Satellite Ocean Color Research Articles

Signals of intense primary production in response to Ulva prolifera bloom in the Yellow Sea during summer 2021

The variability in changing climate has been predicted to lead to significant changes in the world's oceans primary productivity. Recent years have experienced more intense algal blooms during the spring-summer months in many coastal waters. Here we present results from the analysis of satellite ocean color products from MODIS-Terra datasets during June 2 to July 20, 2021, corresponding to the most intense period of the Enteromorpha (Ulva) prolifera algae (U. prolifera) bloom in the Yellow Sea. Because the studies on sea surface temperature (SST) variability over the U. prolifera bloom have not yet been fully explored by satellite measurements in the offshore waters of Qingdao, China, we analyzed the MODIS SST and their relationship with the U. prolifera blooms in the 2021 summer months. Analysis of SST showed a variation in the range of 18–24 °C in near-shore waters in response to the bloom of green algae. We find that a significant thermal front with a gradient magnitude of 0.03 °C/km was developed over the shelf and offshore regions during the algal bloom period. The observed changes in thermal fronts are driven by high-wind speed and Ekman pumping-velocity processes. We concluded that the changes in SSTs with a range of 20–23 °C, high-magnitude thermal fronts and wind speed, and Ekman pumping velocity resulted in the intensification of the largest bloom of U. prolifera in the Yellow Sea during the study period.

Attribution and Predictability of Climate‐Driven Variability in Global Ocean Color

AbstractFor over two decades, satellite ocean color missions have revealed spatio‐temporal variations in marine chlorophyll. Seasonal cycles and interannual changes of the physical environment drive the nutrient and chlorophyll variations. In order to identify contributions of seasonal and interannual components on chlorophyll, the present study investigates total chlorophyll variance (TCV) of a 24 year records (September 1997 to December 2021) across satellite generations. First‐order contributions of the seasonal cycle in the mid‐latitude (25°–35°) oceans in the Northern and Southern Hemispheres explain 59.5% and 69.9% of TCV, respectively. In contrast, the contribution of seasonal cycle only explain 30.9% in the tropical oceans (20°N–20°S). Both seasonal cycle‐ and climate‐driven variability (26.3%) explain 57.2% on TCV in the tropical oceans. A multiple linear regression model was forced by instantaneous and delayed effects of oceanic memory of eight climate indices based on sea surface temperature anomalies to reconstruct chlorophyll anomalies. Delayed climate effects generally boost the anomaly correlation coefficients (ACC) between the observed and reconstructed chlorophyll timeseries (ACC skills: 0.64 to 0.72 in the Indian Ocean, 0.74 to 0.82 in off‐equatorial Northern Pacific, and 0.58 to 0.71 in the off‐equatorial Southern Pacific). Such delayed climate effects provide a source of predicted chlorophyll ACC (ACC_predic) skills one season ahead in some ocean regions (ACC_predic skill: 0.63 in the overall tropical ocean, 0.67 in the tropical Pacific, and 0.60 in the Indian Ocean). The attribution of chlorophyll variability indicates promising avenues for improving marine ecosystem predictions with Earth system models by incorporating delayed climate effects.

Threshold indicators of primary production in the north-east Atlantic for assessing environmental disturbances using 21 years of satellite ocean colour

Primary production (PP) is highly sensitive to changes in the ecosystem and can be used as an early warning indicator for disturbance in the marine environment. Historic indicators of good environmental status of the north-east (NE) Atlantic and north-west (NW) European Seas suggested that daily PP should not exceed 2–3 g C m−2 d−1 during phytoplankton blooms and that annual rates should be <300 g C m−2 yr−1. We use 21 years of Copernicus Marine Service (CMEMS) Ocean Colour data from September 1997 to December 2018 to assess areas in the NE Atlantic with similar peak, climatology, phenology and annual PP values. Daily and annual thresholds of the 90th percentile (P90) of PP are defined for these areas and PP values above these thresholds indicate disturbances, both natural and anthropogenic, in the marine environment. Two case studies are used to test the validity and accuracy of these thresholds. The first is the eruption of the volcano Eyjafjallajökull, which deposited large volumes of volcanic dust (and therefore iron) into the NE Atlantic during April and May 2010. A clear signature in both PP and chlorophyll-a (Chl a) was evident from 28th April to 6th May and from 18th to 27th May 2010, when PP exceeded the PP P90 threshold for the region, which was comparatively more sensitive than Chl a P90 as an indicator of this disturbance. The second case study was for the riverine input of total nitrogen and phosphorus, along the Wadden Sea coast in the North Sea. During years when total nitrogen and phosphorus were above the climatology maximum, there was a lag signature in both PP and Chl a when PP exceeded the PP P90 threshold defined for the study area which was slightly more sensitive than Chl a P90. This technique represents an accurate means of determining disturbances in the environment both in the coastal and offshore waters in the NE Atlantic using remotely sensed ocean colour data.

