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Open AccessArticle

The Inversion of HY-1C-COCTS Ocean Color Remote Sensing Products from High-Latitude Seas

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Remote Sens. 2022, 14(22), 5722; https://doi.org/10.3390/rs14225722 - 12 Nov 2022

Cited by 2 | Viewed by 899

Abstract

China’s first operational ocean color satellite sensor, named the Chinese Ocean Color and Temperature Scanner (HY-1C-COCTS), was launched in September 2018 and began to provide operational data in June 2019. However, as a polar orbiting ocean color satellite sensor, HY-1C-COCTS would inevitably encounter [...] Read more.

China’s first operational ocean color satellite sensor, named the Chinese Ocean Color and Temperature Scanner (HY-1C-COCTS), was launched in September 2018 and began to provide operational data in June 2019. However, as a polar orbiting ocean color satellite sensor, HY-1C-COCTS would inevitably encounter regions impacted by large solar zenith angles when observing the high-latitude seas, especially during the winter. The current atmospheric correction algorithm used by ocean color satellite data processing software cannot effectively process observation data with solar zenith angles greater than 70°. This results in a serious lack of effective ocean color product data from high-latitude seas in winter. To solve this problem, this study developed an atmospheric correction algorithm based on a neural network model for use with HY-1C-COCTS data. The new algorithm used HY-1C-COCTS satellite data collected from latitudes greater than 50°N and between April 2020 and April 2021 to establish a direct relationship between the total radiance received by the satellite and the remote sensing reflectance products. The evaluation using the test dataset shows that the inversion accuracy of the new algorithm is relatively high under different solar zenith angles and different HY-1C-COCTS bands (the relative deviation is 3.37%, 7.05%, 5.10%, 5.29%, and 10.06% at 412 nm, 443 nm, 490 nm, 520 nm, and 565 nm, respectively; the relative deviation is 1.07% when the solar zenith angle is large (70~90°)). Cross comparison with MODIS Aqua satellite products shows that the inversion results are consistent. After verifying the accuracy and stability of the algorithm, we reconstructed the remote sensing reflectance dataset from the Arctic Ocean and surrounding high-latitude seas (latitude greater than 50°N) and successfully retrieved chlorophyll-a data and information on other marine ecological parameters. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessFeature PaperArticle

A Contrast Minimization Approach to Remove Sun Glint in Landsat 8 Imagery

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Frank Fell

Remote Sens. 2022, 14(18), 4643; https://doi.org/10.3390/rs14184643 - 16 Sep 2022

Viewed by 1347

Abstract

Sun glint, i.e., direct solar radiation reflected from a water surface, negatively affects the accuracy of ocean color retrieval schemes if entering the field-of-view of the observing instrument. Herein, a simple and robust method to quantify the sun glint contribution to top-of-atmosphere reflectances [...] Read more.

Sun glint, i.e., direct solar radiation reflected from a water surface, negatively affects the accuracy of ocean color retrieval schemes if entering the field-of-view of the observing instrument. Herein, a simple and robust method to quantify the sun glint contribution to top-of-atmosphere reflectances in the visible and near-infrared is proposed, exploiting concomitant observations of the sun glint’s morphology in the shortwave infrared. The method, termed Glint Removal through Contrast Minimization (GRCM), requires high spatial resolution (ca. 10–50 m) imagery to resolve the sun glint’s characteristic morphology, meeting additional criteria on radiometric resolution, signal-to-noise ratio, and temporal delay between the individual band’s acquisitions. It has been applied with good success to a selection of cloud-free Landsat 8 Operational Land Imager (OLI) scenes, otherwise encompassing a wide range of environmental conditions in terms of observation geometry, glint intensity, water types, as well as aerosol and Rayleigh optical depths. GRCM is entirely image based and does not require ancillary information on the sea surface roughness or related parameters (e.g., surface wind), nor the presence of homogeneous clear water areas in the image under consideration. GRCM’s limitations are discussed, and its potential for sensors other than OLI as well as applications beyond glint removal are sketched. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessArticle

Evaluation of Atmospheric Correction Algorithms over Lakes for High-Resolution Multispectral Imagery: Implications of Adjacency Effect

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Yanqun Pan

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Simon Bélanger

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Yannick Huot

Remote Sens. 2022, 14(13), 2979; https://doi.org/10.3390/rs14132979 - 22 Jun 2022

Cited by 12 | Viewed by 1767

Abstract

Atmospheric correction of satellite optical imagery over inland waters is a key remaining challenge in aquatic remote sensing. This is due to numerous confounding factors such as the complexity of water optical properties, the surface glint, the heterogeneous nature of atmospheric aerosols, and [...] Read more.

Atmospheric correction of satellite optical imagery over inland waters is a key remaining challenge in aquatic remote sensing. This is due to numerous confounding factors such as the complexity of water optical properties, the surface glint, the heterogeneous nature of atmospheric aerosols, and the proximity of bright land surfaces. This combination of factors makes it difficult to retrieve accurate information about the system observed. Moreover, the impact of radiance coming from adjacent land (adjacency effects) in complex geometries further adds to this challenge, especially for small lakes. In this study, ten atmospheric correction algorithms were evaluated for high-resolution multispectral imagery of Landsat-8 Operational Land Imager and Sentinel-2 MultiSpectral Instrument using in situ optical measurements from ~300 lakes across Canada. The results of the validation show that the performance of the algorithms varies by spectral band and evaluation metrics. The dark spectrum fitting algorithm had the best performance in terms of similarity angle (spectral shape), while the neural network-based models showed the lowest errors and bias per band. However, none of the tested atmospheric correction algorithms meet a 30% retrieval accuracy target across all the visible bands, likely due to uncorrected adjacency effects. To quantify this process, three-dimensional radiative transfer simulations were performed and compared to satellite observations. These simulations show that up to 60% of the top of atmosphere reflectance in the near-infrared bands over the lake was from the adjacent lands covered with green vegetation. The significance of these adjacency effects on atmospheric correction has been analyzed qualitatively, and potential efforts to improve the atmospheric correction algorithms are discussed. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessArticle

Evaluation of Sentinel-2/MSI Atmospheric Correction Algorithms over Two Contrasted French Coastal Waters

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Mohamed Abdelillah Mograne

Remote Sens. 2022, 14(5), 1099; https://doi.org/10.3390/rs14051099 - 23 Feb 2022

Cited by 10 | Viewed by 2963

Abstract

The Sentinel-2A and Sentinel-2B satellites, with on-board Multi-Spectral Instrument (MSI), and launched on 23 June 2015 and 7 March 2017, respectively, are very useful tools for studying ocean color, even if they were designed for land and vegetation applications. However, the use of [...] Read more.

The Sentinel-2A and Sentinel-2B satellites, with on-board Multi-Spectral Instrument (MSI), and launched on 23 June 2015 and 7 March 2017, respectively, are very useful tools for studying ocean color, even if they were designed for land and vegetation applications. However, the use of these satellites requires a process called “atmospheric correction”. This process aims to remove the contribution of the atmosphere from the total top of atmosphere reflectance measured by the remote sensors. For the purpose of assessing this processing, seven atmospheric correction algorithms have been compared over two French coastal regions (English Channel and French Guiana): Image correction for atmospheric effects (iCOR), Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (C2RCC), Sentinel 2 Correction (Sen2Cor), Polynomial-based algorithm applied to MERIS (Polymer), the standard NASA atmospheric correction (NASA-AC) and the Ocean Color Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART). The satellite-estimated remote-sensing reflectances were spatially and temporally matched with in situ measurements collected by an ASD FieldSpec4 spectrophotometer. Results, based on 28 potential individual match-ups, showed that the best performance processor is OC-SMART with the highest values for the total score S_tot (16.89) and for the coefficient of correlation R² (ranging from 0.69 at 443 nm to 0.92 at 665 nm). iCOR and Sen2Cor show the less accurate performances with total score S_tot values of 2.01 and 7.70, respectively. Since the size of the in situ observation platform can be significant compared to the pixel resolution of MSI onboard Sentinel-2, it can create bias in the pixel extraction process. Thus, to study this impact, we used different methods of pixel extraction. However, there are no significant changes in results; some future research may be necessary. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessArticle

