How can we use MODIS land surface temperature to validate long-term urban model simulations?

This is the authors accepted manuscript. The published version is available here: http://dx.doi.org/10.1002/2013JD021101.High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.Funded by National Aeronautics and Space Administration. Grant Number: (NNX10AK79G

Modeling the angular dependence of satellite retrieved Land Surface Temperature (LST)

Tese de mestrado em Ciências Geofísicas, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2013A temperatura de superfície do solo (Land Surface Temperature - LST) é definida como a temperatura radiométrica da superfície sobre terra, correspondendo à radiação emitida no infravermelho (IV) térmico por uma camada com espessura da ordem da profundidade de penetração da radiação IV, da ordem do comprimento de onda. A LST é uma variável climatológica importante e, também, um parâmetro de diagnóstico das condições da superfície do solo. Pode ser utilizada para estimar fluxos de calor sensível à superfície, a humidade do solo, a evapotranspiração e propriedades da vegetação, incluindo o seu stress hídrico. A deteção remota, nomeadamente a efetuada através de satélites, constitui o único meio disponível para a obtenção de LST a uma escala espacial global e regular e com elevada frequência temporal. A Land Surface Analysis Satellite Application Facility (LSA-SAF) dissemina, de forma operacional e em tempo quase real, um produto de LST obtido por aplicação de um algoritmo do tipo “generalized split-window” a observações de temperatura de brilho no topo da atmosfera efetuadas pelo Spinning Enhanced Visible and InfraRed Imager (SEVIRI) a bordo dos satélites da série Meteosat Second Generation (MSG). A validação da LST da LSA-SAF envolve não só a sua comparação com medições in situ mas também com a LST obtida por sensores a bordo de outros satélites. As principais fontes de discrepâncias de LST entre satélites são: 1) a calibração do sensor, 2) as funções de resposta, 3) a resolução espacial e temporal, 4) a correção atmosférica aplicada, 5) as estimativas de emissividade de superfície adotadas, 6) a máscara de nuvens utilizadas e 7) a anisotropia angular. Destas, a sensibilidade da LST à anisotropia angular é um dos tópicos menos estudados. No entanto, os produtos de satélite de LST são, em geral, variáveis direcionais, isto é, a LST obtida para uma dada cena, utilizando o mesmo sensor, mas com ângulos de visão diferentes, frequentemente apresenta valores diferentes, dependendo de fatores como o tipo de superfície, as características do solo e a inclinação do terreno. A estrutura da superfície tem uma influência importante na temperatura, devido particularmente a efeitos de sombreamento pelos elementos de vegetação e inclinação do terreno que resultam numa dependência da LST dos ângulos zenital e azimutal de visão. Para superfícies homogéneas, a variabilidade da LST é essencialmente função da direccionalidade da emissividade, enquanto para superfícies heterogéneas a variabilidade angular está na sua maioria associada às proporções observadas pelo satélite de diferentes componentes que possuem as suas próprias temperatura e emissividade. Existem diversos modelos de transferência radiativa que tratam de diferentes formas a anisotropia da radiação em zonas vegetadas. Os modelos Ótico-Geométricos foram desenvolvidos em particular para descrever florestas e outros cobertos vegetais descontínuos. Estes modelos operam assumindo que a copa da vegetação pode ser descrita por objetos geométricos distribuídos espacialmente de acordo com determinado modelo estatístico. A interseção e reflecção de luz são calculadas analiticamente a partir de considerações geométricas. Nestes modelos a radiância de uma dada região é estimada como sendo uma média pesada das radiâncias de cada componente básico (normalmente, o solo ao sol e à sombra e a copa ao sol e à sombra). Neste estudo apresenta-se um modelo geométrico que permite estimar as áreas projetadas de cada componente utilizando geometria de raios paralelos para descrever a iluminação de um único elemento de vegetação tridimensional e a sombra que origina. Dada a forma e tamanho do elemento de vegetação e a geometria de visão e iluminação, as diferentes proporções podem ser estimadas recorrendo ao formalismo do modelo Booleano, desde que se possa assumir que os objetos possuem uma distribuição espacial aleatória. O modelo Booleano inclui ainda a possibilidade de sombreamento mútuo entre objetos e a sobreposição de copas. Este tipo de modelo ótico-geométrico tem sido bastante utilizado por vários autores em estudos de anisotropia de temperatura da superfície. O procedimento proposto no presente trabalho tem a vantagem de recorrer a um método computacional simples para calcular as projeções, em vez de utilizar um método analítico mais rígido e complexo. O método consiste em projetar um elemento de vegetação tridimensional (copa elipsoidal ou cónica) numa malha de elevada resolução, o que permite a utilização de qualquer forma e tamanho para a vegetação e até mesmo a combinação de diferentes formas e tamanhos. As radiâncias das componentes são obtidas a partir de medições in situ da temperatura de brilho