Evaluation of machine learning methods in spatial downscaling of average annual land surface temperature and air temperature

Document Type : Research Paper


1 Department of Environmental Sciences and Engineering, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran

2 Department of Remote Sensing and Geographic Information System, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran


Today, the use of raster data from climate databases such as WorldClim is one of the reliable data sources that have been used instead of the point data of weather stations. However, the use of these climate databases with low spatial resolution has generated limitations for many studies related to biological and ecological studies. This study aims to investigate the relationship between air temperature and land surface temperature and then reproduce the land surface temperature with the high spatial resolution for downscaling of annual average air temperature using two widely used products, namely,  annual average air temperature from the WorldClim database and annual average day and night temperature from MOD11A2 v061 MODIS sensor. In this study, firstly, the performance of machine learning models including random forest, artificial neural network, elastic network regression, and support vector machine for downscaling of MOD11A2 v061 product from 1 km to 250 meters was assessed. For this purpose, continuous and discrete predictor variables including height above sea level, latitude, vegetation cover, soil texture, slope direction, and land cover were used. Then, WorldClim's annual average air temperature was downscaled from 1 km to 250 meters using the land surface temperature with a 3rd-degree polynomial regression model.  Also, the weather data of seven synoptic stations were used to check the validity of the downscaled product. The results of the Taylor diagram represented that the random forest model has the best performance for the downscaled land surface temperatures with a root mean square error of 0.54 degrees Celsius. Also, the 3rd-degree polynomial regression model has a lower relative error rate in producing downscaled air temperature. The value of the root mean square error of the results for the uncorrected and corrected downscaled air temperature was 1.32 and 1.21 degrees Celsius, respectively, which did not show a significant difference at the 0.05 level according to the paired t-test. The findings of this research show that the downscaling of the mean annual air temperature of WorldClim has the required validity.


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