Retrieval of land surface temperature (LST) based on Support Vector Machine (SVM) from HJ-1B data with single-channel

Land surface temperature (LST) is a very key variable for land surface process research. However, the retrieval of LST is still underdetermined issue because of the fact that the unknowns are always more than the measurements even the atmospheric condition can be acquired completely. Currently, Support Vector Machine (SVM) as an effective machine learning tool has been used widely in the domain of quantitative remote sensing because its optimization and generalization. This paper used SVM to retrieve LST based on only one thermal band in HJ-1B satellite launched by China. The radiance and water vapor content were selected as the independent variables. The validation result indicates that the errors of the SVM-MOD07 are lower than the Qin´s-MOD07. Additionally, the sensitivity analysis indicates that when the errors of the water vapor content increase, the errors for the SVM model change insignificantly. In the end the SVM model was applied in Beijing area.