Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate

New Technologies for Monitoring Coastal Ecosystem Dynamics

In recent years, our view of coastal ecosystems has expanded and come into greater focus. We are currently making more types of observations over larger areas and at higher frequencies than ever before. These advances are timely, as coastal ecosystems are facing increasing pressures from climate change and anthropogenic stressors. This article synthesizes recent literature on emerging technologies for coastal ecosystem monitoring, including satellite monitoring, aerial and underwater drones, in situ sensor networks, fiber optic systems, and community science observatories. We also describe how advances in artificial intelligence and deep learning underpin all these technologies by enabling insights to be drawn from increasingly large data volumes. Even with these recent advances, there are still major gaps in coastal ecosystem monitoring that must be addressed to manage coastal ecosystems during a period of accelerating global change.

Detecting the Phaeocystis globosa bloom and characterizing its bloom condition in the northern Beibu Gulf using MODIS measurements

Phaeocystis globosa is the most common species making up harmful algal blooms. For better detect P. globosa bloom, a multispectral approach was developed based on extensive in-situ investigation and MODIS remote sensing reflectance (Rrs) dataset. A novel proxy RPG was created based on the feature of Rrs spectral shape and P. globosa bloom was identified when RPG was >1.6. Normalized Fluorescence Line Height (nFLH) was applied to discriminate the bloom events and nFLH of bloom waters was almost higher than 0.095 Wm-2μm-1sr-1. The RPG associated with nFLH exhibited the P. globosa bloom areas comparable to that in field investigation, which indicated this practical method was successful on the spatial and temporal distribution of P. globosa blooms. Several environmental factors derived from MODIS products and field survey were analyzed to characterize the bloom conditions. Redundancy analysis suggested that nutrients and temperature are vital for triggering P. globosa bloom.

Seasonal patterns and bloom dynamics of phytoplankton based on satellite-derived chlorophyll-a in the eastern yellow sea

Satellite-derived chlorophyll-a concentration (Chl-a) is essential for assessing environmental conditions, yet its application in the optically complex waters of the eastern Yellow Sea (EYS) is challenged. This study refines the Chl-a algorithm for the EYS employing a switching approach based on normalized water-leaving radiance at 555 nm wavelength according to turbidity conditions to investigate phytoplankton bloom patterns in the EYS. The refined Chl-a algorithm (EYS algorithm) outperforms prior algorithms, exhibiting a strong alignment with in situ Chl-a. Employing the EYS algorithm, seasonal and bloom patterns of Chl-a are detailed for the offshore and nearshore EYS areas. Distinct seasonal Chl-a patterns and factors influencing bloom initiation differed between the areas, and the peak Chl-a during the bloom period from 2018 to 2020 was significantly lower than the average year in both areas. Specifically, bimodal and unimodal peak patterns in Chl-a were observed in the offshore and nearshore areas, respectively. By investigating the relationships between environmental factors and bloom parameters, we identified that major controlling factors governing bloom initiation were mixed layer depth (MLD) and suspended particulate matter (SPM) in the offshore and nearshore areas, respectively. Additionally, this study proposed that the recent decrease in the peak Chl-a might be caused by rapid environmental changes such as the warming trend of sea surface temperature (SST) and the limitation of nutrients. For example, external forcing, phytoplankton growth, and nutrient dynamics can change due to increased SST and limitation of nutrients, which can lead to a decrease in Chl-a. This study contributes to understanding phytoplankton dynamics in the EYS, highlighting the importance of region-specific considerations in comprehending Chl-a patterns and bloom dynamics.

Multi-vehicle adaptive 3D mapping for targeted ocean sampling

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

Remote sensing for chromophoric dissolved organic matter (CDOM) monitoring research 2003-2022: A bibliometric analysis based on the web of science core database

Chromophoric dissolved organic matter (CDOM) occupies a critical part in biogeochemistry and energy flux of aquatic ecosystems. CDOM research spans in many fields, including chemistry, marine environment, biomass cycling, physics, hydrology, and climate change. In recent years, a series of remarkable research milestone have been achieved. On the basis of reviewing the research process of CDOM, combined with a bibliometric analysis, this study aims to provide a comprehensive review of the development and applications of remote sensing in monitoring CDOM from 2003 to 2022. The findings show that remote sensing data plays an important role in CDOM research as proven with the increasing number of publications since 2003, particularly in China and the United States. Primary research areas have gradually changed from studying absorption and fluorescence properties to optimization of remote sensing inversion models in recent years. Since the composition of oceanic and freshwater bodies differs significantly, it is important to choose the appropriate inversion method for different types of water body. At present, the monitoring of CDOM mainly relies on a single sensor, but the fusion of images from different sensors can be considered a major research direction due to the complex characteristics of CDOM. Therefore, in the future, the characteristics of CDOM will be studied in depth inn combination with multi-source data and other application models, where inversion algorithms will be optimized, inversion algorithms with low dependence on measured data will be developed, and a transportable inversion model will be built to break the regional limitations of the model and to promote the development of CDOM research in a deeper and more comprehensive direction.

Global climate-change trends detected in indicators of ocean ecology

Strong natural variability has been thought to mask possible climate-change-driven trends in phytoplankton populations from Earth-observing satellites. More than 30 years of continuous data were thought to be needed to detect a trend driven by climate change1. Here we show that climate-change trends emerge more rapidly in ocean colour (remote-sensing reflectance, Rrs), because Rrs is multivariate and some wavebands have low interannual variability. We analyse a 20-year Rrs time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite, and find significant trends in Rrs for 56% of the global surface ocean, mainly equatorward of 40°. The climate-change signal in Rrs emerges after 20 years in similar regions covering a similar fraction of the ocean in a state-of-the-art ecosystem model2, which suggests that our observed trends indicate shifts in ocean colour-and, by extension, in surface-ocean ecosystems-that are driven by climate change. On the whole, low-latitude oceans have become greener in the past 20 years.

