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Predicting land surface temperature with geographically weighed regression and deep learning - 2020

Predicting Land Surface Temperature With Geographically Weighed Regression And Deep Learning

Research Area:  Machine Learning


For prediction of urban remote sensing surface temperature, cloud, cloud shadow and snow contamination lead to the failure of surface temperature inversion and vegetation-related index calculation. A time series prediction framework of urban surface temperature under cloud interference is proposed in this paper. This is helpful to solve the problem of the impact of data loss on surface temperature prediction. Spatial and temporal variation trends of surface temperature and vegetation index are analyzed using Landsat 7/8 remote sensing data of 2010 to 2019 from Beijing. The geographically weighed regression (GWR) method is used to realize the simulation of surface temperature based on the current date. The deep learning prediction network based on convolution and long short-term memory (LSTM) networks was constructed to predict the spatial distribution of surface temperature on the next observation date. The time series analysis shows that the NDBI is less than −0.2, which indicates that there may be cloud contamination. The land surface temperature (LST) modeling results show that the precision of estimation using GWR method on impervious surface and water bodies is superior compared to the vegetation area. For LST prediction using deep learning methods, the result of the prediction on surface temperature space distribution was relatively good. The purpose of this study is to make up for the missing data affected by cloud, snow, and other interference factors, and to be applied to the prediction of the spatial and temporal distributions of LST.


Author(s) Name:  Hongfei Jia, Dehe Yang, Weiping Deng, Qing Wei, Wenliang Jiang

Journal name:  WIREs Data Mining and Knowledge Discovery

Conferrence name:  

Publisher name:  Wiley

DOI:  10.1002/widm.1396

Volume Information:  Volume11, Issue1 January/February 2021