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

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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Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

Remote Sensing ◽

10.3390/rs11192297 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2297 ◽

Cited By ~ 6

Author(s):

Kristi Uudeberg ◽

Ilmar Ansko ◽

Getter Põru ◽

Ave Ansper ◽

Anu Reinart

Keyword(s):

Remote Sensing ◽

Coastal Waters ◽

Atmospheric Correction ◽

Suspended Sediments ◽

Reflectance Spectra ◽

Water Type ◽

Colored Dissolved Organic Matter ◽

Water Color ◽

Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

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Removal of atmospheric effects from hyperspectral imaging data for remote sensing of coastal waters

10.1117/12.411682 ◽

2001 ◽

Author(s):

Bo-Cai Gao ◽

Marcos J. Montes ◽

Curtiss O. Davis

Keyword(s):

Remote Sensing ◽

Hyperspectral Imaging ◽

Coastal Waters ◽

Atmospheric Effects ◽

Imaging Data

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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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Remote sensing of snow and atmosphere properties using Ocean and Land Colour Instrument on board Copernicus Sentinel-3 mission

10.5194/egusphere-egu2020-21774 ◽

2020 ◽

Author(s):

Alexander Kokhanovsky ◽

Jason Box ◽

Baptiste Vandecrux ◽

Michael Kern

Keyword(s):

Remote Sensing ◽

Atmospheric Correction ◽

European Space Agency ◽

Satellite Observations ◽

Snow Properties ◽

Retrieval Technique ◽

Space Agency ◽

Correction Technique ◽

Cloud Screening

In this work we propose a simple technique to derive snow and atmosphere properties from satellite top-of-atmosphere spectral reflectance observations using asymptotic radiative transfer theory valid for the case of weakly absorbing and optically thick media. The following snow properties are derived and analyzed: ice grain size, snow specific surface area, snow pollution load, snow spectral and broadband albedo. The developed retrieval technique includes both atmospheric correction and cloud screening routines and is based on Ocean and Land Colour Instrument (OLCI) measurements on board Sentinel-3A, B. The spectral aerosol optical thickness, total ozone and water vapour column are derived fitting the measured and simulated OLCI-registered spectral reflectances at 21 OLCI channels.The derived results are validated using ground - based observations. It follows that satellite observations can be used to study time series of spectral and broadband albedo over Greenland. The deviations of satellite and ground observations are due to problems with cloud screening over snow and also due to different spatial scale of satellite and ground observations (Kokhanovsky et al., 2020).AcknowledgementsThe work has been supported by the European Space Agency in the framework of ESRIN contract No. 4000118926/16/I-NB &#8216;Scientific Exploitation of Operational Missions (SEOM) Sentinel-3 Snow (Sentinel-3 for Science, Land Study 1: Snow&#8217;) and ESRIN contract 4000125043 &#8211; ESA/AO/1-9101/17/I-NB EO science for society &#8216;Pre-operational Sentinel-3 snow and ice products&#8217;.ReferencesKokhanovsky, A.A., et al. (2020), The determination of snow albedo from satellite observations using fast atmospheric correction technique, Remote Sensing, 12 (2), 234, &#160;https://doi.org/10.3390/rs12020234.

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A multi-channel atmospheric correction algorithm for remote sensing of coastal waters

2007 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2007.4422728 ◽

2007 ◽

Author(s):

Bo-Cai Gao ◽

Marcos J. Montes ◽

Rong-Rong Li

Keyword(s):

Remote Sensing ◽

Coastal Waters ◽

Atmospheric Correction ◽

Correction Algorithm

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New atmospheric correction technique to retrieve the ocean colour from SeaWiFS imagery in complex coastal waters

Journal of Optics A Pure and Applied Optics ◽

10.1088/1464-4258/9/5/016 ◽

2007 ◽

Vol 9 (5) ◽

pp. 511-530 ◽

Cited By ~ 26

Author(s):

Palanisamy Shanmugam ◽

Yu-Hwan Ahn

Keyword(s):

Coastal Waters ◽

Atmospheric Correction ◽

Ocean Colour ◽

Correction Technique

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A preliminary approach on the use of satellite hyperspectral data for geological mapping

Bulletin of the Geological Society of Greece ◽

10.12681/bgsg.17251 ◽

2007 ◽

Vol 40 (4) ◽

pp. 1998

Author(s):