Improvement and Assessment of Ocean Color Algorithms in the Northwest Pacific Fishing Ground Using Himawari-8, MODIS-Aqua, and VIIRS-SNPP

Chlorophyll-a (Chl-a) is an important marine indicator, and the improvement in Chl-a concentration retrieval for ocean color remote sensing is always a major challenge. This study focuses on the northwest Pacific fishing ground (NPFG) to evaluate and improve the Chl-a products of three mainstream remote sensing satellites, Himawari-8, MODIS-Aqua, and VIIRS-SNPP. We analyzed in situ data and found that an in situ Chl-a concentration of 0.3 mg m−3 could be used as a threshold to distinguish the systematic deviation of remote sensing Chl-a data in the NPFG. Based on this threshold, we optimized the Chl-a algorithms of the three satellites by data grouping, and integrated multisource satellite Chl-a data by weighted averaging to acquire high-coverage merged data. The merged data were thoroughly verified by Argo Chl-a data. The Chl-a front of merged Chl-a data could be represented accurately and completely and had a good correlation with the distribution of the NPFG. The most important marine factors for Chl-a are nutrients and temperature, which are affected by mesoscale eddies and variations in the Kuroshio extension. The variation trend of merged Chl-a data is consistent with mesoscale eddies and Kuroshio extension and has more sensitive responses to the marine climatic conditions of ENSO.

Declining greenness in Arctic-boreal lakes

Arctic and boreal regions are undergoing dramatic warming and also possess the world’s highest concentration of lakes. However, ecological changes in lakes are poorly understood. We present a continental-scale trend analysis of satellite lake color in the green wavelengths, which shows declining greenness from 1984 to 2019 in Arctic-boreal lakes across western North America. Annual 30-m Landsat composites indicate lake greenness has decreased by 15%. Our findings show a relationship between lake color, rising air temperatures, and increasing precipitation, supporting the theory that warming may be increasing connectivity between lakes and surrounding landscapes. Overall, our results bring a powerful set of observations in support of the hypothesis that lakes are sentinels for global change in rapidly warming Arctic-boreal ecosystems.

The highest concentration of the world’s lakes are found in Arctic-boreal regions [C. Verpoorter, T. Kutser, D. A. Seekell, L. J. Tranvik, Geophys. Res. Lett. 41, 6396–6402 (2014)], and consequently are undergoing the most rapid warming [J. E. Overland et al., Arctic Report Card (2018)]. However, the ecological response of Arctic-boreal lakes to warming remains highly uncertain. Historical trends in lake color from remote sensing observations can provide insights into changing lake ecology, yet have not been examined at the pan-Arctic scale. Here, we analyze time series of 30-m Landsat growing season composites to quantify trends in lake greenness for >4 × 10⁵ waterbodies in boreal and Arctic western North America. We find lake greenness declined overall by 15% from the first to the last decade of analysis within the 6.3 × 10⁶-km² study region but with significant spatial variability. Greening declines were more likely to be found in areas also undergoing increases in air temperature and precipitation. These findings support the hypothesis that warming has increased connectivity between lakes and the land surface [A. Bring et al., J. Geophys. Res. Biogeosciences 121, 621–649 (2016)], with implications for lake carbon cycling and energy budgets. Our study provides spatially explicit information linking climate to pan-Arctic lake color changes, a finding that will help target future ecological monitoring in remote yet rapidly changing regions.

Chromophoric dissolved organic matter in inland waters: Present knowledge and future challenges

Chromophoric dissolved organic matter (CDOM) plays an important role in the biogeochemical cycle and energy flow of aquatic ecosystems. Thus, systematic and comprehensive understanding of CDOM dynamics is critically important for aquatic ecosystem management. CDOM spans multiple study fields, including analytical chemistry, biogeochemistry, water color remote sensing, and global environmental change. Here, we thoroughly summarize the progresses of recent studies focusing on the characterization, distribution, sources, composition, and fate of CDOM in inland waters. Characterization methods, remote sensing estimation, and biogeochemistry cycle processes were the hotspots of CDOM studies. Specifically, optical, isotope, and mass spectrometric techniques have been widely used to characterize CDOM abundance, composition, and sources. Remote sensing is an effective tool to map CDOM distribution with high temporal and spatial resolutions. CDOM dynamics are mainly determined by watershed-related processes, including rainfall discharge, groundwater, wastewater discharges/effluents, and biogeochemical cycling occurring in soil and water bodies. We highlight the underlying mechanisms of the photochemical degradation and microbial decomposition of CDOM, and emphasize that photochemical and microbial processes of CDOM in inland waters accelerate nutrient cycling and regeneration in the water column and also exacerbate global warming by releasing greenhouse gases. Future study directions to improve the understanding of CDOM dynamics in inland waters are proposed. This review provides an interdisciplinary view and new insights on CDOM dynamics in inland waters.

