Combining spatial and temporal data to create a fine-resolution daily urban air temperature product from remote sensing land surface temperature (LST) data

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities

Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem

Downscaling techniques offer a solution to the lack of high-resolution satellite Thermal InfraRed (TIR) data and can bridge the gap until operational TIR missions accomplishing spatio-temporal requirements are available. These techniques are generally based on the Visible Near InfraRed (VNIR)-TIR variable relations at a coarse spatial resolution, and the assumption that the relationship between spectral bands is independent of the spatial resolution. In this work, we adopted a previous downscaling method and introduced some adjustments to the original formulation to improve the model performance. Maps of Land Surface Temperature (LST) with 10-m spatial resolution were obtained as output from the combination of MODIS/Sentinel-2 images. An experiment was conducted in an agricultural area located in the Barrax test site, Spain (39°03′35″ N, 2°06′ W), for the summer of 2018. Ground measurements of LST transects collocated with the MODIS overpasses were used for a robust local validation of the downscaling approach. Data from 6 different dates were available, covering a variety of croplands and surface conditions, with LST values ranging 300-325 K. Differences within ±4.0 K were observed between measured and modeled temperatures, with an average estimation error of ±2.2 K and a systematic deviation of 0.2 K for the full ground dataset. A further cross-validation of the disaggregated 10-m LST products was conducted using an additional set of Landsat-7/ETM+ images. A similar uncertainty of ±2.0 K was obtained as an average. These results are encouraging for the adaptation of this methodology to the tandem Sentinel-3/Sentinel-2, and are promising since the 10-m pixel size, together with the 3-5 days revisit frequency of Sentinel-2 satellites can fulfill the LST input requirements of the surface energy balance methods for a variety of hydrological, climatological or agricultural applications. However, certain limitations to capture the variability of extreme LST, or in recently sprinkler irrigated fields, claim the necessity to explore the implementation of soil moisture or vegetation indices sensitive to soil water content as inputs in the downscaling approach. The ground LST dataset introduced in this paper will be of great value for further refinements and assessments

Enhancing the spatial resolution of satellite-derived land surface temperature mapping for urban areas

Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. Here, a thermal sharpening technique is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed Super-Resolution Thermal Sharpener (SRTS) does not rely on any surface index, ensuring the independence of the derived LST to be as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: the thermal sharpening algorithm (TsHARP), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM). The privilege of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, even without requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable

Urban surface temperature time series estimation at the local scale by spatial-spectral unmixing of satellite observations

The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities

Spatio-temporal analysis of the urban green infrastructure of the city of Granada (Spain) as a heat mitigation measure using high-resolution images Sentinel 3

At present, and motivated by a substantial growth of the population, a considerable expansion of urban areas is taking place through the modification of land uses. These changes, together with global warming and extreme weather events, produce increases in the temperature of the earth’s surface and a deterioration of the environment that affects people’s quality of life. The green areas of cities are upheld as one of the best for adapting to such phenomena, since they help lower outdoor temperatures. In this research, using high-resolution Sentinel 3 satellite images and the TsHARP algorithm, the Land Surface Temperature (LST) and the Park Cool Island (PCI) were obtained at a resolution of 10 m over green areas in the city of Granada. The objective was to analyze the relationship between surface, PCI effect and cooling distance. In turn, for each of the eight green areas studied, the following variables were taken into account and included in a statistical analysis known as data panel: normalized difference vegetation index, vegetal proportion, sky view factor, landscape shape index, model digital elevation, wind and solar radiation. Our results report diurnal LST decreases of 1 K and night LST of 0.6 K in green areas as compared to urban areas. There is moreover a correlation between the size of the green areas, the decrease in temperature they generate, and distance of the minimizer effect.Universidad de Granada, CBU

Sharpening ECOSTRESS and VIIRS Land Surface Temperature Using Harmonized Landsat-Sentinel Surface Reflectances

Land surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co-collected over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (~70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180–270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data

Εκτίμηση της συμβολής της αστικής μορφολογίας και λειτουργίας στο αστικό θερμικό περιβάλλον με την ανάπτυξη προηγμένων τεχνικών δορυφορικής τηλεπισκόπησης

