Satellite Ocean Color Data Research Articles

Enhancing ocean color retrieval in sunglint regions: the efficacy of IPNOA over conventional atmospheric correction methods

Sunglint significantly impacts the extraction of ocean color information, particularly for sensors lacking tilt capabilities. Traditional atmospheric correction algorithms often fail to retrieve effective data in high-sunglint regions. The polynomial-based POLYMER method, applied to MERIS data, effectively addresses sunglint, although its accuracy decreases by about 15% in such conditions. To enhance data reliability in sunglint regions, we propose the Improved polynomial nonlinear optimization approach (IPNOA), a revision of the POLYMER atmospheric correction. IPNOA employs the QAA-RGR (quasi-analytical algorithm-red-green-bands-ratio) to refine the bio-optical ocean reflectance model. Additionally, due to the nonlinear optimization algorithm’s sensitivity to initial values, this study uses global 8-day average oceanic optical properties at 4 km resolution as the initial setting. The performance of IPNOA was initially evaluated using a synthetic dataset, with retrieved remote sensing reflectance (Rrs) closely matching the simulated Rrs across all wavelengths. The mean absolute percentage error (MAPE) remained below 1% for non-sunglint, moderate sunglint, and high sunglint conditions. Further analysis of in situ data revealed that IPNOA performs better, exceptionally at 412 nm, with a MAPE of 5.27% in sunglint regions. When processed by POLYMER, the dataset exhibited a MAPE of 68.47%. Finally, an analysis of global data from MODIS, VIIRS, and HY1C/D on July 15, 2022, showed good agreement among the three on a global scale. Above all, these results indicate that the IPNOA algorithm has strong potential for retrieving valid products in moderate, even high sunglint regions, offering practical benefits for expanding the spatial coverage of ocean color satellite data.

Characterization of the aerosol contribution to the top-of-atmosphere radiance for satellite ocean color retrievals

A method is proposed for characterization of the aerosol contribution to the top-of-atmosphere (TOA) radiance. The method is based on solving the problem of radiative transfer in the atmosphere-ocean system and expanding the solution in powers of the aerosol optical thickness τ a . We show that the linear term of the expansion is analytically expressed in terms of the bidirectional transmittance/reflectance of the aerosol-free Rayleigh atmosphere. A procedure is also proposed for successively extracting the terms of higher order in τ a from the data of the TOA radiance computation with the DISORT code. As analysis shows, the radiance expansion in τ a is not purely polynomial. Beginning from the quadratic term, the coefficients of the series expansion in powers of τ a become dependent logarithmically on τ a . The approach proposed enables us to reproduce analytically the τ a -dependence of the TOA radiance with controlled accuracy. We determine the expansion coefficients up to the cubic term inclusive and validate our results on the aerosol model embedded in NASA’s SeaDAS algorithm for aerosol loadings, representative for the Barents and Kara seas. In the visible and near-infrared spectral ranges, accounting for the terms up to a quadratic one is found to be sufficient for the atmospheric correction of satellite ocean color data typical for the Arctic region.

Bio-Optical Properties and Ocean Colour Satellite Retrieval along the Coastal Waters of the Western Iberian Coast (WIC)

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data.

Estimation of Phytoplankton Primary Productivity in Qinghai Lake Using Ocean Color Satellite Data: Seasonal and Interannual Variations

Estimation of primary production in Qinghai Lake is crucial for the aquatic ecosystem management in the northeastern Qinghai–Tibet Plateau. This study used the Vertically Generalized Production Model (VGPM) with ocean color satellite data to estimate phytoplankton primary productivity (PP) in Qinghai Lake during the non-freezing period from 2002 to 2023. Field data from 2018 and 2023 were used to calibrate and verify the model. The results showed a seasonal trend in chlorophyll-a and PP, with the lowest values in May and peaks from June to September. Qinghai Lake was identified as oligotrophic, with annual mean chlorophyl-a of 0.24–0.40 µg/L and PP of 40–369 mg C/m2/day. The spatial distribution of PP was low in the center of the lake and high near the shores and estuaries. An interesting periodic increasing trend in PP every 2 to 4 years was observed from 2002 to 2023. This study established a remote sensing method for PP assessment in Qinghai Lake, revealing seasonal and interannual variations and providing a useful example for monitoring large saline mountain lakes.

