La température des surfaces continentales (LST) est une variable météorologiquetrès importante car elle permet l’accès aux bilans d’énergie et d’eau ducontinuum Biosphère-Atmosphère. Sa haute variabilité spatio-temporelle nécessite desmesures à haute résolution spatiale (HRS) et temporelle (HRT) pour suivre au mieuxles états hydriques du sol et des végétations.La télédétection infrarouge thermique (IRT) permet d’estimer la LST à différentesrésolutions spatio-temporelles. Toutefois, les mesures les plus fréquentes sont souventà basse résolution spatiale (BRS). Il faut donc développer des méthodes pour estimerla LST à HRS à partir des mesures IRT à BRS/HRT. Cette solution est connue sous lenom de désagrégation et fait l’objet de cette thèse.Ainsi, une nouvelle approche de désagrégation basée sur l’assimilation de données(AD) est proposée. Il s’agit de contraindre la dynamique des LSTs HRS/HRT simuléespar un modèle en minimisant l’écart entre les LST agrégées et les données IRT àBRS/HRT, sous l’hypothèse d’homogénéité de la LST par type d’occupation des sols àl’échelle du pixel BRS. La méthode d’AD choisie est un lisseur particulaire qui a étéimplémenté dans le modèle de surface SETHYS (Suivi de l’Etat Hydrique du Sol).L’approche a été évaluée dans une première étape sur des données synthétiques etvalidée ensuite sur des données réelles de télédétection sur une petite région au Sud-Est de la France. Des séries de températures Météosat à 5 km de résolution spatialeont été désagrégées à 90m et validées sur une journée à l’aide de données ASTER.Les résultats encourageants nous ont conduit à élargir la région d’étude et la périoded’assimilation à sept mois. La désagrégation des produits Météosat a été validée quantitativementà 1km à l’aide de données MODIS et qualitativement à 30m à l’aide dedonnées Landsat7. Les résultats montrent de bonnes performances avec des erreursinférieures à 2.5K sur les températures désagrégées à 1km. / Land surface temperature (LST) is one of the most important meteorologicalvariables giving access to water and energy budgets governing the Biosphere-Atmosphere continuum. To better monitor vegetation and energy states, we need hightemporal and spatial resolution measures of LST because its high variability in spaceand time.Despite the growing availability of Thermal Infra-Red (TIR) remote sensing LSTproducts, at different spatial and temporal resolutions, both high spatial resolution(HSR) and high temporal resolution (HTR) TIR data is still not possible because ofsatellite resolutions trade-off : the most frequent LST products being low spatial resolution(LSR) ones.It is therefore necessary to develop methods to estimate HSR/HTR LST from availableTIR LSR/HTR ones. This solution is known as "downscaling" and the presentthesis proposes a new approach for downscaling LST based on Data Assimilation (DA)methods. The basic idea is to constrain HSR/HTR LST dynamics, simulated by a dynamicalmodel, through the minimization of their respective aggregated LSTs discrepancytoward LSR observations, assuming that LST is homogeneous at the land cover typescale inside the LSR pixel.Our method uses a particle smoother DA method implemented in a land surfacemodel : SETHYS model (Suivie de l’Etat Hydrique de Sol). The proposed approach hasbeen firstly evaluated in a synthetic framework then validated using actual TIR LSTover a small area in South-East of France. Meteosat LST time series were downscaledfrom 5km to 90m and validated with ASTER HSR LST over one day. The encouragingresults conducted us to expand the study area and consider a larger assimilation periodof seven months. The downscaled Meteosat LSTs were quantitatively validated at1km of spatial resolution (SR) with MODIS data and qualitatively at 30m of SR withLandsat7 data. The results demonstrated good performances with downscaling errorsless than 2.5K at MODIS scale (1km of SR).

L'objectif de cette thèse est la modélisation du rayonnement thermique directionnel des surfaces et de la correction angulaire sur la LST par des méthodes empiriques et physiques ainsi que l'analyse de validation sur le terrain. L'émissivité directionnelle des surfaces naturelles a été obtenue à partir du produit émissivité MODIS et est ensuite utilisée dans l'algorithme de split-window de correction angulaire sur la LST. Les modèles de paramétrage de l'émissivité directionnelle et du rayonnement thermique ont été développés. En ce qui concerne les pixels non iso-thermiques, la méthode de jour-TISI a été proposée pour obtenir l'émissivité directionnelle et la température effective à partir de données multi-angulaires infrarouges médian et thermique. Cela a été validé à l'aide de données aéroportée. Le modèle de noyaux Kernel BRDF a été vérifié dans le domaine de l'infrarouge thermique et son extension a servi à la normalisation angulaire de la LST. Un nouveau modèle, FovMod, qui concerne l'empreinte du capteur au sol, a été développé pour simuler la température de brillance directionnelle de couvert végétal en rang. Basé sur le résultat de la simulation de FovMod, une empreinte optimale pour la validation de champ de vue a été obtenue. Cette thèse a étudié systématiquement le rayonnement thermique directionnel et les corrections angulaires sur la température de surface et ses résultats amélioreront la précision sur la température et émissivité à partir de données de télédétection. Ils fourniront également des indices pour la conception de capteurs infrarouges thermiques multi-angulaires aéro/spatio portés et également pour la mesure au sol des paramètres de surface. / The aim of this thesis is the modeling of surface directional thermal radiation and angular correction on the LST by using empirical and physical methods as well as the analysis of field validation. The work has conducted to some conclusions. The directional emissivity of natural surfaces was obtained from MODIS emissivity product and then used in the split-window algorithm for angular correction on LST. The parameterization models of directional emissivity and thermal radiation were developed. As for the non-isothermal pixels, the daytime-TISI method was proposed to retrieve directional emissivity and effective temperature from multi-angular middle and thermal infrared data. This was validated using an airborne dataset. The kernel-driven BRDF model was checked in the thermal infrared domain and its extension was used to make angular normalization on the LST. A new model, namely FovMod that concerns on the footprint of ground sensor, was developed to simulate directional brightness temperature of row crop canopy. Based on simulation result of the FovMod, an optimal footprintfor field validation of LST was obtained. This thesis has systematically investigated the topic of directional thermal radiation and angular correction on surface temperature and its findings will improve the retrieval accuracy of temperature and emissivity from remotely sensed data and will also provide suggestion for the future design of airborne or spaceborne multi-angular thermal infrared sensors and also for the ground measurement of surface parameters.

