Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

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Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

Remote Sensing ◽

10.3390/rs11232849 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2849

Author(s):

Trung Kien Tran ◽

Lucile Duforêt-Gaurier ◽

Vincent Vantrepotte ◽

Daniel Schaffer Ferreira Jorge ◽

Xavier Mériaux ◽

...

Keyword(s):

Remote Sensing ◽

Coastal Waters ◽

Model Performance ◽

Chemical Compositions ◽

Band Ratio ◽

Data Set ◽

Near Surface ◽

Percentage Difference ◽

The Mean

Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, Rrs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of Rrs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSDlog) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 μg L−1 and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) Rrs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSDlog = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region.

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Assessment of Polymer Atmospheric Correction Algorithm for Hyperspectral Remote Sensing Imagery over Coastal Waters

Sensors ◽

10.3390/s21124125 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4125

Author(s):

Mariana A. Soppa ◽

Brenner Silva ◽

François Steinmetz ◽

Darryl Keith ◽

Daniel Scheffler ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Coastal Waters ◽

Spectral Region ◽

Atmospheric Correction ◽

Hyperspectral Remote Sensing ◽

Water Colour ◽

Percentage Difference ◽

Green Spectral Region

Spaceborne imaging spectroscopy, also called hyperspectral remote sensing, has shown huge potential to improve current water colour retrievals and, thereby, the monitoring of inland and coastal water ecosystems. However, the quality of water colour retrievals strongly depends on successful removal of the atmospheric/surface contributions to the radiance measured by satellite sensors. Atmospheric correction (AC) algorithms are specially designed to handle these effects, but are challenged by the hundreds of narrow spectral bands obtained by hyperspectral sensors. In this paper, we investigate the performance of Polymer AC for hyperspectral remote sensing over coastal waters. Polymer is, in nature, a hyperspectral algorithm that has been mostly applied to multispectral satellite data to date. Polymer was applied to data from the Hyperspectral Imager for the Coastal Ocean (HICO), validated against in situ multispectral (AERONET-OC) and hyperspectral radiometric measurements, and its performance was compared against that of the hyperspectral version of NASA’s standard AC algorithm, L2gen. The match-up analysis demonstrated very good performance of Polymer in the green spectral region. The mean absolute percentage difference across all the visible bands varied between 16% (green spectral region) and 66% (red spectral region). Compared with L2gen, Polymer remote sensing reflectances presented lower uncertainties, greater data coverage, and higher spectral similarity to in situ measurements. These results demonstrate the potential of Polymer to perform AC on hyperspectral satellite data over coastal waters, thus supporting its application in current and future hyperspectral satellite missions.

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Decadal Measurements of the First Geostationary Ocean Color Satellite (GOCI) Compared with MODIS and VIIRS Data

Remote Sensing ◽

10.3390/rs14010072 ◽

2021 ◽

Vol 14 (1) ◽

pp. 72

Author(s):

Myung-Sook Park ◽

Seonju Lee ◽

Jae-Hyun Ahn ◽

Sun-Ju Lee ◽

Jong-Kuk Choi ◽

...

Keyword(s):

