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The spatiotemporal variability of chlorophyll-a (Chl-a) in the Arabian Sea (AS) and Persian Gulf (PG) has been widely studied, but long-term trends and influencing factors remain less understood due to data gaps. This study investigates Chl-a variability and trends from 2001 to 2019 using reconstructed MODIS-Terra monthly Chl-a and sea surface temperature (SST) data, employing the Data Interpolating Empirical Orthogonal Functions (DINEOF) method for high-accuracy reconstruction. Results reveal pronounced seasonal variability, with Chl-a peaks exceeding 3 mg m-3 during southwestern monsoons and ranging between 1–3 mg m-3 during northeastern monsoons, with the lowest levels in transitional months. Spatially, the highest Chl-a concentrations were observed in the western and northeastern AS, influenced by summer southwestern (SW) and winter northeastern (NE) monsoons. Trend analysis using Sen’s slope and the Mann-Kendall test indicates significant Chl-a declines (-0.002 to 0) along ASPG coasts, with slight increases (~0.005) in the southeastern AS and southern PG. Rising SST anomalies (SST_A) correlated with reduced Chl-a anomalies (Chl-a_A) in the western AS, while increased wind anomalies (Wind_A) enhanced Chl-a_A in the western AS but decreased it in the southern PG. These findings enhance our understanding of the complex environmental dynamics shaping the ASPG ecosystems.

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Global Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including the unique 380 nm band. After six years in orbit, a comprehensive evaluation of SGLI products and their temporal consistency is needed. Remote sensing reflectance (Rrs) is the primary product for monitoring water quality, forming the basis for deriving key oceanic constituents such as chlorophyll-a (Chla) and total suspended matter (TSM). The Japan Aerospace Exploration Agency (JAXA) provides Rrs products through two platforms, G-Portal and JASMES, each employing different atmospheric correction methodologies and assumptions. This study aims to evaluate the SGLI full-resolution Rrs products from G-Portal and JASMES at regional scales (Japan and East Asia) and assess G-Portal Rrs products globally between January 2018 and December 2023. The evaluation employs in situ matchups from NASA’s Aerosol Robotic Network-Ocean Color (AERONET-OC) and cruise measurements. We also assess the retrieval accuracy of two water quality indices, Chla and TSM. The AERONET-OC data analysis reveals that JASMES systematically underestimates Rrs values at shorter wavelengths, particularly at 412 nm. While the Rrs accuracy at 412 nm is relatively low, G-Portal’s Rrs products perform better than JASMES at shorter wavelengths, showing lower errors and stronger correlations with AERONET-OC data. Both G-Portal and JASMES show lower agreement with AERONET-OC and cruise datasets at shorter wavelengths but demonstrate improved agreement at longer wavelengths (530 nm, 565 nm, and 670 nm). JASMES generates approximately 12% more matchup data points than G-Portal, likely due to G-Portal’s stricter atmospheric correction thresholds that exclude pixels with high reflectance. In situ measurements indicate that G-Portal provides better overall agreement, particularly at lower Rrs magnitudes and Chla concentrations below 5 mg/m3. This evaluation underscores the complexities and challenges of atmospheric correction, particularly in optically complex coastal waters (Case 2 waters), which may require tailored atmospheric correction methods different from the standard approach. The assessment of temporal consistency and seasonal variations in Rrs data shows that both platforms effectively capture interannual trends and maintain temporal stability, particularly from the 490 nm band onward, underscoring the potential of SGLI data for long-term monitoring of coastal and oceanic environments.

