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

Mengmeng Wang,Xiuguo Liu,Guizhou Wang,Zhengjia Zhang,Guojin He,Zhaoming Zhang,Xiaojie Cao,Zhijie Wu

doi:10.3390/rs9121278

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.

Highlights

Near surface air temperature (NSAT) is a key factor in energy and water exchanges between the land surface and atmosphere [1]
The Root Mean Squared Error (RMSE) for the Multiple linear regression (MLR) model are between 2.4 ◦C and 10.2 ◦C, and the mean RMSEs for 12 months are 5.6 ◦C for monthly minimum NSAT, 5.0 ◦C for monthly mean NSAT, and 4.92 ◦C for monthly maximum NSAT, respectively
As for the MLR model, the RMSEs decrease in the order from monthly minimum to mean maximum NSAT

Summary

Introduction

Near surface air temperature (NSAT) is a key factor in energy and water exchanges between the land surface and atmosphere [1]. Many efforts have been made to obtain spatial distributions of various NSAT variables based on the point station measurements, including annual maximum/minimum/mean NSAT [8], monthly maximum/minimum/mean NSAT [9,10,11,12,13], daily maximum/minimum/mean NSAT [14,15,16,17,18,19], and instantaneous NSAT [20,21]. These NSAT retrieval methods can be divided into three groups: (1) spatial interpolation method [22], (2) physical-based method [20,23], and (3) regression analysis method [8]

Objectives

Methods

Results

Discussion

Conclusion