In 2005, Hurricane Katrina’s diverse impacts on the Greater New Orleans area included damaged and destroyed trees, and other despoiled vegetation, which also increased the exposure of artificial and bare surfaces, known factors that contribute to the climatic phenomenon known as the urban heat island (UHI). This is an investigation of UHI in the aftermath of Hurricane Katrina, which entails the analysis of pre and post-hurricane Katrina thermal imagery of the study area, including changes to surface heat patterns and vegetative cover. Imagery from Landsat TM was used to show changes to the pattern and intensity of the UHI effect, caused by an extreme weather event. Using remote sensing visualization methods, field data, and local knowledge, the author found there was a measurable change in the pattern and intensity of the New Orleans UHI effect, as well as concomitant changes to vegetative land cover. This finding may be relevant for urban planners and citizens, especially in the context of recovery from a large-scale disaster of a coastal city, regarding future weather events, and other natural and human impacts.

Soil attributes directly influence on its surface temperature. Although there are several studies using soil spectra obtained from satellites, soil evaluation through Land Surface Temperature (LST) is still scarce. The broad availability of satellite thermal data and the development of algorithms to retrieve LST facilitated its use in soil studies. The objective of this study was to evaluate soil LST variations due to its composition and verify the potential of using LST on soil attributes quantification, also integrated with reflectance spectra and elevation data. The study area (198 ha) is located in Sao Paulo state, Brazil, and had plowed bare soil during the satellite image acquisition date. Soil samples were collected in a regular grid of 100 x 100 m (depths: 0-0.2 m and 0.8- 1.0 m); soil granulometry, organic matter (OM) and iron oxides were determined by wet chemistry analysis. In this study, an image of Landsat 5 was used for extracting LST using the inversion of Planck\'s function in band 6 (10,400 - 12,500 nm), and land surface emissivity was estimated using Normalized Difference Vegetation Index threshold method. Reflectance values were extracted from bands 1, 2, 3, 4, 5 and 7. Models for soil attributes quantification were performed using Linear Regression (LR), with samples from 62 auger points distributed in 14 toposequences. Simple LR was applied for generating prediction models based on LST and on elevation data (extracted from a Digital Elevation Model). Multiple LR was applied in order to generate prediction models using atmospherically corrected spectral reflectance from Visible, Near-Infrared and Shortwave infrared (Vis-NIR-SWIR) bands as predictors, and also for the prediction of soil attributes using simultaneously Vis-NIR-SWIR, LST and elevation data, and only significant variables identified by T-tests were used. Predictive performance of models was assessed based on adjusted coefficient of determination (R2adj), Root Mean Squared Error (RMSE, g kg-1) and Ratio of Performance to Interquartile Range (RPIQ) obtained in validation. Ordinary kriging was also performed and the resulted interpolated surfaces were compared to the maps obtained from the best LR model. There was significant correlation between soil attributes and reflectance, LST and elevation data, and soils with clay texture were differentiated from sandy soils based on LST mean values. For all soil attributes, models using only elevation presented the worst performance; models using only LST, moderate performance; and using Vis-NIR-SWIR bands, good predictive performance. For clay, the best model obtained had bands 4-7, LST and elevation as predictors; for sand and iron oxides, the best model had bands 4-7 and LST; for OM, band 4, band 7 and LST. The use of LST for estimating soil attributes increases the predictive performance of multiple LR models when associated with other variables obtained through remote sensing, particularly surface reflectance data, improving the validation of models reaching high R2adj, high RPIQ and low RMSE values. Maps for sand, OM and iron oxides obtained through ordinary kriging outperformed those obtained for the