Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem ModelingReportar como inadecuado

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GEOFOREST Group, IUCA, Department of Geography and Spatial Management, University of Zaragoza, Spain, Pedro Cerbuna 12, Zaragoza E-50009, Spain


Department of Environmental Sciences, Technical State University of Quevedo, Quevedo EC120509, Los Rios, Ecuador


Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, Copenhagen K DK-1350, Denmark


Centre for Human and Social Sciences, Spanish Council for Scientific Research, Albasanz 26-28, Madrid 28037, Spain


Center for Spatial Technologies and Remote Sensing CSTARS, Department of Land, Air and Water Resources, University of California, One Shields Avenue, Davis, CA 95616-8617, USA


Author to whom correspondence should be addressed.

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. View Full-Text

Keywords: land surface temperature; Landsat; multitemporal land surface temperature; Landsat; multitemporal

Autor: Lidia Vlassova 1,2,* , Fernando Perez-Cabello 1, Hector Nieto 3, Pilar Martín 4, David Riaño 4,5 and Juan de la Riva 1



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