MAPPING AIR TEMPERATURE BY FOURIER ANALYSIS OF LAND SURFACE TEMPERATURE TIME SERIES OBSERVED BY TERRA/MODIS

Characterization of the spatial patterns of air temperature (Ta) is needed in a wide spectrum of environmental studies, especially when complex landscapes are investigated. In some cases the limited areal density of meteorological stations limits the use of the ordinary interpolation methods (i.e. Kriging, Multiple Linear Regression, IDW). In this work, an alternative approach for spatial interpolation of maximum near surface air temperature has been implemented employing time series of Land Surface temperature (LST) image data.The method was tested employing LST daily observations (overpass around local noon) from TERRA/MODerate-resolution IMaging Spectroradiometer (MODIS) in 2000-2006 over an area of about 200 km2 in the "Telesina" valley in Southern Italy.The starting point of the procedure consists in normalizing spatial variability of LST with observations at a reference location(s). Therefore, the time series of the ratio images of LST at each pixel to LST at a reference point have been calculated. Then, in order to evaluate the interannual variability, harmonic analysis has been applied to model with a Fourier series the pixel-wise ratio images for each year. The temporal stability, i.e. limited interannual variability, of the Fourier coefficients has been verified by means of a variability index to conclude that their mean values are sufficient to characterize the annual temporal profile of the pixel-wise ratio.The second step was the identification of a linear relation between air temperature and surface temperature using near surface temperature measured at the available ground stations. A unique relation Ta vs LST has been established for the area under study. The inverse relation (LST vs Ta) has been established at the reference location. Finally, the model of the time series of normalized LST was combined with the regression coefficients to obtain Ta(x,y.t) as a function of LST(x0,y0,t).Air temperature data at the nodes of a 35 km grid for past (1961-1990), present (2000-2010) and future (2021-2050) scenarios have been provided within the project Agroscenari. The future scenario was derived from a statistical downscaling technique of global circulation models (GCM) outputs. These data were used as reference locations.The procedure was assessed against maximum air temperature measured at the four ground station available in the study area for the period 2000-2006. RMSE was calculated for daily maximum temperature, for 5 and 10 days moving average showing variations respectively in the ranges of 3.2 K - 3.9 K, 1.7 K -2.1 K and 1.5 K -2.0 K. The procedure has been also validated against the fully independent air temperature data for the period 1961-1990. Further, downscaling of future scenario maximum temperature from 35 to 1 km spatial resolution has been performed