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study

This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c) the phytoplankton community structure. Global and regional algorithms were examined, along with a local algorithm tuned using the time series data. The global algorithm produced the largest uncertainties when compared with the in situ data, followed by the regional and local algorithms. Correlation coefficient for the local algorithm was 0.690 - a significant increase from regional’s 0.420 and global’s 0.042. Both the global and the regional algorithms exhibited systemic errors that inversely were related to chlorophyll-a concentration, while the local algorithm displayed some reduction in the systematic errors, highlighting the value of local in situ observations, for improving sub-regional and local algorithms for retrieval of chlorophyll-a concentration from satellite ocean colour data. While the mixed layer has not shown any direct correlation with the uncertainties, it may facilitate exceptionally strong vertical gradients in chlorophyll-a profiles after summer blooms that take role as the main source of high differences between satellite observations and surface chlorophyll-a concentration. As such, it is important to supplement satellite measurements with vertical profiles to ensure valid readings and exercise caution when dealing with data post-blooms. These instances occurred in less than 3% of all cases. Differences in the phytoplankton community structures have shown direct correlation to estimation error - Miozoa is associated with low error, Bacillariophyta with high error, while Phytoflagellates abundance tips the error between underestimation and overestimation.

Effects of the Earth curvature on Mie-scattering radiances at high solar-sensor geometries based on Monte Carlo simulations.

Given the importance of vector radiative transfer models in ocean color remote sensing and a lack of suitable models capable of analyzing the Earth curvature effects on Mie-scattering radiances, this study presents an enhanced vector radiative transfer model for a spherical shell atmosphere geometry by the Monte Carlo method (MC-SRTM), considering the effects of Earth curvature, different atmospheric conditions, flat sea surface reflectance, polarization, high solar and sensor geometries, altitudes and wavelengths. A Monte Carlo photon transport model was employed to simulate the vector radiative transfer processes and their effects on the top-of-atmosphere (TOA) radiances. The accuracy of the MC-SRTM was verified by comparing its scalar model outputs from Henyey-Greenstein (HG) phase function with the Kattawar-Adams model results, and the mean relative differences were less than 2.75% and 4.33% for asymmetry factors (g-values) of 0.5 and 0.7, respectively. The vector mode results of MC-SRTM for a spherical shell geometry with the Mie-scattering phase matrix were compared with the PCOART-SA model results (from Polarized Coupled Ocean-Atmosphere Radiative Transfer model based on the pseudo-spherical assumption), and the mean relative differences were less than 2.67% when solar zenith angles (SZAs) > 70 ∘ and sensor viewing zenith angles (VZAs) < 60 ∘ for two aerosol models (coastal and tropospheric models). Based on the MC-SRTM, the effects of Earth curvature on TOA radiances at high SZAs and VZAs were analyzed. For pure aerosol atmosphere, the effects of Earth curvature on TOA radiances reached up to 5.36% for SZAs > 70 ∘ and VZAs < 60 ∘ and reduced to less than 2.60% for SZAs < 70 ∘ and VZAs > 60 ∘. The maximum Earth curvature effect of pure aerosol atmosphere was nearly same (10.06%) as that of the ideal molecule atmosphere. The results also showed no statistically significant differences for the aerosol-molecule mixed and pure aerosol atmospheres. Our study demonstrates that there is a need to consider the Earth curvature effects in the atmospheric correction of satellite ocean color data at high solar and sensor geometries.

