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

Observation and assessment of model retrievals of surface exchange components over a row canopy using directional thermal data

Land surface temperature is an essential climate variable that can serve as a proxy for detecting water deficiencies in croplands and wooded areas. Its measurement can however be influenced by anisotropic properties of surface targets leading to occurrence of directional effects on the signal. This may lead to an incorrect interpretation of thermal measurements. In this study, we perform model assessments and check the influence of thermal radiation directionality using data over a vineyard. To derive the overall directional surface temperatures, elemental values measured by individual cameras were aggregated according to the respective cover fractions/weights in viewing direction. Aggregated temperatures from the turbid model were compared to corresponding temperatures simulated by the 3D DART radiative transfer model. The reconstructed temperatures were then used in surface-energy-balance (SEB) simulations to assess the impact of the Sun-target-sensor geometry on retrievals. Here, the pseudo-isotropic Soil-Plant-Atmosphere-Remote-Sensing-of-Evapotranspiration (SPARSE) dual-source model together with the non-isotropic version (SPARSE4), were used. Both schemes were able to retrieve overall fluxes satisfactorily, confirming a previous study. However, the sensitivity (of flux and component temperature estimates) of the schemes to viewing direction was tested for the first time using reconstructed sets of directional thermal data to force the models. Degradation (relative to nadir) in flux retrieval cross-row was observed, with better consistency along rows. Overall, it was nevertheless shown that SPARSE4 is less influenced by the viewing direction of the temperature than SPARSE, particularly for strongly off-nadir viewing. Some directional/asymmetrical artefacts are however not well reproduced by the simple Radiative Transfer Methods (RTM), which can then manifest in and influence the subsequent thermal-infrared-driven SEB modelling.This work was supported by the ALTOS project (PRIMA 2018 - Section 2), with grants provided by ANR via the agreement n°ANR-18-PRIM-0011-02 as well as the CNES/TOSCA program for the TRISHNA project. First author acknowledges the financial support of his PhD from CNES and Région Occitanie. The field experiments were carried out in the context of the HiLiaise and ESA WineEO projects. Joan Boldu (proprietor) and David Tous (SafSampling) are also acknowledged for allowing/providing access to the site and other site related data. Nicolas Lauret’s help with preparation of the DART mock-ups is appreciated.info:eu-repo/semantics/publishedVersio

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

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

Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification

DFG, 357874777, FOR 2694: Large-Scale and High-Resolution Mapping of Soil Moisture on Field and Catchment Scales - Boosted by Cosmic-Ray NeutronsDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Land and cryosphere products from Suomi NPP VIIRS: overview and status

[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS

A sensor view model to investigate the influence of tree crowns on effective urban thermal anisotropy

A sensor view model is modified to include trees using a gap probability approach to estimate foliage view factors and an energy budget model for leaf surface temperatures (SUMVEG). The model is found to compare well with airborne thermal infrared (TIR) surface temperature measurements. SUMVEG is used to investigate the influence of trees on thermal anisotropy for narrow field-of-view TIR remote sensors over treed residential urban surfaces. Tests on regularly-spaced arrays of cubes on March 28 and June 21 at latitudes of 47.6°N and 25.8°N show that trees both decrease and increase anisotropy as a function of tree crown and building plan fractions. In compact geometries, anisotropy tends to decrease with tree crown plan fraction, with the opposite in open geometries, though trees taller than building height cause anisotropy to increase for all building plan fractions. These results help better understand and potentially correct urban thermal anisotropy

Asteroid Models from Multiple Data Sources

In the past decade, hundreds of asteroid shape models have been derived using the lightcurve inversion method. At the same time, a new framework of 3-D shape modeling based on the combined analysis of widely different data sources such as optical lightcurves, disk-resolved images, stellar occultation timings, mid-infrared thermal radiometry, optical interferometry, and radar delay-Doppler data, has been developed. This multi-data approach allows the determination of most of the physical and surface properties of asteroids in a single, coherent inversion, with spectacular results. We review the main results of asteroid lightcurve inversion and also recent advances in multi-data modeling. We show that models based on remote sensing data were confirmed by spacecraft encounters with asteroids, and we discuss how the multiplication of highly detailed 3-D models will help to refine our general knowledge of the asteroid population. The physical and surface properties of asteroids, i.e., their spin, 3-D shape, density, thermal inertia, surface roughness, are among the least known of all asteroid properties. Apart for the albedo and diameter, we have access to the whole picture for only a few hundreds of asteroids. These quantities are nevertheless very important to understand as they affect the non-gravitational Yarkovsky effect responsible for meteorite delivery to Earth, or the bulk composition and internal structure of asteroids.Comment: chapter that will appear in a Space Science Series book Asteroids I