Yellow Sea ◽

North Pacific ◽

Western North Pacific ◽

Ocean Color ◽

Atmospheric Correction ◽

East Sea ◽

The Yellow Sea ◽

Chl A ◽

Correction Technique ◽

Band Ratios

The first geostationary ocean color data from the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) have been accumulating for more than ten years from 2010. This study performs a multi-year quality assessment of GOCI chlorophyll-a (Chl-a) and radiometric data for 2012–2021 with an advanced atmospheric correction technique and a regionally specialized Chl-a algorithm. We examine the consistency and stability of GOCI, Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) level 2 products in terms of annual and seasonal climatology, two-dimensional frequency distribution, and multi-year time series. Overall, the GOCI agrees well with MODIS and VIIRS on annual and seasonal variability in Chl-a, as the central biological pattern of the most transparent waters over the western North Pacific, productive waters over the East Sea, and turbid waters over the Yellow Sea are reasonably represented. Overall, an excellent agreement is remarkable for western North Pacific oligotrophic waters (with a correlation higher than 0.91 for Chl-a and 0.96 for band-ratio). However, the sporadic springtime overestimation of MODIS Chl-a values compared with others is notable over the Yellow Sea and East Sea due to the underestimation of MODIS blue-green band ratios for moderate-high aerosol optical depth. The persistent underestimation of VIIRS Chl-a values compared with GOCI and MODIS occurs due to inherent sensor calibration differences. In addition, the artificially increasing trends in GOCI Chl-a (+0.48 mg m−3 per 9 years) arise by the decreasing trends in the band ratios. However, decreasing Chl-a trends in MODIS and VIIRS (−0.09 and −0.08 mg m−3, respectively) are reasonable in response to increasing sea surface temperature. The results indicate GOCI sensor degradation in the late mission period. The long-term application of the GOCI data should be done with a caveat, however; planned adjustments to GOCI calibration (2022) in the following GOCI-II satellite will essentially eliminate the bias in Chl-a trends.

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Evaluation of Remote-Sensing Reflectance Products from Multiple Ocean Color Missions in Highly Turbid Water (Hangzhou Bay)

Remote Sensing ◽

10.3390/rs13214267 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4267

Author(s):

Yuzhuang Xu ◽

Xianqiang He ◽

Yan Bai ◽

Difeng Wang ◽

Qiankun Zhu ◽

...

Keyword(s):

Remote Sensing ◽

Infrared Imaging ◽

Ocean Color ◽

Hangzhou Bay ◽

Turbid Water ◽

A Value ◽

Remote Sensing Reflectance ◽

Percentage Difference ◽

Absolute Percentage Difference

Validation of remote-sensing reflectance (Rrs) products is necessary for the quantitative application of ocean color satellite data. While validation of Rrs products has been performed in low to moderate turbidity waters, their performance in highly turbid water remains poorly known. Here, we used in situ Rrs data from Hangzhou Bay (HZB), one of the world’s most turbid estuaries, to evaluate agency-distributed Rrs products for multiple ocean color sensors, including the Geostationary Ocean Color Imager (GOCI), Chinese Ocean Color and Temperature Scanner aboard HaiYang-1C (COCTS/HY1C), Ocean and Land Color Instrument aboard Sentinel-3A and Sentinel-3B, respectively (OLCI/S3A and OLCI/S3B), Second-Generation Global Imager aboard Global Change Observation Mission-Climate (SGLI/GCOM-C), and Visible Infrared Imaging Radiometer Suite aboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP). Results showed that GOCI and SGLI/GCOM-C had almost no effective Rrs products in the HZB. Among the others four sensors (COCTS/HY1C, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP), VIIRS/SNPP obtained the largest correlation coefficient (R) with a value of 0.7, while OLCI/S3A obtained the best mean percentage differences (PD) with a value of −13.30%. The average absolute percentage difference (APD) values of the four remote sensors are close, all around 45%. In situ Rrs data from the AERONET-OC ARIAKE site were also used to evaluate the satellite-derived Rrs products in moderately turbid coastal water for comparison. Compared with the validation results at HZB, the performances of Rrs from GOCI, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP were much better at the ARIAKE site with the smallest R (0.77) and largest APD (35.38%) for GOCI, and the worst PD for these four sensors was only −13.15%, indicating that the satellite-retrieved Rrs exhibited better performance. In contrast, Rrs from COCTS/HY1C and SGLI/GCOM-C at ARIAKE site was still significantly underestimated, and the R values of the two satellites were not greater than 0.7, and the APD values were greater than 50%. Therefore, the performance of satellite Rrs products degrades significantly in highly turbid waters and needs to be improved for further retrieval of ocean color components.