Spatiotemporal reconstruction of global ocean surface pCO2 based on optimized random forest

The partial pressure of ocean surface CO2 (pCO2) plays an important role in quantifying the carbon budget and assessing ocean acidification. For such a vast and complex ocean system as the global ocean, most current research practices tend to study the ocean into regions. In order to reveal the overall characteristics of the global ocean and avoid mutual influence between zones, a holistic research method was used to detect the correlation of twelve predictive factors, including chlorophyll concentration (Chlor_a), diffuse attenuation coefficient at 490 nm (Kd_490), density ocean mixed layer thickness (Mlotst), eastward velocity (East), northward velocity (North), salinity (Sal), temperature (Temp), dissolved iron (Fe), dissolved silicate (Si), nitrate (NO3), potential of hydrogen (pH), phosphate (PO4), at the global ocean scale. Based on measured data from the Global Surface pCO2 (LDEO) database, combined with National Aeronautics and Space Administration (NASA) Ocean Color satellite data and Copernicus Ocean reanalysis data, an improved optimized random forest (ORF) method is proposed for the overall reconstruction of global ocean surface pCO2, and compared with various machine learning methods. The results indicate that the ORF method is the most accurate in overall modeling at the global ocean scale (mean absolute error of 6.27μatm, root mean square error of 15.34μatm, R2 of 0.92). Based on independent observations from the LDEO dataset and time series observation stations, the ORF model was further validated, and the global ocean surface pCO2 distribution map of 0.25° × 0.25° for 2010 to 2019 was reconstructed, which is of significance for the global ocean carbon cycle and carbon assessment.

Variations and Depth of Formation of Submesoscale Eddy Structures in Satellite Ocean Color Data in the Southwestern Region of the Peter the Great Bay

The aim of this study was to develop methods for determining the most significant contrasts in satellite ocean color data arising in the presence of a submesoscale eddy structure, as well as to determine the corresponding depths of the upper layer of the sea where these contrasts are formed. The research was carried out on the example of the chain of submesoscale eddies identified in the Tumen River water transport area in the Japan/East Sea. MODIS Aqua/Terra satellite data of the remotely sensed reflectance (Rrs) and Rrs band ratio at various wavelengths, chlorophyll-a concentration, and, for comparison, sea surface temperature (sst) were analyzed. Additionally, the results of ship surveys in September 2009 were used to study the influence of eddy vertical structure on the obtained remote characteristics. The best characteristic for detecting the studied eddies in satellite ocean color data was the MODIS chlor_a standard product, which is an estimate of chlorophyll-a concentration obtained by a combination of the three-band reflectance difference algorithm (CI) for low concentrations and the band-ratio algorithm (OCx) for high concentrations. At the same time, the weakest contrasts were in sst data due to similar water heating inside and outside the eddies. The best eddy contrast-to-noise ratio according to Rrs spectra is achieved at 547 nm in the spectral region of seawater with maximum transparency and low relative errors of measurements. The Rrs at 678 nm and associated products may be a significant characteristic for eddy detection if there are many phytoplankton in the eddy waters. The maximum depth of the remotely sensed contrast formation of the considered eddy vertical structure was ~6 m, which was significantly less than the maximum spectral penetration depth of solar radiation for remote sensing, which was in the 14–17 m range. The results obtained can be used to determine the characteristics that provide the best contrast for detecting eddy structures in remotely sensed reflectance data and to improve the interpretation of remote spectral ocean color data in the areas of eddies activity.

Spring phytoplankton bloom phenology during recent climate warming on the Bering Sea shelf

