This paper presents two new schemes for interpolating missing samples in satellite diurnal temperature cycles (DTCs). The first scheme, referred to here as the cosine model, is an improvement of the model proposed in [2] and combines a cosine and exponential function for modelling the DTC. The second scheme uses the notion of a Reproducing Kernel Hilbert Space (RKHS) interpolator [1] for interpolating the missing samples. The application of RKHS interpolators to the DTC interpolation problem is novel. Results obtained by means of computer experiments are presented.

Remote sensing of land surface temperature (LST) is a complex task. From a satellite-based perspective the radiative properties of the land surface and the atmosphere are inextricably linked. Knowledge of both is required if one is to accurately measure the temperature of the land surface from a space-borne platform. In practice, most satellite-based sensors designed to measure LST over the surface of the Earth are polar orbiting. They scan swaths of the order of 2000 km, utilizing zenith angles of observation of up to 60°. As such, satellite viewing geometry is important when comparing estimates of LST between different overpasses of the same point on the Earth's surface. In the case of the atmosphere, the optical path length through which the surfaceleaving radiance propagates increases with increasing zenith angle of observation. A longer optical path may in turn alter the relative contributions which molecular absorption and emission processes make to the radiance measured at the satellite sensor. A means of estimating the magnitudes of these radiative components in relation to the viewing geometry of the satellite needs to be developed if their impacts on the at-sensor radiance are to be accurately accounted for. The problem of accurately describing radiative transfer between the surface and the satellite sensor is further complicated by the fact that the surface-leaving radiance itself may also vary with sensor viewing geometry. Physical properties of the surface such as emissivity are known to vary as the zenith angle of observation changes. The proportions of sunlit and shaded areas with the field-of-view of the sensor may also change with viewing geometry depending on the type of cover (eg vegetation), further impacting the surface emissivity. / Investigation of the change in surface-leaving radiance as the zenith angle of observation varies is then also important in developing a better understanding of the radiative interaction between the land surface and the atmosphere. The work in this study investigates the atmospheric impacts using surface brightness temperature measurements from the ATSR-2 satellite sensor in combination with atmospheric profile data from radiosondes and estimates of the downwelling sky radiance made by a ground-based radiometer. A line-by-line radiative transfer model is used to model the angular impacts of the atmosphere upon the surfaceleaving radiance. Results from the modelling work show that if the magnitude of the upwelling and downwelling sky radiance and atmospheric transmittance are accurately known then the surface-emitted radiance and hence the LST may be retrieved with negligible error. Guided by the outcomes of the modelling work an atmospheric correction term is derived which accounts for absorption and emission by the atmosphere, and is based on the viewing geometry of the satellite sensor and atmospheric properties characteristic of a semi-arid field site near Alice Springs in the Northern Territory (Central Australia). Ground-based angular measurements of surface brightness temperature made by a scanning, self calibrating radiometer situated at this field site are then used to investigate how the surface-leaving radiance varies over a range of zenith angles comparable to that of the ATSR-2 satellite sensor. / Well defined cycles in the angular dependence of surface brightness temperature were observed on both diumal and seasonal timescales in these data. The observed cycles in surface brightness temperature are explained in terms of the interaction between the downwelling sky radiance and the angular dependence of the surface emissivity. The angular surface brightness temperature and surface emissivity information is then applied to derive an LST estimate of high accuracy (approx. 1 K at night and 1-2 K during the day), suitable for the validation of satellite-derived LST measurements. Finally, the atmospheric and land surface components of this work are combined to describe surface-atmosphere interaction at the field site. Algorithms are derived for the satellite retrieval of LST for the nadir and forward viewing geometries of the ATSR-2 sensor, based upon the cycles in the angular dependence of surface brightness temperature observed in situ and the atmospheric correction term developed from the modelling of radiative transfer in the atmosphere. A qualitative assessment of the performance of these algorithms indicates they may obtain comparable accuracy to existing dual angle algorithms (approx. 1.5 K) in the ideal case and an accuracy of 3-4 K in practice, which is limited by knowledge of atmospheric properties (eg downwelling sky radiance and atmospheric transmittance), and the surface emissivity. There are, however, strong prospects of enhanced performance given better estimates of these physical quantities, and if coefficients within the retrieval algorithms are determined over a wider range of observation zenith angles in the future.

