Abstract
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. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 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.
Highlights
Near-surface air temperature (Ta), usually refers to the air temperature at 2 m above the ground, is an essential factor affecting ecology, agriculture, and urban areas (Raja Reddy et al, 1997; Krüger and Emmanuel, 2013; Shamir and Georgakakos, 2014), and is the basis for climate change studies (Alkama and Cescatti, 2016; Bathiany et al, 2018)
Ta can be obtained by establishing the regression relationship between Ta and remote sensing inversion and auxiliary parameters, such as using land surface temperature (Ts), Normalized Difference Vegetation Index (NDVI), wind speed, geographic location and elevation, among which Ts is the most important parameter (Vancutsem et al, 2010; Hachem et al, 2012; Song and Wu, 2018)
Perform parameter inversion based on Landsat8 images, use Radiative Transfer Equation (RTE) to calculate and correct Ts
Summary
Near-surface air temperature (Ta), usually refers to the air temperature at 2 m above the ground, is an essential factor affecting ecology, agriculture, and urban areas (Raja Reddy et al, 1997; Krüger and Emmanuel, 2013; Shamir and Georgakakos, 2014), and is the basis for climate change studies (Alkama and Cescatti, 2016; Bathiany et al, 2018). The air temperature has become the primary driving variable of many land surface models (Nieto et al, 2011), so its spatial fidelity must be higher than that obtained by interpolation of point observation data, and even the most complex geostatistical techniques cannot meet the requirements (Prince et al, 1998). Ta can be obtained by establishing the regression relationship between Ta and remote sensing inversion and auxiliary parameters, such as using land surface temperature (Ts), Normalized Difference Vegetation Index (NDVI), wind speed, geographic location and elevation, among which Ts is the most important parameter (Vancutsem et al, 2010; Hachem et al, 2012; Song and Wu, 2018)
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