Publications

Yang, YZ; Cai, WH; Yang, J (2017). Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China. REMOTE SENSING, 9(5), 410.

Abstract
Air temperature (T-air) 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 T-air 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 (T-max), minimum (T-min), and mean (T-mean) 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 T-max, T-min, and T-mean with relatively high accuracies (Model Efficiency >= 0.90). Both LST and day length (DL) predictors were important in estimating T-max (SRCs: daytime LST = 0.53, DL = 0.35), T-min (SRCs: nighttime LST = 0.74, DL = 0.23), and T-mean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting T-min and T-mean had better performance than the one predicting T-max. Nighttime LST was better at predicting T-min and T-mean than daytime LST data at predicting T-max. Land covers had noticeable influences on estimating T-air, 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 T-air from the MODIS LST production.

DOI:
10.3390/rs9050410

ISSN:
2072-4292