Satellite retrieval of oceanic particulate organic nitrogen concentration

Over the past several decades, satellite ocean color remote sensing has greatly improved our understanding of the biogeochemical properties of the global ocean. Particulate organic nitrogen (PON) refers to the nitrogen contained in biological or other debris particles, and it plays important roles in the ecosystem functions and biogeochemical processes of the marine biology. However, few studies have focused on the satellite retrieval of oceanic PON concentrations. With an attempt to fill the gap, this study aimed to explore the feasibility of retrieving oceanic PON concentrations from remote sensing data, determine the bio-optical proxies for satellite PON retrievals, and develop satellite oceanic PON retrieval models for the global ocean. In situ PON data collected over the global ocean and Moderate-resolution Imaging Spectroradiometer (MODIS) Level-3 products were used. Three different types of models were tested: (1) apparent optical property (AOP)-based models, 2) inherent optical property (IOP)-based models, and 3) biological property-based models. Results showed that ocean color remote sensing could be used for oceanic PON concentration retrieval in the global ocean, and AOP-based models calibrated produced better fitting performance than the other two types; models based on blue-to-green band ratio (BG) and normalized difference nitrogen index (NDNI) produced comparable and better fitting and validation performance; and IOP-based and biological property-based models produced lower but also acceptable performance. With the PON models developed, the monthly variations of PON concentrations in the global ocean were also explored. In further studies, PON models will be used to explore oceanic PON spatiotemporal variations and the underlying driving forces.

Using geostationary satellite ocean color data and superpixel to map the diurnal dynamics of water transparency in the eastern China seas

Polar-orbiting ocean color satellites can monitor daily to interannual variations in water transparency (or Secchi disk depth, SDD) from regional to global oceans. However, diurnal variations in SDD of coastal oceans remain poorly understood. Based on the bio-optical SDD algorithm, we retrieved the SDD products of hourly observations using the Geostationary Ocean Color Imager (GOCI) from 2011 to 2020 in the eastern China seas. The determination coefficient (R2) between the SDD product and the in situ dataset is 0.93, with a root mean squared error (RMSE) of 0.86 m. Based on the pixel-level tempo-spatial analysis, superpixel image segmentation retrieved by a simple linear iterative clustering algorithm (SLIC) was applied to classify the SDD product. The reconstruction SDD superpixel products not only match the spatial distribution well but can also more clearly express the spatial gradient. The percentage of the diurnal change in transparency was high in the nearshore (∼10 %), medium in transitional waters (∼5%), and low in offshore waters (∼3%). Finally, we found a significant negative correlation between SDD and wind speed (R2 = 0.65) and a significantly positive correlation between diurnal change range (DCR) and wind speed (R2 = 0.62). In contrast, sea surface temperature (SST) was positively correlated with SDD (R2 = 0.72) but significantly negatively correlated with DCR (R2 = 0.80). These results provide a basis for studying diurnal SDD changes in highly dynamic waters.

