%0 Dataset
%T A global spatiotemporally seamless daily mean land surface temperature from 2003 to 2019
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/9e664d73-664c-4ca5-9a12-9b8fdabb9222
%W NCDC
%R 10.5281/zenodo.6287052
%A Zhan Wenfeng
%K Surface temperature;diurnal temperature cycle;MODIS
%X The daily average surface temperature (LST) obtained from the following locations is crucial for various applications, such as global and regional climate change analysis, for polar orbiting spacecraft. However, polar orbiting spacecraft can only effectively sample the surface in very limited circumstances, under cloudless conditions, on a daily basis. These limitations result in systematic sampling bias (Δ Tsb) on the daily average LST (Tdm) estimated using traditional methods that directly use clear sky LST observations as Tdm. Several methods have been proposed for estimating Tdm, but they are becoming increasingly rare and can produce spatiotemporal seamless Tdmacross the globe. Based on MODIS and reanalysis data, we propose an improved annual and diurnal temperature cycle based framework (referred to as the IADTC framework) to generate global spatiotemporal seamless Tdmproducts ranging from 2003 to 2019 (named the GADTC product).