This study identifies the characteristics of water regions with negative normalized water-leaving radiance (nLw(λ)) values in the satellite observations of the Second-generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission–Climate (GCOM-C) satellite. SGLI Level-2 data, along with atmospheric and in-water optical properties measured by the sun photometers in the AErosol RObotic NETwork-Ocean Color (AERONET-OC) from 26 sites globally, are utilized in this study. The focus is particularly on Tokyo Bay and the Ariake Sea, semi-enclosed water regions in Japan where previous research has pointed out the occurrence of negative nLw(λ) values due to atmospheric correction with SGLI. The study examines the temporal changes in atmospheric and in-water optical properties in these two regions, and identifies the characteristics of regions prone to negative nLw(λ) values due to atmospheric correction by comparing the optical properties of these regions with those of 24 other AERONET-OC sites. The time series results of nLw(λ) and the single-scattering albedo (ω(λ)) obtained by the sun photometers at the two sites in Tokyo Bay and Ariake Sea, along with SGLI nLw(λ), indicate the occurrence of negative values in SGLI nLw(λ) in blue band regions, which are mainly attributed to the inflow of absorptive aerosols. However, these negative values are not entirely explained by ω(λ) at 443 nm alone. Additionally, a comparison of in situ nLw(λ) measurements in Tokyo Bay and the Ariake Sea with nLw(λ) values obtained from 24 other AERONET-OC sites, as well as the inherent optical properties (IOPs) estimated through the Quasi-Analytical Algorithm version 5 (QAA_v5), identified five sites—Gulf of Riga, Long Island Sound, Lake Vanern, the Tokyo Bay, and Ariake Sea—as regions where negative nLw(λ) values are more likely to occur. These regions also tend to have lower nLw(λ) values at shorter wavelengths. Furthermore, relatively high light absorption by phytoplankton and colored dissolved organic matter, plus non-algal particles, was confirmed in these regions. This occurs because atmospheric correction processing excessively subtracts aerosol light scattering due to the influence of aerosol absorption, increasing the probability of the occurrence of negative nLw(λ) values. Based on the analysis of atmospheric and in-water optical measurements derived from AERONET-OC in this study, it was found that negative nLw(λ) values due to atmospheric correction are more likely to occur in water regions characterized by both the presence of absorptive aerosols in the atmosphere and high light absorption by in-water substances.

Abstract Satellites can help monitor harmful algal blooms in coastal regions. Methods have been developed using different satellite missions. However, it is necessary to develop a simple and useful method for discriminating harmful algal species that could be applied to multi-source satellite remote sensing reflectance spectra ( $${R}_{\mathrm{rs}}(\lambda ))$$ R rs ( λ ) ) . In this study, based on the bio-optical model, a backscattering indicator, b bp-index (green), was found to be useful for species identification (Karenia mikimotoi and Prorocentrum donghaiense) combined with the red tide index (RI) in water blooms in the East China Sea (ECS). The MODIS, GOCI, and MERIS data collected between 2004 and 2020 were consistent for bloom discrimination, determining that K. mikimotoi exhibited lower b bp-index (green) values than P. donghaiense. The classification of the blooms is based on the following criteria: $${R}_{\mathrm{rs}}(555)$$ R rs ( 555 ) < 0.014 sr−1 and RI > 2.8 and (1) b bp-index (green) > 1.2 $$\times {10}^{-3}$$ × 10 - 3 , classified as P. donghaiense blooms or (2) b bp-index (green)

In recent decades, red tides of non-toxic harmful algal blooms have frequently occurred in monsoon-influenced tropical areas, particularly the green form of Noctiluca scintillans (hereafter green Noctiluca). However, our understanding of the mechanism of red tide formation is hindered by spatial and temporal constraints of field data. In this study, we used moderate resolution imaging spectroradiometer (MODIS) ocean color data along with a locally developed algal-bloom classification algorithm to investigate the seasonal variability of dominant red tides across the upper Gulf of Thailand (uGoT). During our July 2018 observation, a super green Noctiluca bloom with extraordinarily high chl-a (>1,469 mg m-3) displayed a distinct spectral reflectance characteristic among red tides in blue-to-green and red-to-near infrared wavelengths. According to the distinctive in situ hyperspectral characteristics of uGoT algal blooms, we developed a classification algorithm for MODIS normalized at 488, 531, and 667 nm, which successfully discriminated green Noctiluca in three levels of blooms, namely, super (100%), strong (>80%), and weak (>40%), from other algal blooms (i.e., dinoflagellates, diatoms, cyanobacteria, and mixed red tide species) as well as non-bloom oceanic and coastal waters using MODIS data, as confirmed by uGoT red tide reports. Monthly MODIS-based discrimination composites from 2003 to 2021 revealed seasonal variability in the surface distribution and bloom frequency of green Noctiluca and other red tides according to the Asian monsoon seasons: the southwest monsoon (May–September) and the northeast monsoon (October–January of the following year). Green Noctiluca blooms occurred farther from the shore and estuaries than other red tides (dinoflagellates and cyanobacteria), and were much more frequent than other red tides between the Tha Chin and Chao Phraya River mouths during the non-monsoon period (February to April). The frequency and distribution of green Noctiluca blooms, as well as other algal blooms, varied with the monsoon season. By comparing MODIS-derived algal blooms to monsoon-induced factors (i.e., sea surface winds, precipitation, and river discharge), we present an unprecedented overview of the spatial and temporal dynamics of red tides throughout the uGoT under Asian monsoon conditions. This research contributes to our understanding of the impact of climate change on phytoplankton dynamics.