same attributes using LR models based on RS co-variables, and for clay, both approaches reached the same accuracy level. Mapping of soil clay, sand, OM and iron oxides contents through multiple LR models using Landsat 5 products is a simple and easy to reproduce technique, appropriate for soil attributes mapping in bare soil agricultural areas. / Os atributos do solo influenciam diretamente na sua temperatura de superfície. Apesar de existir vários estudos utilizando espectros de solos obtidos de satélite, a avaliação do solo por meio da Temperatura de Superfície Terrestre (em inglês Land Surface Temperature, LST) ainda é escassa. A ampla disponibilidade de dados termais de satélite e o desenvolvimento de algoritmos para derivar a LST facilitou o seu uso em estudos de solos. O objetivo desse trabalho foi avaliar variações da LST do solo devidas à sua composição e verificar o potencial de uso da LST na quantificação de atributos do solo, também integrada com dados de espectros de reflectância e elevação. A área de estudo (198 ha) está localizada no estado de São Paulo, Brasil, e estava com solo exposto e arado na data de aquisição da imagem de satélite. Amostras de solo foram coletadas em um grid regular de 100 x 100 m (profundidades: 0.02 m e 0.8-1.0 m); a granulometria do solo, matéria orgânica (MO) e óxidos de ferro foram determinados via análises físicas e químicas laboratoriais. Neste estudo, uma imagem do Landsat 5 foi utilizada para extrair a temperatura de superfície usando a inversão da função da Lei de Planck na banda 6 (10.400 - 12.500 nm), e a emissividade de superfície foi estimada utilizando o método do limiar do Índice de Vegetação da Diferença Normalizada. Valores de reflectância das bandas 1, 2, 3, 4, 5 e 7 foram extraídos. Modelos para quantificação de atributos do solo foram feitos usando Regressão Linear (RL), com amostras de 62 pontos de tradagem distribuídos em 14 topossequências. A RL simples foi aplicada para gerar modelos de predição baseados na LST e também na elevação (extraída de um modelo digital de elevação). A RL múltipla foi aplicada para gerar modelos de predição usando os espectros de reflectância com correção atmosférica das bandas do Visível, Infravermelho próximo e Infravermelho de ondas curtas (Vis-NIR-SWIR) como preditores; também foi aplicada para predição de atributos do solo usando simultaneamente dados do Vis-NIR-SWIR, LST e elevação, e apenas variáveis significativas identificadas por teste T foram usadas. A performance preditiva dos modelos foi avaliada baseada no coeficiente de determinação ajustado (R2adj), raiz do erro quadrático médio (RMSE, g kg-1) e razão de desempenho do intervalo interquartil (RPIQ) obtidos na validação. A krigagem ordinária também foi feita e as superfícies interpoladas resultantes foram comparadas com o melhor modelo de RL. Houve correlação significativa entre os atributos do solo e dados de reflectância, LST e elevação, e solos com textura argilosa foram diferenciados de solos arenosos com base em valores médios de LST. Para todos os atributos do solo, os modelos usando apenas elevação apresentaram a pior performance, modelos usando somente LST, performance moderada, e usando as bandas do Vis-NIR-SWIR, boa performance preditiva. Para argila, o melhor modelo obtido teve as bandas 4-7, LST e elevação como preditores; para areia e óxidos de ferro, o melhor modelo teve as bandas 4-7 e LST; para MO, banda 4, banda 7 e LST. O uso da LST para estimar atributos do solo aumenta a performance preditiva de modelos de RL múltipla quando associada a outras variáveis obtidas via sensoriamento remoto (SR), particularmente dados de reflectância de superfície, melhorando a validação dos modelos atingindo altos valores de R2adj e RPIQ e baixos valores de RMSE. Os mapas para areia, MO e óxidos de ferro obtidos via krigagem ordinária superaram aqueles obtidos para os mesmos atributos usando modelos de RL baseados em co-variáveis obtidas via SR, e para argila, ambas abordagens atingiram o mesmo nível de acurácia. O mapeamento dos conteúdos de argila, areia, matéria orgânica e óxidos de ferro do solo via modelos de RL múltipla utilizando produtos do Landsat 5 é uma técnica simples e fácil de reproduzir, apropriada para o mapeamento de atributos do solo em áreas de agricultura com solo exposto.

