{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T14:57:53Z","timestamp":1770821873761,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T00:00:00Z","timestamp":1631577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China - Heilongjiang Joint Found","award":["U20A2082"],"award-info":[{"award-number":["U20A2082"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["4197115"],"award-info":[{"award-number":["4197115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["TD2019D002"],"award-info":[{"award-number":["TD2019D002"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land surface temperature (LST) is a crucial parameter driving the dynamics of the thermal state on land surface. In high-latitude cold region, a long-term, stable LST product is of great importance in examining the distribution and degradation of permafrost under pressure of global warming. In this study, a generalized additive model (GAM) approach was developed to fill the missing pixels of the MODIS\/Terra 8-day Land Surface Temperature (MODIS LST) daytime products with the ERA5 Land Skin Temperature (ERA5ST) dataset in a high-latitude watershed in Eurasia. Comparison at valid pixels revealed that the MODIS LST was 4.8\u201313.0 \u00b0C higher than ERA5ST, which varies with land covers and seasons. The GAM models fairly explained the LST differences between the two products from multiple covariates including satellite-extracted environmental variables (i.e., normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI) as well as locational information. Considering the dramatic seasonal variation of vegetation and frequent snow in the cold region, the gap-filling was conducted in two seasons. The results revealed the root mean square errors (RMSE) of 2.7 \u00b0C and 3.4 \u00b0C between the valid MODIS LST and GAM-simulated LST data in the growing season and snowing season, respectively. By including the satellite-extracted land surface information in the GAM model, localized variations of land surface temperature that are often lost in the reanalysis data were effectively compensated. Specifically, land surface wetness (NDWI) was found to be the greatest contributor to explaining the differences between the two products. Vegetation (NDVI) was useful in the growing season and snow cover (NDSI) cannot be ignored in the snow season of the study region. The km-scale gap-filled MODIS LST products provide spatially and temporally continuous details that are useful for monitoring permafrost degradation in cold regions in scenarios of global climate change.<\/jats:p>","DOI":"10.3390\/rs13183667","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T21:47:21Z","timestamp":1631656041000},"page":"3667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Gap-Filling of 8-Day Terra MODIS Daytime Land Surface Temperature in High-Latitude Cold Region with Generalized Additive Models (GAM)"],"prefix":"10.3390","volume":"13","author":[{"given":"Dianfan","family":"Guo","sequence":"first","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China"},{"name":"Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin Normal University, Harbin 150025, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0306-9535","authenticated-orcid":false,"given":"Cuizhen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography, University of South Carolina, Columbia, SC 29208, USA"}]},{"given":"Shuying","family":"Zang","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China"},{"name":"Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin Normal University, Harbin 150025, China"}]},{"given":"Jinxi","family":"Hua","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6884-788X","authenticated-orcid":false,"given":"Zhenghan","family":"Lv","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China"},{"name":"Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin Normal University, Harbin 150025, China"}]},{"given":"Yue","family":"Lin","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China"},{"name":"Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin Normal University, Harbin 150025, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4227","DOI":"10.1016\/j.rse.2008.07.009","article-title":"A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales","volume":"112","author":"Anderson","year":"2008","journal-title":"Remote Sens. 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