provenientes da estação de validação de LSA-SAF em Évora. Estas medições são efetuadas a cada minuto por quatro radiómetros que observam o solo ao sol (em dois pontos diferentes), a copa de uma árvore e o céu a um ângulo zenital de 53º, sendo a última medição utilizada para estimar a componente de fluxo radiativo descendente refletido. Assume-se ainda que a temperatura da sombra é determinada pelos valores máximos diários das temperaturas do ar e do solo ao sol. O modelo é posteriormente aplicado ao pixel do MSG que contém a estação de Évora, utilizando-se informação de terreno sobre a densidade de árvores e a sua forma e tamanho médios. A temperatura do compósito resultante da combinação do modelo geométrico e das medições in situ é então comparada com a LST operacional disseminada pela LSA-SAF. Os resultados mostram uma boa concordância entre a temperatura do compósito e a LST, apresentando um viés de cerca de 1ºC e um erro médio quadrático de cerca de 1.5ºC. Acresce que os resultados mostram que existe um impacto significativo de heterogeneidades da superfície na LST e, especialmente, que esse impacto varia ao longo do dia e do ano uma vez que depende das temperaturas relativas do solo ao sol e à sombra e da copa. Em relação a outros estudos efetuados, o presente trabalho proporciona uma avaliação mais pormenorizada deste efeito, em particular graças à análise efetuada a uma grande variedade de ângulos de visão e iluminação, emissividades de superfície e coberto vegetal. A simplicidade do modelo permite a sua aplicação a qualquer satélite, geoestacionário ou de orbita polar. A LST foi, assim, igualmente comparada com o respetivo produto do sensor MODIS. A comparação dos dois produtos mostra a presença de um viés e de um desvio padrão dos erros de cerca de 3ºC. O modelo geométrico foi mais uma vez aplicado às medições in situ, de forma a estimar e corrigir desvios entre as estimativas de LST com base nos dois sensores, que estão associados a geometrias de visão diferentes. A aplicação desta correção resulta numa redução significativa do desvio padrão dos erros, resultado este expectável, dada a geometria de visão variável do MODIS. Quanto ao viés observado entre os dois sensores, este não pode ser atribuído a diferenças na geometria de visão, estando provavelmente relacionado com outras fontes persistentes de erro. As diferenças observadas podem eventualmente ser atribuídas às discrepâncias significativas observadas entre as emissividades utilizadas pela LSA-SAF e pelo MODIS. Com efeito, no período de estudo, as diferenças variam entre 0.005 e 0.01, com o MODIS a apresentar sempre valores mais elevados, facto consistente com o viés negativo observado. Os resultados obtidos sugerem que o procedimento proposto pode constituir uma ferramenta útil para a validação e comparação de LST de diferentes sensores. O modelo geométrico apresentado representa um ponto de partida para a compreensão dos efeitos direcionais na LST. Pode antecipar-se que este modelo virá a ser utilizado num estudo alargado de sensibilidade, a ser realizado para todo o disco MSG – e por isso para uma vasta variedade de tipos de superfície e geometrias de visão e iluminação – de modo a que sejam identificadas áreas e períodos do dia e do ano em que estes efeitos são mais pronunciados.Satellite retrieved values of Land Surface Temperature (LST) over heterogeneous pixels generally depend on viewing and illumination angles as well as on the characteristics of the land cover. A geometrical model is presented that allows estimating LST of a given pixel for any viewing and illumination angles. The Boolean scene model is used to estimate the per-pixel fractions covered by the following three scene components: sunlit background, shaded background and vegetation. Estimates of the average area covered by canopies and by shadow are derived from the projection of a single arbitrarily-shaped vegetation element (e.g. ellipsoidal or conical tree canopies) onto a fine scale regular grid. The model is applied to time-series of continuous in situ brightness temperature measurements as obtained at the LSA-SAF validation site in Évora (Portugal) during 2011 and 2012. Measurements are performed every minute by four radiometers, two of them observing the sunlit background and the other two a tree crown and the sky at 53° zenith angle. It is assumed that the shadow temperature is determined by daily maxima of air and sunlit background temperatures. The resulting composite temperature is compared against LSA-SAF operational LST data as retrieved from the SEVIRI instrument on-board Meteosat-8. Results show a bias of order of 1 K and a RMSE of about 1.5K. LST data are also compared against MODIS (level 3) daily LST. The LST difference between MSG and MODIS shows a strong dependence on viewing geometry that suggests relying on the geometrical model to generate estimates of LST differences between the two sensors.Results obtained with the model reveal a significant decreasing of the standard deviation error between the sensors

Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

The development of a temporal-BRDF model-based approach to change detection, an application to the identification and delineation of fire affected areas.