This study implemented a GIS-based analysis of local climate zones (LCZ) in Denton, TX with data sets from 2009, 2011, 2015, and 2016. The LCZ scheme enables evaluation of distinct regions' thermal characteristics with greater granularity than conventional urban-rural dichotomies. Further, the GIS-based approach to LCZ mapping allows use of high-resolution lidar data, the availability of which for the study area enabled estimation of geometric and surface cover parameters: height of roughness elements, sky-view factor, and building surface fraction. Pervious surface fraction was estimated from National Landcover Database impervious imagery. A regular grid was used to estimate per-cell mean values for each parameter, and with a decision-making algorithm (if/then statements) two maps were produced (2011 and 2015) and six LCZ identified in each: LCZ 6 (open low-rise), LCZ 8 (large low-rise), LCZ 9 (sparsely built), LCZ A (dense trees), LCZ B (scattered trees), and LCZ C (bush/scrub). Post-processing was carried out to ensure identified zones met the spatial minimum for qualification as LCZ. Landsat Collection 2 Level 2 surface temperature products from various seasons of 2011 and 2015 were acquired to examine LCZ thermal differentiability, and preliminary surface urban heat island intensity values were estimated. Particular attention was afforded to issues regarding data quality and classifier threshold adjustment.

Increasing populations, shifting demographics and changes in diet are driving increases in crop production. However, any increases in food demand are ultimately limited by water availability, which is under pressure globally, but especially so in arid and semi-arid regions. To address this challenge, spatially distributed information on crop water use, vegetation health, soil moisture status and a range of other water, energy and carbon variables are all required. However, critical to the determination of many of these processes is an accurate characterization of the land surface temperature (LST). The only feasible manner by which to estimate this variable across a range of spatial and temporal scales is using thermal infrared (TIR) satellite data. Here we investigate the estimation of LST, focusing on its accurate retrieval across a range of different spatial scales. First, we examine the influence of atmospheric correction on retrieval accuracy by employing a radiative transfer model and Landsat data using a variety of available atmospheric profile data, with the aim of identifying an optimal product combination for retrieval. Using these results, we then investigate the potential to downscale coarse resolution (O~103 m) MODIS satellite data to scales appropriate for agricultural application (less than O~102 m), using a machine-learning approach. To further advance the downscaling technique, we explore the utility of novel Cubesat data to produce within-field scale (O~101 m) distributions of land surface temperature. Finally, to expand upon the multi-resolution/multi-satellite LST strategy explored here, we examine the capacity of ultra-high resolution (O~10-1 m) thermal imagery from an unmanned aerial vehicle to characterize surface temperature response and behavior, focusing on the retrieval accuracy and diurnal variability of these spatially and temporally varying land surface temperature estimates. The ultimate goal of this research is to advance the utility of LST for agricultural application by providing description and insights into product development, accuracy issues, and identifying some limitations and opportunities of both current and future remote observation platforms.

Esse trabalho tem como objetivo compreender as relações entre a Land Surface Temperature (LST), Normalized Difference Vegetation Índex (NDVI) e os padrões do uso da terra do município de São Paulo no período de 1985 a 2010. Analisou-se 15 bairros, nos quais foram extraídas 45 amostras aleatórias de diferentes padrões de uso da terra; subdivididas em baixo padrão, médio padrão e médio alto padrão. Com o aporte de geotecnologia, foi feita a extração dos dados das imagens de satélite Landsat 5 (TM) e das Ortofotos do ano de 2010. O comportamento das amostras variou de acordo como os diferentes perfis dos grupos analisados. O grupo de baixo padrão foi o que apresentou as maiores amplitudes térmicas, ausência de arborização urbana atreladas a um baixo padrão construtivo. O grupo de médio padrão é caracterizado pela predominância de área verticalizada e apresenta uma arborização urbana escassa em meio a uma malha urbana consolidada. O grupo de médio alto padrão foi o que mais apresentou arborização urbana, distribuída de forma homogênea na maioria das amostras, portanto foi o grupo que teve baixas amplitudes térmicas e o índice de Normalized Difference Vegetation Index (NDVI) com pouca variação. Os testes mostraram fortes correlações negativas entre as amostras de Land Surface Temperature (LST) e o índice de Normalized Difference Vegetation Index (NDVI), sendo -0,58 em 1985, -0,43 em 2004 e -0,82 em 2010. Os diferentes padrões de uso da terra, relacionados à temperatura de superfície, e o índice de vegetação, aliado à preocupação com o planejamento ambiental, deve resultar na melhoria da qualidade de vida da população. Esta pesquisa faz parte do Projeto Temático processo FAPESP 08/58161 -1, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change. / This study aims to understand the relationship between Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and the patterns of land use in the municipality of São Paulo, from 1985 to 2010. A totoal of 45 random samples were extracted from the 15 districts used in this study, with different patterns of land use which were subdivided into three different clases: low-end, middle and middle-high. Geospatial approaches allowed the extraction of satellite image data from Landsat 5 data (TM) and from Orthophotos from 2010. The behavior of the samples varied accordingly to the different group profiles. The low-end group presented the highest thermal amplitudes and more significant absence of urban vegetation linked, both to low urbanization and construction standards. The average standard group is characterized by the predominance of vertical buildings and lacks urban trees amidst a consolidated urban landscape. The average-high standard group displayed the highest concentration of green urban areas, distributed homogeneously in most samples, so this group presented low variations both in temperature amplitude and in the Normalized Difference Vegetation Index (NDVI). The correlation tests showed strong negative correlations between samples of Land Surface Temperature (LST) and the NDVI samples, of -0.58 in 1985, -0.43 in 2004 and -0.82 in 2010. Understanding the relations between the different patterns of land use, surface temperature and the NDVI (with due concern for environmental planning) is an important step in the identification and rehabilitation of enviromentally. This research is part of the Thematic Project FAPESP 08/58161 -1 process, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change.

