Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images - Liu, Yi; Ortega-Farías, Samuel; Tian, Fei; Wang, Sufen; Li, Sien (Frontiers in Environmental Science, 2021)
Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Also, NDVI was used to correct the Radiative Transfer Equation (RTE) method for calculating Ts, then participate in the Ta estimation model. The results show that: 1) The accuracy of the surface temperature (Ts) did not affect the estimation of Ta using MLR and RF methods; 2) The MLR model is more suitable for estimating Ta in areas with fewer meteorological stations, and its model efficiency (MEF), and mean absolute error (MAE), root mean square error (RMSE) were 0.95, 1.68°C, and 1.92°C, respectively; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.