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However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model\u2019s generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson\u2019s Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale.<\/jats:p>","DOI":"10.3390\/rs16142542","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T15:22:05Z","timestamp":1720624925000},"page":"2542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Spatial Downscaling of Nighttime Land Surface Temperature Based on Geographically Neural Network Weighted Regression Kriging"],"prefix":"10.3390","volume":"16","author":[{"given":"Jihan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Highway Engineering Consulting Group Company Ltd., Beijing 100089, China"}]},{"given":"Laifu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"}]},{"given":"Haoyu","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9925-5788","authenticated-orcid":false,"given":"Yiming","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9322-0149","authenticated-orcid":false,"given":"Sensen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China"}]},{"given":"Renyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-Derived Land Surface Temperature: Current Status and Perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. 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