Esse trabalho tem como objetivo compreender as relações entre a Land Surface Temperature (LST), Normalized Difference Vegetation Índex (NDVI) e os padrões do uso da terra do município de São Paulo no período de 1985 a 2010. Analisou-se 15 bairros, nos quais foram extraídas 45 amostras aleatórias de diferentes padrões de uso da terra; subdivididas em baixo padrão, médio padrão e médio alto padrão. Com o aporte de geotecnologia, foi feita a extração dos dados das imagens de satélite Landsat 5 (TM) e das Ortofotos do ano de 2010. O comportamento das amostras variou de acordo como os diferentes perfis dos grupos analisados. O grupo de baixo padrão foi o que apresentou as maiores amplitudes térmicas, ausência de arborização urbana atreladas a um baixo padrão construtivo. O grupo de médio padrão é caracterizado pela predominância de área verticalizada e apresenta uma arborização urbana escassa em meio a uma malha urbana consolidada. O grupo de médio alto padrão foi o que mais apresentou arborização urbana, distribuída de forma homogênea na maioria das amostras, portanto foi o grupo que teve baixas amplitudes térmicas e o índice de Normalized Difference Vegetation Index (NDVI) com pouca variação. Os testes mostraram fortes correlações negativas entre as amostras de Land Surface Temperature (LST) e o índice de Normalized Difference Vegetation Index (NDVI), sendo -0,58 em 1985, -0,43 em 2004 e -0,82 em 2010. Os diferentes padrões de uso da terra, relacionados à temperatura de superfície, e o índice de vegetação, aliado à preocupação com o planejamento ambiental, deve resultar na melhoria da qualidade de vida da população. Esta pesquisa faz parte do Projeto Temático processo FAPESP 08/58161 -1, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change. / This study aims to understand the relationship between Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and the patterns of land use in the municipality of São Paulo, from 1985 to 2010. A totoal of 45 random samples were extracted from the 15 districts used in this study, with different patterns of land use which were subdivided into three different clases: low-end, middle and middle-high. Geospatial approaches allowed the extraction of satellite image data from Landsat 5 data (TM) and from Orthophotos from 2010. The behavior of the samples varied accordingly to the different group profiles. The low-end group presented the highest thermal amplitudes and more significant absence of urban vegetation linked, both to low urbanization and construction standards. The average standard group is characterized by the predominance of vertical buildings and lacks urban trees amidst a consolidated urban landscape. The average-high standard group displayed the highest concentration of green urban areas, distributed homogeneously in most samples, so this group presented low variations both in temperature amplitude and in the Normalized Difference Vegetation Index (NDVI). The correlation tests showed strong negative correlations between samples of Land Surface Temperature (LST) and the NDVI samples, of -0.58 in 1985, -0.43 in 2004 and -0.82 in 2010. Understanding the relations between the different patterns of land use, surface temperature and the NDVI (with due concern for environmental planning) is an important step in the identification and rehabilitation of enviromentally. This research is part of the Thematic Project FAPESP 08/58161 -1 process, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change.

No description available.

La température de surface terrestre (LST) est un paramètre important dans les systèmes climatiques. Les données infrarouge thermique (TIR) contiennent un nombre d'information de la surface terrestre et de l'atmosphère sont des sources de l'information important pour estimer la LST à l'aide de télédétection. / Land surface temperature (LST) is an important parameter in climate systems. Hyperspectral thermal infrared (TIR) data, containing large information about the surface and the atmosphere, is an important source of information for retrieving LST by remote-sensing.

