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"m"i"n and T"m"a"x, respectively) and soil temperature for a depth of 0-5cm (T"s"o"i"l"-"5"c"m) 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 imagesx6 bands) between 2000 and 2005 were averaged over a monthly basis to temporally match monthly T"m"i"n, T"m"a"x, and T"s"o"i"l"-"5"c"m from 83 meteorological stations. A total of 60 (28 temporally averaged plus 32 time series-based) linear regression models of T"m"i"n, T"m"a"x, and T"s"o"i"l"-"5"c"m 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^oC), 65% (RMSE=5.1^oC), and 57% (RMSE=6.9^oC) of variations in T"m"i"n, T"m"a"x, and T"s"o"i"l"-"5"c"m, respectively, under a wide range of T"m"i"n (-34 to 25^oC), T"m"a"x (0.2-47^oC) and T"s"o"i"l"-"5"c"m (-9 to 40^oC) observed at the 83 stations.

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