High-latitude ecosystems commonly experience large phytoplankton blooms in spring, which provide basal resources for a range of grazers including zooplankton, benthic consumers and fishes. Variation of the timing and intensity of the spring phytoplankton bloom influences the degree of spatial and temporal overlap with consuming organisms. In the Bering Sea, blooms occur in association with ice retreat or as pelagic open-water blooms. In the last few years, the Bering Sea shelf experienced unprecedented and widespread warming. Understanding how those climatic changes subsequently influenced phytoplankton bloom dynamics is critical for evaluating Bering Sea food web responses. We estimate spring bloom timing and type (ice-associated, open-water) across the Bering Sea shelf using a combination of data from the long-running oceanographic moorings on the eastern shelf (M2, M4, M5, M8) and satellite ocean color data from 1998 to 2022. We assess 1) if the Bering Sea shelf experienced noticeable changes in spring bloom timing or type in the last two decades, 2) whether bloom phenology was accentuated by the recent warm period (2018–2019), and 3) what influences do winds and sea surface temperatures have on spring bloom timing and where are these variables influential? Our spatial analyses reinforce the conclusion that ice retreat is the dominant forcing factor of bloom timing for the Bering Sea shelf with some influence of wind for open-water blooms. Overall, bloom timing has not shifted seasonally with climate warming in the last two decades for most of the Bering Sea shelf, except for nearshore areas and mainly in the northern Bering Sea. In warm years when ice retreats early prior to the last week of March, blooms form in open waters and bloom timing on the middle and outer shelf is delayed when wind mixing is prevalent in ice-free springs. In recent years, open-water blooms were more widespread than previously experienced, and even occurred in the northern Bering Sea during 2018–2019. A progression to more open water blooms in a future warmer climate will influence the availability of basal resources for pelagic and benthic consumers.

Regional Algorithm of Quantitative Assessment of Cyanobacteria Blooms in the Eastern Part of the Gulf of Finland Using Satellite Ocean Color Data

Summer blooms of potentially harmful cyanobacteria are common in the Baltic Sea. Under clear sky conditions, the cyanobacterial blooms are easily detectable from space. We propose a new regional algorithm for cyanobacteria biomass estimation from satellite data in the eastern part of the Gulf of Finland, developed on the basis of field measurements in July–August 2012–2014. The multi-regression equation defines the cyanobacteria biomass as a function of the particle backscattering coefficient and chlorophyll concentration. The use of this equation provides the best performance in comparison to the linear one, which is reflected in both R2 and RMSE values (0.61 and 272 mg m−3 respectively). Unlike other algorithms, which determine only the cyanobacteria bloom area in the Baltic Sea, our algorithm allows the determination of both a bloom area and its intensity. Considering the algorithm errors, the bloom detection threshold has been shifted from the 200 mg m−3 determined by biologists to 300 mg m−3. Based on data from the MODIS-Aqua satellite ocean color scanner, the spatial and temporal variability of cyanobacterial blooms in this region from 2003 to 2022 was analyzed. Significant interannual variability of cyanobacteria biomass was revealed in the central part of the studied region, with minimum values in 2014 and maximum in 2004. The record bloom during the studied period occurred in July 2004 (the average cyanobacteria biomass was 780 mg m−3). The weakest blooms were observed in 2009, 2010, and 2014, when both in July and August, the bloom areas did not exceed 30% of the study region.

Marine big data-driven ensemble learning for estimating global phytoplankton group composition over two decades (1997–2020)

Accurate monitoring of the spatial-temporal distribution and variability of phytoplankton group (PG) composition is of vital importance in better understanding of marine ecosystem dynamics and biogeochemical cycles. While existing bio-optical algorithms provide valuable information, relying solely on satellite ocean color data remains insufficient to obtain high-precision retrieval of PG due to the intricate nature of the bio-optical signal and PG composition itself. An interdisciplinary approach combining advancements in machine learning with big data from ocean observations and simulations offers a promising avenue for more accurate quantification of PG composition. In this study, an ensemble learning approach, called the spatial-temporal-ecological ensemble (STEE) model, is developed to construct a robust prediction model for eight distinct phytoplankton groups (i.e., Diatoms, Dinoflagellates, Haptophytes, Pelagophytes, Cryptophytes, Green Algae, Prokaryotes, and Prochlorococcus). The proposed method introduces multiple data simultaneously: ocean color, physical oceanographic, biogeochemical, and spatial and temporal information. An ensemble strategy is applied to increase the performance of the model by merging three advanced machine-learning algorithms. The combined validation of multiple cross-validation (CV) strategies (i.e., standard, spatial block, and temporal block CVs) shows that the proposed STEE model has superior robustness and generalization ability. In addition, the analysis shows a high degree of concordance between the independent datasets and the modeled estimations for long-time series sites, indicating that the STEE model is capable of effectively monitoring long-term trends in phytoplankton group composition. Finally, the proposed model was utilized to retrieve global monthly phytoplankton group products (STEE-PG) over an extended period (September 1997 to May 2020), and comparisons demonstrated better rationality of spatio-temporal distribution than existing satellite-derived phytoplankton group products. Hence, this new model comprehensively integrates all kinds of observation data and yields long-term global PG products with high accuracy, which will enhance our understanding of the response of marine ecosystems to environmental and climate change.