Soil attributes directly influence on its surface temperature. Although there are several studies using soil spectra obtained from satellites, soil evaluation through Land Surface Temperature (LST) is still scarce. The broad availability of satellite thermal data and the development of algorithms to retrieve LST facilitated its use in soil studies. The objective of this study was to evaluate soil LST variations due to its composition and verify the potential of using LST on soil attributes quantification, also integrated with reflectance spectra and elevation data. The study area (198 ha) is located in Sao Paulo state, Brazil, and had plowed bare soil during the satellite image acquisition date. Soil samples were collected in a regular grid of 100 x 100 m (depths: 0-0.2 m and 0.8- 1.0 m); soil granulometry, organic matter (OM) and iron oxides were determined by wet chemistry analysis. In this study, an image of Landsat 5 was used for extracting LST using the inversion of Planck\'s function in band 6 (10,400 - 12,500 nm), and land surface emissivity was estimated using Normalized Difference Vegetation Index threshold method. Reflectance values were extracted from bands 1, 2, 3, 4, 5 and 7. Models for soil attributes quantification were performed using Linear Regression (LR), with samples from 62 auger points distributed in 14 toposequences. Simple LR was applied for generating prediction models based on LST and on elevation data (extracted from a Digital Elevation Model). Multiple LR was applied in order to generate prediction models using atmospherically corrected spectral reflectance from Visible, Near-Infrared and Shortwave infrared (Vis-NIR-SWIR) bands as predictors, and also for the prediction of soil attributes using simultaneously Vis-NIR-SWIR, LST and elevation data, and only significant variables identified by T-tests were used. Predictive performance of models was assessed based on adjusted coefficient of determination (R2adj), Root Mean Squared Error (RMSE, g kg-1) and Ratio of Performance to Interquartile Range (RPIQ) obtained in validation. Ordinary kriging was also performed and the resulted interpolated surfaces were compared to the maps obtained from the best LR model. There was significant correlation between soil attributes and reflectance, LST and elevation data, and soils with clay texture were differentiated from sandy soils based on LST mean values. For all soil attributes, models using only elevation presented the worst performance; models using only LST, moderate performance; and using Vis-NIR-SWIR bands, good predictive performance. For clay, the best model obtained had bands 4-7, LST and elevation as predictors; for sand and iron oxides, the best model had bands 4-7 and LST; for OM, band 4, band 7 and LST. The use of LST for estimating soil attributes increases the predictive performance of multiple LR models when associated with other variables obtained through remote sensing, particularly surface reflectance data, improving the validation of models reaching high R2adj, high RPIQ and low RMSE values. Maps for sand, OM and iron oxides obtained through ordinary kriging outperformed those obtained for the same attributes using LR models based on RS co-variables, and for clay, both approaches reached the same accuracy level. Mapping of soil clay, sand, OM and iron oxides contents through multiple LR models using Landsat 5 products is a simple and easy to reproduce technique, appropriate for soil attributes mapping in bare soil agricultural areas. / Os atributos do solo influenciam diretamente na sua temperatura de superfície. Apesar de existir vários estudos utilizando espectros de solos obtidos de satélite, a avaliação do solo por meio da Temperatura de Superfície Terrestre (em inglês Land Surface Temperature, LST) ainda é escassa. A ampla disponibilidade de dados termais de satélite e o desenvolvimento de algoritmos para derivar a LST facilitou o seu uso em estudos de solos. O objetivo desse trabalho foi avaliar variações da LST do solo devidas à sua composição e verificar o potencial de uso da LST na quantificação de atributos do solo, também integrada com dados de espectros de reflectância e elevação. A área de estudo (198 ha) está localizada no estado de São Paulo, Brasil, e estava com solo exposto e arado