Data-Free Area Detection and Evaluation for Marine Satellite Data Products

The uncertainty verification of satellite ocean color products and the bias analysis of multiple data are both indispensable in the evaluation of ocean color products. Incidentally, ocean color products often have missing information that causes the methods mentioned above to be difficult to evaluate these data effectively. We propose an analysis and evaluation method based on data-free area. The objective of this study is to evaluate the quality of ocean color products with respect to information integrity and continuity. First, we use an improved Spectral Angle Mapper, also called ISAM. It can automatically obtain the optimal threshold value for each class of objects. Then, based on ISAM, we perform spectral information mining on first-level Yellow Sea and Bohai Sea data obtained from the Geostationary Ocean Color Imager (GOCI), Moderate Resolution Imaging Spectroradiometer (MODIS) and Ocean and Land Color Instrument (OLCI). In this manner, quantitative results of information related to data-free areas of ocean data products are obtained. The findings indicate that the product data of OLCI are optimal with respect to both completeness and continuity. GOCI and MODIS have striking similarities in their quantitative or visualization results for both evaluation metrics. Moreover, a concomitant phenomenon of ocean-covered objects is apparent in the data-free area with temporal and spatial distribution characteristics. The two characteristics are subsequently explored for further analysis. The evaluation method adopted in this study can help to enrich the content of ocean color product evaluation, facilitate the research of cloud detection algorithms and further understand the composition of the data-free regional information of marine data products. The method proposed in this study has a wide application value.

Oil spills in China Seas revealed by the national ocean color satellites

<p indent="0mm">Oil spill is one of the key targets in marine environment monitoring. In the process of weathering (e.g., spreading, drift, emulsification, evaporation, dispersion, and biological degradation), various weathered oils with different visual characteristics can form, such as non-emulsified oil slicks, water-in-oil (WO) emulsions and oil-in-water (OW) emulsions. Timely and accurate information about the spatial coverage, concentration, and thickness of various oil types is important for the recovery and cleanup. Satellite observation can serve as an effective technique for the detection and monitoring of spilled oils. Synthetic aperture radar (SAR) and optical remote sensing are the most-frequently used techniques. SAR provides data under all-weather conditions, but it is often restricted to presence/absence detection without classifying oil types. Passive optical remote sensing is another popular tool as it is able to classify different weathered oils and quantify the thickness, concentration, or volume of them. The operational monitoring of marine oil spills via remote sensing requires optical sensors for wide coverage, high spatial resolution, high signal-to-noise ratio (SNR), large radiative dynamic range, and short revisit period. Haiyang-1C (HY-1C) and Haiyang-1D (HY-1D), launched on 7 September 2018 and 11 June 2020, are the first operational ocean color satellites of China. They are equipped with five sensors, including Chinese ocean color and temperature scanner (COCTS), coastal zone imager (CZI), ultraviolet imager (UVI), calibration spectrometer, and automatic identification system. The integrated imaging system composed of HY-1C/D sensors can provide more frequent observations. Particularly, the CZI sensors onboard, which have a spatial resolution of <sc>50 m</sc> with high SNR, and ensure double coverage in China Seas every three days, have demonstrated excellent performance in optical remote sensing of marine oil spills; thus, they can be used for the operational monitoring of oil spills. The results indicate that 57 oil spill incidents were detected by the CZI sensors over the past three years, the image area of spilled oils can be up to <sc>1291.63 km².</sc> More than 47 oil spills occurred in China Seas, particularly in the Bohai Sea, Yellow Sea, and South China Sea, which require further attention. Among these, 13 oil spills could be detected by both HY-1C and HY-1D, indicating that the satellites network can increases observation times and is helpful the oil spill detection. Meanwhile, owing to the wide coverage and high temporal resolution, the CZI sensors can effectively distinguish oil types (i.e., non-emulsified oil slicks and oil emulsions) under different sunglint conditions. The results indicate that almost half of the oil spills have emulsified, and these emulsified spilled oils tend to have larger length and image area, posing a great threat to the marine environment. Moreover, based on the identification result, the thickness and volume of the spilled oils can be estimated preliminarily according to <italic>Bonn Agreement</italic>, and the damage caused by spilled oils can be assessed with less uncertainties. Therefore, the HY-1C/D satellites can provide reliable data and improve the monitoring of marine oil spills in China Seas.