Satellite-derived chlorophyll data and Google Earth Engine (GEE) are used to introduce the first global map of coastal eutrophication potential as a GEE app. The prospects of the app being used as a global framework for eutrophication screening/monitoring are discussed.

This study investigated the seasonal variability of surface chlorophyll-a (chl-a) and the influence of the El Niño Southern Oscillation (ENSO) related to environmental parameters in the Gulf of Thailand (GoT). Monthly chl-a data from MODIS from 2002 to 2020 as well as sea surface temperature (SST), wind, precipitation, and river discharge were used in this analysis. Results from seasonal climatology and Empirical Orthogonal Function (EOF) described high chl-a concentration areas along the western to the southern coasts and near Ca Mau Cape during the northeast monsoon (NEM), and the upper GoT (UGoT), eastern coast, and the GoT mouth during the southwest monsoon (SWM), while low chl-a took place during the non-monsoon (NON). The GoT was divided into six areas based on the EOFs of chl-a, and then the correlation between chl-a variability and environmental parameters was also examined. The results suggested that chl-a in coastal and offshore areas were controlled by different mechanisms. Chl-a in coastal areas responded to precipitation and river discharge as well as the shoreward wind; meanwhile, chl-a in offshore areas correlated with SST and wind magnitude indicating the importance of water mixing and upwelling. The fluctuation of chl-a in each season related to ENSO was captured by EOF based on the seasonal anomaly. The influence of ENSO was strong during NEM and NON but minimal during SWM. El Niño/La Niña generally caused low/high precipitation and high/low SST. Moreover, El Niño/La Niña caused anomalously weak/strong wind during NEM contrary to during NON. Anomalous high/low chl-a were observed in shallow regions during El Niño/La Niña corresponding to strong/weak wind in NON. Abnormal wind under ENSO also created the shifting in the high chl-a area near Ca Mau Cape. These results have improved our understanding of monsoons and ENSO variabilities as the crucial drivers of changes in the tropical marine ecosystem in both seasonal and interannual time scales.

Vertical distribution of phytoplankton composition in the East China Sea (ECS) and Tsushima Strait (TS) was highly variable in the region where the Changjiang River diluted water (CDW), Kuroshio water (KW), and Tsushima water (TW) intersected. An in-situ multiple excitation fluorometer was used to obtain the high-resolution phytoplankton groups data from every meter of the water column. Sharp differences were noted in the distribution of phytoplankton groups in the CDW, KW, and TW. In the CDW, brown algae were generally present ~60% of all depths with exception of subsurface chlorophyll-a maximum (SCM), whereas cyanobacteria (>40%) and green algae plus cryptophytes (>40%) were found above and below the SCM, respectively. In TW, where chlorophyll a (CHL) was lower than in the CDW, brown algae predominated the water column (>60%) and SCM (>80%), except the surface layer where cyanobacteria dominated. In KW, a high fraction of cyanobacteria (>40%) extended up to 40 m, while brown and green algae dominated (>60%) the deeper waters below 40 m at western and eastern stations, respectively. These results can be further related to water property and nutrient concentration of the water masses in each region. This new data show that the in-situ multiple excitation fluorometer can be a powerful tool to estimate high-resolution vertical profiles of phytoplankton groups on a large scale in marine environments.

