Abstract
Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 °C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 °C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components.
Highlights
Land surface temperature (LST) is a state variable that plays a crucial role in many land surface processes [1]
Prior to Land Surface Temperature (LST) retrieval optical bands of Landsat images used in emissivity estimation were corrected for atmospheric effects using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm implemented in the ENVI [45]
The differences with the reference LSTs (LSTref) are due to the fact that the tested methods are based on the different versions of the radiative transfer code: LOWTRAN 7 for Mono-Window algorithm (MW) and MODerate resolution atmospheric TRANsmission (MODTRAN) 4 for SC and Radiative TransferEquation (RTE)-Atmospheric Correction Parameter Calculator (ACPC)
Summary
Land surface temperature (LST) is a state variable that plays a crucial role in many land surface processes [1]. LST is related to the transport of heat between the land surface and the atmospheric boundary layer [1,2,3], and makes possible estimation of sensible heat flux [4] and latent heat flux, or evapotranspiration [5,6]. It is a necessary input for ecosystem modeling [7], which can be performed at local [4], regional, and global scales. In the case of dealing with a waveband, all these parameters are integrated according to the spectral response function of this band
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