Improved accuracy and spatial resolution for bio-logging-derived chlorophyll a fluorescence measurements in the Southern Ocean

The ocean’s meso- and submeso-scales (1-100 km, days to weeks) host features like filaments and eddies that have a key structuring effect on phytoplankton distribution, but that due to their ephemeral nature, are challenging to observe. This problem is exacerbated in regions with heavy cloud coverage and/or difficult access like the Southern Ocean, where observations of phytoplankton distribution by satellite are sparse, manned campaigns costly, and automated devices limited by power consumption. Here, we address this issue by considering high-resolution in-situ data from 18 bio-logging devices deployed on southern elephant seals (Mirounga leonina) in the Kerguelen Islands between 2018 and 2020. These devices have submesoscale-resolving capabilities of light profiles due to the high spatio-temporal frequency of the animals’ dives (on average 1.1 +-0.6 km between consecutive dives, up to 60 dives per day), but observations of fluorescence are much coarser due to power constraints. Furthermore, the chlorophyll a concentrations derived from the (uncalibrated) bio-logging devices’ fluorescence sensors lack a common benchmark to properly qualify the data and allow comparisons of observations. By proposing a method based on functional data analysis, we show that a reliable predictor of chlorophyll a concentration can be constructed from light profiles (14 686 in our study). The combined use of light profiles and matchups with satellite ocean-color data enable effective (1) homogenization then calibration of the bio-logging devices’ fluorescence data and (2) filling of the spatial gaps in coarse-grained fluorescence sampling. The developed method improves the spatial resolution of the chlorophyll a field description from ~30 km to ~12 km. These results open the way to empirical study of the coupling between physical forcing and biological response at submesoscale in the Southern Ocean, especially useful in the context of upcoming high-resolution ocean-circulation satellite missions.

Atmospheric correction of absorbing aerosols for satellite ocean color remote sensing over coastal waters

The atmospheric correction (AC) of satellite ocean color remote sensing, which is affected by the vertical distribution of absorbing aerosols (AA), is prone to large uncertainties in the waters with AA, especially for remote sensing reflectance (Rrs(λ)) retrievals in the blue and ultraviolet bands. In this study, a new AC algorithm called the Ocean Color–XGBoost with radiative transfer (RT) simulation Retrieval Tool (OC-XGBRT), which is based on extensive radiative simulation and machine learning model, was developed to retrieve the Rrs(λ) at the short wavelength bands of visible light in the presence of AA while considering their vertical distributions. The goal of the OC-XGBRT AC algorithm is to improve the quality of ocean color satellite products over inland and coastal waters with AA and heavy aerosol loadings. The images acquired by Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite (MODIS-Aqua) over six regions with the cases of AA were utilized to assess the performance of OC-XGBRT. The Rrs(λ) retrieved by OC-XGBRT were validated with in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and the SeaWiFS Bio-optical Archive and Storage (SeaBASS). The results showed that OC-XGBRT provided more accurate Rrs(412 nm) and Rrs(443 nm) products than the National Aeronautics and Space Administration (NASA) SeaWiFS Data Analysis System (SeaDAS) v7.5 standard near-infrared (NIR) AC algorithms, the POLYnomial-based approach established for the atmospheric correction of MERIS data (PLOYMER), and the Ocean Color-Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) over coastal and inland waters, with mean absolute percentage deviation (APD) and root mean square error (RMSE) values less than ∼36.9% and ∼ 5.5 × 10−4 sr−1, respectively. Application results of OC-XGBRT in six typical regions further demonstrated that the quality of retrieved Rrs(412 nm) and Rrs(443 nm) had substantially improved compared with the original satellite products under the condition of AA. Moreover, the implementation of the OC-XGBRT contributes to a significant enhancement in the spatial coverage of utilizable Rrs(λ) values at blue bands. Our proposed OC-XGBRT AC algorithm has the potential to process ocean color satellite data under the condition of AA.

