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

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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Atmospheric correction of APEX hyperspectral data

Miscellanea Geographica ◽

10.1515/mgrsd-2015-0022 ◽

2016 ◽

Vol 20 (1) ◽

pp. 16-20 ◽

Cited By ~ 12

Author(s):

Sindy Sterckx ◽

Kristin Vreys ◽

Jan Biesemans ◽

Marian-Daniel Iordache ◽

Luc Bertels ◽

...

Keyword(s):

Atmospheric Correction ◽

Hyperspectral Data ◽

Atmospheric Effects ◽

Atmospheric Transmission ◽

Specific Effects ◽

Correction Process ◽

Moderate Resolution ◽

Data Processing Center ◽

Processing Steps ◽

Central Data

Abstract Atmospheric correction plays a crucial role among the processing steps applied to remotely sensed hyperspectral data. Atmospheric correction comprises a group of procedures needed to remove atmospheric effects from observed spectra, i.e. the transformation from at-sensor radiances to at-surface radiances or reflectances. In this paper we present the different steps in the atmospheric correction process for APEX hyperspectral data as applied by the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). The MODerate resolution atmospheric TRANsmission program (MODTRAN) is used to determine the source of radiation and for applying the actual atmospheric correction. As part of the overall correction process, supporting algorithms are provided in order to derive MODTRAN configuration parameters and to account for specific effects, e.g. correction for adjacency effects, haze and shadow correction, and topographic BRDF correction. The methods and theory underlying these corrections and an example of an application are presented.

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Atmospheric Correction of Airborne Hyperspectral CASI Data Using Polymer, 6S and FLAASH

Remote Sensing ◽

10.3390/rs13245062 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5062

Author(s):

Mengmeng Yang ◽

Yong Hu ◽

Hongzhen Tian ◽

Faisal Ahmed Khan ◽

Qingping Liu ◽

...

Keyword(s):

Remote Sensing ◽

Coastal Waters ◽

Atmospheric Correction ◽

Hyperspectral Data ◽

Spectral Shape ◽

Time Difference ◽

The Other ◽

Atmospheric Effects ◽

Aerosol Scattering ◽

Correction Technique

Airborne hyperspectral data play an important role in remote sensing of coastal waters. However, before their application, atmospheric correction is required to remove or reduce the atmospheric effects caused by molecular and aerosol scattering and absorption. In this study, we first processed airborne hyperspectral CASI-1500 data acquired on 4 May 2019 over the Uljin coast of Korea with Polymer and then compared the performance with the other two widely used atmospheric correction approaches, i.e., 6S and FLAASH, to determine the most appropriate correction technique for CASI-1500 data in coastal waters. Our results show the superiority of Polymer over 6S and FLAASH in deriving the Rrs spectral shape and magnitude. The performance of Polymer was further evaluated by comparing CASI-1500 Rrs data with those obtained from the MODIS-Aqua sensor on 3 May 2019 and processed using Polymer. The spectral shapes of the derived Rrs from CASI-1500 and MODIS-Aqua matched well, but the magnitude of CASI-1500 Rrs was approximately 0.8 times lower than MODIS Rrs. The possible reasons for this difference were time difference (1 day) between CASI-1500 and MODIS data, higher land adjacency effect for MODIS-Aqua than for CASI-1500, and possible errors in MODIS Rrs from Polymer.

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The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

Atmospheric Measurement Techniques ◽

10.5194/amt-8-1593-2015 ◽

2015 ◽

Vol 8 (3) ◽

pp. 1593-1604 ◽

Cited By ~ 12

Author(s):

C. Bassani ◽

C. Manzo ◽

F. Braga ◽

M. Bresciani ◽

C. Giardino ◽

...

Keyword(s):

Accuracy Assessment ◽

Atmospheric Correction ◽

Solar Spectrum ◽

Hyperspectral Data ◽

High Spectral Resolution ◽

Quality Analysis ◽

Microphysical Properties ◽

Quantitative Remote Sensing ◽

The Impact ◽

Aerosol Types

Abstract. Hyperspectral imaging provides quantitative remote sensing of ocean colour by the high spectral resolution of the water features. The HICO™ (Hyperspectral Imager for the Coastal Ocean) is suitable for coastal studies and monitoring. The accurate retrieval of hyperspectral water-leaving reflectance from HICO™ data is still a challenge. The aim of this work is to retrieve the water-leaving reflectance from HICO™ data with a physically based algorithm, using the local microphysical properties of the aerosol in order to overcome the limitations of the standard aerosol types commonly used in atmospheric correction processing. The water-leaving reflectance was obtained using the HICO@CRI (HICO ATmospherically Corrected Reflectance Imagery) atmospheric correction algorithm by adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer code. The HICO@CRI algorithm was applied on to six HICO™ images acquired in the northern Mediterranean basin, using the microphysical properties measured by the Acqua Alta Oceanographic Tower (AAOT) AERONET site. The HICO@CRI results obtained with AERONET products were validated with in situ measurements showing an accuracy expressed by r2 = 0.98. Additional runs of HICO@CRI on the six images were performed using maritime, continental and urban standard aerosol types to perform the accuracy assessment when standard aerosol types implemented in 6SV are used. The results highlight that the microphysical properties of the aerosol improve the accuracy of the atmospheric correction compared to standard aerosol types. The normalized root mean square (NRMSE) and the similar spectral value (SSV) of the water-leaving reflectance show reduced accuracy in atmospheric correction results when there is an increase in aerosol loading. This is mainly when the standard aerosol type used is characterized with different optical properties compared to the local aerosol. The results suggest that if a water quality analysis is needed the microphysical properties of the aerosol need to be taken into consideration in the atmospheric correction of hyperspectral data over coastal environments, because aerosols influence the accuracy of the retrieved water-leaving reflectance.

