Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature  

作　　者：Shiqi YANG Dejun ZHANG Liang SUN Yongqian WANG Yanghua GAO 

机构地区：[1]Chongqing Institute of Meteorological Sciences,Chongqing 401147 [2]College of Resources and Environment,Chengdu University of Information Technology,Chengdu 610225 [3]Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Remote Sensing,Ministry of Agriculture and Rural Affairs of the People’s Republic of China,Beijing 100081

出　　处：《Journal of Meteorological Research》2020年第2期264-279,共16页气象学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(41631180 and 41801315);Science and Technology Department of Chongqing Municipality(cstc2019jcyj-msxm X0649);Innovation Project of Chinese Academy of Agricultural Sciences(960-3)。

摘　　要：Temperature vegetation dryness index(TVDI)in a triangular or trapezoidal feature space can be calculated from the land surface temperature(LST)and normalized difference vegetation index(NDVI),and has been widely applied to regional drought monitoring.However,thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels.In cloudy areas,LST data include a large number of cloudy pixels,seriously degrading the spatial and temporal continuity of drought monitoring.In this paper,the Remotely Sensed Daily Land Surface Temperature Reconstruction model(RSDAST)is combined with the LST reconstructed(RLST)by the RSDAST and applied to drought monitoring in a cloudy area.The drought monitoring capability of the reconstructed temperature vegetation drought index(RTVDI)under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction.Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI(OTVDI)in 2011.In addition,RLST/NDVI scatter plots cover a wide range of values,with the fitted dry–wet boundaries more representative of real soil moisture conditions.Under continuously cloudy conditions,the OTVDI inverted from the original LST(OLST)loses its drought monitoring capability,whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area.The correlation between TVDI and soil moisture is stronger for RTVDI(R=-0.45)than that for OTVDI(R=-0.33).In terms of the spatial and temporal distributions,the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI.Hence,in continuously cloudy areas,RTVDI not only expands drought monitoring capability in time and space,but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.

关 键 词：LAND SURFACE TEMPERATURE RECONSTRUCTION Remotely Sensed Daily LAND SURFACE TEMPERATURE RECONSTRUCTION model(RSDAST) TEMPERATURE vegetation DRYNESS index(TVDI) soil moisture drought 

分 类 号：P407[天文地球—大气科学及气象学] P426.616