This paper presents the characterization of Land Surface Temperature (LST) variability according to land cover, in order to derive the properties of evapotranspiration and improve the monitoring of a catchment. The land cover can be represented by its Normalized Difference Vegetation Index (NDVI) and first results underscore the relation between T and NDVI at NOAA-AVHRR pixels scale. However, due to their rough resolution, these pixels include several land cover types and this study revealed not useful for catchment monitoring. Therefore, Land Surface Temperature has to be specified with a more precise representation. We employ a physical model of temperature, which requires several parameters such as proportion and emissivity for each component within the pixel; these values are obtained with learning process using high resolution data such as Landsat TM. These results are then extrapolated to the global region with NOAA-AVHRR acquisitions and allow to analyze the land cover effects on Land Surface Temperature variability. By this way, the characterization of evapotranspiration according to land use for a global catchment is improved.