Predicting conversion of photosynthetic electron transport to inorganic carbon uptake rates (the so-called electron requirement for carbon fixation, KC) is central to the broad scale deployment of Fast Repetition Rate fluorometry (FRRf) for primary productivity studies. However, reconciling variability of KC over space and time to produce robust algorithms remains challenging, given the large number of factors that influence KC. We have previously shown that light appears to be a proximal driver of Kc in several ocean regions and we therefore examined whether and how light similarly regulated KC variability in the Arctic Ocean, during a summer cruise in 2016. Sampling transited ice-free and ice-covered waters, with temperature, salinity and Chl-a concentrations all higher for the ice-free than ice covered surface waters. Micro- and pico-phytoplankton generally dominated the ice-free and ice-covered waters, respectively. Values of KC, determined from parallel measures of daily integrated electron transport rates and 14C-uptake, were overall lower for the ice-covered vs. ice-free stations. As in our previous studies, KC was strongly linearly correlated to daily PAR (r = 0.68, n = 46, p < 0.001) and this relationship could be further improved (r = 0.84, n = 46, p < 0.001) by separating samples into ice-free (micro-phytoplankton dominated) vs. ice-covered (Nano- and Pico-phytoplankton dominated water. We subsequently contrasted the PAR-KC relationship form the Arctic waters with the previous relationships from the Ariake Bay and East China Sea and revealed that these various PAR-KC relationships can be systematically explained across regions by phytoplankton community size structures. Specifically, the value of the linear slope describing PAR-KC decreases as water bodies have an increasing fraction of larger phytoplankton. We propose that this synoptic trend reflects how phytoplankton community structure integrates past and immediate environmental histories and hence may be a better broad-scale predictor of KC than specific environmental factors such as temperature and nutrients. We provide a novel algorithm that may enable broad-scale retrieval of CO2 uptake from FRRf with knowledge of light and phytoplankton community size information.

We investigated the spatio-temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) associated with spring–neap tidal cycles in the Ariake Sea, Japan. Our study relied on significantly improved, regionally-tuned datasets derived from the ocean color sensor Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua over a 16-year period (2002–2017). The results revealed that spring–neap tidal variations in Chl-a and TSM within this macrotidal embayment (the Ariake Sea) are clearly different regionally and seasonally. Generally, the spring–neap tidal variability of Chl-a in the inner part of the Ariake Sea was controlled by TSM for seasons other than summer, whereas it was controlled by river discharge for summer. On the other hand, the contribution of TSM to the variability of Chl-a was not large for two areas in the middle of Ariake Sea where TSM was not abundant. This study demonstrates that ocean color satellite observations of Chl-a and TSM in the macrotidal embayment offer strong advantages for understanding the variations during the spring–neap tidal cycle.

The raphidophyte Chattonella spp. and diatom Skeletonema spp. are the dominant harmful algal species of summer blooms in Ariake Sea, Japan. A new bio-optical algorithm based on backscattering features has been developed to differentiate harmful raphidophyte blooms from diatom blooms using MODIS imagery. Bloom waters were first discriminated from other water types based on the distinct spectral shape of the remote sensing reflectance R r s (λ) data derived from MODIS. Specifically, bloom waters were discriminated by the positive value of Spectral Shape, SS (645), which arises from the R r s (λ) shoulder at 645 nm in bloom waters. Next, the higher cellular-specific backscattering coefficient, estimated from MODIS data and quasi-analytical algorithm (QAA) of raphidophyte, Chattonella spp., was utilized to discriminate it from blooms of the diatom, Skeletonema spp. A new index b b p − i n d e x ( 555 ) was calculated based on a semi-analytical bio-optical model to discriminate the two algal groups. This index, combined with a supplemental Red Band Ratio (RBR) index, effectively differentiated the two bloom types. Validation of the method was undertaken using MODIS satellite data coincident with confirmed bloom observations from local field surveys, which showed that the newly developed method, based on backscattering features, could successfully discriminate the raphidophyte Chattonella spp. from the diatom Skeletonema spp. and thus provide reliable information on the spatial distribution of harmful blooms in Ariake Sea.

