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

Remote Sensing ◽

10.3390/rs13224536 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4536

Author(s):

Martin Bachmann ◽

Kevin Alonso ◽

Emiliano Carmona ◽

Birgit Gerasch ◽

Martin Habermeyer ◽

...

Keyword(s):

Atmospheric Correction ◽

Hyperspectral Data ◽

Quality Information ◽

Atmospheric Effects ◽

End User ◽

Operational Approach ◽

Hyperspectral Sensors ◽

Metadata Quality ◽

Global Mapping ◽

Processing Steps

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 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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Comparison of observed and modeled cloud-free longwave downward radiation (2010–2016) at the high mountain BSRN Izaña station

Geoscientific Model Development ◽

10.5194/gmd-11-2139-2018 ◽

2018 ◽

Vol 11 (6) ◽

pp. 2139-2152 ◽

Cited By ~ 2

Author(s):

Rosa Delia García ◽

Africa Barreto ◽

Emilio Cuevas ◽

Julian Gröbner ◽

Omaira Elena García ◽

...

Keyword(s):

Longwave Radiation ◽

Precipitable Water ◽

Surface Radiation ◽

Transmission Model ◽

Standard Group ◽

High Mountain ◽

Atmospheric Transmission ◽

Night Time ◽

Moderate Resolution ◽

Downward Radiation

Abstract. A 7-year (2010–2016) comparison study between measured and simulated longwave downward radiation (LDR) under cloud-free conditions was performed at the Izaña Atmospheric Observatory (IZO, Spain). This analysis encompasses a total of 2062 cases distributed approximately evenly between day and night. Results show an excellent agreement between Baseline Surface Radiation Network (BSRN) measurements and simulations with libRadtran V2.0.1 and MODerate resolution atmospheric TRANsmission model (MODTRAN) V6 radiative transfer models (RTMs). Mean bias (simulated − measured) of <  1.1 % and root mean square of the bias (RMS) of <  1 % are within the instrumental error (2 %). These results highlight the good agreement between the two RTMs, proving to be useful tools for the quality control of LDR observations and for detecting temporal drifts in field instruments. The standard deviations of the residuals, associated with the RTM input parameters uncertainties are rather small, 0.47 and 0.49 % for libRadtran and MODTRAN, respectively, at daytime, and 0.49 to 0.51 % at night-time. For precipitable water vapor (PWV)  >  10 mm, the observed night-time difference between models and measurements is +5 W m−2 indicating a scale change of the World Infrared Standard Group of Pyrgeometers (WISG), which serves as reference for atmospheric longwave radiation measurements. Preliminary results suggest a possible impact of dust aerosol on infrared radiation during daytime that might not be correctly parametrized by the models, resulting in a slight underestimation of the modeled LDR, of about −3 W m−2, for relatively high aerosol optical depth (AOD  >  0.20).

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Determination of land surface reflectance using the AATSR dual-view capability

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-7451-2014 ◽

2014 ◽

Vol 7 (7) ◽

pp. 7451-7494

Author(s):

L. Sogacheva ◽

P. Kolmonen ◽

T. H. Virtanen ◽

E. Rodriguez ◽

A.-M. Sundström ◽

...

Keyword(s):

Land Surface ◽

Atmospheric Correction ◽

Correlation Coefficients ◽

Absolute Error ◽

Surface Reflectance ◽

Imaging Spectrometer ◽

Comparison Results ◽

Moderate Resolution ◽

The Difference ◽

Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using reflectance measured at the top of the atmosphere for the two views provided by the Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated vs. surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (MODerate resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with some (up to 0.05) overestimation by ADV over bright surfaces. The difference between the ADV and MODIS retrieved surface reflectance is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation of the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for the both AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover 17 years period of measurements (1995–2012), as well as a prototype for The Sea and Land Surface Temperature Radiometer (SLSTR) to be launched in 2015 onboard the Sentinel-3 satellite.

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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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Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

Remote Sensing ◽

10.3390/rs11232771 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2771 ◽

Cited By ~ 2

Author(s):

Lu She ◽

Hankui Zhang ◽

Weile Wang ◽

Yujie Wang ◽

Yun Shi

Keyword(s):

Mean Squared Error ◽

Atmospheric Correction ◽

Remote Sensing Data ◽

Japan Meteorological Agency ◽

Absolute Bias ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Highly Correlated ◽

New Generation

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.

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Atmospheric Correction for Tower-Based Solar-Induced Chlorophyll Fluorescence Observations at O2-A Band

Remote Sensing ◽

10.3390/rs11030355 ◽

2019 ◽

Vol 11 (3) ◽

pp. 355 ◽

Cited By ~ 6

Author(s):

Xinjie Liu ◽

Jian Guo ◽

Jiaochan Hu ◽

Liangyun Liu

Keyword(s):

Chlorophyll Fluorescence ◽

Atmospheric Correction ◽

Field Measurements ◽

Correction Method ◽

Oxygen Absorption ◽

Atmospheric Transmission ◽

Absorption Bands ◽

Transfer Path ◽

The Difference ◽

Atmospheric Transmittance

Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O2-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O2-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E790/E660). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O2-A band.

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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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