Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

Bhatia, Nitin; Tolpekin, V.A.; Stein, Alfred; Reusen, I.

doi:10.3390/rs10060947

Cited by 17 publications

(6 citation statements)

References 55 publications

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“…Deviations in AOD or AE will change the shape of the AOD spectra, yielding inaccurate estimates of mineral concentrations. We must also acknowledge that uncertainty is an inherent property of satellite AOD derivations and cannot be entirely eliminated and will likely propagate to further derivations [64], i.e., to mineral concentrations in our case. An analysis of uncertainty involved in mineral derivations due to the deviations in AOD and AE (Figures 9-11) is, therefore, a beneficial measure to determine the model's performance.…”

Section: Discussionmentioning

confidence: 99%

Understanding Haze: Modeling Size-Resolved Mineral Aerosol from Satellite Remote Sensing

Sanwlani

Das

2022

Remote Sensing

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Mineral dust aerosols are composed of a complex mixture of silicates, carbonates, oxides, and sulfates. The minerals’ chemical composition and size distribution are vital parameters to evaluate dust environmental impacts. However, the quantification of minerals remains a challenge due to the sparse in situ measurements of dust samples. Here we derive the size-resolved mineralogical composition of airborne dust aerosols from MODIS (Terra and Aqua) satellite-acquired optical measurements and compare it with chemically analyzed elemental (Al, Fe, Ca, Mg) concentrations of aerosols for PM2.5 and PM10 from Chonburi, Chiang Rai, and Bangkok in Thailand, and from Singapore. MODIS-derived mineral retrievals exhibited high correlations with elemental concentrations with R2 ≥ 0.84 for PM2.5 and ≥0.96 for PM10. High mineral dust activity was detected in the vicinity of biomass-burning areas with gypsum and calcite exhibiting tracer characteristics of combustion. The spatiotemporal pattern of the MODIS-derived minerals matched with Ozone Monitoring Instrument (OMI)-derived dust, sulfates, and carbonaceous aerosols, indicating the model’s consistency. Variation in aerosol loading by ±90% led to deviation in the mineral concentration by <10%. An uncertainty of 6.4% between AERONET-measured and MODIS-derived AOD corresponds to a < ± 2% uncertainty in MODIS-derived mineral concentration, demonstrating the robustness of the model.

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Section: Discussionmentioning

confidence: 99%

Understanding Haze: Modeling Size-Resolved Mineral Aerosol from Satellite Remote Sensing

Sanwlani

Das

2022

Remote Sensing

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“…The research objective was to develop quality indicators and quality layers for airborne hyperspectral imagery and data products, which also included a joint and harmonized data format and metadata standards [10,11]. The outcomes of the uncertainty estimation approach resulted in a number of publication such as [12] addressing the expected uncertainty of EnMAP L2A data, in [13] for the uncertainty in Aerosol Optical Density retrieval using APEX as well as the APEX instrument calibration uncertainty in [14]. Other approaches for the estimation of uncertainty in hyperspectral datasets can be found in [15].…”

Section: Eufar Hyquapromentioning

confidence: 99%

Analysis-Ready Data from Hyperspectral Sensors—The Design of the EnMAP CARD4L-SR Data Product

et al. 2021

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Today, the ground segments of the Landsat and Sentinel missions provide a wealth of well-calibrated, characterized datasets which are already orthorectified and corrected for atmospheric effects. Initiatives such as the CEOS Analysis Ready Data (ARD) propose and ensure guidelines and requirements so that such datasets can readily be used, and interoperability within and between missions is a given. With the increasing availability of data from operational and research-oriented spaceborne hyperspectral sensors such as EnMAP, DESIS and PRISMA, and in preparation for the upcoming global mapping missions CHIME and SBG, the provision of analysis ready hyperspectral data will also be of increasing interest. Within this article, the design of the EnMAP Level 2A Land product is illustrated, highlighting the necessary processing steps for CEOS Analysis Ready Data for Land (CARD4L) compliant data products. This includes an overview of the design of the metadata, quality layers and archiving workflows, the necessary processing chain (system correction, orthorectification and atmospheric correction), as well as the resulting challenges of this procedure. Thanks to this operational approach, the end user will be provided with ARD products including rich metadata and quality information, which can readily be integrated in analysis workflows, and combined with data from other sensors.

