000060626 001__ 60626
000060626 005__ 20180531095509.0
000060626 0247_ $$2doi$$a10.3390/rs6054345
000060626 0248_ $$2sideral$$a85703
000060626 037__ $$aART-2014-85703
000060626 041__ $$aeng
000060626 100__ $$aVlassova, L.
000060626 245__ $$aAssessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-grass Ecosystem Modeling
000060626 260__ $$c2014
000060626 5060_ $$aAccess copy available to the general public$$fUnrestricted
000060626 5203_ $$aLand 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.
000060626 536__ $$9info:eu-repo/grantAgreement/ES/DGA-CAIXA/GA-LC-042-2011$$9info:eu-repo/grantAgreement/ES/MINECO/CGL2008-02301-CLI$$9info:eu-repo/grantAgreement/ES/MINECO/CGL2012-34383
000060626 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000060626 590__ $$a3.18$$b2014
000060626 591__ $$aREMOTE SENSING$$b5 / 28 = 0.179$$c2014$$dQ1$$eT1
000060626 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000060626 700__ $$aPerez-Cabello, F.
000060626 700__ $$aNieto, H.
000060626 700__ $$aMartín, P.
000060626 700__ $$aRiaño, D.
000060626 700__ $$0(orcid)0000-0003-2615-270X$$ade la Riva, J.$$uUniversidad de Zaragoza
000060626 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDepartamento de Geografía y Ordenación del Territorio$$cAnálisis Geográfico Regional
000060626 773__ $$g6, 5 (2014), 4345-4368$$pRemote sens. (Basel)$$tRemote sensing (Basel)$$x2072-4292
000060626 8564_ $$s1029772$$uhttp://zaguan.unizar.es/record/60626/files/texto_completo.pdf$$yVersión publicada
000060626 8564_ $$s108100$$uhttp://zaguan.unizar.es/record/60626/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000060626 909CO $$ooai:zaguan.unizar.es:60626$$particulos$$pdriver
000060626 951__ $$a2018-05-31-09:48:47
000060626 980__ $$aARTICLE