{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T21:47:46Z","timestamp":1772920066861,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T00:00:00Z","timestamp":1590537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"International Partnership Program of the Chinese Academy of Sciences","award":["161461KYSB20170013"],"award-info":[{"award-number":["161461KYSB20170013"]}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road","award":["XDA20040301"],"award-info":[{"award-number":["XDA20040301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002\u2013present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9\u20130.95 and 1\u20132 \u00b0C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000\u20133000 m and 4000\u20135000 m, whereas the elevation interval at 6000\u20137000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming.<\/jats:p>","DOI":"10.3390\/rs12111722","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"1722","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau"],"prefix":"10.3390","volume":"12","author":[{"given":"Mingxi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Life Sciences, Faculty of Science, University of Technology Sydney, P.O. Box 123, Broadway, Sydney 2007, Australia"},{"name":"NSW Department of Planning, Industry and Environment, 4 Parramatta Square, Parramatta 2150, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2731-7150","authenticated-orcid":false,"given":"James","family":"Cleverly","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Faculty of Science, University of Technology Sydney, P.O. Box 123, Broadway, Sydney 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2574-1908","authenticated-orcid":false,"given":"De Li","family":"Liu","sequence":"additional","affiliation":[{"name":"NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia"},{"name":"Climate Change Research Centre, University of New South Wales, Sydney 2052, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Puyu","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Faculty of Science, University of Technology Sydney, P.O. Box 123, Broadway, Sydney 2007, Australia"},{"name":"NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Faculty of Science, University of Technology Sydney, P.O. Box 123, Broadway, Sydney 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2809-2376","authenticated-orcid":false,"given":"Alfredo","family":"Huete","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Faculty of Science, University of Technology Sydney, P.O. Box 123, Broadway, Sydney 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5990-2186","authenticated-orcid":false,"given":"Xihua","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Faculty of Science, University of Technology Sydney, P.O. Box 123, Broadway, Sydney 2007, Australia"},{"name":"NSW Department of Planning, Industry and Environment, 4 Parramatta Square, Parramatta 2150, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6950-1821","authenticated-orcid":false,"given":"Qiang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Faculty of Science, University of Technology Sydney, P.O. Box 123, Broadway, Sydney 2007, Australia"},{"name":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&amp;F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1038\/454393a","article-title":"The third pole","volume":"454","author":"Qiu","year":"2008","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1175\/1520-0469(1974)031<0003:TEOMOT>2.0.CO;2","article-title":"The effects of mountains on the general circulation of the atmosphere as identified by numerical experiments","volume":"31","author":"Manabe","year":"1974","journal-title":"J. Atmos. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1007\/s00376-012-1220-y","article-title":"Weather and climate effects of the Tibetan Plateau","volume":"29","author":"Duan","year":"2012","journal-title":"Adv. Atmos. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3979","DOI":"10.1002\/2015JD024728","article-title":"Review on climate change on the Tibetan plateau during the last half century","volume":"121","author":"Kuang","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"015101","DOI":"10.1088\/1748-9326\/5\/1\/015101","article-title":"Review of climate and cryospheric change in the Tibetan Plateau","volume":"5","author":"Kang","year":"2010","journal-title":"Environ. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.gloplacha.2013.12.001","article-title":"Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review","volume":"112","author":"Yang","year":"2014","journal-title":"Glob. Planet. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dimri, A.P., Bookhagen, B., Stoffel, M., and Yasunari, T. (2019). Himalayan Weather and Climate and their Impact on the Environment. Himalayan Weather and Climate and their Impact on the Environment, Springer Nature.","DOI":"10.1007\/978-3-030-29684-1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1038\/nclimate2563","article-title":"Elevation-dependent warming in mountain regions of the world","volume":"5","author":"Pepin","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111191","DOI":"10.1016\/j.rse.2019.05.010","article-title":"A physical model-based method for retrieving urban land surface temperatures under cloudy conditions","volume":"230","author":"Fu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_10","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. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2013.10.022","article-title":"A Surface Temperature Initiated Closure (STIC) for surface energy balance fluxes","volume":"141","author":"Mallick","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ouyang, X., Chen, D., Duan, S.-B., Lei, Y., Dou, Y., and Hu, G.J.R.S. (2017). Validation and analysis of long-term AATSR land surface temperature product in the Heihe River basin, China. Remote Sens., 9.","DOI":"10.3390\/rs9020152"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Noi, P., Degener, J., and Kappas, M. (2017). Comparison of multiple linear regression, cubist regression, and random forest algorithms to estimate daily air surface temperature from dynamic combinations of modis lst data. Remote Sens., 9.","DOI":"10.3390\/rs9050398"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, Y., Cai, W., and Yang, J. (2017). Evaluation of MODIS land surface temperature data to estimate near-surface air temperature in northeast China. Remote Sens., 9.","DOI":"10.3390\/rs9050410"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/2016JD025154","article-title":"Estimating daily air temperatures over the Tibetan Plateau by dynamically integrating MODIS LST data","volume":"121","author":"Zhang","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.rse.2011.12.019","article-title":"A daily merged MODIS Aqua\u2013Terra land surface temperature data set for the conterminous United States","volume":"119","author":"Crosson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.rse.2017.12.010","article-title":"Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States","volume":"206","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2012.10.034","article-title":"Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products","volume":"130","author":"Zhu","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.rse.2007.02.025","article-title":"Estimation of diurnal air temperature using MSG SEVIRI data in West