na data de aquisição da imagem de satélite. Amostras de solo foram coletadas em um grid regular de 100 x 100 m (profundidades: 0.02 m e 0.8-1.0 m); a granulometria do solo, matéria orgânica (MO) e óxidos de ferro foram determinados via análises físicas e químicas laboratoriais. Neste estudo, uma imagem do Landsat 5 foi utilizada para extrair a temperatura de superfície usando a inversão da função da Lei de Planck na banda 6 (10.400 - 12.500 nm), e a emissividade de superfície foi estimada utilizando o método do limiar do Índice de Vegetação da Diferença Normalizada. Valores de reflectância das bandas 1, 2, 3, 4, 5 e 7 foram extraídos. Modelos para quantificação de atributos do solo foram feitos usando Regressão Linear (RL), com amostras de 62 pontos de tradagem distribuídos em 14 topossequências. A RL simples foi aplicada para gerar modelos de predição baseados na LST e também na elevação (extraída de um modelo digital de elevação). A RL múltipla foi aplicada para gerar modelos de predição usando os espectros de reflectância com correção atmosférica das bandas do Visível, Infravermelho próximo e Infravermelho de ondas curtas (Vis-NIR-SWIR) como preditores; também foi aplicada para predição de atributos do solo usando simultaneamente dados do Vis-NIR-SWIR, LST e elevação, e apenas variáveis significativas identificadas por teste T foram usadas. A performance preditiva dos modelos foi avaliada baseada no coeficiente de determinação ajustado (R2adj), raiz do erro quadrático médio (RMSE, g kg-1) e razão de desempenho do intervalo interquartil (RPIQ) obtidos na validação. A krigagem ordinária também foi feita e as superfícies interpoladas resultantes foram comparadas com o melhor modelo de RL. Houve correlação significativa entre os atributos do solo e dados de reflectância, LST e elevação, e solos com textura argilosa foram diferenciados de solos arenosos com base em valores médios de LST. Para todos os atributos do solo, os modelos usando apenas elevação apresentaram a pior performance, modelos usando somente LST, performance moderada, e usando as bandas do Vis-NIR-SWIR, boa performance preditiva. Para argila, o melhor modelo obtido teve as bandas 4-7, LST e elevação como preditores; para areia e óxidos de ferro, o melhor modelo teve as bandas 4-7 e LST; para MO, banda 4, banda 7 e LST. O uso da LST para estimar atributos do solo aumenta a performance preditiva de modelos de RL múltipla quando associada a outras variáveis obtidas via sensoriamento remoto (SR), particularmente dados de reflectância de superfície, melhorando a validação dos modelos atingindo altos valores de R2adj e RPIQ e baixos valores de RMSE. Os mapas para areia, MO e óxidos de ferro obtidos via krigagem ordinária superaram aqueles obtidos para os mesmos atributos usando modelos de RL baseados em co-variáveis obtidas via SR, e para argila, ambas abordagens atingiram o mesmo nível de acurácia. O mapeamento dos conteúdos de argila, areia, matéria orgânica e óxidos de ferro do solo via modelos de RL múltipla utilizando produtos do Landsat 5 é uma técnica simples e fácil de reproduzir, apropriada para o mapeamento de atributos do solo em áreas de agricultura com solo exposto.

Temperature characteristics of surface using remote sensing methods Abstract The aim of this thesis is to design a methodology for refining the land surface temperature values obtained from Landsat 8 satellite data in areas with diverse land cover. The research section describes factors influencing the radiation of the Earth's surface. Also mentioned are current methods used for processing infrared thermal data and calculate land surface temperature. The practical part describes satellite and airborne data used in the analytical and verification process. All parts of the applied method leading to the subpixel value of the land surface temperature are described in detail in the method part. The results are then compared with airborne verification data with better spatial resolution and with currently used methods. Finally, the pros and cons of this method and its possible improvement in the future are mentioned. Key words: land surface temperature, land surface emissivity, satellite data, Landsat 8, airborne data, subpixel method, Czech Republic

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.

