Land surface temperature estimation from airborne hyperspectral data

Number of different methods has been developed and successfully used for Land Surface Temperature (LST) estimation; among them, some require algorithms that exploit the different atmospheric absorption in adjacent thermal infrared (TIR) channels. The nowadays accuracy of these two-channel methods, validated for current satellite sensors with different central wavelength and width of the TIR channels, is typically below 1.2K. In this work, we aim to present the approach followed to select the optimal TIR bands for the LST inversion problem and to provide Split Window (SW) coefficients that can be used to retrieve LST from the Thermal Airborne Spectrographic Imager (TASI-600) airborne data. TASI is a commercial hyperspectral infrared sensor produced by ITRES, Canada, with 32 TIR bands covering the range between 8 and 11.5 µm with a spectral resolution of 109.5 nm. TASI spectral bands have been numerically analysed in order to select the optimal bands for the algorithm design. The outcomes of this analysis can be exploited for the optimal band selection for new satellite TIR hyperspectral sensors. The SW algorithm according to the Jiménez-Muñoz and José A. Sobrino 2008 formulation with seven coefficients was calibrated and validated using a datasets of profiles applied to configure MODTRAN to simulate Top of the Atmosphere (TOA) brightness temperatures. These datasets were selected from a training database compiled by Borbas et al. (SeeBor database) which consists of 15,704 clear-sky profiles of temperature, Total Column Water Vapour (TCWV) and ozone; and other ancillary data such as spectral emissivity, elevation, site latitude and longitude, skin temperature, surface pressure and International Geosphere-Biosphere Programme (IGBP) classification. Simulations have been performed on a subset of the SeeBor dataset resized on Europe. The algorithm coefficients have been retrieved by multiple regression analysis and their statistical significance was evaluated by simultaneous hypothesis using a Student’s t test and a P-value lower than 0.05. Among the TASI 32 spectral bands the optimal regression was obtained by using TASI band 19 (10.034 µm) and band 28 (11.051 µm). For the overall land cover types and for the optimal bands selection we obtained an R2 of 0.93 and a RMSE of 0.635K. The algorithm, when applied to the single IGBP classes, showed a good performance for the majority of IGBP classes, except for barren profiles. This low performance for the Barren class is anyhow physically consistent; since barren emissivities have larger spectral variations in the window region between 10 and 12 µm due to the variable abundances of vegetation and soil units within the IGBP Barren class. To date, the SW algorithm validation is foreseen by using real TASI data acquired in the Basilicata region in Italy on the urban and industrial district.