Satellite-based and mesoscale regression modeling of monthly air and soil temperatures over complex terrain in Turkey

Simple regression algorithms were developed to quantify spatio-temporal dynamics of minimum and maximum air temperatures (T-min and T-max respectively) and soil temperature for a depth of 0-5 cm (Tsoil-5cm) across complex terrain in Turkey using Moderate Resolution Imaging Spectroradiometer (MODIS) data at a 500-m resolution. A total of 762 16-day MODIS composites (127 images x 6 bands) between 2000 and 2005 were averaged over a monthly basis to temporally match monthly T-min, T-max, and Tsoil-5cm from 83 meteorological stations. A total of 60 (28 temporally averaged plus 32 time series-based) linear regression models of T-min, T-max, and Tsoil-5cm were developed using best subsets procedure as a function of a combination of 12 explanatory variables: six MODIS bands of blue, red, near infrared (NIR), middle infrared (MIR), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI); four geographical variables of latitude, longitude, altitude, and distance to sea (DtS); and two temporal variables of month, and year. The best multiple linear regression models elucidated 65% (RMSE = 5.9 degrees C), 65% (RMSE = 5.1 degrees C), and 57% (RMSE = 6.9 degrees C) of variations in T-min, T-max, and Tsoil-5cm, respectively, under a wide range of T-min (-34 to 25 degrees C), T-max (0.2-47 degrees C) and Tsoil-5cm (-9 to 40 degrees C) observed at the 83 stations. (C) 2011 Elsevier Ltd. All rights reserved.