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Remote Sens. 2016, 8(8), 656; doi:10.3390/rs8080656

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

1
State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
4
National Satellite Meteorological Center, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Academic Editors: Benjamin Bechtel, Iphigenia Keramitsoglou, Simone Kotthaus, James A. Voogt, Klemen Zakšek, Zhaoliang Li, Richard Müller and Prasad S. Thenkabail
Received: 17 June 2016 / Revised: 3 August 2016 / Accepted: 9 August 2016 / Published: 13 August 2016
View Full-Text   |   Download PDF [15362 KB, uploaded 16 August 2016]   |  

Abstract

Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001–2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R2) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18–2.60 K in RMSE and 0.00–0.15 in R2). View Full-Text
Keywords: air temperature; land surface temperature; remote sensing; statistical model; MODIS air temperature; land surface temperature; remote sensing; statistical model; MODIS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, Y.; Quan, J.; Zhan, W.; Guo, Z. Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data. Remote Sens. 2016, 8, 656.

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