Algorithm of Additional Correction of Level 2 Remote Sensing Reflectance Data Using Modelling of the Optical Properties of the Black Sea Waters

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Elena N. Korchemkina

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Daria V. Kalinskaya

Remote Sens. 2022, 14(4), 831; https://doi.org/10.3390/rs14040831 - 10 Feb 2022

Cited by 4 | Viewed by 1252

Abstract

Atmospheric correction of satellite optical data is based on an assessment of the optical characteristics of the atmosphere, such as the aerosol optical depth of the atmosphere and the spectral slope of its spectrum, the so-called Angstrom parameter. Inaccurate determination of these parameters [...] Read more.

Atmospheric correction of satellite optical data is based on an assessment of the optical characteristics of the atmosphere, such as the aerosol optical depth of the atmosphere and the spectral slope of its spectrum, the so-called Angstrom parameter. Inaccurate determination of these parameters is one of the causes of errors in the retrieval of the remote sensing reflectance spectra. In this work, the obtained large array of field and satellite data for the northeastern part of the Black Sea is used, including ship-based measurements of atmospheric characteristics and sea reflectance, MODIS Aqua/Terra and OLCI Sentinel-3 A/B Level 2 remote sensing reflectance and atmospheric data. The purpose of this study is to show the numerical differences between the atmospheric parameters measured from the surface level and from the satellite and demonstrate their relationship with the differences between in situ and satellite remote sensing reflectance. Based on the information received, we propose an algorithm for the additional correction of satellite Level 2 data that uses a two-parametric model of the Black Sea remote sensing reflectance as a first approximation. This method does not require any in situ information. It is shown that additional correction significantly reduces the discrepancy between in situ and retrieved remote sensing reflectance, especially in short-wave spectral bands. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessArticle

Consistency between Satellite Ocean Colour Products under High Coloured Dissolved Organic Matter Absorption in the Baltic Sea

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Remote Sens. 2022, 14(1), 89; https://doi.org/10.3390/rs14010089 - 25 Dec 2021

Cited by 6 | Viewed by 2352

Abstract

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua [...] Read more.

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua OC radiometric products were assessed using Baltic Sea in situ remote sensing reflectance (

R_{r s}

) from ferry tracks (Alg@line) and at two Aerosol Robotic Network for Ocean Colour (AERONET-OC) sites from April 2016 to September 2018. A range of atmospheric correction (AC) processors for OLCI-A were evaluated. POLYMER performed best with <23 relative % difference at 443, 490 and 560 nm compared to in situ

R_{r s}

and 28% at 665 nm, suggesting that using this AC for deriving Chl a will be the most accurate. Suomi-VIIRS and MODIS-Aqua underestimated

R_{r s}

by 35, 29, 22 and 39% and 34, 22, 17 and 33% at 442, 486, 560 and 671 nm, respectively. The consistency between different AC processors for OLCI-A and MODIS-Aqua and VIIRS products was relatively poor. Applying the POLYMER AC to OLCI-A, MODIS-Aqua and VIIRS may produce the most accurate

R_{r s}

and Chl a products and OC time series for the Baltic Sea. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessArticle

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

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Remote Sens. 2021, 13(21), 4267; https://doi.org/10.3390/rs13214267 - 23 Oct 2021

Cited by 8 | Viewed by 1613

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessArticle

Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

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Remote Sens. 2021, 13(10), 1927; https://doi.org/10.3390/rs13101927 - 14 May 2021

Cited by 2 | Viewed by 1785

Abstract

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. [...] Read more.

