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Remote Sens. 2017, 9(12), 1278; doi:10.3390/rs9121278

Comparison of Spatial Interpolation and Regression Analysis Models for an Estimation of Monthly Near Surface Air Temperature in China

1
Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
2
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3
College of Resources Engineering, Longyan University, Longyan 364012, China
*
Authors to whom correspondence should be addressed.
Received: 24 October 2017 / Revised: 5 December 2017 / Accepted: 7 December 2017 / Published: 8 December 2017
(This article belongs to the Special Issue Remote Sensing of Land-Atmosphere Interactions)
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Abstract

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e., Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR)) models for predicting monthly minimum, mean, and maximum NSAT in China, a domain with a large area, complex topography, and highly variable station density. This was conducted for a period of 12 months of 2010. The accuracy of the GWR model is better than the MLR model with an improvement of about 3 °C in the Root Mean Squared Error (RMSE), which indicates that the GWR model is more suitable for predicting monthly NSAT than the MLR model over a large scale. For three spatial interpolation models, the RMSEs of the predicted monthly NSAT are greater in the warmer months, and the mean RMSEs of the predicted monthly mean NSAT for 12 months in 2010 are 1.56 °C for the Kriging model, 1.74 °C for the IDW model, and 2.39 °C for the Spline model, respectively. The GWR model is better than the Kriging model in the warmer months, while the Kriging model is superior to the GWR model in the colder months. The total precision of the GWR model is slightly higher than the Kriging model. The assessment result indicated that the higher standard deviation and the lower mean of NSAT from sample data would be associated with a better performance of predicting monthly NSAT using spatial interpolation models. View Full-Text
Keywords: near surface air temperature; multiple linear regression; spatial interpolation; geographically weighted regression near surface air temperature; multiple linear regression; spatial interpolation; geographically weighted regression
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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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Wang, M.; He, G.; Zhang, Z.; Wang, G.; Zhang, Z.; Cao, X.; Wu, Z.; Liu, X. Comparison of Spatial Interpolation and Regression Analysis Models for an Estimation of Monthly Near Surface Air Temperature in China. Remote Sens. 2017, 9, 1278.

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