{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:29:52Z","timestamp":1774466992117,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,8]],"date-time":"2017-12-08T00:00:00Z","timestamp":1512691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Programs of China","award":["2016YFA0600302"],"award-info":[{"award-number":["2016YFA0600302"]}]},{"name":"the National Key Research and Development Programs of China","award":["2016YFB0502502"],"award-info":[{"award-number":["2016YFB0502502"]}]},{"name":"the Hainan Provincial Department of Science and Technology","award":["ZDKJ2016021"],"award-info":[{"award-number":["ZDKJ2016021"]}]},{"name":"the Hainan Provincial Department of Science and Technology","award":["ZDKJ2016015-1"],"award-info":[{"award-number":["ZDKJ2016015-1"]}]},{"name":"the programs of the National Natural Science Foundation of China","award":["61401461"],"award-info":[{"award-number":["61401461"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e., Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR)) models for predicting monthly minimum, mean, and maximum NSAT in China, a domain with a large area, complex topography, and highly variable station density. This was conducted for a period of 12 months of 2010. The accuracy of the GWR model is better than the MLR model with an improvement of about 3 \u00b0C in the Root Mean Squared Error (RMSE), which indicates that the GWR model is more suitable for predicting monthly NSAT than the MLR model over a large scale. For three spatial interpolation models, the RMSEs of the predicted monthly NSAT are greater in the warmer months, and the mean RMSEs of the predicted monthly mean NSAT for 12 months in 2010 are 1.56 \u00b0C for the Kriging model, 1.74 \u00b0C for the IDW model, and 2.39 \u00b0C for the Spline model, respectively. The GWR model is better than the Kriging model in the warmer months, while the Kriging model is superior to the GWR model in the colder months. The total precision of the GWR model is slightly higher than the Kriging model. The assessment result indicated that the higher standard deviation and the lower mean of NSAT from sample data would be associated with a better performance of predicting monthly NSAT using spatial interpolation models.<\/jats:p>","DOI":"10.3390\/rs9121278","type":"journal-article","created":{"date-parts":[[2017,12,8]],"date-time":"2017-12-08T11:37:40Z","timestamp":1512733060000},"page":"1278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":95,"title":["Comparison of Spatial Interpolation and Regression Analysis Models for an Estimation of Monthly Near Surface Air Temperature in China"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5379-5773","authenticated-orcid":false,"given":"Mengmeng","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"given":"Guojin","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhaoming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Guizhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhengjia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"given":"Xiaojie","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhijie","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Resources Engineering, Longyan University, Longyan 364012, China"}]},{"given":"Xiuguo","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1175\/JHM-D-12-014.1","article-title":"Mapping mean monthly temperatures over a coastal hilly area incorporating terrain aspect effects","volume":"14","author":"Guan","year":"2013","journal-title":"J. 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