Analysis of Seasonal Driving Factors and Inversion Model Optimization of Soil Moisture in the Qinghai Tibet Plateau Based on Machine Learning

• Published in: Water
• Main Authors: Qinghai Deng, Jingjing Yang, Liping Zhang, Zhenzhou Sun, Guizong Sun, Qiao Chen, Fengke Dou
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2023
• Subjects: Sentinel-1; semi-empirical model; artificial neural networks; decision tree algorithm; permafrost
• Online Access: https://doi.org/10.3390/w15162859

The accuracy of soil moisture retrieval based on traditional microwave remote sensing models in the Qinghai Tibet Plateau (QTP) is unstable due to its unique plateau climate. However, considering the impact of multiple multi-scale factors effectively improves the accuracy and stability of soil moisture inversion. This article uses Sentinel-1 and seasonal climate data to analyze factors and influencing mechanisms of soil moisture in the QTP. First, an artificial neural network (ANN) was used to conduct a significance analysis to screen significant influencing factors to reduce the redundancy of the experimental design and insert information. Second, the normalization effect of each factor on the soil moisture inversion was determined, and the factors with significant normalization influences were input to fit the model. Third, different fitting methods combined the semi-empirical models for soil moisture inversion. The decision tree Chi-square Automatic Interaction Detector (CHAID) analyzed the model accuracy, and the Pearson correlation coefficient between the sample and measured data was tested to further validate the accuracy of the results to obtain an optimized model that effectively inverts soil moisture. Finally, the influencing mechanisms of various factors in the optimization model were analyzed. The results show that: (1) The terrain factors, such as elevation, slope gradient, aspect, and angle, along with climate factors, such as temperature and precipitation, all have the greatest normalized impact on soil moisture in the QTP. (2) For spring (March), summer (June), and autumn (September), the greatest normalized factor of soil moisture is the terrain factor. In winter (December), precipitation was the greatest factor due to heavy snow cover and permafrost. (3) Analyzing the impact mechanism from various factors on the soil moisture showed a restricted relationship between the inversion results and the accuracy of the power fitting model, meaning it is unsuitable for general soil moisture inversion. ...