Dynamic partitioning of tropical Indian Ocean surface waters using ocean colour data - management and modelling applications

Over the past few decades, partitioning of the surface ocean into ecologically-meaningful spatial domains has been approached using a range of data types, with the aim of improving our understanding of open ocean processes, supporting marine management decisions and constraining coupled ocean-biogeochemical models. The simplest partitioning method, which could provide low-latency information for managers at low cost, remains a purely optical classification based on ocean colour remote sensing. The question is whether such a simple approach has value. Here, the efficacy of optical classifications in constraining physical variables that modulate the epipelagic environment is tested for the tropical Indian Ocean, with a focus on the Chagos marine protected area (MPA). Using remote sensing data, it was found that optical classes corresponded to distinctive ranges of wind speed, wind stress curl, sea surface temperature, sea surface slope, sea surface height anomaly and geostrophic currents (Kruskal-Wallis and post-hoc Tukey honestly significantly different tests, α = 0.01). Between-class differences were significant for a set of sub-domains that resolved zonal and meridional gradients across the MPA and Seychelles-Chagos Thermocline Ridge, whereas between-domain differences were only significant for the north-south gradient (PERMANOVA, α = 0.01). A preliminary test of between-class differences in surface CO₂ concentrations from the Orbiting Carbon Observatory-2 demonstrated a small decrease in mean pCO₂ with increasing chlorophyll (chl), from 418 to 398 ppm. Simple optical class maps therefore provide an overview of growth conditions, the spatial distribution of resources - from which habitat fragmentation metrics can be calculated, and carbon sequestration potential. Within the 17 year study period, biotic variables were found to have decreased at up to 0.025%a^-1 for all optical classes, which is slower than reported elsewhere (Mann-Kendall-Sen regression, α = 0.01). Within the MPA, positive Indian Ocean Dipole conditions and negative Southern Oscillation Indices were weakly associated with decreasing chl, fluorescence line height (FLH), eddy kinetic energy, easterly wind stress and wind stress curl, and with increasing FLH/chl, sea surface temperature, SSH gradients and northerly wind stress, consistent with reduced surface mixing and increased stratification. The optical partitioning scheme described here can be applied in Google Earth Engine to support management decisions at daily or monthly scales, and potential applications are discussed.

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

Biases from incorrect reflectance convolution

Reflectance, a crucial earth observation variable, is converted from hyperspectral to multispectral through convolution. This is done to combine time series, validate instruments, and apply retrieval algorithms. However, convolution is often done incorrectly, with reflectance itself convolved rather than the underlying (ir)radiances. Here, the resulting error is quantified for simulated and real multispectral instruments, using 18 radiometric data sets (N = 1799 spectra). Biases up to 5% are found, the exact value depending on the spectrum and band response. This significantly affects extended time series and instrument validation, and is similar in magnitude to errors seen in previous validation studies. Post-hoc correction is impossible, but correctly convolving (ir)radiances prevents this error entirely. This requires publication of original data alongside reflectance.

Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy

Remote sensing reflectance (Rrs) values measured by satellite sensors involve large amounts of uncertainty leading to non-negligible noise in remote Chlorophyll-a (Chl-a) concentration estimation. This work distinguished between two main stages in the case of estimating distributions of Chl-a within the Gulf of St. Lawrence (Canada). At the model building stage, the retrieval algorithm used both in-situ Chl-a measurements and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) L2-level data estimated Rrs at 412, 443, 469, 488, 531, 547, 555, 645, 667, 678 nm at a 1 km spatial resolution during 2004-2013. Through the training and validation of various models and Rrs combinations of the considered eight techniques (including support vector regression, artificial neural networks, gradient boosting machine, random forests, standard CI-OC3M, multiple linear regression, generalized addictive regression, principal component regression), the support vector regression (SVR) technique was shown to have the best performance in Chl-a concentration estimation using Rrs at 412, 443, 488, 531 and 678 nm. The accuracy indicators for both the training (850) and the validation (213) datasets were found to be very good to excellent (e.g., the R² value varied between 0.7058 and 0.9068). At the space-time estimation stage, this work took a step forward by using the Bayesian maximum entropy (BME) theory to further process the SVR estimated Chl-a concentrations by incorporating the inherent spatiotemporal dependency of physical Chl-a distribution. A 56% improvement was achieved in the reduction of the mean uncertainty of the validation data decreased considerably (from 1.2222 to 0.5322 mg/m³). Then, this novel BME/SVR framework was employed to estimate the daily Chl-a concentrations in the Gulf of St. Lawrence during Jan 1-Dec 31 of 2017 (1 km spatial resolution). The results showed that the daily mean Chl-a concentration varied from 1.6630 to 3.3431 mg/m³, and that the daily mean Chl-a uncertainty reduction of the composite BME/SVR vs. the SVR estimation had a maximum reduction value of 1.0082 and an average reduction value of 0.6173 mg/m³. The monthly spatial Chl-a distribution covariances showed that the highest Chl-a concentration variability occurred during November and that the spatiotemporal Chl-a concentration pattern changed a lot during the period August to November. In conclusion, the proposed BME/SVR was shown to be a promising remote Chl-a retrieval approach that exhibited a significant ability in reducing the non-negligible uncertainty and improving the accuracy of remote sensing Chl-a concentration estimates.

Correcting non-photochemical quenching of Saildrone chlorophyll-a fluorescence for evaluation of satellite ocean color retrievals

In vivo chlorophyll fluorescence (ChlF) can serve as a reasonable estimator of in situ phytoplankton biomass with the benefits of efficiently and affordably extending the global chlorophyll (Chl) data set in time and space to remote oceanic regions where routine sampling by other vessels is uncommon. However, in vivo ChlF measurements require correction for known, spurious biases relative to other measures of Chl concentration, including satellite ocean color retrievals. Spurious biases affecting in vivo ChlF measurements include biofouling, colored dissolved organic matter (CDOM) fluorescence, calibration offsets, and non-photochemical quenching (NPQ). A more evenly distributed global sampling of in vivo ChlF would provide additional confidence in estimates of uncertainty for satellite ocean color retrievals. A Saildrone semi-autonomous, ocean-going, solar- and wind-powered surface drone recently measured a variety of ocean and atmospheric parameters, including ChlF, during a 60-day deployment in mid-2018 in the California Current region. Correcting the Saildrone ChlF data for known biases, including deriving an NPQ-correction, greatly improved the agreement between the drone measurements and satellite ocean color retrievals from MODIS-Aqua and VIIRS-SNPP, highlighting that once these considerations are made, Saildrone semi-autonomous surface vehicles are a valuable, emerging data source for ocean and ecosystem monitoring.

