Retrieval of Bare-surface Soil Moisture from Simulated Brightness Temperature Using Least Squares Support Vector Machines Technique
Soil moisture is an important parameter for hydrological and climatic investigations.It also plays a critical role in the prediction of erosion,flood or drought.In this paper,we explore the use of the support vector machine technique for modeling soil moisture inversion.LS-SVM is improved by the standard SVM and has more attractive properties.Experimental tests are carried by using the different set of training and test data.The methodologies have been applied to two sets of data to retrieve soil moisture and obtained the root mean squared error(RMSE)and the determination coefficient(R2).The emissivity model(Q/H model)is applied to acquire the brightness temperature.The frequencies of interest include 1.4 GHz(L-band)of the soil moisture and ocean salinity(SMOS)sensor at two incidence angles and 6.9 GHz(C-band)of the advanced microwave scanning radiometer(AMSR)viewing angle of 55 deg.The effectiveness is assessed by considering various combinations of the input features.This study demonstrates the great potential of LS-SVM in the retrial of soil moisture from passive microwave remotely sensed data.
Fei Xu Qinghe Zhang Qiyuan Zou
School of Science,Three Gorges University,Yichang,Hubei 443002,China
国际会议
Progress in Electromagnetics Research Symposium 2014(2014年电磁学研究新进展学术研讨会)
广州
英文
1508-1512
2014-08-01（万方平台首次上网日期，不代表论文的发表时间）