Although large quantities of southern Africa burn every year, minimal information is available relating to the fire regimes of this area. This study develops a new, generic approach to change detection, applicable to the identification of land cover change from high temporal and moderate spatial resolution satellite data. Traditional change detection techniques have several key limitations which are identified and addressed in this work. In particular these approaches fail to account for directional effects in the remote sensing signal introduced by variations in the solar and sensing geometry, and are sensitive to underlying phenological changes in the surface as well as noise in the data due to cloud or atmospheric contamination. This research develops a bi-directional, model-based change detection algorithm. An empirical temporal component is incorporated into a semi-empirical linear BRDF model. This may be fitted to a long time series of reflectance with less sensitivity to the presence of underlying phenological change. Outliers are identified based on an estimation of noise in the data and the calculation of uncertainty in the model parameters and are removed from the sequence. A "step function kernel" is incorporated into the formulation in order to detect explicitly sudden step-like changes in the surface reflectance induced by burning. The change detection model is applied to the problem of locating and mapping fire affected areas from daily moderate spatial resolution satellite data, and an indicator of burn severity is introduced. Monthly burned area datasets for a 2400km by 1200km area of southern Africa detailing the day and severity of burning are created for a five year period (2000-2004). These data are analysed and the fire regimes of southern African ecosystems during this time are characterised. The results highlight the extent of the burning which is taking place within southern Africa, with between 27-32% of the study area burning during each of the five years of observation. Higher fire frequencies are exhibited by savanna and grassland ecosystems, while more dense vegetation types such as shrublands and deciduous broadleaf forests burn less frequently. In addition the areas which burn more frequently do so with a greater severity, with a positive relationship identified between the frequency and the severity of burning

Variability in Surface BRDF at Different Spatial Scales (30 m-500 m) Over a Mixed Agricultural Landscape as Retrieved from Airborne and Satellite Spectral Measurements

Over the past decade, the role of multiangle remote sensing has been central to the development of algorithms for the retrieval of global land surface properties including models of the bidirectional reflectance distribution function (BRDF), albedo, land cover/dynamics, burned area extent, as well as other key surface biophysical quantities represented by the anisotropic reflectance characteristics of vegetation. In this study, a new retrieval strategy for fine-to-moderate resolution multiangle observations was developed, based on the operational sequence used to retrieve the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 reflectance and BRDF/albedo products. The algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model to provide estimates of intrinsic albedo (i.e., directional-hemispherical reflectance and bihemispherical reflectance), model parameters describing the BRDF, and extensive quality assurance information. The new retrieval strategy was applied to NASA's Cloud Absorption Radiometer (CAR) data acquired during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC) over the well-instrumented Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site in Oklahoma, USA. For the case analyzed, we obtained approx.1.6 million individual surface bidirectional reflectance factor (BRF) retrievals, from nadir to 75 off-nadir, and at spatial resolutions ranging from 3 m - 500 m. This unique dataset was used to examine the interaction of the spatial and angular characteristics of a mixed agricultural landscape; and provided the basis for detailed assessments of: (1) the use of a priori knowledge in kernel-driven BRDF model inversions; (2) the interaction between surface reflectance anisotropy and instrument spatial resolution; and (3) the uncertain ties that arise when sub-pixel differences in the BRDF are aggregated to a moderate resolution satellite pixel. Results offer empirical evidence concerning the influence of scale and spatial heterogeneity in kernel-driven BRDF models; providing potential new insights into the behavior and characteristics of different surface radiative properties related to land/use cover change and vegetation structure

