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Article

Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

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School of Life Sciences, Faculty of Science, University of Technology Sydney, P.O. Box 123, Broadway, Sydney 2007, Australia
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NSW Department of Planning, Industry and Environment, 4 Parramatta Square, Parramatta 2150, Australia
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NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia
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Climate Change Research Centre, University of New South Wales, Sydney 2052, Australia
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State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1722; https://doi.org/10.3390/rs12111722
Received: 20 April 2020 / Revised: 18 May 2020 / Accepted: 25 May 2020 / Published: 27 May 2020
(This article belongs to the Section Atmosphere Remote Sensing)
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–present). 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 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming. View Full-Text
Keywords: near-surface air temperature; MODIS LST; machine learning; Tibetan Plateau near-surface air temperature; MODIS LST; machine learning; Tibetan Plateau
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MDPI and ACS Style

Zhang, M.; Wang, B.; Cleverly, J.; Liu, D.L.; Feng, P.; Zhang, H.; Huete, A.; Yang, X.; Yu, Q. Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau. Remote Sens. 2020, 12, 1722. https://doi.org/10.3390/rs12111722

AMA Style

Zhang M, Wang B, Cleverly J, Liu DL, Feng P, Zhang H, Huete A, Yang X, Yu Q. Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau. Remote Sensing. 2020; 12(11):1722. https://doi.org/10.3390/rs12111722

Chicago/Turabian Style

Zhang, Mingxi, Bin Wang, James Cleverly, De L. Liu, Puyu Feng, Hong Zhang, Alfredo Huete, Xihua Yang, and Qiang Yu. 2020. "Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau" Remote Sensing 12, no. 11: 1722. https://doi.org/10.3390/rs12111722

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