Evapotranspiration (ET) is a significant hydrological process. It can be studied and estimated using remote sensing based methods at multiple spatial and temporal scales. Most commonly and widely used remote sensing based methods to estimate actual evapotranspiration (AET) are a) methods based on energy balance equations, b) vegetation coefficient based method and c) contextual methods. These three methods require reflectance and land surface temperature (LST) data measured at optical and thermal portion of the electromagnetic spectrum. However, these data are available only for clear sky conditions and fail to be retrieved under overcast conditions creating gaps in the data, which result in discontinuous of AET product. Moreover, energy balance equation based methods and evaporative fraction (EF) based contextual methods are difficult to apply over overcast conditions. In this context, vegetation coefficient based (Tasumi et al., 2005; Allen et al., 2005) and microwave remote sensing based methods can be applied under cloudy sky conditions (Sun et al., 2012), since microwave radiations can penetrate through clouds, but these data are available at coarse resolution. In the vegetation coefficient method temporal upscaling can be avoided. Therefore in this research vegetation coefficient based method is employed over Cauvery basin to estimate daily AET for clear and cloudy sky conditions. Required critical variables for this method such as reference evapotranspiration (ETo) and vegetation coefficients are obtained using LST and optical vegetation indices for all sky conditions. In this study, all sky conditions refer to both clear and cloudy sky conditions. Most important variable for estimation of ETo using radiation and temperature based models is air temperature (Ta). In this study, for better accuracy of Ta, two satellite based approaches namely, Temperature Vegetation Index (TVX) and Advance Statistical Approaches (ASA) were evaluated. In the TVX approach, in addition to traditional Normalized Difference Vegetation Index (NDVI), other vegetation indices such as Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI) were also examined. In case of ASA, bootstrap technique was used to generate calibration and validation samples and Levenberg Marquardt algorithm was used to find the solution of the models. The better of the Ta results obtained out of these two approaches were employed in the ETo models and are referred as Ta based ETo models. Instead of Ta, processed LST data obtained directly from the satellite (Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS)) was applied in the ETo models and these are referred as LST based ETo models. These Ta and LST based Hargreaves-Samani (H-S), Makkink (Makk) and Penman Monteith Temperature (PMT) models were evaluated by comparing with the FAO56 PM model. Additionally, simple LST based equation (SLBE) proposed by Rivas et al. (2004) was also examined. Required solar radiation (Rs) data for ETo estimation was obtained from Kalpana1/VHRR satellite data. Results implied that, Ta based PMT model performed better than the Ta based H-S, Makk and SLBE with less RMSE, MAPE and MBE values for all land cover classes and for various climatic regions for clear sky conditions. LST based H-S, PMT, Makk and Ta based Makkink advection models predominantly overestimated ETo for the study region. In the case of TVX approach, to estimate maximum Ta (Tmax), GVMI performed better than NDVI and EVI. Nevertheless, TVX approach poorly estimated Tmax in comparison with statistical approach. ASA performed better for both Tmax and minimum Ta. This study demonstrates the applicability of satellite based Ta and ETo models by considering very few variables for clear sky conditions. Spatially distributed vegetation coefficients (Kv) data with high temporal resolution is another important variable in vegetation coefficient method for daily AET estimation and also it is in demand for crop condition assessment, irrigation scheduling, etc. But available Kv models application hinders because of two main reasons i.e 1) Spectral reflectance based Kv accounts only for transpiration factor but not evaporation, which fails to account for total AET. 2) Required optical spectral reflectances are available only during clear sky conditions, which creates gaps in the Kv data. Hence there is a necessity of a model which accounts for both transpiration and evaporation factors and also gap filling method, which produces accurate continuous quantification of Kv values. Therefore, different combinations of EVI, GVMI and temperature vegetation dryness index (TVDI) have been employed in linear and non linear regression techniques to obtain best model. This best Kv model had been compared with Guershman et al. (2009) Kv model. To fill the gaps in the data, initially, temporal fitting of Kv values have been examined using Savitsky-Goley (SG) filter for three years of data (2012 to 2014), but this fails when sufficient high quality Kv values were unavailable. In this regard, three gap filling