Floodplain wetland-river flow synergy in the White Volta River basin, Ghana

The Upper East Region of Ghana is characterized by an unreliable mono-modal rainfall pattern making rain-fed agriculture a risky business. Therefore, farmers along the White Volta River cultivate on the floodplain to make use of residual moisture. This has prompted the Government of Ghana through the Ministry of Food and Agriculture to encourage dry season floodplain wetland cultivation, using pumped river water as a source of irrigation. The increasing use of floodplains for dry season cultivation has placed pressures on land allocation practices and floodplain wetlands resources. To ensure good management and sustainable water resource use, the role of floodplain-wetlands in regulating stream flow needs to be recognized, understood and taken into account when modeling hydrological processes within the basin. This study examines floodplain wetland-river flow synergy within the White Volta River basin. The methods applied in this study involve generating a floodplain wetland probability map and selection of floodplain wetlands, the use of HYDRUS-1D to model floodplain hydrodynamics, the result served as an input into the MODFLOW model to simulate interaction between floodplain wetland and the White Volta River; and applying isotopic tracers δ18O and δ2H to derive a water balance. The mapping process used a combination of geographic information system, remote sensing and statistical techniques. Logistic regression, a statistical technique used within the GIS platform, identified distance, texture, log transformation of ETM- band4 and evapotranspiration as the parameters having the greatest predictive power in wetland mapping, at 95 per cent confidence level. The map generated enabled the selection of Pwalugu and Tindama wetland sites as suitable for detailed hydrological analysis. Applied to the Pwalugu floodplain wetland as a test site, the HYDRUS-1D model indicated that the infiltration contribution to sub-surface storage over 16 months was 444 mm. The period of highest contribution occurred between July and September. In addition, the estimated vertical gradient indicates a low upwelling of sub-surface water in areas close to the river. The results of the isotope analysis of Oxygen-18 and deuterium showed that the trajectory of the tropical continental and tropical maritime air masses influenced isotopic composition of the rainfall over the Pwalugu and Tindama wetlands. Using the Rayleigh equation, evaporative fractions from Pwalugu wetland and Tindama were estimated to be 53.25% and 16.79%. However, to verify surface and sub-surface water interaction for the Pwalugu wetland, within August and September, the isotope signatures showed a similar ratio and plot around the local meteoric water line indicating some form of interaction. To determine wetland-river flow interaction, HYDRUS-1D bottom flux was used as groundwater recharge, an input into PW-WIN (MODFLOW). The PW-WIN (MODFLOW) simulation showed a systematic variation in hydraulic head of the wetland to changes in rainfall pattern, the observed interaction between floodplain wetlands and the White Volta River was bidirectional in terms of horizontal direction. Sensitivity analysis was performed using the dimensionless and dimension scaled methods, and model outputs were found to be highly sensitive to the parameters such as horizontal hydraulic conductivity, specific storage and specific yield. The study assisted to understand the relationship between recurrent spatial and temporal patterns of water table response within the floodplain and their controlling factors. Additionally, the study showed that a combination of methods such as tracers and hydrological models can be successful used to understand the dynamics of floodplain wetlands in White Volta River basin.Wechselwirkung zwischen einem Feuchtgebiet und dem Flusssystem im White Volta River basin, Ghana Ghanas Upper East Region ist durch ein unzuverlässiges monomodales Regenfallmuster gekennzeichnet, das Regenfeldbau zu einem riskanten Unternehmen macht. Darum kultivieren Bauern Felder in den Auengebieten entlang des White Volta Flusses, um von deren Bodenfeuchte zu profitieren. Dies bewog die Ghanaische Regierung, repräsentiert durch das Ministry of Food and Agriculture, den Feldbau in Auengebieten durch gepumptes Flusswasser während der Trockenzeit zu fördern. Die verstärkte Nutzung der Feuchtgebiete in der Trockenzeit hatte Auswirkungen auf Praktiken der Landvergabe sowie anderer Ressourcen dieser Feuchtgebiete. Um ein gutes Management und die nachhaltige Wassernutzung sicherzustellen, bedarf es der Anerkennung und des Verständnisses der Rolle der Feuchtgebiete für die Regulation des Abflusses, um hydrologische Prozesse im Volta Flussbecken modellieren zu können. Diese Studie befasst sich mit der der Synergie von Feuchtgebieten und Abfluss innerhalb des Becken des White Volta. Zu den angewandten Methoden zählen die Erstellung einer Auenwahrscheinlichkeitskarte und die Auswahl von Feuchtgebieten sowie die Nutzung des HYDRUS_1D zur Modellierung der Hydrodynamik des Feuchtgebietes. Das Ergebnis dient als Input für das MODFLOW-Modell zur Simulierung der Interaktion zwischen Feuchtgebiet und dem White Volta Fluss. Die isotopischen Tracer δ18O und δ2H wurden angewandt, um die Wasserbilanz zu ermitteln. Die Kartierung basierte auf einer Kombination von GIS (geographic information system), Fernerkundung und statistischen Techniken. Eine logistische Regression, eine im GIS verwendete Technik, identifiziert Distanzen, B4-Beschaffenheit, die logistische Transformation von ETM-band4 und Evapotranspiration als Parameter, die die größte Vorhersagekraft für Feuchtgebietskartierung haben, bei einem Konfidenzintervall von 95%. Die erhaltene Karte ermöglicht die Selektion der Orte Pwalugu und Tindama, deren Feuchtgebiete detailliert untersucht und hydrologisch analysiert wurden. Auf das Pwalugu Feuchtgebiet angewandt, zeigte das HYDRUS-1D-Modell, dass die Infiltration in 16 Monaten zu 444mm unterirdischer Speicherung beitrug. Der höchste Beitrag wurde zwischen Juli und September gemessen. Außerdem zeigt der geschätzte vertikale Gradient einen geringen Auftrieb des Grundwassers in flussnahen Gebieten. Das Ergebnis der Isotopenanalyse mit den Tracern δ18O und δ2H zeigt, dass die Bewegung der tropischen, kontinentalen sowie tropischen maritimen Luftmassen die isotopische Zusammensetzung des Regenfalls über den Feuchtgebieten von Pwalugu und Tindama beeinflussten. Unter Verwendung der Rayleigh- Gleichung wird der evaporative Anteil der Feuchtgebiete von Pwalugu und Tindama auf 53,25% und 16,79% geschätzt. Um die Interaktion von Oberflächen- und Grundwasser in Pwalugu während August und September zu verifizieren, zeigen die isotopischen signatures eine ähnliches ratio und plot um die meteorische Wasserlinie, was auf eine Interaktion hindeutet. Um diese Interaktion zu bestimmen, wurde HYDRUS-1D bottom flux für Grundwasserrecharge, ein Input für PW-WIN (MODFLOW, genutzt). Die PW-WIN (MODFLOW)– Simulation zeigt eine systematische Variation der Piezometerhöhe des Feuchtgebietes aufgrund von Änderungen im Regelfallmuster an. Die beobachtete Interaktion zwischen Feuchtgebiet und White Volta verlief horizontal bidirektional. Eine Sensitivitätsanalyse wurde ausgeführt, in der dimensionslose sowie die durch Dimensionen skalierte Methoden genutzt wurden. Die modellierten Ergebnisse waren sehr sensitiv im Hinblick auf die Parameter horizontale hydraulische Durchlässigkeit, spezifischer Speicher und spezifisches Wasserdargebot. Die Studie trägt zum Verständnis der Beziehung zwischen periodischen, räumlichen und zeitlichen Mustern des Grundwasserstandes in Abhängigkeit zu den Feuchtgebieten und den sie bestimmenden Faktoren bei. Außerdem belegt die Studie, dass eine Kombination von Methoden, wie Tracern und hydrologischen Modelle, erfolgreich eingesetzt werden können, um die Dynamik der Feuchtgebiete des White Volta Becken zu verstehen