Impacts of pure seawater absorption coefficient on remotely sensed inherent optical properties in oligotrophic waters

The spectral absorption coefficient of pure seawater (a_w(λ)) in published studies differ significantly in the blue domain, yet the impacts of such discrepancies on the inherent optical properties (IOPs) derived from ocean color have been scarcely documented. In this study, we confirm that changes in a_w(λ) may have significant impacts on retrieved IOPs in oligotrophic waters, especially for the phytoplankton absorption coefficient (a_ph(λ)). Two sets of a_w(λ) data, a_w_PF97 (Appl. Opt. 36, 8710, 1997) and a_w_Lee15 (Appl. Opt. 54, 546, 2015), were selected for optical inversion analysis. It is found that a_ph(λ) retrieved with a_w_Lee15 agree better with the in-situ measurements in oligotrophic waters. Further applications to satellite images show that the derived a_ph(λ) using a_w_Lee15 can be up to 238% higher than the retrievals using a_w_PF97 in the core zone of the subtropical ocean gyres. Given that a_w_PF97 is commonly accepted as the "standard" a_w(λ) by the ocean color community in the past decades, this study highlights the need and importance to update a_w(λ) with a_w_Lee15 for IOPs retrievals in oligotrophic waters.

Factors Regulating the Relationship Between Total and Size-Fractionated Chlorophyll-a in Coastal Waters of the Red Sea

Phytoplankton biomass and size structure are recognized as key ecological indicators. With the aim to quantify the relationship between these two ecological indicators in tropical waters and understand controlling factors, we analyzed the total chlorophyll-a concentration, a measure of phytoplankton biomass, and its partitioning into three size classes of phytoplankton, using a series of observations collected at coastal sites in the central Red Sea. Over a period of 4 years, measurements of flow cytometry, size-fractionated chlorophyll-a concentration, and physical-chemical variables were collected near Thuwal in Saudi Arabia. We fitted a three-component model to the size-fractionated chlorophyll-a data to quantify the relationship between total chlorophyll and that in three size classes of phytoplankton [pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm)]. The model has an advantage over other more empirical methods in that its parameters are interpretable, expressed as the maximum chlorophyll-a concentration of small phytoplankton (pico- and combined pico-nanophytoplankton, Cpm and Cp,nm, respectively) and the fractional contribution of these two size classes to total chlorophyll-a as it tends to zero (D_p and D_p,n). Residuals between the model and the data (model minus data) were compared with a range of other environmental variables available in the dataset. Residuals in pico- and combined pico-nanophytoplankton fractions of total chlorophyll-a were significantly correlated with water temperature (positively) and picoeukaryote cell number (negatively). We conducted a running fit of the model with increasing temperature and found a negative relationship between temperature and parameters Cpm and Cp,nm and a positive relationship between temperature and parameters D_p and D_p,n. By harnessing the relative red fluorescence of the flow cytometric data, we show that picoeukaryotes, which are higher in cell number in winter (cold) than summer (warm), contain higher chlorophyll per cell than other picophytoplankton and are slightly larger in size, possibly explaining the temperature shift in model parameters, though further evidence is needed to substantiate this finding. Our results emphasize the importance of knowing the water temperature and taxonomic composition of phytoplankton within each size class when understanding their relative contribution to total chlorophyll. Furthermore, our results have implications for the development of algorithms for inferring size-fractionated chlorophyll from satellite data, and for how the partitioning of total chlorophyll into the three size classes may change in a future ocean.

UV-NIR approach with non-zero water-leaving radiance approximation for atmospheric correction of satellite imagery in inland and coastal zones

In the atmospheric correction process of the satellite ocean color data, the removal of the aerosol scattering contribution over the coastal and inland water bodies has been a major challenge with the standard algorithms. In this work, a practical method is proposed based on a combination of NIR and ultraviolet (UV) bands (named as UVNIR-ex) for the succeeding generation of space borne multispectral and hyperspectral sensors. This scheme replaces the black-ocean assumption and accounts for non-zero water-leaving radiance contributions in the NIR and UV bands. The aerosol contributions are thus deduced for these two bands and used to select the appropriate aerosol models to retrieve aerosol optical properties and hence, water-leaving radiances in the UV, Visible and NIR bands. The performance of the UVNIR-ex algorithm was tested and evaluated based on match-ups between HICO and in-situ observations in optically complex coastal and inland waters and by comparison with three alternative aerosol correction methods based on UV-NIR, Spectral Shape Parameter (SSP) and iterative NIR (INIR) approaches. A preliminary comparison with in-situ aerosol optical thickness (AOT) measurements from AERONET-OC sites revealed that the UVNIR-ex algorithm significantly improved the AOT retrievals with a mean relative error (MRE) around 25%, while the UVNIR, SSP and INIR algorithms showed performance degradation with a MRE of 27%, 34%, and 42%, respectively. The comparison with AERONET-OC and regional in-situ measurements from turbid and productive waters further showed that the INIR algorithm underestimated the nL_w retrievals in blue bands in turbid waters (MRE > 100%) and negligible nL_w in red-NIR bands and high anomalous radiances in UV-Blue bands in productive waters (MRE 53%). The SSP and UVNIR algorithms performed better in retrieving the nL_w in green-NIR bands but showed significant errors in UV-blue bands in both turbid and productive waters. Based on these match-up analyses, the UVNIR-ex algorithm yielded best nL_w retrievals across all the UV-NIR bands in terms of accuracy and performance. The highest accuracy and consistency of the UVNIR-ex algorithm indicates that it is more suited for estimating the aerosol optical properties and water-leaving radiance and has a significant advantage over the requirement of shortwave infrared bands for turbid and productive waters.

