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Advances in Meteorology
Volume 2016, Article ID 4294219, 11 pages
http://dx.doi.org/10.1155/2016/4294219
Research Article

Estimating the Surface Air Temperature by Remote Sensing in Northwest China Using an Improved Advection-Energy Balance for Air Temperature Model

1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Florida, FL 33431, USA
4State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
5Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

Received 2 February 2016; Accepted 29 May 2016

Academic Editor: Philippe Ricaud

Copyright © 2016 Suhua Liu et al. 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.

Abstract

To estimate the surface air temperature by remote sensing, the advection-energy balance for the surface air temperature (ADEBAT) model is developed which assumes the surface air temperature is driven by the local driving force and the advective driving force. The local driving force produces a local surface air temperature whereas the advective driving force changes it by adding an exotic air temperature. An advection factor is defined to measure the quantity of the exotic air brought by the advection. Since the is determined by the advection, this paper improves it to a regional scale by using the Inverse Distance Weighting (IDW) method whereas the original ADEBAT model uses a constant of for a block of area. Results retrieved by the improved ADEBAT (IADEBAT) model are evaluated and comparison was made with the in situ measurements, with an (correlation coefficient) of 0.77, an RMSE (Root Mean Square Error) of 0.31 K, and a MAE (Mean Absolute Error) of 0.24 K. The evaluation shows that the IADEBAT model has higher accuracy than the original ADEBAT model. Evaluations together with a -test of the MAD (Mean Absolute Deviation) reveal that the IADEBAT model has a significant improvement.