AN INVESTIGATION OF REMOTELY SENSED URBAN HEAT ISLAND CLIMATOLOGY

Satellite remotely sensed temperatures are widely used for urban heat island (UHI) studies. However, the abilities of satellite surface and atmospheric data to assess the climatology of UHI face many unknowns and challenges. This research addresses the problems and potential for satellite remotely sensed UHI climatology by examining three different issues. The first issue is related to the temporal aggregation of land surface temperature (LST) and the potential biases that are induced on the surface UHI (SUHI) intensity. Composite LST data usually are preferred to avoid the missing values due to clouds for long-term UHI monitoring. The impact of temporal aggregation shows that SUHI intensities are more notably enhanced in the daytime than nighttime with an increasing trend for larger composite periods. The cause of the biases is highly related to the amount and distribution of clouds. The second issue is related to model validation and the appropriate use of LST for comparison to modeled radiometric temperatures in the urban environment. Sensor view angle, cloud distribution, and cloud contaminated pixels can confound comparisons between satellite LST and modeled surface radiometric temperature. Three practical sampling methods to minimize the confounding factors are proposed and evaluated for validating different aspects of model performance. The third issue investigated is to assess to what extent remotely sensed atmospheric profiles collected over the urban environment can be used to examine the UHI. The remotely sensed air and dew-point temperatures are compared with the ground observations, showing an ability to capture the temporal and spatial dynamics of atmospheric UHI at a fine scale. Finally, a new metric for quantifying the urban heat island is proposed. The urban heat island curve (UHIC), is developed to represent UHI intensity by integrating the urban surface heterogeneity in a curve. UHIC illustrates the relationship between the air temperature and the urban fractions, and emphasizes the temperature gradients, consequently decreasing the impact of the data biases. This research illustrates the potential for satellite data to monitor and increase our understanding of UHI climatology