Estimates of diurnal and daily net primary productivity using the Geostationary Ocean Color Imager (GOCI) data

For the past three decades, polar-orbiting ocean color satellites have provided local, regional to global scale estimates of oceanic net primary production that have greatly aided studies of ocean carbon cycling, food web dynamics and climate change. Despite considerable progress, accurate estimates of daily ocean productivity from space have not been realized because polar-orbiting satellites are unable to account for variations in phytoplankton physiology and carbon fixation rates from changing incident irradiance levels over the course of the day. Here we have attempted to exploit the unique short-temporal measurements provided by the Korean Geostationary Ocean Color Imager (GOCI), to obtain for the first time, diurnal and daily measurements of surface- and euphotic-column integrated Net Primary Productivity (NPP). These estimates are based on the Absorption Based Productivity Model (AbPM), and are calibrated with a bio-optical database of measurements made at different times of the day during the Korea-US Ocean Color (KORUS-OC) cruise in May–June 2016. Photo-physiological rate estimates, varied across different water types encountered around the Korean Peninsula, but on account of their limited number, were regionally scaled through the use of dynamic optical-biogeochemical (O-BGC) Biomes. Comparisons of GOCI derived estimates of daily and weekly integrated NPP against in-situ measurements, clearly underscore the superiority of geostationary over polar-orbiting ocean color satellites, which consider the strong short-term fluctuations in phytoplankton light absorption and incident irradiance fields over the course of the day.

Validation of ocean color remote sensing reflectance data: Analysis of results at European coastal sites

From its initial measurements 20 years ago, the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) has produced large validation data sets to assess the ocean color satellite data records. This study, applied to the standard atmospheric correction algorithm l2gen of the National Aeronautics and Space Administration, analyses the populations of residuals (differences between matching satellite and field data) of remote sensing reflectance RRS and, secondarily, aerosol optical thickness τa, and their validation statistics associated with data collected at seven AERONET-OC sites located in European coastal regions for six satellite missions. Validation statistics do not appear particularly sensitive to observation conditions, represented by viewing and solar geometry, aerosol optical thickness τa, Ångström exponent or water properties indexed by RRS, the most obvious exceptions being a systematic increase of the residuals for high solar zenith angles for all missions and sites. Residuals of RRS and τa tend to be inversely correlated as deviations in aerosol radiance and water-leaving radiance compensate each other. The spectra of a RRS uncertainty estimate σ decrease in a monotonous manner from 0.7 to 1.5 10−3 sr−1 for wavelengths around 412nm to 0.1–0.35 10−3 sr−1 around 667nm, with σ usually relatively high for waters associated with high amplitudes of RRS. At 412nm, σ for τa is mostly in the interval 0.03–0.04. For RRS, variations across missions of σ or of the relative differences between satellite and field data are overall smaller than variations across sites, which suggests that the products derived from various missions are rather consistent at a given location when processed with the considered algorithm. For a given mission, RRS residuals are well correlated between bands (inter-band correlation coefficient mostly larger than 0.5), which has implications on the propagation of uncertainties through bio-optical algorithms. For a given wavelength, residuals are correlated between missions to various degrees (most often with inter-mission correlation coefficients larger than 0.5). Besides implications as far as uncertainties of multi-mission merged products are concerned, this inter-mission correlation among residuals suggest that the residuals are not random and could be reduced.

Correction of inter-mission inconsistencies in merged ocean colour satellite data

Consistency in a time series of ocean colour satellite data is essential when determining long-term trends and statistics in Essential Climate Variables. For such a long time series, it is necessary to merge ocean colour data sets from different sensors due to the finite life span of the satellites. Although bias corrections have been performed on merged data set products, significant inconsistencies between missions remain. These inconsistencies appear as sudden steps in the time series of these products when a satellite mission is launched into- or removed from orbit. This inter-mission inconsistency is not caused by poor correction of sensor sensitivities but by differences in the ability of a sensor to observe certain waters. This study, based on a data set compiled by the ‘Ocean Colour Climate Change Initiative’ project (OC-CCI), shows that coastal waters, high latitudes, and areas subject to changing cloud cover are most affected by coverage variability between missions. The “Temporal Gap Detection Method” is introduced, which temporally homogenises the observations per-pixel of the time series and consequently minimises the magnitude of the inter-mission inconsistencies. The method presented is suitable to be transferred to other merged satellite-derived data sets that exhibit inconsistencies due to changes in coverage over time. The results provide insights into the correct interpretation of any merged ocean colour time series.

