This work is linked to the future Indian–French high spatio-temporal TRISHNA (Thermal infraRed Imaging Satellite for High-resolution natural resource Assessment) mission, which includes shortwave and thermal infrared bands, and is devoted amongst other things to the monitoring of urban heat island events. In this article, the performance of seven empirical thermal unmixing techniques applied on simulated TRISHNA satellite images of an urban scenario is studied across spatial resolutions. For this purpose, Top Of Atmosphere (TOA) images in the shortwave and Thermal InfraRed (TIR) ranges are constructed at different resolutions (20 m, 40 m, 60 m, 80 m, and 100 m) and according to TRISHNA specifications (spectral bands and sensor properties). These images are synthesized by correcting and undersampling DESIREX 2008 Airborne Hyperspectral Scanner (AHS) images of Madrid at 4 m resolution. This allows to compare the Land Surface Temperature (LST) retrieval of several unmixing techniques applied on different resolution images, as well as to characterize the evolution of the performance of each technique across resolutions. The seven unmixing techniques are: Disaggregation of radiometric surface Temperature (DisTrad), Thermal imagery sHARPening (TsHARP), Area-To-Point Regression Kriging (ATPRK), Adaptive Area-To-Point Regression Kriging (AATPRK), Urban Thermal Sharpener (HUTS), Multiple Linear Regressions (MLR), and two combinations of ground classification (index-based classification and K-means classification) with DisTrad. Studying these unmixing techniques across resolutions also allows to validate the scale invariance hypotheses on which the techniques hinge. Each thermal unmixing technique has been tested with several shortwave indices, in order to choose the best one. It is shown that (i) ATPRK outperforms the other compared techniques when characterizing the LST of Madrid, (ii) the unmixing performance of any technique is degraded when the coarse spatial resolution increases, (iii) the used shortwave index does not strongly influence the unmixing performance, and (iv) even if the scale-invariant hypotheses behind these techniques remain empirical, this does not affect the unmixing performances within this range of resolutions.

Urban Heat Islands (UHIs) at the surface and canopy levels are major issues in urban planification and development. For this reason, the comprehension and quantification of the influence that the different land-uses/land-covers have on UHIs is of particular importance. In order to perform a detailed thermal characterisation of the city, measures covering the whole scenario (city and surroundings) and with a recurrent revisit are needed. In addition, a resolution of tens of meters is needed to characterise the urban heterogeneities. Spaceborne remote sensing meets the first and the second requirements but the Land Surface Temperature (LST) resolutions remain too rough compared to the urban object scale. Thermal unmixing techniques have been developed in recent years, allowing LST images during day at the desired scales. However, while LST gives information of surface urban heat islands (SUHIs), canopy UHIs and SUHIs are more correlated during the night, hence the development of thermal unmixing methods for night LSTs is necessary. This article proposes to adapt four empirical unmixing methods of the literature, Disaggregation of radiometric surface Temperature (DisTrad), High-resolution Urban Thermal Sharpener (HUTS), Area-To-Point Regression Kriging (ATPRK), and Adaptive Area-To-Point Regression Kriging (AATPRK), to unmix night LSTs. These methods are based on given relationships between LST and reflective indices, and on invariance hypotheses of these relationships across resolutions. Then, a comparative study of the performances of the different techniques is carried out on TRISHNA synthesized images of Madrid. Since TRISHNA is a mission in preparation, the synthesis of the images has been done according to the planned specification of the satellite and from initial Aircraft Hyperspectral Scanner (AHS) data of the city obtained during the DESIREX 2008 capaign. Thus, the coarse initial resolution is 60 m and the finer post-unmixing one is 20 m. In this article, we show that: (1) AATPRK is the most performant unmixing technique when applied on night LST, with the other three techniques being undesirable for night applications at TRISHNA resolutions. This can be explained by the local application of AATPRK. (2) ATPRK and DisTrad do not improve significantly the LST image resolution. (3) HUTS, which depends on albedo measures, misestimates the LST, leading to the worst temperature unmixing. (4) The two main factors explaining the obtained performances are the local/global application of the method and the reflective indices used in the LST-index relationship.

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.

TRUST (Thermal Remote sensing Unmixing for Subpixel Temperature) is a spectral unmixing method developed to provide subpixel abundances and temperatures from radiance images in the thermal domain. By now, this method has been studied in simple study cases, with a low number of endmembers, high spatial resolutions (1 m) and more than 30 spectral bands in the thermal domain. Thus, this article aims to show the applicability of TRUST on a highly challenging study case: the analysis of a heterogeneous urban environment with airborne multispectral (eight thermal bands) images at 8-m resolution. Thus, this study is necessary to generalize the use of TRUST in the analysis of urban thermography. Since TRUST allows linking intrapixel temperatures to specific materials, it appears as a very useful tool to characterize Surface Urban Heat Islands and its dynamics at high spatial resolutions. Moreover, this article presents an improved version of TRUST, called TRUST-DNS (Day and Night Synergy), which takes advantage of daytime and nighttime acquisitions to improve the unmixing performances. In this study, both TRUST and TRUST-DNS were applied on daytime and nighttime airborne thermal images acquired over the center of Madrid during the DESIREX (Dual-use European Security IR Experiment) campaign in 2008. The processed images were obtained with the Aircraft Hyperspectral Scanner (AHS) sensor at 4-m spatial resolution on 4 July. TRUST-DNS appears to be more stable and slightly outperforms TRUST on both day and night images. In addition, TRUST applied on daytime outperforms TRUST on nighttime, illustrating the importance of the temperature contrasts during day for thermal unmixing.

This article aims to show that TRUST thermal unmixing, providing intra-pixel information 1 of material abundances and their temperatures, allows a better description of the urban thermography, 2 which not only supplies land surface temperatures at better resolutions but also allows to link them to 3 specific materials. Moreover, this article presents an improved version of TRUST, called TRUST-DNS 4 (Day and Night Synergy), which takes advantage of daytime and nighttime acquisitions to improve 5 the unmixing performances. Both TRUST and TRUST-DNS are applied on airborne thermal images 6 of Madrid city center obtained during the DESIREX campaign 2008, initially at 4m resolution and 7 undersampled to 8m resolution. This undersampling allows to use the 4m ground classification done 8 during the DESIREX campaign as reference for the abundance retrieval study. Thus, this article aims at 9 showing the applicability of TRUST thermal unmixing on a highly challenging study case: the analysis 10 of a heterogeneous urban environment with airborne multispectral (8 thermal bands) images at 8m 11 resolution. In this study, seven different endmembers (natural and manmade materials) are considered as 12 composing the Madrid city center, and TRUST and TRUST-DNS are used to recover intra-pixel abundance 13 and temperature maps at 8m resolution for each endmember.