techniques namely regression, Artificial Neural Networks (ANNs) and interpolation techniques have been analyzed. Microwave polarization difference index (MPDI) has been employed in ANN technique to estimate Kv values under cloudy sky conditions. The results revealed that the combination of GVMI and TVDI using linear regression technique performed better than other combinations and also yielded better results than Guershman et al. (2009) Kv model. Furthermore, the results indicated that SG filter can be used for temporal fitting and for filling the gaps, regression technique can be used as it performed better than other techniques for Berambadi station. Land Surface Temperature (LST) with high spatiotemporal resolution is required in the estimation of ETo to obtain AET. MODIS is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe, but is incapable of providing LST data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements have a capability to penetrate under clouds. The current study proposes a methodology by exploring this property to predict high spatiotemporal resolution LST under cloudy conditions during daytime and night time without employing in-situ LST measurements. To achieve this, ANN based models were employed for different land cover classes, utilizing MPDI at finer resolution with ancillary data. MPDI was derived using resampled (from 0.250 to 1 km) brightness temperatures (Tb) at 36.5 GHz channel of dual polarization from Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 sensors. The proposed methodology was quantitatively evaluated through three performance measures namely correlation coefficient (r), Nash Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE). Results revealed that during daytime, AMSR-E(AMSR2) derived LST under clear sky conditions corresponds well with MODIS LST resulting in values of r ranging from 0.76(0.78) to 0.90(0.96), RMSE from 1.76(1.86) K to 4.34(4.00) K and NSE from 0.58(0.61) to 0.81(0.90) for different land cover classes. For night time, r values ranged from 0.76(0.56) to 0.87(0.90), RMSE from 1.71(1.70) K to 2.43(2.12) K and NSE from 0.43 (0.28) to 0.80(0.81) for different land cover classes. RMSE values found between predicted LST and MODIS LST during daytime under clear sky conditions were within acceptable limits. Under cloudy conditions, results of microwave derived LST were evaluated with Ta which indicated that the approach performed well with RMSE values lesser than the results obtained under clear sky conditions for land cover classes for both day and nighttimes. These predicted LSTs can be applied for the estimation of soil moisture in hydrological studies, in climate studies, ecology, urban climate and environmental studies, etc. AET was estimated for all sky conditions using vegetation coefficient method. Essential parameter ETo under cloudy conditions was estimated using LST and Ta based PMT and H-S models and required solar radiation (Rs) in these two models estimated using equation proposed by Samani (2000). In this equation it was found that the differences between LSTmax or Tmax and LSTmin or Tmin could able to capture the variations due to cloudy sky conditions and hence can be used for estimating ETo under cloudy sky conditions. Results revealed that the estimated Rs correlated well with observed Rs for Berambadi station under cloudy conditions for the year 2013. PMT based ETo values were corresponded with observed ETo under cloudy sky condition. The difference between LST and Ta was less during cloudy conditions, therefore LST or Ta can be used as the only input in temperature based PMT model to estimate ETo. AET estimated correlated well with the observed AET values for clear and cloudy sky conditions. In addition, AET estimated using vegetation coefficient method was compared with two source energy balance (TSEB) method developed by Nishida et al. (2003) under clear sky conditions. It was found that the improved vegetation coefficient method performed better than the TSEB method for Berambadi station. Other microwave vegetation indices such as Microwave Vegetation Indices (MVIs) and Emissivity Difference Vegetation Index (EDVI) are available in literature. Therefore in this study, MVIs are used to predict LST under cloudy conditions using proposed methodology to check whether the MVIs could yield better LST values. Results showed that MPDI performed better than MVIs to predict LST under cloudy sky conditions. Furthermore, MPDI obtained using dual polarizations of 37 GHz channel Tb has advantage of having fine spatial resolution compared to MVIs, as it requires Tb of 19 GHz in addition to Tb of 37 GHz channel which is of coarse resolution and therefore uncertainties resulting from re-sampling technique can be minimized. x

