Abstract
The monitoring of the Land Surface Temperature (LST) by remote sensing in urban areas is of great interest to study the Surface Urban Heat Island (SUHI) effect. Thus, it is one of the goals of the future spaceborne mission TRISHNA, which will carry a thermal radiometer onboard with four bands at a 60-m spatial resolution, acquiring daytime and nighttime. In this study, TRISHNA-like data are simulated from Airborne Hyperspectral Scanner (AHS) data over the Madrid urban area at 4-m resolution. To retrieve the LST, the Temperature and Emissivity Separation (TES) algorithm is applied with four spectral bands considering two main original approaches compared with the classical TES algorithm. First, calibration and validation datasets with a large number of artificial materials are considered (called urban-oriented database), contrary to most of the previous studies that do not use a large number of artificial material spectra during the calibration step, thus impacting the LST retrieval over these materials. This approach produces one TES algorithm with one empirical relationship, called 1MMD TES. Second, two empirical relationships are used, one for the artificial materials and the other for the natural ones. These relationships are defined thanks to two calibration datasets (artificial-surface-oriented database and natural-surface-oriented database, respectively), one containing mainly artificial materials and the other mainly natural ones. Finally, in order to use two empirical relationships, a ground cover classification map is given to the TES algorithm to separate artificial pixels from natural ones. This approach produces one material-oriented TES algorithm with two empirical relationships, called 2MMD TES. In order to perform a complete comparison of these two addenda in the TES algorithm and their impact on the LST retrieval, both AHS and TRISHNA spatial resolutions are studied, i.e., 4-m and 60-m resolutions, respectively. Relative to the calibration of the TES algorithm, we conclude that (1) the urban-oriented database is more representative of the urban areas than previous databases from the state-of-the-art, and (2) using two databases (artificial-surface-oriented and natural-surface-oriented) instead of one prevents the overestimation of the LST over natural materials and the underestimation over artificial ones. Thus, for both studied spatial resolutions (AHS and TRISHNA), we find that the 2MMD TES outperforms the 1MMD TES. This difference is especially important for artificial materials, corroborating the above conclusion. Furthermore, the comparison with ground measurements shows that, on 4-m spatial resolution images, the 2MMD TES outperforms both the 1MMD TES and the TES from the state-of-the-art used in this study. Finally, we conclude that the 2MMD TES method, with only four spectral bands, better retrieves the LST over artificial and natural materials and that the future TRISHNA sensor is suited for the monitoring of the LST over urban areas and the SUHI effect.
Highlights
54% of the world’s population lives in urban areas, and an increase to 66% is expected by 2050 [1]
The statistical analysis for the comparison of Temperature and Emissivity Separation (TES) Land Surface Temperature (LST) with ground measurements as well as the Surface Urban Heat Island (SUHI) values are performed on all the acquisitions
When comparing with ground LSTs, (Tables 6–8), the 2MMD-4-band TES outperforms the 1MMD-4-band TES over both natural and artificial materials. These results show the capacity of the double MMD relationship to recover a large variability of LST values, which become very important in urban environments where both natural and artificial materials are present
Summary
54% of the world’s population lives in urban areas, and an increase to 66% is expected by 2050 [1]. A recent study highlighted that the mean air temperature rising over urban areas could reach 4.4 K by 2100 [2]. One-third of the world population will be possibly subject to a higher risk of mortality due to the heat waves, and this amount can increase from 48% to 74% by 2100 [3]. This temperature rising is generally due to global warming and is accentuated in cities by the Urban Heat Island (UHI) effect, defined as the difference between the urban and rural (urban surroundings) mean air temperatures. Remote sensing data from the Thermal InfraRed (TIR) spectral domain allows to retrieve the Land Surface Temperatures (LSTs) leading to Surface Urban
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