Riverine nutrients act in concert with local hydrographic conditions to create distinct ecological niches for phytoplankton communities across river-ocean continuums. Here we compare two of the world’s largest river-ocean systems, the Amazon River Plume (ARP) which outflows into the Western Tropical North Atlantic and the Changjiang Diluted Water (CDW) which empties into the East China Sea to show how distinctly different N: P supply ratios of their source waters, shape phytoplankton communities along the river-ocean continuum. Sampling in the relatively unpolluted surface waters of the ARP during peak river discharge revealed that phytoplankton communities along the river-ocean continuum were strongly limited by Dissolved Inorganic Nitrogen (DIN, nitrate plus nitrite) which was low or beyond detectable, while Dissolved Inorganic Phosphorous (DIP, phosphate) and Silica were not limiting. The resulting low N:P supply ratio allowed diazotrophs to co-exist with non-diazotrophs. Diatom-Diazotroph Associations (DDAs) such as Hemiaulus hauckii-Richelia proliferated, while in the oligotrophic oceanic waters, Trichodesmium spp. thrived. In contrast, in the CDW, anthropogenic nitrogen inputs from human pressures in the Changjiang River system has led to a system where the changing supply rate of the single nutrient (DIP) is responsible for the interannual variability seen in the phytoplankton community structure of the CDW. During years of low discharge, DIP limitation can be ameliorated by on-shelf upwelling of DIP rich Kuroshio Intermediate Waters leading to domination of diatoms and dinoflagellates. Conversely, during years of heavy discharge, the westward flowing CDW plume was severely DIP limited, probably because water column stratification dampened upwelling of subsurface waters. The consequent DIP limitation led to the proliferation of small phytoplankton such as Chlorophytes and Cyanobacteria. The absence of diazotrophs in the CDW, leads us to hypothesize that river-ocean continuums, whose source waters are heavily impacted by anthropogenic activities and with high nitrate concentrations often substantially in excess of Redfield ratios, may not support diatoms offshore on account of DIP limitation nor diazotrophy because of excess DIN.

The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335.

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical Kd(λ) retrieval model (SAKM) and Jamet's neural network model (JNNM), and then develop a new neural network Kd(λ) retrieval model (NNKM). Based on the comparison of Kd(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in Kd(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The Kd(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces

Despite extensive research on chlorophyll-a (Chla) concentration retrieval methods from remote sensing reflectance (Rrs, sr−1) data, there remains a need for more reliable Chla retrieval techniques. In this study, we introduce a deep learning approach based on a 1D convolutional neural network (1D CNN) architecture. In addition, we provide a new method of representing the Rrs as a sequential vector. The model architecture targets the Sentinel-2 MultiSpectral Instrument (MSI) sensor. The proposed model was trained and tested on simulated and in situ data collected from broad trophic states in Japan and Vietnam waters with Chla concentrations ranging from 0.02 to 148.26 mg/m³. The proposed model was evaluated against well-accepted state-of-the-art methods: ocean color three-band (OC3), ocean color index (OCI), two-band ratio, Blend, and a neural network model with a mixture density network. The evaluation shows that the proposed method outperforms other methods with a 7.48–38.02% reduction in root mean squared error (RMSE) and an 11.50–39.17% lower mean absolute error (MAE) than the other methods. The promising performance of the proposed model suggests that more attention should be paid to the domain of sequence modeling for Rrs and the architecture of 1D CNN. [ABSTRACT FROM AUTHOR]