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AT2ES: Simultaneous Atmospheric Transmittance-Temperature-Emissivity Separation Using Online Upper Midwave Infrared Hyperspectral Images

Remote Sensing ◽

10.3390/rs13071249 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1249

Author(s):

Sungho Kim ◽

Jungsub Shin ◽

Sunho Kim

Keyword(s):

Air Temperature ◽

Atmospheric Correction ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Co2 Absorption ◽

Infrared Band ◽

Data Regression ◽

Midwave Infrared ◽

Novel Method ◽

Atmospheric Transmittance

This paper presents a novel method for atmospheric transmittance-temperature-emissivity separation (AT2ES) using online midwave infrared hyperspectral images. Conventionally, temperature and emissivity separation (TES) is a well-known problem in the remote sensing domain. However, previous approaches use the atmospheric correction process before TES using MODTRAN in the long wave infrared band. Simultaneous online atmospheric transmittance-temperature-emissivity separation starts with approximation of the radiative transfer equation in the upper midwave infrared band. The highest atmospheric band is used to estimate surface temperature, assuming high emissive materials. The lowest atmospheric band (CO2 absorption band) is used to estimate air temperature. Through onsite hyperspectral data regression, atmospheric transmittance is obtained from the y-intercept, and emissivity is separated using the observed radiance, the separated object temperature, the air temperature, and atmospheric transmittance. The advantage with the proposed method is from being the first attempt at simultaneous AT2ES and online separation without any prior knowledge and pre-processing. Midwave Fourier transform infrared (FTIR)-based outdoor experimental results validate the feasibility of the proposed AT2ES method.

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New Approaches to Processing Ground-based SAR (GBSAR) Data for Deformation Monitoring

Remote Sensing ◽

10.3390/rs10121936 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1936 ◽

Cited By ~ 1

Author(s):

Sichun Long ◽

Aixia Tong ◽

Ying Yuan ◽

Zhenhong Li ◽

Wenhao Wu ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Atmospheric Correction ◽

Deformation Monitoring ◽

Atmospheric Effects ◽

Threshold Optimization ◽

Before And After ◽

Multiple Threshold ◽

The Stability ◽

Average Coherence

In this paper, aiming at the limitation of persistence scatterers (PS) points selection, a new method for selecting PS points has been introduced based on the average coherence coefficient, amplitude dispersion index, estimated signal-to-noise ratio and displacement standard deviation of multiple threshold optimization. The stability and quality of this method are better than that of a single model. In addition, an atmospheric correction model has also been proposed to estimate the atmospheric effects on Ground-based synthetic aperture radar (GBSAR) observations. After comparing the monitoring results before and after correction, we clearly found that the results are in good agreement with the actual observations after applying the proposed atmospheric correction approach.

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Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery

Remote Sensing ◽

10.3390/rs12162623 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2623 ◽

Cited By ~ 1

Author(s):

Marcel König ◽

Gerit Birnbaum ◽

Natascha Oppelt

Keyword(s):

Sea Ice ◽

Hyperspectral Imagery ◽

Atmospheric Correction ◽

Hyperspectral Data ◽

Arctic Sea Ice ◽

Atmospheric Conditions ◽

Melt Pond ◽

Melt Ponds ◽

Arctic Sea ◽

Highly Correlated

Hyperspectral remote-sensing instruments on unmanned aerial vehicles (UAVs), aircraft and satellites offer new opportunities for sea ice observations. We present the first study using airborne hyperspectral imagery of Arctic sea ice and evaluate two atmospheric correction approaches (ATCOR-4 (Atmospheric and Topographic Correction version 4; v7.0.0) and empirical line calibration). We apply an existing, field data-based model to derive the depth of melt ponds, to airborne hyperspectral AisaEAGLE imagery and validate results with in situ measurements. ATCOR-4 results roughly match the shape of field spectra but overestimate reflectance resulting in high root-mean-square error (RMSE) (between 0.08 and 0.16). Noisy reflectance spectra may be attributed to the low flight altitude of 200 ft and Arctic atmospheric conditions. Empirical line calibration resulted in smooth, accurate spectra (RMSE < 0.05) that enabled the assessment of melt pond bathymetry. Measured and modeled pond bathymetry are highly correlated (r = 0.86) and accurate (RMSE = 4.04 cm), and the model explains a large portion of the variability (R2 = 0.74). We conclude that an accurate assessment of melt pond bathymetry using airborne hyperspectral data is possible subject to accurate atmospheric correction. Furthermore, we see the necessity to improve existing approaches with Arctic-specific atmospheric profiles and aerosol models and/or by using multiple reference targets on the ground.

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SHARC method for fast atmospheric correction of hyperspectral data

Remote Sensing of Clouds and the Atmosphere XXIII ◽

10.1117/12.2323455 ◽

2018 ◽

Cited By ~ 1

Author(s):

Dimitry Ivanov ◽

Leonid Katkovsky ◽

Anton Martenov ◽

Volha Siliuk

Keyword(s):

Atmospheric Correction ◽

Hyperspectral Data

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Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

Remote Sensing ◽

10.3390/rs11121469 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1469 ◽

Cited By ~ 8

Author(s):

Marcela Pereira-Sandoval ◽

Ana Ruescas ◽

Patricia Urrego ◽

Antonio Ruiz-Verdú ◽

Jesús Delegido ◽

...

Keyword(s):

Near Infrared ◽

Atmospheric Correction ◽

Inland Waters ◽

Atmospheric Effects ◽

Image Correction ◽

Lakes And Reservoirs ◽

Dark Pixel ◽

Organic Particles ◽

Sentinel 2

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

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