The major aim of this work is to develop a framework for the estimation of Evapotranspiration (ET) over the Indian landmass using remote sensing (RS) datasets in a repeated and consistent manner with improved spatial resolution. Different RS based ET models exist in the literature, out of which, the triangle, the S-SEBI and the Sim-ReSET models were compared for the estimation of daytime integrated latent heat flux (λEday). These three models were chosen as they can be driven only with RS based inputs without the need for any ground measurements. The results showed that the application of simpler contextual models may yield better results than physically based models when ground data is limited or not available. To improve the spatial resolution of one of the key surface variable, Land Surface Temperature (LST), the performance of five different vegetation indices Normalised Difference Vegetation Index (NDVI), Fraction Vegetation Cover (FVC), Normalised Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI) and Modified SAVI (MSAVI) were tested in the existing DisTrad disaggregation model. Results suggested that the most commonly used vegetation indices NDVI and FVC yielded better results only under wet conditions. Under drier surface conditions, using NDWI for disaggregation resulted in relatively higher accurate LST. A model for spatial disaggregation of Evaporative Fraction (EF) called DEFrac (Disaggregation of Evaporative Fraction) was developed based on the relationship between EF and NDVI to obtain finer spatial resolution EF from coarser resolution estimates. The experimental results suggested that the DEFrac model developed in this study, yielded more accurate disaggregated EF. The disaggregated EF was further used to get disaggregated λEday. Finally, The issue of lack of proper ET dataset over India was addressed by developing two data products one over entire India at 0.05° spatial resolution and the second product over the Kabini basin at 1 km spatial resolution. Both the products were developed with a temporal resolution of 8-day and for the period 2001–2014. The developed ET products were validated against ground observed data at seven sites across India and against ET simulated by a hydrological model over a forested watershed. Further the developed ET products were compared with some other global ET products such as MOD16, LandFlux Eval synthesis ET and GLEAM ET. Analyses revealed that only in regions where ET is predominantly driven by rainfall and where irrigation is not applied at very large scales, the global ET products tend to capture the ET patterns satisfactorily. On the other hand, the ET products developed in this work captured the spatial and temporal patterns of ET quite realistically all across India.

Les données de télédétection dans l'infrarouge thermique (IRT) sont une source indispensable d'information pour estimer les flux de surface et suivre le fonctionnement des agro-écosystèmes. Cependant, les mesures de température de surface sont sujettes à des effets directionnels très importants (présence de 'hot spot') pouvant entraîner une erreur allant jusqu'à une dizaine de degrés Celsius. Ils doivent être pris en compte en vue des applications opérationnelles. Le travail proposé ici vise à modéliser l'anisotropie directionnelle des couverts végétaux pour mettre au point des méthodes opérationnelles de correction des mesures satellitaires de température de surface. Il est largement motivé par les projets du CNES visant à élaborer une mission spatiale nouvelle combinant une haute résolution spatiale et des capacités fortes de revisite dans l'IRT. Deux étapes de travail ont été menées. La première repose sur l'utilisation du modèle déterministe de transfert Sol-Végétation-Atmosphère SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), capable de simuler les radiances directionnelles dans l'optique et l'IRT. Dans ce manuscrit, il est validé par rapport à des mesures de terrain et sa capacité à simuler correctement les effets d'anisotropie démontrée. Il est ensuite utilisé pour étudier de façon systématique la sensibilité de l'anisotropie directionnelle à la structure de la canopée, à son état hydrique, au forçage météorologique et aux configurations angulaires solaire et de visée. Les conséquences en terme d'impact combiné des caractéristiques orbitales des satellites, de la position géographique des sites observés et de la date d'acquisition sur l'anisotropie sont discutées. La seconde étape vise à proposer un modèle paramétrique simplifié (dit RL). SCOPE est ici utilisé en tant que générateur de données. Le modèle RL se révèle robuste et capable de restituer avec succès les signatures directionnelles sur le plan géométrique (position du hot spot) comme pour l'amplitude des effets directionnels. Une comparaison avec le seul autre modèle paramétrique utilisé jusqu'alors en télédétection IRT (le modèle de Vinnikov) confirme les qualités du modèle RL, ce qui en fait un candidat potentiel pour les chaines de traitement des futures données satellitaires. / Remotely-sensed data in thermal infrared (TIR) are an essential source of information to estimate surface fluxes and to monitor the functioning of agro-ecosystems. However, surface temperature measurements are prone to directional effects ('hot spot' phenomenon)which may result in an error