The distribution of the phytoplankton size class (PSC) and the variations in the size classes are key to understanding ocean biogeochemical processes and ecosystem. Remote sensing of the PSC in the East China Sea (ECS) remains a challenge, although many PSC algorithms have been developed. Here based on a local dataset from the ECS, a regional model was tuned to infer the PSC from the spectral features of normalized phytoplankton absorption (αph) using a principal component analysis approach. Before applying the refined model to the real MODIS (Moderate Resolution Imaging Spectroradiometer) data, reconstructing satellite Rrs at 412 and 443 nm becomes critical through modeling them from Rrs between 469 and 555 nm using multiple regression analyses. Satellite-derived PSC values compare well with those derived from pigment composition, which demonstrates the potential of satellite ocean color data to estimate PSC distributions in the ECS from space. The refined model was applied to αph derived from Rrs observations collected by MODIS over the ECS from 2003 to 2016. Seasonal images show that the PSC distribution was heterogeneous in both temporal and spatial scales. Seasonal variations of the PSC in the ECS were probably affected by a combination of the water column stability, upwelling, sea surface temperature, and the Kuroshio Current. Additionally, human activity and riverine discharge may also influence the PSC distributions in the ECS, especially in coastal regions. [ABSTRACT FROM AUTHOR]

Abstract  Remote sensing reflectance [R rs(λ)] and absorption coefficients of red tides were measured in Isahaya Bay, southwestern Japan, to investigate differences in the optical properties of red tide and non-red tide waters. We defined colored areas of the sea surface, visualized from shipboard, as “red tides”. Peaks of the R rs(λ) spectra of non-red tide waters were at 565 nm, while those of red tides shifted to longer wavelengths (589 nm). The spectral shape of R rs(λ) was close to that of the reciprocal of the total absorption coefficient [1/a(λ)], implying that the R rs(λ) peak is determined by absorption. Absorption coefficients of phytoplankton [a ph(λ)], non-pigment particles and colored dissolved organic matter increased with increasing chlorophyll a concentration (Chl a), and those coefficients were correlated with Chl a for both red tide and non-red tide waters. Using these relationships between absorption coefficients and Chl a, variation in the spectrum of 1/a(λ) as a function of Chl a was calculated. The peak of 1/a(λ) shifted to longer wavelengths with increasing Chl a. Furthermore, the relative contribution of a ph(λ) to the total absorption in red tide water was significantly higher than in non-red tide water in the wavelength range 550–600 nm, including the peak. Our results show that the variation of a ph(λ) with Chl a dominates the behavior of the R rs(λ) peak, and utilization of R rs(λ) peaks at 589 and 565 nm may be useful to discriminate between red tide and non-red tide waters by remote sensing. [ABSTRACT FROM AUTHOR]

Abstract  The Vertically Generalized Production Model (VGPM) was verified by the primary production data of the Sagami Bay, Japan. The VGPM with open ocean parameters including P B opt , maximum primary production per unit of chlorophyll a in the water column, explained only 40% of the variability of integrated primary production. Formulations of the open ocean P B opt showed no correlation with in situ P B opt . Adjustment of the parameters of chlorophyll a and temperature dependent P B opt improved the estimation of integrated primary production to 47% of the variation. Vertical integration parameters of VGPM also have to be adjusted to improve the estimation. Integrated primary production calculated with a stronger light dependency and with the adjusted P B opt model can explain 74% of the variation. This model was used to estimate primary production of the Sagami Bay during 2003 with satellite data. In situ measurements on cloudy days indicate that the use of satellite data from sunny days only overestimates primary production. [ABSTRACT FROM AUTHOR]

The first CO2 exposure experiments on several species of pelagic copepods inhabiting surface and deep layers in the western North Pacific were conducted. Living organisms were collected from two layers between the surface and 1,500 m between latitudes of 11 and 44°N, and they were exposed aboard ship to various pCO2 up to about 98,000 μatm. Mortality of copepods from both shallow and deep layers in subarctic to subtropical regions increased with increasing pCO2 and exposure time. Deep-living copepods showed higher tolerance to pCO2 than shallow-living copepods. Furthermore, deep-living copepods from subarctic and transitional regions had higher tolerances than the subtropical copepods. The higher tolerances of the deep-living copepods from subarctic and transitional regions may be due to the adaptation to the natural pCO2 conditions in the subarctic ocean. [ABSTRACT FROM AUTHOR]