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“…No entanto, efeitos como ruído de detectores, dependência do tipo de superfície, geometrias de iluminação e visada, podem contribuir para a incerteza das medidas obtidas (Bhatia et al, 2018). Assim, recentemente, a implementação do algoritmo MAIAC teve por objetivo reduzir as fontes de incerteza relacionadas a efeitos provocados pela reflectância bidirecional em observações MODIS.…”

Section: Visibilidade Atmosférica E Profundidade óPtica De Aerossolunclassified

Correção Atmosférica em Sensoriamento Remoto: Uma Revisão

Gaida

Breunig

Galvão

et al. 2020

Rev. Bras. Geog. Fis.

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A correção atmosférica é um procedimento necessário em estudos de sensoriamento remoto com foco nas propriedades físicas, químicas e biológicas de alvos. Em abordagens multitemporais e/ou multisensor, a correção atmosférica é fundamental para a obtenção de medidas de reflectância de superfície representativas das características espectrais dos alvos. Embora de grande importância, este procedimento e seus conceitos ainda são pouco difundidos e estudados no âmbito da Geografia brasileira. O objetivo do estudo foi apresentar uma revisão dos principais métodos e parâmetros envolvidos no processo de correção atmosférica de imagens de sensoriamento remoto. Este procedimento visa minimizar os efeitos decorrentes da interação dos constituintes atmosféricos com a radiação eletromagnética, como a absorção provocada pelos gases e o espalhamento produzido por partículas de aerossóis. Sua eficiência depende de um conjunto de fatores, como o método de correção adotado e as características do sistema sensor. Diversos métodos têm sido propostos, sendo classificados em: métodos físicos, fundamentados em modelos de transferência radiativa; métodos empíricos, que estimam os efeitos atmosféricos a partir de alvos específicos da imagem; e métodos híbridos, que combinam informações físicas da atmosfera e estatísticas da cena. Embora os métodos empíricos sejam de fácil aplicação, eles são menos precisos do que os métodos físicos. Estes, por sua vez, necessitam a priori do conhecimento de alguns parâmetros atmosféricos que, dependendo da resolução espectral do sensor, podem ser obtidos da própria imagem. Os métodos híbridos constituem uma alternativa por demandarem um menor conhecimento de parâmetros atmosféricos. Um exemplo dos efeitos atmosféricos sobre o cálculo de índices de vegetação é apresentado. Atmospheric Correction in Remote Sensing: A Review A B S T R A C TAtmospheric correction is a necessary procedure in remote sensing studies focusing on the physical, chemical and biological properties of targets. In multi-temporal and/or multi-sensor approaches, the atmospheric correction is fundamental to obtain surface reflectance measurements representative of the spectral characteristics of targets. Although of great importance, this procedure and its concepts are still little diffused and studied in the scope of the Geography in Brazil. The objective of the study was to present an overview of the main methods and parameters involved in the process of atmospheric correction of remote sensing images. This procedure aims to minimize the effects of the interaction of atmospheric constituents with electromagnetic radiation, such as the absorption caused by gases and the scattering produced by aerosol particles. Its efficiency depends on a set of factors such as the selected correction method and the technical specifications of the sensor system. Several methods have been proposed. They are classified in physical methods, based on radiative transfer models; empirical methods, which estimate atmospheric effects from selected targets in the scene; and hybrid methods, which combine physical information from the atmosphere and scene statistics. Although empirical methods are easy to apply, they are less precise than physical methods. Physical approaches require a priori knowledge of some atmospheric parameters. However, depending on the spectral resolution of the sensor, these parameters can be estimated from the image itself. The hybrid methods are an alternative because they require less knowledge of atmospheric parameters. An example of the atmospheric effects on the determination of vegetation indices is presented.Key-words: image processing, atmospheric effects, spectral behavior, radiometric errors.

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Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

Cited by 17 publications

References 55 publications

Understanding Haze: Modeling Size-Resolved Mineral Aerosol from Satellite Remote Sensing

Understanding Haze: Modeling Size-Resolved Mineral Aerosol from Satellite Remote Sensing

Analysis-Ready Data from Hyperspectral Sensors—The Design of the EnMAP CARD4L-SR Data Product

Correção Atmosférica em Sensoriamento Remoto: Uma Revisão

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