Construction and Validation of Surface Soil Moisture Inversion Model Based on Remote Sensing and Neural Network
Rencai Lin,
Zheng Wei,
Rongxiang Hu,
He Chen,
Yinong Li,
Baozhong Zhang,
Fengjing Wang,
Dongxia Hu
Affiliations
Rencai Lin
State Key Laboratory of Watershed Water Cycle Simulation and Regulation, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Zheng Wei
State Key Laboratory of Watershed Water Cycle Simulation and Regulation, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Rongxiang Hu
Zhejiang Institute of Hydraulics & Estuary (Zhejiang Institute of Marine Planning and Design), Hangzhou 310020, China
He Chen
State Key Laboratory of Watershed Water Cycle Simulation and Regulation, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Yinong Li
State Key Laboratory of Watershed Water Cycle Simulation and Regulation, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Baozhong Zhang
State Key Laboratory of Watershed Water Cycle Simulation and Regulation, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Fengjing Wang
State Key Laboratory of Watershed Water Cycle Simulation and Regulation, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Dongxia Hu
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.
