Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area

文献类型： 外文期刊

第一作者： Yang, Guijun

作者： Yang, Guijun;Yang, Guijun;Yang, Guijun;Zhao, Chunjiang;Huang, Wenjiang;Wang, Jihua;Pu, Ruiliang

作者机构：

关键词： Artificial neural network;ASTER;Endmember index;MODIS;Self-organizing feature map;Subpixel temperature

期刊名称：REMOTE SENSING OF ENVIRONMENT （ 影响因子：10.164； 五年影响因子：11.057 ）

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收录情况： SCI

摘要： Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990m and 90m resolutions, respectively. Secondly, the relationship between the 990m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90m data (R2=0.709 and RMSE=2.702K).

分类号： X83

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