A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature from AMSR-E Data

Abstract

A stepwise downscaling method is proposed for generating high-resolution land surface temperature (LST) from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) data to benefit the fusion of thermal infrared and microwave data for high quality all-weather LST. This method sets a series of intermediate resolution levels between the initial (0.25°) and target (0.01°) resolutions, then downscales AMSR-E LST from one resolution to the next one step at a time, starting from 0.25° and ending with 0.01°. The geographically weighted regression model is adopted in each step to construct the relationship between LST and environmental variables, including normalized differential vegetation index, elevation and slope. The stepwise method is verified over three regions in China that represent different characteristics of landscape heterogeneity varying from the highest to the lowest: the Yunnan-Guizhou Plateau (YGP), the border of Shanxi Province and Henan Province (BSH), and the center part of Inner Mongolia (CIM). Verified using the emulated AMSR-E LST resampled from reference MODIS LST available in 2010, the results show that the proportions of dates when the stepwise method is better are 100%, 78.1%, and 51.5% in the YGP, BSH, and CIM regions, respectively, which means the stepwise method has advantage over the direct method in the regions with high heterogeneity. For real AMSR-E LST, the downscaled LST exhibits a similar spatial pattern to that of emulated data but suffers from reduced accuracy and contrast, which is caused by the smooth spatial pattern and low accuracy of the real AMSR-E LST.

Cloud-Free Land Surface Temperature Reconstructions Based on MODIS Measurements and Numerical Simulations for Characterizing Surface Urban Heat Islands

“…MKF-M LST product is generated based on MKF fusion thermal infrared and PMW LST. Both TRIMS LST product and MKF-M LST product were obtained from the National Qinghai-Tibet Plateau Data Center http://data.tpdc.ac.cn/ [72,73], and we compared them in spatial mode and quantitative accuracy (Fig. 9).…”

Important role of precipitation in controlling a more uniform spring phenology in the Qinba Mountains, China

“…To this end, we used a geographically weighted regression (GWR) model to downscale the WT, ST, and SP data to a spatial resolution of 0.01° ( Lu et al., 2018 ; Lu et al., 2019 ). We selected the EVI and elevation as influencing factors and downscaled the WT, ST, and SP data by three levels with an intermediate resolution of 0.05° ( Figure S1 ) ( Zhang et al., 2020 ; Zhang and Cheng, 2020 ; Xu and Cheng, 2021 ). The values were calculated as follows:…”

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

“…Land surface models and reanalysis datasets release simulated hourly skin temperatures continuously, which have been fused with satellite-retrieved LSTs to generate gap-free LSTs (Dumitrescu et al, 2020;Long et al, 2020;Marullo et al, 2014;Ma et al, 2022;Muñoz-Sabater et al, 2021); however, the accuracy of the recovered LSTs is highly dependent on simulation accuracy, especially for continuous cloudy days. In addition, passive microwave (PMW) observations can penetrate clouds and estimate LSTs under all-sky conditions (Zhang et al, 2019;Wu et al, 2022), and studies have explored fusing such data with TIR LSTs from sensors of polar-orbiting satellites (Zhang et al, 2020;Xu and Cheng, 2021). However, PMW data have limited overpass times in a day; thus, they cannot match well with GEO observations.…”