up to 10°C. They have to be taken into account in the framework of operational applications. The work proposed here aims at modelling the directional anisotropy of continuous vegetated canopies in order to develop operational methods for correcting land surface temperature measurements carried out by TIR satellites. This work is mainly motivated by the CNES projects aiming at developing a new TIR spatial mission combining both high spatial resolution and high revisit time capacities. Two steps were carried out. The first is based on the use of the deterministic SVAT model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), able to simulate directional radiances at top of canopy in both optical and TIR domains. In this thesis, it is validated against experimental measurements and its ability to successfully simulate TIR directional anisotropy demonstrated. Then it is used to study the sensitivity of anisotropy to canopy structure, water status of soil and vegetation, meteorological forcing and solar and observer angular configurations. The consequences of the combined features of satellites orbits, geographical position of the scanned sites and acquisition date on anisotropy are discussed. In the second part, we propose a simplified parametric model (called 'RL'). SCOPE is used as a data generator. The RL model is deemed suitable and able to correctly reproduce directional signatures both in terms of geometry (hot spot position) and amplitude of these effects. A comparison with the only one parametric model previously used in TIR remote sensing (Vinnikov's approach) confirms the good capacities of the RL model. The RL model is thus a potential candidate to the future satellite processing chains.

As urbanization and global warming increase, an increasing importance is set on that urban planning processes take the possible effects of urban heat islands into account. In order to provide local city planners with site specific knowledge of the current situation in Stockholm, this study has explored the intra-urban heat island effect in Stockholm municipality by identifying major problem areas as well as addressing the statistical relationship between temperature and factors relating to land cover and urban planning strategies. Remotely sensed land surface temperature (LST) and the Swedish National Land Cover Database (NMD) have formed the data basis for the analyses that were carried out using GIS. The LST and land cover information have been extracted from randomly placed circle polygons in order to create a sample for the statistical analyses.  The results show that there exist differences in temperature within Stockholm municipality, both within the municipality as a whole and between various urban characters. In both cases, land cover is identified as an important, but not sole, factor to explain the differences. On land areas, artificial non-vegetated surfaces and forest is identified as the land cover classes of most relevance when it comes to the urban heat island effect. For both land cover classes, a strong correlation to LST is seen. Even though certain uncertainties and limitations are embedded in the data as well as in the method choices, the study can conclude in that the urban heat island effect is present in Stockholm municipality and that it can be derived from both land cover and urban characters. / I takt med att urbaniseringen och den globala uppvärmningen ökar kommer högre krav ställas på att stadsplaneringen tar hänsyn till de effekter som väntas uppstå kopplat till urbana värmeöar. I syfte att bistå lokala stadsplanerare med platsspecifik kunskap om den nuvarande situationen i Stockholm har den här studien utforskat intraurbana temperaturvariationer i Stockholms stad genom att identifiera de mest utsatta områdena samt genom att undersöka det statistiska sambandet mellan temperatur och faktorer kopplat till marktäcke och stadsutformning. Markens yttemperatur uppmätt från satellit och Nationella marktäckedata (NMD) har utgjort det främsta dataunderlaget för analyserna som genomförts med hjälp av GIS. Genom att extrahera information om yttemperatur och marktäcke från slumpmässigt placerade cirkelpolygoner kunde ett urval till de statistiska analyserna skapas.  Resultaten från studien visar att det finns skillnader i temperatur inom Stockholms stad, både inom kommunen som helhet och mellan olika stadsbyggnadskaraktärer. I båda fallen kan marktäcke identifieras som en viktig, men inte ensam, faktor till att förklara skillnaderna. På landområdena identifieras exploaterad mark och skog som de marktäcken med störst betydelse när det kommer till urbana värmeöar. För båda marktäckena ses i studien en stark korrelation till yttemperatur. Trots att vissa osäkerheter och begräsningar kan kopplas till både använd data och de metoder som använts kan slutsatsen att effekten från urbana värmeöar finns i Stockholms kommun dras. Dessutom kan konstateras att effekten kan härledas både till marktäcke och stadsbyggnadskaraktär.

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).