水量的分布具有空间异质性。因此，很难准确监测大面积土壤湿度的分布。为了研究各种因素对土壤湿度的直接或间接影响，并获得准确的土壤湿度反演结果，我们使用结构方程模型（SEM）来确定这些因素之间的结构关系及其对土壤湿度的影响程度。然后，将这些模型转换为人工神经网络（ANN）的拓扑结构。最后，构建了一个结构方程模型与人工神经网络相结合的土壤湿度反演模型（SEM-ANN）。结果表明：（1）4 月土壤湿度空间变异性的最重要预测因子是温度-植被干燥指数，而 8 月最重要的预测因子是地表温度；（2）改进 ANN 模型后，SEM-ANN 模型对表层土壤湿度的反演精度有所提高，验证集的 R 值在 4 月和 8 月分别提高了 0.01 和 0.02，相对分析误差分别降低了 0.5%和 1.13%。（3）4 月和 8 月土壤湿度分布趋势没有显著差异。