CHLOROPHYLL ALGORITHMS FOR OCEAN COLOR SENSORS - OC4, OC5 & OC6

A high degree of consistency and comparability among chlorophyll algorithms is necessary to meet the goals of merging data from concurrent overlapping ocean color missions for increased coverage of the global ocean and to extend existing time series to encompass data from recently launched missions and those planned for the near future, such as PACE, OLCI, HawkEye, EnMAP and SABIA-MAR. To accomplish these goals, we developed 65 empirical ocean color (OC) maximum band ratio (MBR) algorithms for 25 satellite instruments using the largest available and most globally representative database of coincident in situ chlorophyll a and remote sensing reflectances. Excellent internal consistency was achieved across these OC 'Version -7' algorithms, as demonstrated by a median regression slope and coefficient of determination (R²) of 0.985 and 0.859, respectively, between 903 pairwise comparisons of OC-modeled chlorophyll. SeaWiFS and MODIS-Aqua satellite-to-in situ match-up results indicated equivalent, and sometimes superior, performance to current heritage chlorophyll algorithms. During the past forty years of ocean color research the violet band (412 nm) has rarely been used in empirical algorithms to estimate chlorophyll concentrations in oceanic surface water. While the peak in chlorophyll-specific absorption coincides with the 443 nm band present on most ocean color sensors, the magnitude of chlorophyll-specific absorption at 412 nm can reach upwards of ~70% of that at 443 nm. Nearly one third of total chlorophyll-specific absorption between 400 and 700 nm occurs below 443 nm, suggesting that bands below 443 nm, such as the 412 nm band present on most ocean color sensors, may also be useful in detecting chlorophyll under certain conditions and assumptions. The 412 nm band is also the brightest band (that is, with the most dominant magnitude) in remotely sensed reflectances retrieved by heritage passive ocean color instruments when chlorophyll is less than ~0.1 mg m^-3, which encompasses ~24% of the global ocean. To attempt to exploit this additional spectral information, we developed two new families of OC algorithms, the OC5 and OC6 algorithms, which include the 412 nm band in the MBR. By using this brightest band in MBR empirical chlorophyll algorithms, the highest possible dynamic range of MBR may be achieved in these oligotrophic areas. The terms oligotrophic, mesotrophic, and eutrophic get frequent use in the scientific literature to designate trophic status; however, quantitative definitions in terms of chlorophyll levels are arbitrarily defined. We developed a new, reproducible, bio-optically based index for trophic status based on the frequency of the brightest, maximum band in the MBR for the OC6_SEAWIFS algorithm, along with remote sensing reflectances from the entire SeaWiFS mission. This index defines oligotrophic water as chlorophyll less than ~0.1 mg m^-3, eutrophic water as chlorophyll above 1.67 mg m^-3 and mesotrophic water as chlorophyll between 0.1 and 1.67 mg m^-3. Applying these criteria to the 40-year mean global ocean chlorophyll data set revealed that oligotrophic, mesotrophic, and eutrophic water occupy ~24%, 67%, and 9%, respectively, of the area of the global ocean on average.

Evaluation of Chlorophyll-a and POC MODIS Aqua Products in the Southern Ocean

The Southern Ocean (SO) is highly sensitive to climate change. Therefore, an accurate estimate of phytoplankton biomass is key to being able to predict the climate trajectory of the 21st century. In this study, MODerate resolution Imaging Spectroradiometer (MODIS), on board EOS Aqua spacecraft, Level 2 (nominal 1 km × 1 km resolution) chlorophyll-a (C S a t ) and Particulate Organic Carbon (POC s a t ) products are evaluated by comparison with an in situ dataset from 11 research cruises (2008–2017) to the SO, across multiple seasons, which includes measurements of POC and chlorophyll-a (C i n s i t u ) from both High Performance Liquid Chromatography (C H P L C ) and fluorometry (C F l u o ). Contrary to a number of previous studies, results highlighted good performance of the algorithm in the SO when comparing estimations with HPLC measurements. Using a time window of ±12 h and a mean satellite chlorophyll from a 5 × 5 pixel box centered on the in situ location, the median C S a t :C i n s i t u ratios were 0.89 (N = 46) and 0.49 (N = 73) for C H P L C and C F l u o respectively. Differences between C H P L C and C F l u o were associated with the presence of diatoms containing chlorophyll-c pigments, which induced an overestimation of chlorophyll-a when measured fluorometrically due to a potential overlap of the chlorophyll-a and chlorophyll-c emission spectra. An underestimation of ∼0.13 mg m − 3 was observed for the global POC algorithm. This error was likely due to an overestimate of in situ POC i n s i t u measurements from the impact of dissolved organic carbon not accounted for in the blank correction. These results highlight the important implications of different in situ methodologies when validating ocean colour products.