Στο πλαίσιο της παρούσας διδακτορικής διατριβής επιχειρήθηκε μία πολυδιάστατη μελέτη του θερμικού περιβάλλοντος του πολεοδομικού συγκροτήματος της Αθήνας. Η ερευνητική προσπάθεια είχε ως στόχο την εκτίμηση της επίδρασης της αστική μορφολογίας και λειτουργίας στο αστικό θερμικό περιβάλλον. Η μελέτη υλοποιήθηκε κατά κύριο λόγο με την ανάπτυξη τεχνικών δορυφορικής τηλεπισκόπησης και επικουρικά με την εφαρμογή Γεωγραφικών Συστημάτων Πληροφοριών και αριθμητικής προσομοίωσης. Τα υφιστάμενα δορυφορικά συστήματα δεν διαθέτουν την ταυτόχρονη υψηλή χωρική και χρονική διακριτική ικανότητα η οποία απαιτείται για τη λεπτομερειακή διερεύνηση των ενδοαστικών διαφοροποιήσεων. Για τον σκοπό αυτό, πραγματοποιήθηκε αρχικά βελτίωση των υφιστάμενων μεθοδολογιών της στατιστικής υποκλιμάκωσης, κατά την οποία το LST ενισχύεται χωρικά βάσει της σχέσης που εμφανίζει με επιφανειακές παραμέτρους. Η υποκλιμάκωση πραγματοποιήθηκε με τη χρήση πολλαπλών μεταβλητών πρόβλεψης, υψηλής χωρικής ανάλυσης τιμών του συντελεστή εκπομπής έπειτα από φασματική ταξινόμηση, και εξετάζοντας διαφορετικούς γραμμικούς και μη γραμμικούς αλγορίθμους παλινδρόμησης. Εφαρμόστηκε στις καταγραφές θερμικής ακτινοβολίας του αισθητήρα Moderate Resolution Imaging Spectroradiometer (MODIS) των δορυφόρων Aqua και Terra, για τη χωρική ενίσχυσή τους από το 1 km στα 100 m. Βρέθηκε ότι η προτεινόμενη μέθοδος υποκλιμάκωσης —με τη χρήση του αλγορίθμου παλινδρόμησης τύπου ridge— παρείχε αξιόπιστες, χωρικά ενισχυμένες τιμές LST με σφάλμα μικρότερο των 2 K (τετραγωνική ρίζα μέσου τετραγωνικού σφάλματος, Root Mean Square Error – RMSE) και με σταθερά καλύτερη ακρίβεια (∼0.5 K) συγκριτικά με τις μεθόδους αναφοράς. Στο επόμενο στάδιο της μελέτης πραγματοποιήθηκε, σε υψηλή χωρική διακριτική ικανότητα (100 m), ο προσδιορισμός των χαρακτηριστικών της αστικής μορφολογίας, της λειτουργίας και των ροών ενέργειας της Αθήνας, και επακόλουθα ο συνδυασμός τους σε έναν δείκτη θερμικής επιβάρυνσης. Χρησιμοποιώντας πλήθος γεωχωρικών δεδομένων, κατέστη δυνατή η πλήρης περιγραφή του κτηριακού περιβάλλοντος, συμπεριλαμβανομένου του λόγου του ύψους των κτηρίων προς το πλάτος των δρόμων —αναλογία διαστάσεων αστικής χαράδρας (H/W)— για κάθε οδό της πόλης. Επακόλουθα, αξιοποιώντας το υψηλής χωρικής και χρονικής ανάλυσης LST από το πρώτο τμήμα της εργασίας, πραγματοποιήθηκε ο προσδιορισμός της αισθητής (QH) και της λανθάνουσας ροής θερμότητας (QE). Χρησιμοποιήθηκαν επιπρόσθετα παρατηρήσεις από μετεωρολογικούς σταθμούς και εφαρμόστηκε η μέθοδος της «αεροδυναμικής αντίστασης». Για την αξιολόγηση της ακρίβειας της εκτίμησης των ροών χρησιμοποιήθηκαν μετρήσεις μικρομετεωρολογικού πύργου· βρέθηκε μέσο σφάλμα RMSE ∼45 W/m2 για το QH και ∼15 W/m2 για το QE. Από τις παραπάνω τυρβώδεις ροές ενέργειας υπολογίστηκε στη συνέχεια ο λόγος Bowen β = QH/QE. Η ανθρωπογενής ροή θερμότητας (QF) προσδιορίστηκε μέσω της ανάπτυξης αλγορίθμου που συνδυάζει τις «bottom-up» και «top-down» προσεγγίσεις και είναι προσαρμοσμένος στα διαθέσιμα ενεργειακά δεδομένα της Αθήνας. Εντοπίστηκαν υψηλές ανθρωπογενείς εκπομπές θερμότητας για το κέντρο της πόλης (QF > 100 W/m2). Οι παραπάνω μεταβλητές (H/W, β και QF) μαζί με την εκτιμώμενη «καθαρή» μεταβολή του ρυθμού αποθήκευσης θερμότητας (ΔQs) ενσωματώθηκαν κατόπιν στον προτεινόμενο δείκτη θερμικής έκθεσης (Urban Heat Exposure, UHeatEx), χρησιμοποιώντας τη μέθοδο της ανάλυσης σε κύριες συνιστώσες (Principal Component Analysis, PCA). Ο παραπάνω δείκτης αποτύπωσε τα σημεία του αστικού ιστού της Αθήνας με τη δυσμενέστερη θερμική ποιότητα και κατά συνέπεια μπορεί να καταστεί ιδιαίτερα πολύτιμος σε μελέτες αστικού σχεδιασμού. Επιπρόσθετα, ο UHeatEx κατάφερε να αναδείξει τα ιδιαίτερα χαρακτηριστικά ως προς τη μορφολογία και το ενεργειακό ισοζύγιο της πόλης, κάτι που σε σημαντικό βαθμό δεν μπορούσε να επιτευχθεί μέσω της ταξινόμησης των «Τοπικών Κλιματικών Ζωνών» (Local Climate Zones, LCZ). Στο τελευταίο μέρος το ενδιαφέρον μετατοπίστηκε στην αξιολόγηση της μέσης επίδρασης στο θερμικό περιβάλλον εκτενέστερων αστικών ενοτήτων τοπικής κλίμακας (1 km). Συγκεκριμένα, αρχικά διερευνήθηκε η επίδραση βασικών αστικών μορφολογικών παραμέτρων —το ποσοστό των αδιαπέρατων επιφανειών, το ποσοστό της επιφάνειας κάλυψης από κτήρια και το ύψος των κτηρίων— στην επιφανειακή θερμοκρασία όπως αυτή καταγράφεται από τον δορυφορικό αισθητήρα MODIS. Με σκοπό μια ευρύτερη γενίκευση των συμπερασμάτων ως προς την επίδραση της αστικής μορφολογίας στο LST, παράλληλα με την περίπτωση της Αθήνας εξετάστηκαν 24 επιπλέον ευρωπαϊκές πόλεις. Η στατιστική ανάλυση κατέδειξε ότι η πυκνή και υψηλή δόμηση έχει εν γένει ασθενή θετική ή ακόμα και αρνητική σύνδεση με το LST κατά τη διάρκεια της ημέρας, ενώ αντίθετα εμφανίζει ισχυρή θετική επίδραση τη νύχτα. Το παραπάνω ήταν ιδιαίτερα εμφανές για την Αθήνα, όπου και εξετάστηκαν επιπλέον μορφολογικές παράμετροι και πραγματοποιήθηκε ερμηνεία των αποτελεσμάτων με βάση τις τάξεις των LCZ. Στη συνέχεια, τα χαρακτηριστικά της αστικής μορφολογίας και λειτουργίας της Αθήνας ενσωματώθηκαν στο ατμοσφαιρικό μοντέλο WRF για τη διερεύνηση της προγνωστικής ικανότητάς του, όσον αφορά το αστικό θερμικό περιβάλλον. Τα αποτελέσματα της εφαρμογής του WRF (σε πλέγμα χωρικής ανάλυσης 1 km) σε συνδυασμό με ένα τροποποιημένο σχήμα αστικής παραμετροποίησης έδειξαν ότι το μοντέλο μπορεί να αναπαραγάγει τις κύριες διαφοροποιήσεις εντός του αστικού ιστού, αναφορικά με την επιφανειακή θερμοκρασία (RMSE ∼2.4 K) και τη θερμοκρασία αέρα κοντά στο έδαφος (RMSE ∼1.7 K).In this PhD thesis, a multifaceted study of the thermal environment of Athens was conducted. The motivation of the research work was to assess the influence of urban form and function on the urban thermal environment. The work was carried out primarily by applying and developing satellite remote sensing techniques, and to a lesser extent via Geographical Information Systems (GIS) methodologies and the implementation of numerical simulations. The current satellite systems do not have the synchronous spatial and temporal frequency which is needed in a detailed study of intra-urban variability. To this end, an improvement of the standard statistical downscaling methodologies was firstly developed, where LST is disaggregated based on its relationship with surface parameters. The downscaling was accomplished using multiple predictor variables, high resolution land cover-based emissivity values, and assessing various linear and non-linear regression algorithms. It was applied to sharpen the thermal observations of the Moderate Resolution Imaging Spectroradiometer (MODIS) from 1 km to 100 m. It was