{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:23:09Z","timestamp":1774797789150,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T00:00:00Z","timestamp":1590624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006245","name":"Ministry of Science and Technology, Israel","doi-asserted-by":"publisher","award":["63365"],"award-info":[{"award-number":["63365"]}],"id":[{"id":"10.13039\/501100006245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006245","name":"Ministry of Science and Technology, Israel","doi-asserted-by":"publisher","award":["MOST- PRC 2018-2020"],"award-info":[{"award-number":["MOST- PRC 2018-2020"]}],"id":[{"id":"10.13039\/501100006245","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30ES023515"],"award-info":[{"award-number":["P30ES023515"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R00ES023450"],"award-info":[{"award-number":["R00ES023450"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 \u00d7 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 \u00d7 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 \u00d7 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners\u2014random forest (RF) and extreme gradient boosting (XGBoost)\u2014by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004\u20132017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 \u00b0C, mean absolute error (MAE) of 0.6 and 0.7 \u00b0C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 \u00b0C, MAE of 0.5 \u00b0C, and R2 of 0.63. The generated hourly 1 \u00d7 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies.<\/jats:p>","DOI":"10.3390\/rs12111741","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"1741","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Estimation of Hourly near Surface Air Temperature Across Israel Using an Ensemble Model"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8853-0724","authenticated-orcid":false,"given":"Bin","family":"Zhou","sequence":"first","affiliation":[{"name":"Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sde Boqer Campus, Beer Sheva 8499000, Israel"},{"name":"Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O.B. 60 12 03, D-14412 Potsdam, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0855-3745","authenticated-orcid":false,"given":"Evyatar","family":"Erell","sequence":"additional","affiliation":[{"name":"Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sde Boqer Campus, Beer Sheva 8499000, Israel"},{"name":"Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 8410501, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7948-6995","authenticated-orcid":false,"given":"Ian","family":"Hough","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 8410501, Israel"},{"name":"Universit\u00e9 Grenoble Alpes, Inserm, CNRS, IAB, Site Sante, All\u00e9e des Alpes, 38700 La Tronche, France"}]},{"given":"Alexandra","family":"Shtein","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 8410501, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4312-5957","authenticated-orcid":false,"given":"Allan C.","family":"Just","sequence":"additional","affiliation":[{"name":"Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029-5674, USA"}]},{"given":"Victor","family":"Novack","sequence":"additional","affiliation":[{"name":"Clinical Research Center, Soroka University Medical Center, P.O.B. 151, Beer Sheva 8410101, Israel"}]},{"given":"Jonathan","family":"Rosenblatt","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, Ben Gurion University of the Negev, P.O.B. 653, Beer Sheva 8410501, Israel"}]},{"given":"Itai","family":"Kloog","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva 8410501, Israel"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1038\/s41467-018-04040-y","article-title":"Increasing occurrence of cold and warm extremes during the recent global warming slowdown","volume":"9","author":"Johnson","year":"2018","journal-title":"Nat. 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