A Fast Detection Strategy for Cyanobacterial blooms and associated cyanotoxins (FDSCC) reveals the occurrence of lyngbyatoxin A in campania (South Italy)

Fast Detection Strategy for Cyanobacterial blooms and associated Cyanotoxins (FDSCC) is a multidisciplinary strategy that allows early detection, in 24 man-hours, of cyanobacteria and related cyanotoxins in water and bivalve samples. This approach combines the advantages of remote/proximal sensing with those of analytical/bioinformatics analyses, namely, LC-HRMS-based molecular networking. The detection of Lyngbyatoxin A, a lipophilic cyanotoxin, in all analyzed water samples and in bivalves, commonly used as food, was the proof of the reliability of the new method.

Progressive scheme for blending empirical ocean color retrievals of absorption coefficient and chlorophyll concentration from open oceans to highly turbid waters

To achieve a smooth transition between algorithms for "clear" water and "turbid" water, we propose a single formula to calculate the input parameter (ip) used for empirical retrieval of absorption coefficients (a) or chlorophyll concentration ([Chl]) from remote-sensing reflectance (R_rs). This formula for ip takes the ratio of the maximum R_rs in the blue-green bands to the sum of R_rs(green) and the scaled R_rs in the red and infrared bands (termed as ip_Max-Sum). We found that, compared to the widely used OC4-type formula for ip, ip_Max-Sum can improve the coefficient of determination from ∼0.88 to 0.99 for absorption coefficient at 440 nm [a(440)] in ∼0.01-20.0  m^-1 ([Chl] ∼0.01-500  mg m^-3). Especially, the sensitivity of ip_Max-Sum to the change in a(440) is about five times greater than that of OC4-type for a(440)>∼1.0  m^-1 ([Chl]>∼10  mg m^-3). These results indicate an advantage of ip_Max-Sum for generating robust and seamless a(440) or [Chl] from clear to highly turbid waters. The inclusion of such a scheme in a quasi-analytical algorithm is also presented.

Deriving inherent optical properties from classical water color measurements: Forel-Ule index and Secchi disk depth

Secchi disk depth (ZSD) and Forel-Ule index (FUI) are the two oldest and easiest measurements of water optical properties based on visual determination. With an overarching objective to obtain water inherent optical properties (IOPs) using these historical measurements, this study presents a model for associating remote-sensing reflectance (Rrs) with FUI and ZSD. Based upon this, a scheme (FZ2ab) for converting FUI and ZSD to absorption (a) and backscattering coefficients (bb) is developed and evaluated. For a data set from HydroLight simulations, the difference is <11% between FZ2ab-derived a and known a, and <28% between FZ2ab-derived bb and known bb. Further, for a data set from field measurements, the difference is < 30% between FZ2ab-derived a and measured a. These results indicate that FZ2ab can bridge the gap between historical measurements and the focus of IOP measurements in modern marine optics, and potentially extend our knowledge on the bio-optical properties of global seas to the past century through the historical measurements of FUI and ZSD.

Monitoring Algal Blooms in drinking water reservoirs using the Landsat 8 Operational Land Imager

In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance (LSR) product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-a concentrations using spectral data. We found that using the 3 band reflectance approach with a combination of OLI spectral bands 1, 3, and 5, produced the most promising results for accurately estimating chl-a concentrations in lakes (R² value of 0.66; RMSE value of 8.9 μg l^-1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.

Reconciling fisheries catch and ocean productivity

Photosynthesis fuels marine food webs, yet differences in fish catch across globally distributed marine ecosystems far exceed differences in net primary production (NPP). We consider the hypothesis that ecosystem-level variations in pelagic and benthic energy flows from phytoplankton to fish, trophic transfer efficiencies, and fishing effort can quantitatively reconcile this contrast in an energetically consistent manner. To test this hypothesis, we enlist global fish catch data that include previously neglected contributions from small-scale fisheries, a synthesis of global fishing effort, and plankton food web energy flux estimates from a prototype high-resolution global earth system model (ESM). After removing a small number of lightly fished ecosystems, stark interregional differences in fish catch per unit area can be explained (r = 0.79) with an energy-based model that (i) considers dynamic interregional differences in benthic and pelagic energy pathways connecting phytoplankton and fish, (ii) depresses trophic transfer efficiencies in the tropics and, less critically, (iii) associates elevated trophic transfer efficiencies with benthic-predominant systems. Model catch estimates are generally within a factor of 2 of values spanning two orders of magnitude. Climate change projections show that the same macroecological patterns explaining dramatic regional catch differences in the contemporary ocean amplify catch trends, producing changes that may exceed 50% in some regions by the end of the 21st century under high-emissions scenarios. Models failing to resolve these trophodynamic patterns may significantly underestimate regional fisheries catch trends and hinder adaptation to climate change.

Space station image captures a red tide ciliate bloom at high spectral and spatial resolution

Mesodinium rubrum is a globally distributed nontoxic ciliate that is known to produce intense red-colored blooms using enslaved chloroplasts from its algal prey. Although frequent enough to have been observed by Darwin, blooms of M. rubrum are notoriously difficult to quantify because M. rubrum can aggregate into massive clouds of rusty-red water in a very short time due to its high growth rates and rapid swimming behavior and can disaggregate just as quickly by vertical or horizontal dispersion. A September 2012 hyperspectral image from the Hyperspectral Imager for the Coastal Ocean sensor aboard the International Space Station captured a dense red tide of M. rubrum (10(6) cells per liter) in surface waters of western Long Island Sound. Genetic data confirmed the identity of the chloroplast as a cryptophyte that was actively photosynthesizing. Microscopy indicated extremely high abundance of its yellow fluorescing signature pigment phycoerythrin. Spectral absorption and fluorescence features were related to ancillary photosynthetic pigments unique to this organism that cannot be observed with traditional satellites. Cell abundance was estimated at a resolution of 100 m using an algorithm based on the distinctive yellow fluorescence of phycoerythrin. Future development of hyperspectral satellites will allow for better enumeration of bloom-forming coastal plankton, the associated physical mechanisms, and contributions to marine productivity.