found that the suggested downscaling method —using the ridge regression downscaling algorithm— produced a robust, spatially sharpened LST, with an average Root Mean Square Error (RMSE) less than 2 K and a consistent better performance compared to the reference methods. At the next stage, the urban form, function, and energy fluxes were mapped at a high resolution (100 m) and subsequently combined in an urban heat exposure indicator. Utilizing a wide range of spatial data, a full description of the building environment was accomplished, as well as the derivation of the building height to road width ratio —urban canyon aspect ratio (H/W)— at street level. Next, using the downscaled satellite-derived LST from the first part of the study and meteorological observations, the sensible (QH) and latent heat flux (QE) were calculated, applying the “aerodynamic resistance” methodology. To assess the accuracy of the calculations, micrometeorological observations; an overall RMSE error of ∼45 Wm2 for QH and ∼15 Wm2 for QE was obtained. From the above turbulent fluxes, the Bowen ratio β = QH/QE was subsequently derived. To determine the anthropogenic heat flux (QF) a new algorithm was developed, combining the “bottom-up” and “top-down” methodologies, adapted to the available data for the study area. Particularly high anthropogenic heat emissions were found for the city center (QF > 100 W/m2). Subsequently, the above urban parameters (H/W, β και QF) together with the net heat storage (ΔQs) were integrated into the proposed Urban Heat Exposure (UHeatEx) indicator through Principal Component Analysis (PCA). The indicator outlined the diverging thermal quality of the different building blocks in Athens and thereby can be valuable to urban planning adaptation responses. Moreover, UHeatEx managed to highlight the city-specific features of the urban form and energy budget of the city, which to a great extent could not be captured by the classification of the Local Climate Zones (LCZ). At the final stage of the study, focus was shifted to the study of the integrated neighborhood-scale effect (1 km) on the urban thermal climate. Specifically, it was initially assessed how basic urban morphological parameters —the impervious fraction, the building fraction, and the building height— are interlinked to the surface temperature variations, as captured by a spaceborne sensor (MODIS). To promote the generalization of conclusions, in addition to Athens, 24 additional European cities were examined. The statistical analysis showed that the closely spaced and high-rise buildings have generally a weak positive or even a negative relation to LST in daytime and a strong positive effect at night. This finding was significantly pronounced for Athens, where further urban parameters were also evaluated and the results were linked to the LCZs classes. Next, urban form and function of Athens were incorporated in the WRF model to study its predicting ability of the thermal environment. Using WRF along with a modified urban parameterization scheme (at a 1 km grid), results indicated that the prevailing intra-urban spatial patterns can be reproduced in the simulations, regarding the surface temperature (RMSE ∼2.4 K) and the near-surface air temperature (RMSE ∼1.7 K)

