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Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai-Tibet Plateau, China

文献类型：期刊论文

作者Wang, Jie1,2; Xu, Duanyang1
刊名REMOTE SENSING
出版日期2021-12-01
卷号13期号:24页码:21
关键词soil moisture artificial neural network AMSR-2 SMAP Qinghai-Tibet Plateau
DOI10.3390/rs13245156
通讯作者Xu, Duanyang(xudy@igsnrr.ac.cn)
英文摘要Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai-Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate.
WOS关键词TIME-SERIES ; SMAP ; VEGETATION ; AMSR2 ; RETRIEVALS ; PRODUCTS ; MODEL ; SMOS ; SIMULATIONS ; FUSION
资助项目National Natural Science Foundation of China[41971253]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000737445600001
出版者MDPI
资助机构National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/169109]  
专题中国科学院地理科学与资源研究所
通讯作者Xu, Duanyang
作者单位1.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Dept Environm & Resources, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Jie,Xu, Duanyang. Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai-Tibet Plateau, China[J]. REMOTE SENSING,2021,13(24):21.
APA Wang, Jie,&Xu, Duanyang.(2021).Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai-Tibet Plateau, China.REMOTE SENSING,13(24),21.
MLA Wang, Jie,et al."Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai-Tibet Plateau, China".REMOTE SENSING 13.24(2021):21.

入库方式： OAI收割

来源：地理科学与资源研究所

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