Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data

文献类型： 外文期刊

第一作者： Sun, Liang

作者： Sun, Liang;Chen, Zhongxin;Wang, Limin;Sun, Liang;Gao, Feng;Anderson, Martha;Yang, Yun;Song, Lisheng;Hu, Bo

作者机构：

关键词： MODIS;Land surface temperature;Reconstruction;Remotely Sensed DAily land Surface;Temperature reconstruction (RSDAST) model;TVDI

期刊名称：COMPUTERS & GEOSCIENCES （ 影响因子：3.372； 五年影响因子：3.696 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Land surface temperature (LST) is a critical parameter in environmental studies and resource management. The MODIS LST data product has been widely used in various studies, such as drought monitoring, evapotranspiration mapping, soil moisture estimation and forest fire detection. However, cloud contamination affects thermal band observations and will lead to inconsistent LST results. In this study, we present a new Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST) model that recovers clear sky LST for pixels covered by cloud using only clear-sky neighboring pixels from nearby dates. The reconstructed LST was validated using the original LST pixels. Model shows high accuracy for reconstructing one masked pixel with R-2 of 0.995, bias of -0.02 K and RMSE of 0.51 K. Extended spatial reconstruction results show a better accuracy for flat areas with R-2 of 0.72-0.89, bias of -0.02-0.21 K, and RMSE of 0.92-1.16 K, and for mountain areas with R-2 of 0.81-0.89, bias of 0.35-1.52 K, and RMSE of 1.42-2.24 K. The reconstructed areas show spatial and temporal patterns that are consistent with the clear neighbor areas. In the reconstructed LST and NDVI triangle feature space which is controlled by soil moisture, LST values distributed reasonably and correspond well to the real soil moisture conditions. Our approach shows great potential for reconstructing clear sky LST. under cloudy conditions and provides consistent daily LST which are critical for daily drought monitoring.

分类号： TP39`P

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