Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast ChinaReport as inadecuate


Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China


Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China - Download this document for free, or read online. Document in PDF available to download.

1

Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China

2

University of Chinese Academy of Sciences, Beijing 100049, China

3

Department of Forestry, TP Cooper Building, University of Kentucky, Lexington, KY 40546, USA





*

Author to whom correspondence should be addressed.



Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail

Abstract Air temperature Tair near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution Tair from the Moderate Resolution Imaging Spectroradiometer MODIS land surface temperature LST products, covering complex terrain in Northeast China. The All Subsets Regression ASR method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum Tmax, minimum Tmin, and mean Tmean air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients SRCs method. The results indicated that the optimal models could estimate the Tmax, Tmin, and Tmean with relatively high accuracies Model Efficiency ≥ 0.90. Both LST and day length DL predictors were important in estimating Tmax SRCs: daytime LST = 0.53, DL = 0.35, Tmin SRCs: nighttime LST = 0.74, DL = 0.23, and Tmean SRCs: nighttime LST = 0.72, DL = 0.28. Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface Tair from the MODIS LST production. View Full-Text

Keywords: air temperature; land surface temperature; remote sensing; MODIS; statistical models; Northeast China air temperature; land surface temperature; remote sensing; MODIS; statistical models; Northeast China





Author: Yuan Z. Yang 1,2, Wen H. Cai 1 and Jian Yang 1,3,*

Source: http://mdpi.com/



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