Modeling ocean surface chlorophyll-a concentration from ocean color remote sensing reflectance in global waters using machine learning

The spatial and temporal variations of Chlorophyll-a (Chl-a) in clear and coastal waters are critical for assessing the health of the marine environment. Machine learning models have been proven to model complex relationships and provide better accuracy estimates of the derived parameters compared to traditional empirical models. The present study proposes a novel approach to derive Chl-a by using multi-layer perceptron Neural Network (MLPNN) with Resilient backpropagation method based on the four ocean color bands existent in most of the ocean color sensors. The NNs are trained on NASA's bio-optical Marine Algorithm Dataset (NOMAD) and tested on three different datasets (i) SeaWiFS and, (ii) MODIS Aqua matchup dataset, and (iii) simulated dataset for the Red Sea. These three datasets cover significant variations range in Chl-a levels under both oligotrophic and eutrophic conditions. The influence of different variations in inputs used in NN training is assessed and hyperparameter tuning of the NN is performed to obtain best NN configuration to derive Chl-a. Accuracy assessment of the present study with other global algorithms are performed by comparing the modeled and observed values of the Chl-a. The performance matrices computed from the developed model were promising. Therefore, this study provides a potential approach for the retrieval of improved Chl-a estimates in the global clear and coastal waters as compared to the traditional blue-green band ratio algorithms. Furthermore, the developed algorithm and existing algorithms are applied to SeaWiFS, MODIS, VIIRS, and Hawkeye satellite ocean color data to demonstrate how it may be utilized to accurately depict the spatial distribution of ocean color features in global waters, phytoplankton blooms and some of the physical processes in the Arabian Sea and the Red Sea. The findings of this work have potential to advance the ocean color remote sensing and biogeochemical cycles and processes in coastal and open ocean waters.

Phytoplankton Blooms Expanding Further Than Previously Thought in the Ross Sea: A Remote Sensing Perspective

Accurate and robust measurements from ocean color satellites are critical to studying spatial and temporal changes of surface ocean properties. Satellite-derived Chlorophyll-a (Chl) is an important parameter to monitor phytoplankton blooms on synoptical scales, particularly in remote seas. However, the present NASA standard Chl algorithm tends to strongly underestimate the Chl in the Ross Sea. Based on a locally-tuned Chl algorithm in the Ross Sea and using the data record from MODIS between 2002 and 2020, here we investigated the spatial expansion of phytoplankton blooms in the Ross Sea. Our results show the geometric areas of the phytoplankton blooms could reach (7.20 ± 2.8) × 104 km2 on average, which was ~3.1 times that of those identified using the NASA default Chl algorithm. Spatially, blooms were frequently identified on the shelf of the Ross Sea polynya with a typical chance of ≥80%. In the context of climate change and global warming, the general decrease and interannual dynamics of sea ice cover tends to affect solar light penetration and surface seawater temperature, which were found to regulate the spatial expansion of the phytoplankton blooms over the years. Statistical analyses showed that the spatial coverages of the phytoplankton blooms were significantly correlated with sea surface temperature (Spearman correlation coefficient R = 0.55, at p &lt; 0.05), sea surface wind speed (R = 0.42, at p &lt; 0.05), and sea ice concentration (R = −0.84, at p &lt; 0.05), yet without significant long-term (&gt;10 years) trends over the study period. The stronger phytoplankton blooms than those previously observed may indicate larger carbon sequestration, which needs to be investigated in the future. More valid satellite observations under cloud covers will further constrain the estimates.

Genesis and Evolution of NASA’s Satellite Ocean Color Program

We recount, based on our involvements in NASA ocean color flight projects, the chronology of technical challenges, lessons learned, and key developments over the past 40 + years of NASA satellite ocean color, beginning with the Nimbus-7/Coastal Zone Color Scanner, that have led to the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Topics include the evolution of 1) satellite ocean color and field bio-optical data access, 2) satellite sensor capabilities, i.e., CZCS to PACE’s hyperspectral Ocean Color Imager, OCI, 3) atmospheric corrections, 4) pre- and post-launch sensor characterization and calibration, 5) bio-optical algorithms, 6) in situ-derived radiometry and photosynthetic pigment data measurement quality, and 7) applications of hyperspectral satellite observations.

