PROJECT TITLE :

Downscaling of Landsat and MODIS Land Surface Temperature Over the Heterogeneous Urban Area of Milan

ABSTRACT:

Remotely sensed pictures of land surface temperature (LST) with high spatial resolution are required for various environmental applications. For instance, finer resolutions (FRs) are essential to capture thermal details in urban textures. To meet the wants of sharper and sharper pictures, this study carries out a downscaling from coarser spatial resolution LST images to FRs using relationships between LST and spectral indexes (SIs) representative of different land cowl types over the heterogeneous area of Milan. Different regressive schemes were applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS pictures during four summer passages. The regressions were 1st evaluated on Landsat pictures aggregated at 960 m resolution and downscaled to 480, 240, and 120 m. For the four Landsat scenes, the best regression models embrace each vegetation and engineered-up/soil SIs: the basis mean sq. (rms) error, around 1 K for 480 m and a couple of K for one hundred twenty m, is clearly below the LST normal deviation of every reference image, assumed as LST spatial variability. Then, up to date MODIS information were downscaled from 960 m to the on top of FRs, and the simplest models embody again both vegetation and built-up/soil SIs. The rms error is on top of the correspondent Landsat one (in some cases exceeds three K), however continuously below the LST spatial variability. A compression of the vary of LST values for the MODIS-downscaled images was found with respect to the Landsat disaggregated pictures: this shortcoming in the LST retrieval affects the MODIS downscaling accuracy.