ESTIMATION OF MONTHLY NEAR SURFACE AIR TEMPERATURE USING GEOGRAPHICALLY WEIGHTED REGRESSION IN CHINA

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Wang, M. M.^1¹  He, G. J.^2² 

[1] Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China^1;Institute of Remote Sensing and Digital Earth, Chinese Academy of sciences, Beijing 10094, China^2

关键词: Near Surface Air Temperature;    Land Surface Temperature;    Geographically Weighted Regression;    MODIS;    Remote Sensing;    Geographically Information System;   

DOI  :  10.5194/isprs-archives-XLII-3-1747-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

【 授权许可】

CC BY