High resolution SeaWiFS data was used to detect red tide events that occurred in the Ariake Sound, Japan, a small embayment known as one of the most productive areas in Japan. SeaWiFS chlorophyll data clearly showed that a large red tide event, which damaged seaweed (Nori) cultures, started early in December 2000 in Isahaya Bay, expanded to the whole sound and persisted to the end of February 2001. The monthly average of SeaWiFS data from May 1998 to December 2001 indicated that the chlorophyll peaks appeared twice a year, in early summer and in fall, after the peaks of rain and river discharge. The SeaWiFS data showed that the red tide event during 2000–2001 winter was part of the fall bloom; however, it started later and continued significantly longer than other years. Satellite ocean color data is useful to detect the red tide; however the algorithms require improvement to accurately estimate chlorophyll in highly turbid water and in red tide areas. [ABSTRACT FROM AUTHOR]

The spatial and seasonal variability of primary production in the Japan Sea from 1998 to 2002 was estimated using a satellite primary production model. A size-fractionated primary production model was validated by in situ primary production data measured in the Japan Sea. Estimated primary production and in situ primary production showed a good positive correlation. Estimated primary production showed spatial variability. Annual primary production levels were 170, 161, 191 and 222 gC m−2year−1 at the Russian coast, in the middle of the Japan Basin, the southeastern area and the southwestern area, respectively. It was higher to the south around 40°N than to the north, and higher in the western area than in the eastern one. Peaks of primary production appeared twice, in spring and fall, in the southern area, while a single peak appeared in the northern area. Primary production along the Russian coast was higher than in other areas during summer. The spring bloom contributed 42% to the annual primary production in these four areas. Furthermore, estimated primary production showed an interannual variability that was largest in spring. Primary production in fall also showed interannual variability, especially in the middle of the Japan Basin and the southwestern area. This corresponded mainly to the size of the phytoplankton bloom in each year. Winter convection by wind and the depth of nutrient-rich, cold subsurface water underlying the Tsushima Current may contribute to the nutrient supply to upper layer and interannual variations of primary production in spring. [ABSTRACT FROM AUTHOR]

Abstract In order to estimate primary production from ocean color satellite data using the Vertical Generalized Production Model (VGPM; Behrenfeld and Falkowski, 1997), we propose a two-phytoplankton community model. This model is based on the two assumptions that changes in chlorophyll concentration result from changes of large-sized phytoplankton abundance, and chlorophyll specific productivity of phytoplankton tends to be inversely proportional to phytoplankton size. Based on the analysis of primary production data, PoptB, which was one parameter in the VGPM, was modeled as a function of sea surface temperature and sea surface chlorophyll concentration. The two-phytoplankton community model incorporated into the VGPM gave good estimates in a relatively high productive area. Size-fractionated primary production was estimated by the two-phytoplankton community model, and PoptB of small-sized phytoplankton was 4.5 times that of large-sized phytoplankton. This result fell into the ranges observed during field studies. [ABSTRACT FROM AUTHOR]

Abstract An algorithm was evaluated for estimating primary production in the Kuroshio front of the East China Sea. The algorithm involves three principal inputs: chlorophyll-a (Chl-a) vertical profile, water column photosynthetic parameter and underwater irradiance parameter. We construct empirical models for estimating those three inputs from sea surface variables accessible by means of satellite remote sensing. Types of Chl-a vertical profile were closely associated with water mass features, and sea surface temperature seemed to be a potential variable to distinguish profile types. The consequence of the deep Chl-a maximum (DCM) presence was investigated by comparing the vertically non-uniform Chl-a profile against the uniform Chl-a profile in terms of the integrated biomass and primary production estimates. Since the DCM in the frontal region occurred in a relatively shallow layer with relatively high light level, allowing intensive photosynthetic processes to take place in the DCM layer. In contrast, as the DCM in the Kuroshio region occurred in the low-light, deep layer, it contributed less to the integrated primary production. We inferred that the DCM formation is more important in the frontal region than in the Kuroshio region, as they potentially contributed within 30.9% (±9.1%) and 20.9% (±5.4%) to the integrated primary production in the frontal and the Kuroshio regions, respectively. Such different degrees in the DCM contribution was then revealed by the higher integrated primary production in the frontal region than in the Kuroshio region. [ABSTRACT FROM AUTHOR]