Africa","volume":"110","author":"Stisen","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2294","DOI":"10.1002\/2013JD020803","article-title":"Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution","volume":"119","author":"Kilibarda","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Metz, M., Andreo, V., and Neteler, M. (2017). A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data. Remote Sens., 9.","DOI":"10.3390\/rs9121333"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.scitotenv.2019.02.077","article-title":"Reconstruction of high spatial resolution surface air temperature data across China: A new geo-intelligent multisource data-based machine learning technique","volume":"665","author":"Zhu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.isprsjprs.2018.01.018","article-title":"Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data","volume":"137","author":"Yoo","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kalra, A., and Ahmad, S. (2009). Using oceanic-atmospheric oscillations for long lead time streamflow forecasting. Water Resour. Res., 45.","DOI":"10.1029\/2008WR006855"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"180177","DOI":"10.1038\/sdata.2018.177","article-title":"High resolution temperature data for ecological research and management on the Southern Ocean Islands","volume":"5","author":"Leihy","year":"2018","journal-title":"Sci. Data"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2017.12.001","article-title":"Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation","volume":"101","author":"Meyer","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0168-1923(84)90017-0","article-title":"On the relationship between incoming solar radiation and daily maximum and minimum temperature","volume":"31","author":"Bristow","year":"1984","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, M., Wang, B., Liu, D.L., Liu, J., Zhang, H., Feng, P., Kong, D., Cleverly, J., Yang, X., and Yu, Q. (2020). Incorporating dynamic factors for improving a GIS-based solar radiation model. Trans. GIS.","DOI":"10.1111\/tgis.12607"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5738","DOI":"10.1029\/2018JD029798","article-title":"An Examination of Temperature Trends at High Elevations Across the Tibetan Plateau: The Use of MODIS LST to Understand Patterns of Elevation-Dependent Warming","volume":"124","author":"Pepin","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X. (2020). The first high-resolution meteorological forcing dataset for land process studies over China. Sci. Data, 7.","DOI":"10.1038\/s41597-020-0369-y"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"171091","DOI":"10.1038\/sdata.2017.191","article-title":"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015","volume":"5","author":"Abatzoglou","year":"2018","journal-title":"Sci. Data"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1080\/19475683.2018.1534890","article-title":"Spatial prediction based on Third Law of Geography","volume":"24","author":"Zhu","year":"2018","journal-title":"Ann. GIS"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0165-0114(03)00089-7","article-title":"A complete fuzzy decision tree technique","volume":"138","author":"Olaru","year":"2003","journal-title":"Fuzzy Sets Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classification in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.rse.2018.09.006","article-title":"A methodology to derive global maps of leaf traits using remote sensing and climate data","volume":"218","author":"Kattge","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.1002\/joc.5995","article-title":"High-resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States","volume":"39","author":"Hashimoto","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_39","unstructured":"Rashmi, K.V., and Gilad-Bachrach, R. (2015, January 9\u201312). DART: Dropouts meet Multiple Additive Regression Trees. Proceedings of the AISTATS, San Diego, CA, USA."},{"key":"ref_40","unstructured":"Quinlan, J.R. (1992, January 16\u201318). Learning with continuous classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Astralia."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.spasta.2015.04.001","article-title":"Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D+ T: The Cook Agronomy Farm data set","volume":"14","author":"Gasch","year":"2015","journal-title":"Spat. Stat."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Meyer, H., Katurji, M., Appelhans, T., M\u00fcller, M., Nauss, T., Roudier, P., and Zawar-Reza, P.J.R.S. (2016). Mapping daily air temperature for Antarctica based on MODIS LST. Remote Sens., 8.","DOI":"10.3390\/rs8090732"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, B., Chen, Y., and Shi, X. (2020). Does elevation dependent warming exist in high mountain Asia?. Environ. Res. Lett., 15.","DOI":"10.1088\/1748-9326\/ab6d7f"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1029\/WR018i001p00107","article-title":"Techniques of trend analysis for monthly water quality data","volume":"18","author":"Hirsch","year":"1982","journal-title":"Water Resour. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1002\/joc.2007","article-title":"Local atmospheric decoupling in complex topography alters climate change impacts","volume":"30","author":"Daly","year":"2009","journal-title":"Int. J. Climatol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1175\/JCLI-D-16-0343.1","article-title":"Spatiotemporal Temperature Variability over the Tibetan Plateau: Altitudinal Dependence Associated with the Global Warming Hiatus","volume":"30","author":"Cai","year":"2017","journal-title":"J. Clim."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2019.06.014","article-title":"A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine","volume":"155","author":"Kong","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2018.05.034","article-title":"Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States","volume":"215","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s10584-009-9733-9","article-title":"The altitudinal dependence of recent rapid warming over the Tibetan Plateau","volume":"97","author":"Qin","year":"2009","journal-title":"Clim. Chang."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/S0168-1923(02)00196-X","article-title":"Spatial estimation of air temperature differences for landscape-scale studies in montane environments","volume":"114","author":"Todd","year":"2003","journal-title":"Agric. For. Meteorol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1002\/joc.4520","article-title":"Rapid warming in the Tibetan Plateau from observations and CMIP5 models in recent decades","volume":"36","author":"You","year":"2016","journal-title":"Int. J. Climatol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1038\/nature06019","article-title":"Warming trends in Asia amplified by brown cloud solar absorption","volume":"448","author":"Ramanathan","year":"2007","journal-title":"Nature"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1002\/joc.3781","article-title":"The dramatic climate warming in the Qaidam Basin, northeastern Tibetan Plateau, during 1961\u20132010","volume":"34","author":"Wang","year":"2014","journal-title":"Int. J. Climatol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1722\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:33:10Z","timestamp":1760175190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/11\/1722"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,27]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["rs12111722"],"URL":"https:\/\/doi.org\/10.3390\/rs12111722","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,27]]}}}