A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA

The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body

A hierarchical Dempster-Shafer evidence combination framework for urban area land cover classification

This paper presents a novel evidence combination framework for urban area land cover classification by using Light Detection And Ranging (LIDAR) data fused with co-registered near infrared and color images. The newly developed combination framework is built with a hierarchical structure involving an improved Dempster-Shafer (DS) theory of evidence for decision making. In the framework, a fuzzy basic probability assignment (BPA) function with fuzzy classes is firstly established based on the DS theory of evidence, and a probability is then assigned to each data source, that is derived from the original airborne LIDAR and the co-registered images. Secondly, an interesting approach is to introduce noise removal in an interim stage at the output of the probability distribution, and then the probability assigned to each data source is redistributed with a designated rule. Finally, a decision is made based on a “maximum normal support” rule, leading to the classification results. The proposed framework has been tested on two datasets. The testing results have shown that it can dramatically reduce the computational time in the classification process, and significantly improve the classification accuracy, i.e. 8.22% on Test 1 and 5.76% on Test 2 compared to the basic DS method. Due to its non-iterative and unsupervised nature, the proposed method is fast in computation, does not require training samples, and has achieved high classification accuracy

Enjeux de la réduction d'échelle dans l'estimation par télédétection des déterminants climatiques

Ce travail s'inscrit dans le cadre de recherche sur les maladies vectorielles de Lyme et Virus du Nil au sein de l'Agence de Santé Publique du Canada (ASPC) ayant pour finalité d'évaluer et de cartographier les risques sanitaires associés à ces maladies infectieuses liées au climat aux échelles municipales, provinciales et fédérale. Dans ce contexte, cette recherche vise à démontrer la faisabilité, la pertinence et les enjeux de recourir aux méthodes de réduction d'échelle pour obtenir à une haute résolution spatio-temporelle (100/30 m et 1 jour) avec au plus des marges d'erreur de 2 unités, des déterminants climatiques et microclimatiques (DCMC) en milieu hétérogène du Canada. Un cadre méthodologique d'application des méthodes de réduction d'échelle, Random Forest Regression (RFR), Thermal sharpening (TsHARP), Pixel block intensity modulation (PBIM), a été proposé pour estimer la température de surface (LST) de MODIS 1000 m à 100/30 m. Des expérimentations basées sur cette approche ont été effectuées sur trois sites au Québec à différentes époques. Les résultats, spatialement représentatifs, ont été validés avec les températures de l'air et celles prises par de Landsat 08 avec des marges d'erreur autour de 2°C. L'analyse des résultats démontre la capacité effective des méthodes de réduction d'échelle à discriminer la LST dans l'espace. Toutefois, dans le contexte du projet de l'ASPC, ces résultats sont non concluants à 100/30 m en l'absence d'une plus-value significative au plan spatial de LST. Cette analyse a conduit à discuter des enjeux temporels, spatiaux, méthodologiques et de gestion de gros volumes de données en lien avec la réduction d'échelle dans le contexte du projet.This research is part of the Public Health Agency of Canada's (PHAC) research on Lyme and West Nile Virus vector-borne diseases, which aims to assess and map the health risks associated with these climate-related infectious diseases at the municipal, provincial and federal levels. In this context, this research aims to demonstrate the feasibility, relevance and challenges of using downscaling methods to obtain high spatial and temporal resolution (100/30 m and 1 day), with margins of error of no more than 2 units, of climatic and microclimatic determinants (CMDs) in a heterogeneous Canadian environment. A methodological framework for the application of downscaling methods, Random Forest Regression (RFR), Thermal sharpening (TsHARP), Pixel block intensity modulation (PBIM), has been proposed to estimate the surface temperature (LST) from MODIS 1000 m to 100/30 m. Experiments with our approach were carried out at three sites in Quebec at different times. The spatially representative results were validated with air and Landsat 08 temperatures with error margins around 2°C. The analysis of our results demonstrates the effective capacity of downscaling methods to discriminate LST in space. However, in the context of the ASPC project, these results are inconclusive at 100/30 m in the absence of a significant, expected increase in the spatial accuracy of LST. This analysis led to a discussion of the temporal, spatial, methodological and large data volume management issues related to downscaling in the context of the project