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Remote Sens. 2015, 7(5), 6005-6025; doi:10.3390/rs70506005

A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale

1
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing 100101, China
2
State Nuclear Electric Power Planning Design and Research Institute, Beijing 100095, China
3
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
4
Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
5
Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Sciences (CAS), Beijing 100081, China
6
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Gloaguen and Prasad S. Thenkabail
Received: 17 March 2015 / Revised: 30 April 2015 / Accepted: 5 May 2015 / Published: 13 May 2015
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Abstract

This paper presents a method of estimating regional distributions of surface air temperature (Ta) and surface vapor pressure (ea), which uses remotely-sensed data and meteorological data as its inputs. The method takes into account the effects of both local driving force and horizontal advection on Ta and ea. Good correlation coefficients (R2) and root mean square error (RMSE) between the measurements of Ta/ea at weather stations and Ta/ea estimates were obtained; with R2 of 0.77, 0.82 and 0.80 and RMSE of 0.42K, 0.35K and 0.20K for Ta and with R2 of 0.85, 0.88, 0.88 and RMSE of 0.24hpa, 0.35hpa and 0.16hpa for ea, respectively, for the three-day results. This result is much better than that estimated from the inverse distance weighted method (IDW). The performance of Ta/ea estimates at Dongping Lake illustrated that the method proposed in the paper also has good accuracy for a heterogeneous surface. The absolute biases of Ta and ea estimates at Dongping Lake from the proposed method are less than 0.5Kand 0.7hpa, respectively, while the absolute biases of them from the IDW method are more than 2K and 3hpa, respectively. Sensitivity analysis suggests that the Ta estimation method presented in the paper is most sensitive to surface temperature and that the ea estimation method is most sensitive to available energy. View Full-Text
Keywords: air temperature; vapor pressure; surface energy balance; horizontal advection; regional scale air temperature; vapor pressure; surface energy balance; horizontal advection; regional scale
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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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MDPI and ACS Style

Zhang, R.; Rong, Y.; Tian, J.; Su, H.; Li, Z.-L.; Liu, S. A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale. Remote Sens. 2015, 7, 6005-6025.

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