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.
This is an open access article distributed under the
Creative Commons Attribution License which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is properly cited.
Export to BibTeX
MDPI and ACS Style
Vlassova, L.; Perez-Cabello, F.; Nieto, H.; Martín, P.; Riaño, D.; de la Riva, J. Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling. Remote Sens. 2014, 6, 4345-4368.
Vlassova L, Perez-Cabello F, Nieto H, Martín P, Riaño D, de la Riva J. Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling. Remote Sensing. 2014; 6(5):4345-4368.
Vlassova, Lidia; Perez-Cabello, Fernando; Nieto, Hector; Martín, Pilar; Riaño, David; de la Riva, Juan. 2014. "Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling." Remote Sens. 6, no. 5: 4345-4368.