Impact of Pacific Ocean heatwaves on phytoplankton community composition

Since 2013, marine heatwaves have become recurrent throughout the equatorial and northeastern Pacific Ocean and are expected to increase in intensity relative to historic norms. Among the ecological ramifications associated with these high temperature anomalies are increased mortality of higher trophic organisms such as marine mammals and seabirds, which are likely triggered by changes in the composition of phytoplankton, the base of the marine trophic food web. Here, we assimilated satellite ocean color data into an ocean biogeochemical model to describe changes in the abundance of phytoplankton functional types (PFTs) during the last decade’s (2010s) warm anomalies in the equatorial and northeastern Pacific Ocean. We find important changes associated with the “Blob” warm anomaly in the Gulf of Alaska, where reduced silica supply led to a switch in community composition from diatoms to dinoflagellates, resulting in an increase in surface ocean chlorophyll during the Summer–Fall of 2014. A more dramatic change was observed in the equatorial Pacific, where the extreme warm conditions of the 2016 El Niño resulted in a major decline of about 40% in surface chlorophyll, which was associated with a nearly total collapse in diatoms.

Bio-optical trends of waters around Valdés Biosphere Reserve: An assessment of the temporal variability based on 20 years of ocean color satellite data

Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions which aim to provide a high spatial resolution coverage of the Earth every few days. Bio-optical characteristics and their variation over time have been poorly studied in the Patagonian shelf. In this paper, we present the trends of time series analysis from satellite images that allows us to interpret the variations of bio-optical properties throughout time and their implications for planktonic organisms. The annual and seasonal trends of six variables were analyzed for two different gulfs, Nuevo and San José, in northern Patagonia from January 2003–December 2021. We present the dynamic temporal changes of chlorophyll a (Chla-sat), phytoplankton absorption (Ab_phy), detritus absorption as well as environmental features changes for the sea surface temperature (SST), depth of the euphotic zone (Z_eu) and photosynthetically active radiation (PAR). We found positive trends for SST, Ab_phy at 443 nm and PAR, but negative for Z_eu in Nuevo and San José gulfs. The positive trendlines for SST and negative for Z_eu suggest less availability of nutrients and light. These trends could change the bloom phenology and modify the phytoplankton community structure with implications for the entire food web and the ecosystem services in the VBR.

An Overview of the Special Issue on Seawater Bio-Optical Characteristics from Satellite Ocean Color Data

Satellite ocean color data provide an opportunity to effectively observe possible changes in the state of marine ecosystems [...]

Revised chlorophyll-a algorithms for satellite ocean color sensors in the East/Japan Sea

The East/Japan Sea (hereafter, East Sea (ES)) is a semi-enclosed marginal sea and considered as a miniature ocean with oceanic features (e.g., currents, eddies, fronts, upwelling) and dynamic marine environments. The ES is significantly influenced by the global ocean warming with fast increases in sea surface temperature during the last several decades. The climate-induced environmental changes can affect marine ecosystem and food webs in the ES. Therefore, more accurate estimates in phytoplankton biomass in the ES with high spatial and temporal resolutions are necessary to investigate phytoplankton production, fisheries, and carbon budget as well as biological responses to the environmental and climate changes. The NASA ocean chlorophyll-type (OCx) chlorophyll-a (Chl-a) algorithms based on blue to green band ratio for the satellite ocean color data in the global ocean have been evaluated using the in situ radiometric and Chl-a measurements in the ES. The model-derived Chl-a data using the OC Chl-a algorithms present systematic overestimation in the lower Chl-a concentrations between about 0.1 and 1.0 mg/m3 in the ES (with ∼40% uncertainties). New Chl-a algorithms for the ES has been derived from the 3rd polynomial regression fits of in situ Chl-a and maximum band ratios of remote sensing reflectance (Rrs). The revised Chl-a algorithms considerably improve the Chl-a data in the ES from the satellite ocean color data in the lower Chl-a concentrations. Thus, the new Chl-a algorithms can provide more accurate assessments in biological and biogeochemical processes such as primary productivity, phytoplankton phenology, phytoplankton dynamics, and carbon cycles.

