The land surface emissivity (LSE) is an intrinsic property of natural materials that is deemed particularly relevant to estimate the surface energy budget. The energy budget is one of the key processes in climatic, hydrological, ecological, and biogeochemical models. Besides, a full exploitation of the thermal infrared imagery to notably estimate the land surface temperature (LST) relies on the knowledge of LSE. Therefore, an accurate retrieval of LSE from remotely sensed observations remain a primary objective of the scientific community. Herein, the proposed work aims at improving the methodology for processing high resolution thermal infrared satellite data of Landsat like sensors; i.e.: only one or two channels in thermal wavelength [10-12.5μm], nadir direction of acquisition, high resolution with simultaneous visible and near infrared (VNIR) eventually medium infrared (MIR) data at compatible resolution. For such, we are revisiting in the frame of this study a classic method based on a statistical relationship between the vegetation indices derived from (VNIR) bands and the LSE. In order to account for vegetation canopies, we simulated a range of emissivity vs. vegetation indices thanks to the SAIL and the SAIL-thermal radiative transfer models. Indeed previous studies considered either spectrum from soil and leaf samples obtained from spectral libraries without accounting for the canopy processes (i.e. the cavity effect). Other previous studies considered in-situ measurements of emissivity, but in any cases only very few measurements (only specific vegetation cases) were available. In order to simulate emissivity for vegetation stand, we used leaf spectra and soil spectrum based on the new ECOSTRESS spectral library and the USGS / ONERA database. The two databases encompass a set of leaf and soil reflectance in a spectrum ranging from visible to thermal infrared, i.e. [0.3µm to 15μm]. The other parameters required as input parameters for SAIL and SAIL-thermal radiative transfer model were chosen in order to represent a wide range of vegetation canopies, soils and geometric configurations. In final, we obtain a synthetic and database of coherent canopy reflectance and emissivity covering solar and thermal infrared (TIR) spectra. This yields a background for investigating the possible relationships with a minimum degree of confidence between VNIR bands (Landsat bands as starting point) and TIR emissivity. In parallel to this effort, we analyze the measurements acquired during the hyper-spectral airborne campaign AHSPECT that took place in 2015 on a summer day and a fall day in the South-West of France. The knowledge of land cover types for the areas fly over by the aircraft with a typical metric resolution allows us to extract the associated spectrum per each land unit. Owing to a large extraction over manifold land cover types, the robustness of the relations theoretically found between VNIR and TIR data are verified and even consolidated. As a result we concluded on the possibility to estimate accurately (with associated error) the LSE at high resolution with associated VNIR remotely sensed data.