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessArticle

Atmospheric Correction of Satellite Optical Imagery over the Río de la Plata Highly Turbid Waters Using a SWIR-Based Principal Component Decomposition Technique

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Juan Ignacio Gossn

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Robert Frouin

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Ana Inés Dogliotti

Remote Sens. 2021, 13(6), 1050; https://doi.org/10.3390/rs13061050 - 10 Mar 2021

Cited by 1 | Viewed by 1694

Abstract

Estimating water reflectance accurately from satellite optical data requires implementing an accurate atmospheric correction (AC) scheme, a particularly challenging task over optically complex water bodies, where the signal that comes from the water prevents using the near-infrared (NIR) bands to separate the perturbing [...] Read more.

Estimating water reflectance accurately from satellite optical data requires implementing an accurate atmospheric correction (AC) scheme, a particularly challenging task over optically complex water bodies, where the signal that comes from the water prevents using the near-infrared (NIR) bands to separate the perturbing atmospheric signal. In the present work, we propose a new AC scheme specially designed for the Río de la Plata—a funnel-shaped estuary in the Argentine–Uruguayan border—highly scattering turbid waters. This new AC scheme uses far shortwave infrared (SWIR) bands but unlike previous algorithms relates the atmospheric signal in the SWIR to the signal in the near-infrared (NIR) and visible (VIS) bands based on the decomposition into principal components of the atmospheric signal. We describe the theoretical basis of the algorithm, analyze the spectral features of the simulated principal components, theoretically address the impact of noise on the results, and perform match-ups exercises using in situ measurements and Moderate Resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) imagery over the region. Plausible water reflectance retrievals were obtained in the NIR and VIS bands from both simulations and match-ups using field data—with better performance (i.e., lowest errors and offsets, and slopes closest to 1) compared to existing AC schemes implemented in the NASA Data Analysis Software (SeaDAS). Moreover, retrievals over images in the VIS and NIR bands showed low noise, and the correlation was low between aerosol and water reflectance spatial fields. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessArticle

Automatic Detection of Optical Signatures within and around Floating Tonga-Fiji Pumice Rafts Using MODIS, VIIRS, and OLCI Satellite Sensors

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Remote Sens. 2021, 13(3), 501; https://doi.org/10.3390/rs13030501 - 31 Jan 2021

Cited by 6 | Viewed by 3034

Abstract

An underwater volcanic eruption off the Vava’u island group in Tonga on 7 August 2019 resulted in the creation of floating pumice on the ocean’s surface extending over an area of 150 km². The pumice’s far-reaching effects from its origin in [...] Read more.

An underwater volcanic eruption off the Vava’u island group in Tonga on 7 August 2019 resulted in the creation of floating pumice on the ocean’s surface extending over an area of 150 km². The pumice’s far-reaching effects from its origin in the Tonga region to Fiji and the methods of automatic detection using satellite imagery are described, making it possible to track the westward drift of the pumice raft over 43 days. Level 2 Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-3 Ocean and Land Color Instrument (OLCI), and Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) imagery of sea surface temperature, chlorophyll-a concentration, quasi-surface (i.e., Rayleigh-corrected) reflectance, and remote sensing reflectance were used to distinguish consolidated and fragmented rafts as well as discolored and mesotrophic waters. The rafts were detected by a 1 to 3.5 °C enhancement in the MODIS-derived “sea surface temperature” due to the emissivity difference of the raft material. Large plumes of discolored waters, characterized by higher satellite reflectance/backscattering of particles in the blue than surrounding waters (and corresponding to either submersed pumice or associated white minerals), were associated with the rafts. The discolored waters had relatively lower chlorophyll-a concentration, but this was artificial, resulting from the higher blue/red reflectance ratio caused by the reflective pumice particles. Mesotrophic waters were scarce in the region of the pumice rafts, presumably due to the absence of phytoplanktonic response to a silicium-rich pumice environment in these tropical oligotrophic environments. As beach accumulations around Pacific islands surrounded by coral shoals are a recurrent phenomenon that finds its origin far east in the ocean along the Tongan trench, monitoring the events from space, as demonstrated for the 7 August 2019 eruption, might help mitigate their potential economic impacts. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Review