Atmospheric correction algorithm based on the interpolation of ultraviolet and shortwave infrared bands.

Traditional atmospheric correction algorithms of ocean color remote sensing are mostly based on the extrapolation of aerosol scattering radiance from a reference band (near infrared, shortwave infrared, or ultraviolet bands), which inevitably leads to the problem of extrapolation error amplification with the increase of extrapolation spectral distance. In this study, we propose a practical interpolation-based algorithm (named the UV-SWIR-AC algorithm) using three reference bands (one ultraviolet and two shortwave infrared bands) for turbid waters. According to 6SV radiative transfer simulations with 15 customized aerosol types, we establish a fitting function framework for the aerosol scattering radiance in the wavelength range of 322-1643 nm. We apply the UV-SWIR-AC algorithm to the real satellite ocean color data observed by the Second-Generation Global Imager aboard the Global Change Observation Mission-Climate (SGLI/GCOM-C) and compare the retrieved remote sensing reflectance with the in-situ data from the observation platform of Hangzhou Bay in the East China Sea and typical bays. The results show that the UV-SWIR-AC algorithm can achieve a better performance than the traditional, extrapolation-based algorithm in turbid waters. Moreover, in the typical regional analysis, this new algorithm also demonstrates a high applicability. The UV-SWIR-AC algorithm should be helpful to improve the atmospheric correction accuracy for next-generation ocean color missions (e.g., NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission and China's Haiyang-1E/F (HY-1E/F) mission) with wider spectral ranges from the ultraviolet to shortwave infrared wavelengths.

Two-decade dynamics of MODIS-derived Secchi depth in Peninsula Malaysia waters

Secchi disk depth (Z sd ) is an essential environmental factor for studying ecosystem dynamics and biogeochemical processes in aquatic environments. Monitoring the long-term changes in water transparency is critical to predict the cascading impacts of climate change on marine ecosystems. We investigated the seasonal and interannual dynamics of Z sd in the eastern coast of Peninsula Malaysia (ECPM) and the Straits of Malacca (SoM) using a 21-year time series of MODIS ocean color data. To enable the reliable assessment of Z sd and its long-term variability, the performance of existing and regional algorithms was investigated using in-situ optical measurements collected during different monsoon seasons and in various environmental conditions. Our validation results showed that the existing Z sd algorithms performed adequately, but exhibited large errors, especially at relatively high Z sd values. On the other hand, the regional empirical algorithm based on a direct relationship between remote sensing reflectance and Z sd showed significant improvements by reducing the overall bias observed in existing Z sd schemes. The results indicated that the monthly climatological Z sd over the period 2000–2020 showed distinct patterns in different seasons. The ECPM waters had deeper Z sd than SoM waters. Maximum transparency usually occurred during the southwest and spring inter-monsoon and minimum transparency occurred during the northeast monsoon. Long-term seasonal evolution of Z sd reveals that widespread and persistent anomalies dominated the ECPM and SoM regions. Interannual trends indicate notable and complex changes in Z sd that were probably driven by variability in the ocean-atmosphere dynamics of Niño-Southern Oscillation (ENSO) and local environmental conditions. This study highlights the extensive analysis of Z sd status and its spatio-temporal pattern from space, which can significantly benefit long-term ocean monitoring efforts in the ECPM and SoM regions. • A new regional empirical algorithm was proposed to derive Z sd from satellite ocean color data. • The new algorithm showed the best performance compared to other existing models. • Strong seasonal and interannual variations of Z sd were observed in the ECPM and SoM. • Long-term variations in Z sd were associated with the ENSO cycle and environmental conditions.