G. K. Nikolakopoulos ◽

D. A. Vaiopoulos ◽

G. A. Skianis

Keyword(s):

Remote Sensing ◽

Narrow Band ◽

Signal To Noise Ratio ◽

Mineral Exploration ◽

Hyperspectral Data ◽

Geological Mapping ◽

Atmospheric Effects ◽

Extensive Information ◽

Hyperion Image ◽

Noise Fraction

During the last decades remote sensing imagery has contributed significantly to mineral exploration. Motivated by the increasing importance of hyperspectral remote sensing, this study investigates the potential of the current-generation satellite hyperspectral data for geological mapping. A narrow-band Hyperion image, acquired in summer 2001, was used. The study area is situated at Milos island. Two different approaches were used for the reduction of the Hyperion bands. First, on the basis of histogram statistics the uncalibrated bands were selected and removed. Then the Minimum Noise Fraction was used to classify the bands according to their signal to noise ratio. The noisiest bands were removed and sixty bands were selected for further processing. In order to make meaningful comparisons between image spectra and laboratory reflectance spetra, the image radiance values must be corrected (calibrated) to reflectance by removing the atmospheric effects. Atmospheric corrections techniques were applied to the selected Hyperion bands. The comparison of the Hyperion hyperspectral data with the JPL spectral library gave quite encouraging results. Further processing of the data has to be done using the image analysis algorithms that have been developed specifically to exploit the extensive information contained in hyperspectral imagery.

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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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Agricultural Stress Monitoring using Remote Sensing Data

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c4688.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 335-337

Keyword(s):

Remote Sensing ◽

Stress Level ◽

Hyperspectral Image ◽

Atmospheric Correction ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Hyperspectral Data ◽

Stress Monitoring ◽

Chlorophyll Contents ◽

Sensing Data

This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.

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Application of Sentinel-2A/B satellites to retrieve turbidity in the Guadalquivir estuary (Southern Spain)

10.5194/egusphere-egu21-15445 ◽

2021 ◽

Author(s):

Masuma Chowdhury ◽

César Vilas ◽

Stef VanBergeijk ◽

Gabriel Navarro ◽

Irene Laiz ◽

...

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Water Quality ◽

Coastal Waters ◽

Atmospheric Correction ◽

Southern Spain ◽

Guadalquivir Estuary ◽

Sensing Applications ◽

Sentinel 2A ◽

Sentinel 2

Application of Sentinel-2A/B satellites to retrieve turbidity in the Guadalquivir estuary (Southern Spain)Due to climate change, contamination, and diverse anthropogenic effects, water quality monitoring is intensifying its importance nowadays. Remote sensing techniques are becoming an important tool, in parallel with fieldwork, for supporting the cost-effective accomplishment of water quality mapping and management. In the recent years, Sentinel-2A/B twin satellites of the European Commission Earth Observation Copernicus programme emerged as a promising way to monitor complex coastal waters with higher spatial, spectral and temporal resolution. However, atmospheric and sunglint correction for the Sentinel-2 data over the coastal and inland waters is one of the major challenges in terms of accurate water quality retrieval. This study aimed at evaluating the ACOLITE atmospheric correction processor in order to develop a regional turbidity model for the Guadalquivir estuary (southern Spain) and its adjacent coastal region using Sentinel-2 imagery at a 10 m spatial resolution. Two settings for the atmospheric correction algorithm within the ACOLITE software were applied: the standard dark spectrum fitting (DSF) and the DSF with an additional option for sunglint correction. Turbidity field data were collected for calibration/validation purposes from the monthly Guadalquivir Estuary-LTER programme by Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA) using a YSI-EXO2 multiparametric sonde for the period 2017-2020 at 2 fixed stations (Bonanza and Tarfia) sampling 4 different water masses along the estuary salinity gradient. Several regional models were evaluated using the red band (665 nm) and the red-edge bands (i.e. 704, 740, 783 nm) of the Sentinel-2 satellites. The results revealed that DSF with glint correction performs better than without glint correction, especially for this region where sunglint is a major concern during summer, affecting most of the satellite scenes. This study demonstrates the invaluable potential of the Sentinel-2A/B mission to monitor complex coastal waters even though they were not designed for aquatic remote sensing applications. This improved knowledge will be a helpful guideline and tool for the coastal managers, policy-makers, stakeholders and the scientific community for ensuring sustainable ecosystem-based coastal resource management under a global climate change scenario.

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