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Open AccessReview

Evolution of Ocean Color Atmospheric Correction: 1970–2005

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Howard R. Gordon

Remote Sens. 2021, 13(24), 5051; https://doi.org/10.3390/rs13245051 - 13 Dec 2021

Cited by 12 | Viewed by 2406

Abstract

Retrieval of water properties from satellite-borne imagers viewing oceans and coastal areas in the visible region of the spectrum requires removing the effect of the atmosphere, which contributes approximately 80–90% of the measured radiance over the open ocean in the blue spectral region. [...] Read more.

Retrieval of water properties from satellite-borne imagers viewing oceans and coastal areas in the visible region of the spectrum requires removing the effect of the atmosphere, which contributes approximately 80–90% of the measured radiance over the open ocean in the blue spectral region. The Gordon and Wang algorithm originally developed for SeaWiFS (and used with other NASA sensors, e.g., MODIS) forms the basis for many atmospheric removal (correction) procedures. It was developed for application to imagery obtained over the open ocean (Case 1 waters), where the aerosol is usually non-absorbing, and is used operationally to process global data from SeaWiFS, MODIS and VIIRS. Here, I trace the evolution of this algorithm from early NASA aircraft experiments through the CZCS, OCTS, SeaWiFs, MERIS, and finally the MODIS sensors. Strategies to extend the algorithm to situations where the aerosol is strongly absorbing are examined. Its application to sensors with additional and unique capabilities is sketched. Problems associated with atmospheric correction in coastal waters are described. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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Open AccessTechnical Note

A Multi-Band Atmospheric Correction Algorithm for Deriving Water Leaving Reflectances over Turbid Waters from VIIRS Data

by

Bo-Cai Gao

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Rong-Rong Li

Remote Sens. 2023, 15(2), 425; https://doi.org/10.3390/rs15020425 - 10 Jan 2023

Viewed by 661

Abstract

The current operational multi-band atmospheric correction algorithms implemented by NASA and NOAA for global remote sensing of ocean color from VIIRS (Visible Infrared Imaging Radiometer Suite) data are mostly based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. These [...] Read more.

The current operational multi-band atmospheric correction algorithms implemented by NASA and NOAA for global remote sensing of ocean color from VIIRS (Visible Infrared Imaging Radiometer Suite) data are mostly based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. These algorithms generally use two NIR bands, one centered near 0.75 μm and the other near 0.865 μm, and a band ratio method for deriving aerosol information. The algorithms work quite well over open ocean waters. However, water leaving reflectances over turbid coastal waters are frequently not derived. We describe here a spectrum-matching algorithm using shortwave IR (SWIR) bands above 1 μm for retrieving water leaving reflectances in the visible from VIIRS data. The SWIR bands centered near 1.24, 1.61, and 2.25 μm are used in a spectrum-matching process to obtain spectral aerosol information, which is subsequently extrapolated to the visible region for the derivation of water leaving reflectances of visible bands. We present retrieval results for four VIIRS scenes acquired over turbid waters. We demonstrate that the spatial coverages of our retrieving results can be improved significantly in comparison with those retrieved with the current NOAA operational algorithm. If our SWIR algorithm is implemented for operational data processing, the algorithm can potentially be complimentary to current NASA and NOAA VIIRS algorithms over turbid waters to increase spatial coverages. Full article

(This article belongs to the Special Issue Atmospheric Correction for Remotely Sensed Ocean Color Data)

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