The urban heat island (UHI) is a phenomenon of noticeably higher temperatures in the cities as compared to their respective surrounding areas. This thesis aims at characterizing the influence of city expansion to the urban heat island phenomenon. The study is carried out in a city of Caceres in the Spanish province of the same name. A model input data is obtained with Landsat multispectral images. The analysis of satellite images shows that functional vegetation cover and water surfaces help in mitigating urban heat island effect. However, the Caceres city expansion does not influence the urban heat island intensity. A possible explanation for it is as the city expanded the ratio of vegetation to dry land remains constant in time.

<div><br></div><div>Extreme heat is one of the deadliest health hazards that is projected to increase in intensity and persistence in the near future. Temperatures are further exacerbated in the urban areas due to the Urban Heat Island (UHI) effect resulting in increased heat-related mortality and morbidity. However, the spatial distribution of urban temperatures is highly heterogeneous. As a result, metrics such as UHI Intensity that quantify the difference between the average urban and non-urban air temperatures, often fail to characterize this spatial and temporal heterogeneity. My objective in this thesis is to understand and characterize the spatio-temporal dynamics of UHI for cities across the world. This has several applications, such as targeted heat mitigation, energy load estimation, and neighborhood-level vulnerability estimation.</div><div><br></div><div>Towards this end, I have developed a novel multi-scale framework of identifying emerging heat clusters at various percentile-based thermal thresholds T_thr and refer to them collectively as <i>intra-Urban Heat Islets</i>. Using the Land Surface Temperatures from Landsat for 78 cities representative of the global diversity, I have showed that the heat islets have a fractal spatial structure. They display properties analogous to that of a percolating system as T_thr varies. At the percolation threshold, the size distribution of these islets in all cities follows a power-law, with a scaling exponent = 1.88 and an aggregated Area-Perimeter Fractal Dimension =1.33. This commonality indicates that despite the diversity in urban form and function across the world, the urban temperature patterns are different realizations with the same aggregated statistical properties. In addition, analogous to the UHI Intensity, the mean islet intensity, i.e., the difference between mean islet temperature and thermal threshold, is estimated for each islet, and their distribution follows an exponential curve. This allows for a single metric (exponential rate parameter) to serve as a comprehensive measure of thermal heterogeneity and improve upon the traditional UHI Intensity as a bulk metric.</div><div><br></div><div><br></div><div>To study the impact of urban form on the heat islet characteristics, I have introduced a novel lacunarity-based metric, which quantifies the degree of compactness of the heat islets. I have shown that while the UHIs have similar fractal structure at their respective percolation threshold, differences across cities emerge when we shift the focus to the hottest islets (T_thr = 90^th percentile). Analysis of heat islets' size distribution demonstrates the emergence of two classes where the dense cities maintain a power law, whereas the sprawling cities show an exponential deviation at higher thresholds. This indicates a significantly reduced probability of encountering large heat islets for sprawling cities. In contrast, analysis of heat islet intensity distributions indicates that while a sprawling configuration is favorable for reducing the mean Surface UHI Intensity of a city, for the same mean, it also results in higher local thermal extremes. </div><div><br></div><div>Lastly, I have examined the impact of external forcings such as heatwaves (HW) on the heat islet characteristics. As a case study, the European heatwave of 2018 is simulated using the Weather Research Forecast model with a focus on Paris. My results indicate that the UHI Intensity under this HW reduces during night time by 1^oC on average. A surface energy budget analysis reveals that this is due to drier and hotter rural background temperatures during the HW period.</div><div>To analyze the response of heat islets at every spatial scale, power spectral density analysis is done. The results show that large contiguous heat islets (city-scale) persist throughout the day during a HW, whereas the smaller islets (neighborhood-scale) display a diurnal variability that is the same as non-HW conditions. </div><div><br></div><div>In conclusion, I have presented a new viewpoint of the UHI as an archipelago of intra-urban heat islets. Along the way, I have introduced several properties that enable a seamless comparison of thermal heterogeneity across diverse cities as well as under diverse climatic conditions. This thesis is a step towards a comprehensive characterization of heat from the spatial scales of an urban block to a megalopolis.</div><div><br></div>