Since decades, the land surface temperature (LST) is a parameter widely considered in the urban area mapping from space. LST has been often retrieved and mapped to evaluate the surface urban heat island (SUHI) using different spaceborne platforms, such as AATSR, ASTER, MODIS and Landsat. Several factors need to be assessed in the LST retrieval from satellite thermal infrared data: sensor radiometric calibration, atmospheric correction, surface emissivity estimation. Particularly, in urban area mapping issue, the satellite sensor spatial resolution may be a limiting factor in detailing the fine scale spatial variability, especially in the presence of impervious surfaces and sharp transitions (e.g., buildings, roads, parking lots, riverside, restricted vegetated zones), such as in a urban texture. The growing demand of remote sensing maps with finer and finer spatial resolution to successfully monitor the SUHI effects at district level and to avoid temperature underestimation stimulates the development of downscaling techniques when the actual sensor measurements do not meet the spatial detail requirements. In this work we perform the downscaling of coarse resolution LST maps from MODIS and Landsat to finer resolutions with the aim to increase the information content of the original maps, using summer satellite images over Milan, Rome and Florence. The downscaling is the enhancement of the spatial resolution of the original pixel data using ancillary information at higher spatial resolution. Different physical and statistical downscaling approaches have been proposed in literature: in this work, a statistical LST downscaling approach regression-based using different spectral indices over heterogeneous urban landscape is proposed, and the reliability assessed. This analysis allows to select the spectral indices and their combinations providing the best results in the LST image sharpening. First, the downscaling was performed using the Landsat TM data over Milan and Rome, assuming the 120 m spatial resolution image as reference. Then, the same downscaling regressive schemes were applied on the contemporary coarse resolution LST MODIS image and verified with the reference LST Landsat map. A further downscaling assessment at finer resolution was carried out using the LST retrieved from Landsat TM over the city of Florence: in this case the sharpened image was compared with a high-resolution thermal image provided by an airborne survey carried out on July 18, 2010. Two Landsat scenes were processed before and after the flight, with the aim to evaluate the impact of the Landsat TM thermal channel resolution (120 m) on the LST estimation over a heterogeneous urban texture. Again, thermal data were downscaled at 30 m with a statistical algorithm using a regression on different spectral indices. The proposed approaches and comparisons allows us to assess potentials and limits of the LST downscaling performed over an heterogeneous urban area.

Mapping the Land Surface Temperature over Urban Areas from Space: a Downscaling Approach

BONAFONI, Stefania;
2015

Abstract

Since decades, the land surface temperature (LST) is a parameter widely considered in the urban area mapping from space. LST has been often retrieved and mapped to evaluate the surface urban heat island (SUHI) using different spaceborne platforms, such as AATSR, ASTER, MODIS and Landsat. Several factors need to be assessed in the LST retrieval from satellite thermal infrared data: sensor radiometric calibration, atmospheric correction, surface emissivity estimation. Particularly, in urban area mapping issue, the satellite sensor spatial resolution may be a limiting factor in detailing the fine scale spatial variability, especially in the presence of impervious surfaces and sharp transitions (e.g., buildings, roads, parking lots, riverside, restricted vegetated zones), such as in a urban texture. The growing demand of remote sensing maps with finer and finer spatial resolution to successfully monitor the SUHI effects at district level and to avoid temperature underestimation stimulates the development of downscaling techniques when the actual sensor measurements do not meet the spatial detail requirements. In this work we perform the downscaling of coarse resolution LST maps from MODIS and Landsat to finer resolutions with the aim to increase the information content of the original maps, using summer satellite images over Milan, Rome and Florence. The downscaling is the enhancement of the spatial resolution of the original pixel data using ancillary information at higher spatial resolution. Different physical and statistical downscaling approaches have been proposed in literature: in this work, a statistical LST downscaling approach regression-based using different spectral indices over heterogeneous urban landscape is proposed, and the reliability assessed. This analysis allows to select the spectral indices and their combinations providing the best results in the LST image sharpening. First, the downscaling was performed using the Landsat TM data over Milan and Rome, assuming the 120 m spatial resolution image as reference. Then, the same downscaling regressive schemes were applied on the contemporary coarse resolution LST MODIS image and verified with the reference LST Landsat map. A further downscaling assessment at finer resolution was carried out using the LST retrieved from Landsat TM over the city of Florence: in this case the sharpened image was compared with a high-resolution thermal image provided by an airborne survey carried out on July 18, 2010. Two Landsat scenes were processed before and after the flight, with the aim to evaluate the impact of the Landsat TM thermal channel resolution (120 m) on the LST estimation over a heterogeneous urban texture. Again, thermal data were downscaled at 30 m with a statistical algorithm using a regression on different spectral indices. The proposed approaches and comparisons allows us to assess potentials and limits of the LST downscaling performed over an heterogeneous urban area.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1360445
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