Mito, CO; Boiyo, RK; Laneve, G (2012). A simple algorithm to estimate sensible heat flux from remotely sensed MODIS data. INTERNATIONAL JOURNAL OF REMOTE SENSING, 33(19), 6109-6121.
Sensible heat flux (H) has a large impact on energy exchange between the surface and the atmosphere and, thus, affects climate change and climatic and hydrological modelling. In the past, remote sensing of H has been a major area of interest and, as a result, various methods have been established for its retrieval. However, large discrepancies between measured and simulated values of H have been observed over land surfaces because of various assumptions and simplifications. This article presents a generalized algorithm for the estimation of sensible heat flux that is suitable for a wide range of atmospheric and terrestrial conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Standard built-in atmospheric profiles in Fast Atmospheric Signature Code (FASCODE) together with atmospheric conditions obtained by periodic radio sounding, once a week, performed at the Broglio Space Centre in Malindi, Kenya, were used in simulating MODIS data at 11.03 and 12.02 mu m wavelengths using PcLnWin software. This new approach improves the form of the Mito algorithm, developed to determine surface temperature, by removing some of the assumptions underlying the algorithm - for example, the assumption that air temperature T-a is approximately equal to surface temperature T-s. The resulting bulk aerodynamic resistance equation allows the formulation of a general algorithm for the determination of H, which takes into account the surface emittance effect, water vapour column (WVC), canopy properties, air temperature and different atmospheric stabilities. Unlike other conventional methods developed earlier for the determination of H, a prior knowledge of surface temperature as an auxiliary input is not necessary in this new algorithm. The estimates of sensible heat flux derived from MODIS using the proposed algorithm compared well with in situ measurements, giving a good correlation coefficient of r = 0.9.