Neural-variational algorithm adaptation from SeaWiFS to MODIS sensor for analysis of atmospheric and oceanic parameters

2021 ◽  
Author(s):  
Khassoum Correa ◽  
Eric Machu ◽  
Hervé Demarcq ◽  
Daouda Diouf
Keyword(s):  
Optical Thickness ◽  
Input Parameter ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
General Distribution ◽  
Chl A ◽  
Marine Food Chain ◽  
Leading Role ◽  
Production Models ◽  
Phytoplankton Communities

<p>Particularly interesting because of its socio-economic contribution, the Canary upwelling system encompasses a number of regions with very special characteristics. The wind that blow over this system induces a permanent upwelling off Mauritania and a seasonal upwelling in the south off Senegal, which boosts the development of phytoplankton. To refine the understanding of the phytoplankton in this region (its distribution, variability, response to physical forcings), we combine a number of tools and methods to arrive at a better estimate, and a better monitoring of the concentration of chlorophyll-a (Chl-a), an input parameter for primary production models. Remote sensing of ocean color has particularly interesting advantages, both in terms of global sampling and data acquisition frequency. This method is all the more interesting since ocean color algorithms can be adapted to reduce bias when standard methods have limitations. The regional ocean color algorithm called SOM-NV (Self-Organized Map-Neuro-variational) offers the advantage of making atmospheric correction in the presence of absorbent aerosols, especially desert dust, which sweeps this area permanently and which compels the standard algorithm to apply a mask when atmospheric optical thickness exceeds a threshold of 0.3. This contribution of SOM-NV in the process of atmospheric correction allowed us to 1 : obtain a better reflectance spectra, and as a consequence offer a better estimate of the Chl-a concentrations ; 2 : acquire a larger number of pixels by processing pixels with an optical thickness greater than 0.3 ; 3 : go beyond the general distribution towards the distribution of dominant groups according to the Physat spectral method. The synthesis of 16 years of data from the MODIS-Aqua sensor, allowed us to revisit the seasonality of Chl-a distribution and its cross-shore particularityand an extension towards the open sea which differs according to the season. The highest coastal values are measured in winter and spring, when upwelling intensifies, while the lowest values are measured in summer, when warm, nutrient-poor equatorial waters freplace upwelling waters along the Senegalese coast. This change in water masses impacts phytoplankton communities. According to the work of some authors, nanoplankton gradually replaces diatoms, known to be present during the upwelling season. This makes this region a particularly interesting zone for monitoring dominant groups of phytoplankton, knowing that the change in community impacts the upper levels of the marine food chain, with phytoplankton playing a leading role.</p><p>Keywords: Phytoplankton, ocean color, upwelling, atmospheric correction, dust</p>

scholarly journals Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing

2021 ◽  
Vol 13 (4) ◽  
pp. 675
Author(s):  
Afonso Ferreira ◽  
Vanda Brotas ◽  
Carla Palma ◽  
Carlos Borges ◽  
Ana C. Brito
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Phytoplankton Bloom ◽  
Ocean Color ◽  
Coastal Region ◽  
Coastal Areas ◽  
Ocean Color Remote Sensing ◽  
Chl A ◽  
Peak Biomass ◽  
Phytoplankton Communities

Phytoplankton bloom phenology studies are fundamental for the understanding of marine ecosystems. Mismatches between fish spawning and plankton peak biomass will become more frequent with climate change, highlighting the need for thorough phenology studies in coastal areas. This study was the first to assess phytoplankton bloom phenology in the Western Iberian Coast (WIC), a complex coastal region in SW Europe, using a multisensor long-term ocean color remote sensing dataset with daily resolution. Using surface chlorophyll a (chl-a) and biogeophysical datasets, five phenoregions (i.e., areas with coherent phenology patterns) were defined. Oceanic phytoplankton communities were seen to form long, low-biomass spring blooms, mainly influenced by atmospheric phenomena and water column conditions. Blooms in northern waters are more akin to the classical spring bloom, while blooms in southern waters typically initiate in late autumn and terminate in late spring. Coastal phytoplankton are characterized by short, high-biomass, highly heterogeneous blooms, as nutrients, sea surface height, and horizontal water transport are essential in shaping phenology. Wind-driven upwelling and riverine input were major factors influencing bloom phenology in the coastal areas. This work is expected to contribute to the management of the WIC and other upwelling systems, particularly under the threat of climate change.

