Retrieval of Land Surface Emissivities Over Partially Vegetated Surfaces From Satellite Data Using Radiative Transfer Models

Land surface emissivity (LSE) is a key variable for land surface temperature (LST) retrieval from satellite data. In this study, five emissivity radiative transfer models (RTMs) for vegetation canopies are investigated together with a classification-based methodology to produce canopy LSE maps over the Iberian Peninsula with EOS Aqua–Moderate Resolution Imaging Spectroradiometer (MODIS) data. The five canopy RTMs are FR97, Mod3, REN15, 4SAIL, and CE-P. The analysis of the RTMs’ performance with satellite data gains interest over partially vegetated surfaces, for which these models can obtain accurate emissivities. The sensitivity analyses showed that FR97, REN15, 4SAIL, and CE-P models have higher uncertainty for low leaf area indexes (LAIs), while the Mod3 model increases the uncertainty with LAI. The produced LSEs were first intercompared with emissivities from the MODIS MYD21A1 product, which is obtained with the temperature and emissivity separation (TES) method. The RTMs agreed with the TES emissivities within the given uncertainty. In addition, the RTM emissivities were compared with MYD11A1 and MYD11B1 MODIS products, and the IREMIS and CAMEL databases, and they were used to estimate the LST at three specific homogeneous sites: shrubland, vineyard, and olive orchard. The LSTs estimated with each modeled emissivity and emissivity products were validated against reference data at these sites. All RTMs provided accurate LST data, equal to or even better than the MODIS products, with median values of differences between −0.7 K and 0.4 K depending on the site. Therefore, the canopy emissivity RTMs used in this study, together with the classification-based methodology, showed to be suitable for satellite LST retrieval.