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Primary production in the Yellow Sea determined by ocean color remote sensing

Marine Ecology Progress Series ◽

10.3354/meps303091 ◽

2005 ◽

Vol 303 ◽

pp. 91-103 ◽

Cited By ~ 40

Author(s):

SH Son ◽

J Campbell ◽

M Dowell ◽

S Yoo ◽

J Noh

Keyword(s):

Remote Sensing ◽

Primary Production ◽

Yellow Sea ◽

Ocean Color ◽

The Yellow Sea ◽

Ocean Color Remote Sensing

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Spectral Classification of the Yellow Sea and Implications for Coastal Ocean Color Remote Sensing

Remote Sensing ◽

10.3390/rs8040321 ◽

2016 ◽

Vol 8 (4) ◽

pp. 321 ◽

Cited By ~ 14

Author(s):

Huping Ye ◽

Junsheng Li ◽

Tongji Li ◽

Qian Shen ◽

Jianhua Zhu ◽

...

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

Ocean Color ◽

Coastal Ocean ◽

The Yellow Sea ◽

Spectral Classification ◽

Ocean Color Remote Sensing

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Hypothetical Product Generation of Geostationary Ocean Color Imager Bands over the Yellow Sea and Bohai Sea Using Deep Learning Technique

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3098781 ◽

2021 ◽

pp. 1-1

Author(s):

Sumin Ryu ◽

Sungwook Hong

Keyword(s):

Deep Learning ◽

Yellow Sea ◽

Bohai Sea ◽

Ocean Color ◽

The Yellow Sea ◽

Product Generation ◽

Learning Technique

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Atmospheric correction algorithm based on vector radiative transfer modeling for hyperspectral remote sensing of ocean color

10.1117/12.366316 ◽

1999 ◽

Cited By ~ 2

Author(s):

Bo-Cai Gao ◽

Marcos J. Montes ◽

Ziauddin Ahmad ◽

Curtiss O. Davis

Keyword(s):

Remote Sensing ◽

Radiative Transfer ◽

Ocean Color ◽

Atmospheric Correction ◽

Hyperspectral Remote Sensing ◽

Correction Algorithm ◽

Radiative Transfer Modeling

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Improvement of Atmospheric Correction of Satellite Sentinel-3/OLCI Data for Oceanic Waters in Presence of Sargassum

Remote Sensing ◽

10.3390/rs14020386 ◽

2022 ◽

Vol 14 (2) ◽

pp. 386

Author(s):

Léa Schamberger ◽

Audrey Minghelli ◽

Malik Chami ◽

François Steinmetz

Keyword(s):

Near Infrared ◽

Ocean Color ◽

Atmospheric Correction ◽

Correction Algorithm ◽

Infrared Band ◽

Economic Issues ◽

Optical Signature ◽

Color Data ◽

The Caribbean ◽

Satellite Ocean Color

The invasive species of brown algae Sargassum gathers in large aggregations in the Caribbean Sea, and has done so especially over the last decade. These aggregations wash up on shores and decompose, leading to many socio-economic issues for the population and the coastal ecosystem. Satellite ocean color data sensors such as Sentinel-3/OLCI can be used to detect the presence of Sargassum and estimate its fractional coverage and biomass. The derivation of Sargassum presence and abundance from satellite ocean color data first requires atmospheric correction; however, the atmospheric correction procedure that is commonly used for oceanic waters needs to be adapted when dealing with the occurrence of Sargassum because the non-zero water reflectance in the near infrared band induced by Sargassum optical signature could lead to Sargassum being wrongly identified as aerosols. In this study, this difficulty is overcome by interpolating aerosol and sunglint reflectance between nearby Sargassum-free pixels. The proposed method relies on the local homogeneity of the aerosol reflectance between Sargassum and Sargassum-free areas. The performance of the adapted atmospheric correction algorithm over Sargassum areas is evaluated. The proposed method is demonstrated to result in more plausible aerosol and sunglint reflectances. A reduction of between 75% and 88% of pixels showing a negative water reflectance above 600 nm were noticed after the correction of the several images.

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