Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-grass Ecosystem Modeling
Resumen: 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.
Idioma: Inglés
DOI: 10.3390/rs6054345
Año: 2014
Publicado en: Remote sensing (Basel) 6, 5 (2014), 4345-4368
ISSN: 2072-4292

Factor impacto JCR: 3.18 (2014)
Categ. JCR: REMOTE SENSING rank: 5 / 28 = 0.179 (2014) - Q1 - T1
Factor impacto SCIMAGO:

Financiación: info:eu-repo/grantAgreement/ES/DGA-CAIXA/GA-LC-042-2011
Financiación: info:eu-repo/grantAgreement/ES/MINECO/CGL2008-02301-CLI
Financiación: info:eu-repo/grantAgreement/ES/MINECO/CGL2012-34383
Tipo y forma: Article (Published version)
Área (Departamento): Análisis Geográfico Regional (Departamento de Geografía y Ordenación del Territorio)
Exportado de SIDERAL (2018-05-31-09:48:47)

Este artículo se encuentra en las siguientes colecciones:
articulos > articulos-por-area > analisis_geografico_regional



 Notice créée le 2017-03-13, modifiée le 2018-05-31


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