Spatial Variability of In Situ Above-Water Reflectance in Coastal Dynamic Waters: Implications for Satellite Match-Up Analysis

The validation of ocean color satellite retrievals generally relies on analyzing match-ups between in situ measurements and satellite retrievals. These analyses focus on the quality of the satellite data, however, of the same importance is the quality of the in situ data. Here, we present the spatial variability of in situ above-water reflectance (Rrs(0+)) within the spatial resolution of different ocean color satellites—300, 900, 1500, and 3000 m spatial resolutions, mimicking Sentinel 3 OLCI and MODIS-Aqua satellites, and possible 3 × 3 and 5 × 5 windows. Radiometric data was acquired with autonomous radiometric sensors installed in the British Columbia Ferry Services Inc. vessel “Queen of Alberni” from May to September 2019, crossing the optically dynamic waters of the Strait of Georgia, Canada. The dataset followed optimal geometry of acquisition and processing, including corrections for skylight radiance signals, ship superstructure, the non-isotropic distribution of the water-leaving radiances, and quality control. A total of 33,073 spectra at full resolution, corresponding to 10 days, were considered for the analysis presented here. The results showed that, overall, the subpixel variability increased as the spatial resolution of the sensor or the window size increased, mainly in a linear fashion. Specifically, spatial variability of Rrs(0+) was the largest (∼18% and 68% for 900 and 3000 m pixel resolution, respectively) in Near Field Plume Interface waters, followed by in the Ocean Water Interface (∼28% and 35%, respectively), thus indicating spatial heterogeneity of interface waters. Further, we found that the estuarine waters showed higher subpixel Rrs(0+) variability (∼8% and 16% for 900 and 3000 m, respectively) compared with plume and oceanic waters. We showed that the high spatial variability in Rrs(0+) was primarily associated with the spatial dynamics of the optical water constituents, thus limiting the use of these datasets as Fiducial Reference Measurements and for validation of satellite-derived atmospherically corrected reflectance. We suggest that spatial variability of the in situ Rrs(0+) should also be considered in the selection criteria for good match-up data, especially for data acquired in coastal dynamic systems. As a result, it will advocate for the exclusion of interface or transition water pixel grids in order to avoid compromising the statistical result of satellite validation.

Biological dipole mode indices: New parameters to characterize the physical and biological processes of the Indian Ocean Dipole event

The Indian Ocean Dipole (IOD) is a basin-wide ocean–atmosphere phenomenon that has profound impacts on the global climate, land, and ocean. The Dipole Mode Index (DMI), which is defined as the difference of the sea surface temperature (SST) anomaly in the east IOD zone and the west IOD zone has long been used to characterize and quantify the strength of IOD events. In this study, we propose biological dipole mode indices (BDMIs) based on the dipolar observations of chlorophyll-a (Chl-a) anomalies (difference and relative difference) in the east and west IOD zones during the IOD event. The two BDMIs, which are based on Chl-a difference and relative difference, not only represent the dipolar biological activities in the Equatorial Indian Ocean, but also reflect the thermocline dynamics in the east IOD zone and west IOD zone. The in situ measurements in the east and west IOD zones show the clear linkage between the BDMIs and the dynamics of the 20°C isothermal depth. This linkage is attributed to the changes of the nutrient supplies driven by the various ocean physical processes in the IOD event, thus the BDMIs could also act as the surrogate for the thermocline dynamics in the two IOD zones. The BDMIs from satellite ocean color observations show that they can identify and characterize all the major IOD events in the last two and half decades. The SST-based DMI and Chl-a-based BDMIs may depict some different aspects of the IOD events (e.g., surface versus subsurface properties). The performance comparison between the two BDMIs and DMI also shows that the BDMIs and traditional DMI can effectively detect IOD signal for the major IOD events. Indeed, the BDMIs and DMI are complementary for characterizing the IOD events, and the combination of these indices can provide a better understanding of the atmosphere and ocean processes for both surface and subsurface, as well as biological processes in the Equatorial Indian Ocean.