Lake surface temperature (LSTlake) can be obtained and studied in different ways: using in situ measurements, satellite imagery and modeling. Collecting spatially representative in situ data over lakes, especially for large and deep ones, is a real challenge. Satellite data products provide the opportunity to collect continuous data over very large geographic areas even in remote regions. Numerical modeling is also an approach to study the response and the role of lakes in the climate system. Satellite instruments provide spatial information unlike in situ measurements and one-dimensional (1-D) lake models that give vertical information at a single point or a few points in lakes. The advantage of remote sensing also applies to land where temperature measurements are usually taken at meteorological stations whose network is extremely sparse in northern regions. This thesis therefore examined the value of land/lake surface (skin) temperature (LSTland/lake) measurements from satellites as a complement to in situ point measurements and numerical modeling. The thesis is organized into two parts. The first part tested, two 1-D numerical models against in situ and satellite-derived LST measurements. LSTlake and ice phenology were simulated for various points at different depths on Great Slave Lake (GSL) and Great Bear Lake (GBL), two large lakes located in the Mackenzie River Basin in Canada’s Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002-2010 period. Input data from three weather stations (Yellowknife, Hay River and Deline) were used for model simulations. LSTlake model results are compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. The main goal was to examine the performance of the FLake and CLIMo models in simulating LSTlake and ice-cover under different conditions against satellite data products. Both models reveal a good agreement with daily average MODIS LSTlake from GSL and GBL on an annual basis. CLIMo showed a generally better performance than FLake for both lakes, particularly during the ice-cover season. Secondly, MODIS-derived lake and land surface temperature (LSTland/lake) products are used to analyze land and lake surface temperature patterns during the open-water and snow/ice growth seasons for the same period of time in the regions of both GBL and GSL. Land and lake temperatures from MODIS were compared with near-surface air temperature measurements obtained from nearby weather stations and with in situ temperature moorings in GBL. Results show a good agreement between satellite and in situ observations. MODIS data were found to be very useful for investigating both the spatial and temporal (seasonal) evolution of LSTland/lake over lakes and land, and for improving our understanding of thermodynamic processes (heat gains and heat loses) of the lake/land systems. Among other findings, the MODIS satellite imagery showed that the surface temperature of lakes is colder in comparison to the surrounding land from April-August and warmer from September until spring thaw.

Lake surface temperature (LSTlake) can be obtained and studied in different ways: using in situ measurements, satellite imagery and modeling. Collecting spatially representative in situ data over lakes, especially for large and deep ones, is a real challenge. Satellite data products provide the opportunity to collect continuous data over very large geographic areas even in remote regions. Numerical modeling is also an approach to study the response and the role of lakes in the climate system. Satellite instruments provide spatial information unlike in situ measurements and one-dimensional (1-D) lake models that give vertical information at a single point or a few points in lakes. The advantage of remote sensing also applies to land where temperature measurements are usually taken at meteorological stations whose network is extremely sparse in northern regions. This thesis therefore examined the value of land/lake surface (skin) temperature (LSTland/lake) measurements from satellites as a complement to in situ point measurements and numerical modeling. The thesis is organized into two parts. The first part tested, two 1-D numerical models against in situ and satellite-derived LST measurements. LSTlake and ice phenology were simulated for various points at different depths on Great Slave Lake (GSL) and Great Bear Lake (GBL), two large lakes located in the Mackenzie River Basin in Canada’s Northwest Territories, using the 1-D Freshwater Lake model (FLake) and the Canadian Lake Ice Model (CLIMo) over the 2002-2010 period. Input data from three weather stations (Yellowknife, Hay River and Deline) were used for model simulations. LSTlake model results are compared to those derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observing System Terra and Aqua satellite platforms. The main goal was to examine the performance of the FLake and CLIMo models in simulating LSTlake and ice-cover under different conditions against satellite data products. Both models reveal a good agreement with daily average MODIS LSTlake from GSL and GBL on an annual basis. CLIMo showed a generally better performance than FLake for both lakes, particularly during the ice-cover season. Secondly, MODIS-derived lake and land surface temperature (LSTland/lake) products are used to analyze land and lake surface temperature patterns during the open-water and snow/ice growth seasons for the same period of time in the regions of both GBL and GSL. Land and lake temperatures from MODIS were compared with near-surface air temperature measurements obtained from nearby weather stations and with in situ temperature moorings in GBL. Results show a good agreement between satellite and in situ observations. MODIS data were found to be very useful for investigating both the spatial and temporal (seasonal) evolution of LSTland/lake over lakes and land, and for improving our understanding of thermodynamic processes (heat gains and heat loses) of the lake/land systems. Among other findings, the MODIS satellite imagery showed that the surface temperature of lakes is colder in comparison to the surrounding land from April-August and warmer from September until spring thaw.