{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T10:52:52Z","timestamp":1769251972134,"version":"3.49.0"},"reference-count":75,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Research Program for Higher Education Institutions of Jiangsu","award":["23KJB420004"],"award-info":[{"award-number":["23KJB420004"]}]},{"name":"Natural Science Research Program for Higher Education Institutions of Jiangsu","award":["2022r040"],"award-info":[{"award-number":["2022r040"]}]},{"DOI":"10.13039\/501100013156","name":"Startup Foundation for Introducing Talent of NUIST","doi-asserted-by":"publisher","award":["23KJB420004"],"award-info":[{"award-number":["23KJB420004"]}],"id":[{"id":"10.13039\/501100013156","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013156","name":"Startup Foundation for Introducing Talent of NUIST","doi-asserted-by":"publisher","award":["2022r040"],"award-info":[{"award-number":["2022r040"]}],"id":[{"id":"10.13039\/501100013156","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The estimation of spatially resolved near-surface air temperature (NSAT) has been extensively performed in previous studies using satellite-derived land surface temperature (LST) from MODIS. However, there remains a need for estimating daily NSAT based on LST data from other satellites, which has important implications for integrating multi-source LST in estimating NSAT and ensuring the continuity of satellite-derived estimates of NSAT over long-term periods. In this study, we conducted a comprehensive comparison of LST derived from Metop with MODIS LST in the modeling and mapping of daily NSAT. The results show that Metop LST achieves consistent predictive performance with MODIS LST in estimating daily NSAT, and models based on Metop LST or MODIS LST have overall predictive performance of about 1.2\u20131.4 K, 1.5\u20132.0 K, and 1.8\u20131.9 K in RMSE for estimating Tavg, Tmax, and Tmin, respectively. Compared to models based on nighttime LST, daytime LST can improve the predictive performance of Tmax by about 0.26\u20130.28 K, while performance for estimating Tavg or Tmin using different schemes of LST is comparable. Models based on Metop LST also exhibit high consistency with models utilizing MODIS LST in terms of the variability in predictive performance across months, with RMSE of 1.03\u20131.82 K, 1.3\u20132.49 K, and 1.26\u20132.66 K for Tavg, Tmin, and Tmax, respectively. This temporal variability in performance is not due to sampling imbalance across months, which is confirmed by comparing models trained using bootstrapped samples in balance, and our results imply that sampling representativeness, complicated by retrieval gaps in LST, is an important issue when analyzing the variability in predictive performance for estimating NSAT. To fully assess the predictive capability of Metop LST in estimating daily NSAT, more studies need to be performed using different methods across areas with a range of scales and geographical environments.<\/jats:p>","DOI":"10.3390\/rs16203754","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T07:53:05Z","timestamp":1728546785000},"page":"3754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Remotely Sensed Estimation of Daily Near-Surface Air Temperature: A Comparison of Metop and MODIS"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3200-6525","authenticated-orcid":false,"given":"Zhenwei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Technology Innovation Center for Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, Nanjing 210044, China"},{"name":"Jiangsu Province Engineering Research Center of Collaborative Navigation\/Positioning and Smart Application, Nanjing 210044, China"}]},{"given":"Peisong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Xiaodi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China"}]},{"given":"Hongwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8197","DOI":"10.1002\/jgrd.50615","article-title":"Instrumental Temperature Series in Eastern and Central China Back to the Nineteenth Century","volume":"118","author":"Cao","year":"2013","journal-title":"J. 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