Introducing Two Fixed Platforms in the Yellow Sea and East China Sea Supporting Long-Term Satellite Ocean Color Validation: Preliminary Data and Results

Following the Aerosol Robotic Network-Ocean Color (AERONET-OC) network scheme and instrument deployment protocols, two fixed platforms (Muping and Dong’ou) in the Yellow Sea and East China Sea were implemented with the support of the China National Satellite Ocean Application Service. Optical radiometry instruments were established at the two sites to autonomously determine remote sensing reflectance (Rrs) and aerosol optical depth (AOD). Details about location selection, platform design, instrument deployment, and the associated data processing procedure are reported in this study. Rrs and AOD measured by independent instruments at the Muping site were compared and results showed that they were consistent, with a median relative percentage difference (MRPD) &lt; 0.6% for AOD and &lt;10% for Rrs. The spectral feature and temporal pattern of Rrs and AOD at the two sites were examined and compared with data from 14 AERONET-OC sites. Rrs and AOD data measured at the two sites were used to evaluate ocean color operational products of MODIS/Aqua (MODISA), OLCI/Sentinel-3A (OLCI-3A), and OLCI/Sentinel-3B (OLCI-3B). Comparison showed that the three satellite sensor-derived Rrs agreed well with in situ measurements, with an MRPD &lt; 25% for MODISA, &lt;30% for OLCI-3A, and &lt;40% for OLCI-3B, respectively. Large uncertainties were observed in the blue bands for the three satellite sensors, particularly for OLCI-3B at 400 nm. AOD at 865 nm derived from the three satellite sensors also agreed well with in situ measurements, with an MRPD of 28.1% for MODISA, 30.6% for OLCI-3A, and 39.9% for OLCI-3B. Two commonly used atmospheric correction (AC) processors, the ACOLITE and SeaDAS, were also evaluated using in situ measurements at two sites and 20 m-resolution MSI/Sentinel-2A data. Close agreements were achieved for both AC processors, while the SeaDAS performed slightly better than ACOLITE. The optimal band selection in the AC models embedded in two AC processors was a combination of one near-infrared and one short-wave infrared band such as 865 and 1609 nm, shedding light on MSI data applications in the aquatic environment.

Global chlorophyll responses to marine heatwaves in satellite ocean color

Marine heatwaves (MHWs), prolonged ocean temperature extremes, have been enhanced by global warming in recent decades. More intense and longer MHWs have increasingly negative impacts on marine organisms that threaten their resilience of marine ecosystems. In this study, we investigated global marine phytoplankton biomass (chlorophyll) estimated by satellite ocean color and its response to MHWs on global and regional scales. We find that MHWs typically decreases chlorophyll concentrations in the tropics and mid-latitudes, with increases at high latitudes. The magnitude of chlorophyll responses to MHWs is increased in response to higher intensity and longer duration of MHWs. We find a change in the response from negative to positive chlorophyll responses to MHWs across the 40°–50° latitude bands in both hemispheres where the strongest meridional gradient in nitrate concentration exists. In these response-changing regions, the latitudinal contrast of the chlorophyll response is more distinctive in the warm season rather than in the cold season because of the shallower climatological mixed layer. The present study highlights the global phytoplankton responses to MHWs and their sensitivity to MHWs properties that imply the importance of upper-ocean interactions between phytoplankton and the mixed-layer.

Development of the Regional Carbon Cycle Model in the Central Pacific Sector of the Southern Ocean

