气候变化领域集成服务门户(CCPortal): Analysis of Seasonal Driving Factors and Inversion Model Optimization of Soil Moisture in the Qinghai Tibet Plateau Based on Machine Learning

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DOI	10.3390/w15162859
	Analysis of Seasonal Driving Factors and Inversion Model Optimization of Soil Moisture in the Qinghai Tibet Plateau Based on Machine Learning
	Deng, Qinghai; Yang, Jingjing; Zhang, Liping; Sun, Zhenzhou; Sun, Guizong; Chen, Qiao; Dou, Fengke
发表日期	2023
EISSN	2073-4441
卷号	15 期号:16
英文摘要	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. However, among the selected models, the accuracy of the linear fit was generally higher than 79.2%, the Pearson index was greater than 0.4, and the restricted relationship between the inversion results and accuracy was weak, making it suitable for the general inversion of soil moisture in the QTP.
关键词	Sentinel-1 semi-empirical model artificial neural networks decision tree algorithm
英文关键词	ALPINE GRASSLAND; PERMAFROST; SCATTERING; RETRIEVAL
WOS研究方向	Environmental Sciences ; Water Resources
WOS记录号	WOS:001056190300001
来源期刊	WATER
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283347
作者单位	Shandong University of Science & Technology
推荐引用方式 GB/T 7714	Deng, Qinghai,Yang, Jingjing,Zhang, Liping,et al. Analysis of Seasonal Driving Factors and Inversion Model Optimization of Soil Moisture in the Qinghai Tibet Plateau Based on Machine Learning[J],2023,15(16).
APA	Deng, Qinghai.,Yang, Jingjing.,Zhang, Liping.,Sun, Zhenzhou.,Sun, Guizong.,...&Dou, Fengke.(2023).Analysis of Seasonal Driving Factors and Inversion Model Optimization of Soil Moisture in the Qinghai Tibet Plateau Based on Machine Learning.WATER,15(16).
MLA	Deng, Qinghai,et al."Analysis of Seasonal Driving Factors and Inversion Model Optimization of Soil Moisture in the Qinghai Tibet Plateau Based on Machine Learning".WATER 15.16(2023).