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

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.

scholarly journals iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2

2021 ◽  
Vol 13 (4) ◽  
pp. 654
Author(s):  
Erwin Wolters ◽  
Carolien Toté ◽  
Sindy Sterckx ◽  
Stefan Adriaensen ◽  
Claire Henocq ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Negative Bias ◽  
Surface Reflectance ◽  
Temporal Consistency ◽  
Correction Algorithm ◽  
Land Surfaces ◽  
Relative Differences ◽  
Level 2

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR’s statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences < ±6%, except for bands Oa02 (Blue) and Oa21 (NIR), which is likely related to the reported OLCI “excess of brightness”. The intercomparison between iCOR and SYN L2 showed R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

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

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.

Effects of Earth curvature on atmospheric correction for ocean color remote sensing

2018 ◽  
Vol 209 ◽  
pp. 118-133 ◽  
Author(s):  
Xianqiang He ◽  
Knut Stamnes ◽  
Yan Bai ◽  
Wei Li ◽  
Difeng Wang
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Ocean Color Remote Sensing

Wave induced turbulence effect on oceanic biogeochemistry and study of ocean color response to changing wave climate

2021 ◽  
Author(s):  
Chinglen Meetei Tensubam ◽  
Alexander V. Babanin
Keyword(s):  
Net Primary Productivity ◽  
Mixed Layer Depth ◽  
Marine Ecosystem ◽  
Ocean Color ◽  
Ocean Waves ◽  
Wave Climate ◽  
Upper Ocean ◽  
P Value ◽  
Chl A ◽  
Wave Induced

&lt;p&gt;The role of surface ocean waves becomes substantial in the upper ocean layer mixing. Due to turbulence induced by the surface waves (both broken and unbroken waves), the upper ocean mixing is enhanced, and important upper ocean parameters are affected such as lowering of sea surface temperature (SST), deepening of mixed layer depth (MLD) and most interestingly, the changes in oceanic biogeochemistry. The main objective of this study is to analyze the effect of wave induced turbulence on oceanic biogeochemistry such as the supply and distribution of nutrients to tiny plants in the ocean called phytoplanktons, and how it affects their concentrations. Marine phytoplanktons formed the basis of marine ecosystem which accounts for about 45 percent of global net primary productivity and play an important part in global carbon cycle. The population of phytoplanktons depends mainly on nutrients (both micro and macro), availability of sunlight and grazing organisms. For this study, we use global coupled ocean-sea ice model ACCESS-OM2 with biogeochemical module called WOMBAT to estimate the effect of wave induced turbulence and study the difference between &amp;#8216;with waves&amp;#8217; and &amp;#8216;without waves&amp;#8217; effect on oceanic biogeochemistry. The same effect of wave induced turbulence on oceanic biogeochemistry are also studied by incorporating the change in wave climate such as increase in significant wave height and wind speed. From the investigation of merged satellite ocean color data from ESA&amp;#8217;s GlobColour project for the period of 23 years between 1997 and 2019, it was found that chlorophyll-a (Chl-a, an index of phytoplankton biomass) concentration showed increasing trend of 0.015 mg/m3 globally and 0.062 mg/m3 in the Southern Ocean (SO) for the study period with p-value less than 0.01. It was also found that most of the increasing trends are shown spatially in the open ocean and decreasing trend in the coastal regions during the study period.&lt;/p&gt;

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