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Article

8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

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College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
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Changjiang Institute of Survey, Planning, Design and Research, 1863 Jiefang Street, Wuhan 430010, China
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National Engineering Research Center of Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China
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School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
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Guangxi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China
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Changjiang River Scientific Research Institute, Changjiang River Water Resources Commission, Huangpu Street No. 23, Wuhan 430010, China
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Guanxi Key Laboratory of Water Engineering Materials and Structures, Guanxi Institute of Water Resources Research, Nanning 530023, China
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School of Environmental Studies & State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences (Wuhan), Wuhan 430074, China
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Author to whom correspondence should be addressed.
Academic Editor: Ka Lok Chan
Remote Sens. 2021, 13(12), 2355; https://doi.org/10.3390/rs13122355
Received: 3 March 2021 / Revised: 22 May 2021 / Accepted: 9 June 2021 / Published: 16 June 2021
(This article belongs to the Section AI Remote Sensing)
Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale. View Full-Text
Keywords: MODIS; air temperature estimation; remote sensing; land surface temperature; nighttime LST MODIS; air temperature estimation; remote sensing; land surface temperature; nighttime LST
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MDPI and ACS Style

Zeng, L.; Hu, Y.; Wang, R.; Zhang, X.; Peng, G.; Huang, Z.; Zhou, G.; Xiang, D.; Meng, R.; Wu, W.; Hu, S. 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale. Remote Sens. 2021, 13, 2355. https://doi.org/10.3390/rs13122355

AMA Style

Zeng L, Hu Y, Wang R, Zhang X, Peng G, Huang Z, Zhou G, Xiang D, Meng R, Wu W, Hu S. 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale. Remote Sensing. 2021; 13(12):2355. https://doi.org/10.3390/rs13122355

Chicago/Turabian Style

Zeng, Linglin, Yuchao Hu, Rui Wang, Xiang Zhang, Guozhang Peng, Zhenyu Huang, Guoqing Zhou, Daxiang Xiang, Ran Meng, Weixiong Wu, and Shun Hu. 2021. "8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale" Remote Sensing 13, no. 12: 2355. https://doi.org/10.3390/rs13122355

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