Predicting land surface temperature with geographically weighed regression and deep learning

Abstract

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. This article is categorized under: Technologies > Machine Learning

An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona

“…The neighborhoods (also known as bandwidths) are the distance metrics used to weigh observations. GWR has been used to build the relationship between dependent variables (ECOSTRESS‐based LST and OWU in 2019) and percent land cover (tree%, grass%, soil%, building%, and road%) as independent variables (Brunsdon et al., 1998; Fan et al., 2015; Jia et al., 2021; C. Wang et al., 2021; Z. Wang et al., 2020; Zhao et al., 2018). Z‐score methods normalize all dependent and independent variables prior to the input of the GWR.…”

Spatial-Multitemporal Analysis of Heatwaves in Thailand: Discrepancies between In-Situ Air Temperature and Remote Sensing-Derived Land Surface Temperature

“…Gaps in the daily LST data retrieved from MODIS platforms are noted. Recently, some of the related studies focused on reconstructing the LST of satellite datasets, forecasting daily LST from time series data, and fusing multisource data to estimate subpixel LST ( [84][85][86][87]. Although machine learning methods have been used in LST retrieval, they have not yet been widely used [88].…”

Integrating Remote Sensing and Ground-Based Data for Enhanced Spatial–Temporal Analysis of Heatwaves: A Machine Learning Approach

“…Addressing gaps in daily LST data derived from MODIS platforms poses a significant challenge, necessitating innovative solutions for data reconstruction and prediction. Recent studies have explored various methods, including reconstructing LST from satellite datasets, forecasting daily LST using time series data, and estimating subpixel LST by fusing multisource data [80][81][82][83]. Although machine learning techniques have been introduced to LST retrieval, their application remains limited [84].…”