Temperature-Correlated Changes in Phytoplankton Community Structure Are Restricted to Polar Waters

Globally distributed observations of size-fractionated chlorophyll a and temperature were used to incorporate temperature dependence into an existing semi-empirical model of phytoplankton community size structure. The additional temperature-dependent term significantly increased the model’s ability to both reproduce and predict observations of chlorophyll a size-fractionation at temperatures below 2°C. The most notable improvements were in the smallest (picoplankton) size-class, for which overall model fit was more than doubled, and predictive skill was increased by approximately 40%. The model was subsequently applied to generate global maps for three phytoplankton size classes, on the basis of satellite-derived estimates of surface chlorophyll a and sea surface temperature. Polar waters were associated with marked decline in the chlorophyll a biomass of the smallest cells, relative to lower latitude waters of equivalent total chlorophyll a. In the same regions a complementary increase was seen in the chlorophyll a biomass of larger size classes. These findings suggest that a warming and stratifying ocean will see a poleward expansion of the habitat range of the smallest phytoplankton, with the possible displacement of some larger groups that currently dominate. There was no evidence of a strong temperature dependence in tropical or sub-tropical regions, suggesting that future direct temperature effects on community structure at lower latitudes may be small.

Multi-Pixel Photon Counters for Optofluidic Characterization of Particles and Microalgae

We have developed an optofluidic biosensor to study microscale particles and different species of microalgae. The system is comprised of a microchannel with a set of chevron-shaped grooves. The chevrons allows for hydrodynamic focusing of the core stream in the center using a sheath fluid. The device is equipped with a new generation of highly sensitive photodetectors, multi-pixel photon counter (MPPC), with high gain values and an extremely small footprint. Two different sizes of high intensity fluorescent microspheres and three different species of algae (Chlamydomonas reinhardtii strain 21 gr, Chlamydomonas suppressor, and Chlorella sorokiniana) were studied. The forward scattering emissions generated by samples passing through the interrogation region were carried through a multimode fiber, located in 135 degree with respect to the excitation fiber, and detected by a MPPC. The signal outputs obtained from each sample were collected using a data acquisition system and utilized for further statistical analysis. Larger particles or cells demonstrated larger peak height and width, and consequently larger peak area. The average signal output (integral of the peak) for Chlamydomonas reinhardtii strain 21 gr, Chlamydomonas suppressor, and Chlorella sorokiniana falls between the values found for the 3.2 and 10.2 μm beads. Different types of algae were also successfully characterized.

Empirical and semi-analytical chlorophyll a algorithms for multi-temporal monitoring of New Zealand lakes using Landsat

The concentration of chlorophyll a (chl a; as a proxy for phytoplankton biomass) provides an indication of the water quality and ecosystem health of lakes. An automated image processing method for Landsat images was used to derive chl a concentrations in 12 Rotorua lakes of North Island, New Zealand, with widely varying trophic status. Semi-analytical and empirical models were used to process 137 Landsat 7 Enhanced Thematic Mapper (ETM+) images using records from 1999 to 2013. Atmospheric correction used radiative transfer modelling, with atmospheric conditions prescribed with Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and AIRS data. The best-performing semi-analytical and empirical equations resulted in similar levels of variation explained (r (2) = 0.68 for both equations) and root-mean-square error (RMSE = 10.69 and 10.43 μg L(-1), respectively) between observed and estimated chl a. However, the symbolic regression algorithm performed better for chl a concentrations <5 μg L(-1). Our Landsat-based algorithms provide a valuable method for synoptic assessments of chl a across the 12 lakes in this region. They also provide a basis for assessing changes in chl a individual lakes through time. Our methods provide a basis for cost-effective hindcasting of lake trophic status at a regional scale, informing on spatial variability of chl a within and between lakes.

Physical forcing mechanisms controlling the variability of chlorophyll-a over the Royal-Charlotte and Abrolhos Banks-Eastern Brazilian Shelf

The Abrolhos Bank is part of the so-called Eastern Brazilian Shelf and is an area of high ecological and economic importance. The bank supports the largest and richest coral reefs in the South Atlantic and the largest rhodolith bed in the world. The spatial and seasonal variation of phytoplankton concentration, however, and the dynamic processes controlling that variability have remained poorly known. The present study investigates the seasonal and spatial distributions of chlorophyll-a (Chl-a) and water conditions by analyzing nine years (2003-2011) of level-3 Moderate-resolution Imaging Spectroradiometer (MODIS) derived Chl-a, National Centers for Environmental Prediction (NCEP)/ETA model-derived winds, NCEP model-derived heat fluxes, thermohaline and velocity results from the Hybrid Circulation Ocean Model (HYCOM) 1/12o assimilated simulation. The results show that low/high concentrations occurred in austral spring-summer (wet season)/autumn-winter (dry season), with the highest values observed in the northern portion of the Abrolhos Bank. The typical meteorological and oceanographic conditions during austral summer favor the development of strong stratification. These conditions are 1) N-NE winds that favor an upwelling-type Ekman circulation; 2) coupling between the open ocean and the continental shelf through the western boundary current, which promotes cooler subsurface water to rise onto the shelf break; and 3) positive net heat flux. In contrast, the S-SE winds during autumn are in the opposite direction of the predominant current system over the Abrolhos Bank, thus reducing their speed and inducing an inverse shear. The warmer ocean and a somewhat cool and dry atmosphere promote the evaporative cooling of the surface layer. The above processes drive mixed layer cooling and deepening that reaches its maximum in winter. The blooming of phytoplankton in the Abrolhos Bank waters appears to be regulated by changes in the mixed layer depth, with Chl-a levels that start to increase during autumn and reach their peak in June-July.

