A Satellite Remote Sensing Monitoring Model for Soil Moisture Based on Artificial Neural Network

摘要：

Based on the satellite data such as precipitation estimation, incident radiation, brightness temperature etc. and the assimilation data of CLSMDAS, combined with B-P neural network to develop a new model of soil moisture monitoring. The model mining the relationship between the soil moisture and the satellite products, then calculate the weight and build models using artificial neural network which has ability of nonlinear processing, and finally output the soil moisture data which is high precision, continuous time and space. Experiments show that the monitor product of the soil moisture by the new model is more accurate than inversion by the AMSR-E, so that it can be used in large-scale to monitor the soil moisture by remote sensing.

关键词： artificial neural network soil moisture CLSMDA

作者: He Li Xiao-yan Huang Yong-ming Luo Chun-xiang Shi

作者单位: Guangxi Research Institute of Meteorological Disasters Mitigation Nanning, 530022,China National Satellite Meteorological Center Beijng, 100081,China

会议类型: 国际会议

会议名称: The Fourth International Joint Conference on Computational Science and Optimization(第四届计算科学与优化国际大会 CSO 2011)

会议地点: 昆明、丽江

会议语种:英文

页码: 1339-1342

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）