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Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by day/night LST algorithm / M. Momeni in Remote sensing of environment, vol 106 n° 2 (30/01/2007)  

Public [article]  inRemote sensing of environment > vol 106 n° 2 (30/01/2007) . - pp 190 - 198 Titre : Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by day/night LST algorithm Type de document : Article/Communication Auteurs : M. Momeni, Auteur ; M.R. Saradjan, Auteur Année de publication : 2007 Article en page(s) : pp 190 - 198 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande spectrale [Termes IGN] écart type [Termes IGN] emissivité [Termes IGN] erreur systématique [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] occupation du sol [Termes IGN] régression linéaire [Termes IGN] seuillage d'image [Termes IGN] température au sol [Termes IGN] température de surface [Termes IGN] variation diurne [Termes IGN] zone aride Résumé : (Auteur) Surface emissivity estimation is a significant factor for the land surface temperature estimation from remotely sensed data. For fully vegetated surfaces, the emissivity estimation is performed in a simple manner since the emissivity is relatively uniform. However, for arid land with sparse vegetation, the estimation is more complicated since the emissivity of the exposed soil and rock is highly variable. In this study, mean and difference emissivity for bands 31 and 32 of MODIS sensor have been derived based on NDVI values. First, the NDVI thresholds have been determined to separate bare soil, partially vegetated soil and fully vegetated land. Then regression relations have been derived to estimate mean and difference emissivity of the bare soil samples and partially vegetated surfaces. A constant emissivity is also used for fully vegetated area. Along with the correlations, standard deviations of the regression relations have been examined for a set of representative soil types. Standard deviations smaller than 0.003 in mean emissivity and smaller than 0.004 in difference emissivity are resulted in regression linear relations. Evaluation of the NDVI derived regression relations has been performed using the results of MODIS Day/Night Land Surface Temperature (LST) algorithm on a pair of MODIS images. Using around 45,500 pixels with different soil and land cover types, emissivity of each pixel in bands 31 and 32 have been estimated. The calculated emissivities have been compared with emissivities calculated by MODIS Day/Night LST algorithm. Biases and standard deviations of NDVI-based relations show relatively high agreement for mean and difference emissivity relations with Day/Night method results. It may be concluded that the proposed algorithm can be used as a rather simple alternative to complex emissivity estimation algorithms. Copyright Elsevier Numéro de notice : A2007-021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.08.005 En ligne : https://doi.org/10.1016/j.rse.2006.08.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28387 [article]