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 central 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 an 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.
Highlights
L AND surface temperature (LST) is one of the critical indicators that gauge the energy balance and material exchange near the Earth’s surface, which is extensively applied in the fields such as evapotranspiration estimation, crop yield estimation, urban heat island research, hydrologic cycle research, vegetation monitoring, and disaster prediction [1]–[7]
A few missing values in the original MODIS land surface temperature (LST) could result in some missing values in the 0.25° MODIS LST, they do not affect the completeness of the downscaled LST because geographically weighted regression (GWR) can establish a regression model for any location within the extent of the samples
In the Yunnan-Guizhou Plateau (YGP) and BSH regions, the spatial patterns of the downscaled LSTs from the stepwise method are more similar to the original MODIS LSTs in the black circles, whereas the direct method results in lower LST values in the YGP region and higher LST values in the BSH region
Summary
L AND surface temperature (LST) is one of the critical indicators that gauge the energy balance and material exchange near the Earth’s surface, which is extensively applied in the fields such as evapotranspiration estimation, crop yield estimation, urban heat island research, hydrologic cycle research, vegetation monitoring, and disaster prediction [1]–[7]. TIR algorithms are recognized as much more developed, and the retrieved LST has a relatively high spatial resolution and accuracy [8]–[10]. The TIR signal is sensitive to the atmosphere and cannot penetrate clouds, which prevents onboard sensors from capturing information from the land surface and leads to serious missing data problems in the LST products [11], [12]. The respective drawbacks of TIR and MW data hinder the single-source LST product from meeting the higher quality requirement in various fields [14], [15]; it is urgent to develop methods that can obtain LST data with high spatial resolution and high accuracy under all-weather condition simultaneously
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