AbstractThis study develops a new regional model of the Southern Ocean including an improved representation of the iron biogeochemistry and ecosystem component, nesting within a biogeochemical ocean state estimate, and benchmarking with a suite of observations. The regional domain focuses on the Udintsev Fracture Zone (UFZ) in the central Pacific sector of the Southern Ocean. The UFZ is characterized by the deep gap between the Pacific‐Antarctic Ridge and the East Pacific Rise, which is one of the key “choke points” of the Antarctic Circumpolar Current where major Southern Ocean fronts are constrained within close proximity to this topographic feature. It is also a region of elevated mesoscale eddy activity, especially downstream of the UFZ. The model reproduces observed partial pressure of carbon dioxide in the surface water (pCO2) remarkably well from seasonal to interannual timescales relative to prior studies (r = 0.89). The seasonality of pCO2 is difficult to simulate correctly because it is a small residual between the opposing influences of temperature and carbon. This model represents an intermittent double peak pattern of pCO2; one driven by the summertime high temperature and another from the wintertime high of dissolved inorganic carbon. The model also captures the spatial and temporal structure of the regional net primary production with respect to the satellite ocean color products (r = 0.57). The model is further validated by comparing it with biogeochemical float observations from the Southern Ocean Carbon and Climate Observations and Modeling project, revealing the model performance and challenges to accurately represent physical and biogeochemical properties in frontal regions.

Changing Hydrographic, Biogeochemical, and Acidification Properties in the Gulf of Maine as Measured by the Gulf of Maine North Atlantic Time Series, GNATS, Between 1998 and 2018

The Gulf of Maine North Atlantic Time Series (GNATS) has been run since 1998, across the Gulf of Maine (GoM), between Maine and Nova Scotia. GNATS goals are to provide ocean color satellite validation and to examine change in this coastal ecosystem. We have sampled hydrographical, biological, chemical, biogeochemical, and bio‐optical variables. After 2008, warm water intrusions (likely North Atlantic Slope Water [NASW]) were observed in the eastern GoM at 50–180 m depths. Shallow waters (<50 m) significantly warmed in winter, summer, and fall but cooled during spring. Surface salinity and density of the GoM also significantly increased over the 20 years. Phytoplankton standing stock and primary production showed highly‐significant decreases during the period. Concentrations of phosphate increased, silicate decreased, residual nitrate [N*; nitrate‐silicate] increased, and the ratio of dissolved inorganic nitrogen:phosphate decreased, suggesting increasing nitrogen limitation. Dissolved organic carbon (DOC) and its optical indices generally increased over two decades, suggesting changes to the DOC cycle. Surface seawater carbonate chemistry showed winter periods where the aragonite saturation (Ωar) dropped below 1.6 gulf‐wide due to upward winter mixing of cool, corrosive water. However, associated with increased average GoM temperatures, Ωar has significantly increased. These results reinforce the hypothesis that the observed decrease in surface GoM primary production resulted from a switch from Labrador Sea Water to NASW entering the GoM. A multifactor analysis shows that decreasing GoM primary production is most significantly correlated to decreases in chlorophyll and particulate organic carbon plus increases in N* and temperature.

Fluorescence Line Height Extraction Algorithm for the Geostationary Ocean Color Imager

Chlorophyll fluorescence is an important indicator of the physiological state of phytoplankton in water bodies. The new generation of ocean color satellite remote sensors usually sets fluorescence bands to detect the phytoplankton fluorescence line height (FLH). Yet, the Geostationary Ocean Color Imager (GOCI) offers no FLH products so far, and the FLH results calculated using the fluorescence band (680 nm) and the two baseline bands (660 and 745 nm) have numerous negative values and are quite different from the FLH products of other satellite ocean color sensors. To address this problem, we established an FLH retrieval algorithm suitable for GOCI. We simulated the spectral datasets of different water types using the radiative transfer model HydroLight and established the band conversion relationship from 680 to 685 nm based on the simulated datasets. The remote sensing reflectance after band conversion was applied to the FLH product inversion, significantly reducing the number of negative FLH values and appreciably improving data availability for GOCI FLH products (from 14.78% to 66.73% on average). The new FLH product has a good correlation with the field-measured data (R2 = 0.73), and the relative error was 6.95%. Moreover, after band conversion, the FLH products retrieved by GOCI are in good agreement with the FLH products of MODIS, and fusion products can be further produced to improve the spatiotemporal resolution of the data. In addition, the radiative transfer simulation datasets also revealed that changes in solar zenith angle have little effect on FLH inversion. The hourly GOCI-derived FLH has good spatiotemporal continuity and can clearly reflect the diurnal variation of FLH. It can provide a stable FLH algorithm for further recovery of time-series GOCI FLH products and research on diurnal changes in FLH.