Retrieval of phytoplankton and colored detrital matter absorption coefficients with remote sensing reflectance in an ultraviolet band

The light absorption of phytoplankton and colored detrital matter (CDM), which includes contribution of gelbstoff and detrital matters, has distinctive yet overlapping features in the ultraviolet (UV) and visible domain. The CDM absorption (a(dg)) increases exponentially with decreasing wavelength while the absorption coefficient of phytoplankton (a(ph)) generally decreases toward the shorter bands for the range of 350-450 nm. It has long been envisioned that including ocean color measurements in the UV range may help the separation of these two components from the remotely sensed ocean color spectrum. An attempt is made in this study to provide an analytical assessment of this expectation. We started with the development of an absorption decomposition model [quasi-analytical algorithm (QAA)-UV], analogous to the QAA, that partitions the total absorption coefficient using information at bands 380 and 440 nm. Compared to the retrieval results relying on the absorption information at 410 and 440 nm of the original QAA, our analyses indicate that QAA-UV can improve the retrieval of a(ph) and a(dg), although the improvement in accuracy is not significant for values at 440 nm. The performance of the UV-based algorithm is further evaluated with in situ measurements. The limited improvement observed with the field measurements highlights that the separation of a(dg) and a(ph) is highly dependent on the accuracy of the ocean color measurements and the estimated total absorption coefficient.

Life-history traits of the common snook Centropomus undecimalis in a Caribbean estuary and large-scale biogeographic patterns relevant to management

The ecology of common snook Centropomus undecimalis in Amatique Bay, a tropical estuary in eastern Guatemala, was investigated and life-history traits were used to conduct a meta-analysis of the species from Florida to Brazil. The reproduction cycle of C. undecimalis in Amatique was strongly related to the precipitation cycle, with a lag of 2 months. Spawning occurred from April to November with a peak spawning after the onset of the summer rains. Protandric sex reversal occurred early in the dry season (December) before somatic recovery from spawning. The growth cycle preceded that of body condition by c. 1 month, and was out of phase with the reproductive cycle. Growth was fast, as many individuals reached >70% of the maximum observed total length (LT , 102 cm) after 3 years. Sex transition occurred within a relatively narrow LT range (70-79 cm), but over a wide range of ages, indicating plasticity in this respect. The meta-analysis indicated a latitudinal-temperature gradient in life-history traits, as well as different seasonal patterns relative to temperature and hydrographical cycles. Centropomus undecimalis from cooler winter waters (e.g. Florida) reach larger maximum LT and LT at sex change, as well as greater gonado-somatic indices and longer life spans. Further, increased fishing mortality results in younger age at sex reversal and male predominance in the populations compared. Recognition of large-scale biogeographic patterns in this important, but little studied, fish species helps in the formulation of management advice in other areas of its occurrence.

Energetic plasticity underlies a variable response to ocean acidification in the pteropod, Limacina helicina antarctica

Ocean acidification, caused by elevated seawater carbon dioxide levels, may have a deleterious impact on energetic processes in animals. Here we show that high PCO(2) can suppress metabolism, measured as oxygen consumption, in the pteropod, L. helicina forma antarctica, by ∼20%. The rates measured at 180-380 µatm (MO(2)  =  1.25 M(-0.25), p  =  0.007) were significantly higher (ANCOVA, p  =  0.004) than those measured at elevated target CO(2) levels in 2007 (789-1000 µatm,  =  0.78 M(-0.32), p  =  0.0008; Fig. 1). However, we further demonstrate metabolic plasticity in response to regional phytoplankton concentration and that the response to CO(2) is dependent on the baseline level of metabolism. We hypothesize that reduced regional Chl a levels in 2008 suppressed metabolism and masked the effect of ocean acidification. This effect of food limitation was not, we postulate, merely a result of gut clearance and specific dynamic action, but rather represents a sustained metabolic response to regional conditions. Thus, pteropod populations may be compromised by climate change, both directly via CO(2)-induced metabolic suppression, and indirectly via quantitative and qualitative changes to the phytoplankton community. Without the context provided by long-term observations (four seasons) and a multi-faceted laboratory analysis of the parameters affecting energetics, the complex response of polar pteropods to ocean acidification may be masked or misinterpreted.

Differential extinction and the contrasting structure of polar marine faunas

Background

The low taxonomic diversity of polar marine faunas today reflects both the failure of clades to colonize or diversify in high latitudes and regional extinctions of once-present clades. However, simple models of polar evolution are made difficult by the strikingly different faunal compositions and community structures of the two poles.

Methodology/Principal Findings

A comparison of early Cenozoic Arctic and Antarctic bivalve faunas with modern ones, within the framework of a molecular phylogeny, shows that while Arctic losses were randomly distributed across the tree, Antarctic losses were significantly concentrated in more derived families, resulting in communities dominated by basal lineages. Potential mechanisms for the phylogenetic structure to Antarctic extinctions include continental isolation, changes in primary productivity leading to turnover of both predators and prey, and the effect of glaciation on shelf habitats.

Conclusions/Significance

These results show that phylogenetic consequences of past extinctions can vary substantially among regions and thus shape regional faunal structures, even when due to similar drivers, here global cooling, and provide the first phylogenetic support for the “retrograde” hypothesis of Antarctic faunal evolution.