A decade of satellite ocean color observations

Atlas of phytoplankton phenology indices in selected Eastern Mediterranean marine ecosystems

Phytoplankton is a fundamental component of marine food webs and play a crucial role in marine ecosystem functioning. The phenology (timing of growth) of these microscopic algae is an important ecological indicator that can be utilized to observe its seasonal dynamics, and assess its response to environmental perturbations. Ocean colour remote sensing is currently the only means of obtaining synoptic estimates of chlorophyll-a (a proxy of phytoplankton biomass) at high temporal and spatial resolution, enabling the calculation of phenology metrics. However, ocean colour observations have acknowledged weaknesses compromising its reliability, while the scarcity of long-term in situ data has impeded the validation of satellite-derived phenology estimates. To address this issue, we compared one of the longest available in situ time series (20 years) of chlorophyll-a concentrations in the Eastern Mediterranean Sea (EMS), along with concurrent remotely-sensed observations. The comparison revealed a marked coherence between the two datasets, indicating the capability of satellite-based measurements in accurately capturing the phytoplankton seasonality and phenology metrics (i.e., timing of initiation, duration, peak and termination) in the studied area. Furthermore, by studying and validating these metrics we constructed a satellite-derived phytoplankton phenology atlas, reporting in detail the seasonal patterns in several sub-regions in coastal and open seas over the EMS. The open waters host higher concentrations from late October to April, with maximum levels recorded during February and lowest during the summer period. The phytoplankton growth over the Northern Aegean Sea appeared to initiate at least a month later than the rest of the EMS (initiating in late November and terminating in late May). The coastal waters and enclosed gulfs (such as Amvrakikos and Maliakos), exhibit a distinct seasonal pattern with consistently higher levels of chlorophyll-a and prolonged growth period compared to the open seas. The proposed phenology atlas represents a useful resource for monitoring phytoplankton growth periods in the EMS, supporting water quality management practices, while enhancing our current comprehension on the relationships between phytoplankton biomass and higher trophic levels (as a food source).

Patterns in the temporal complexity of global chlorophyll concentration

Decades of research have relied on satellite-based estimates of chlorophyll-a concentration to identify oceanographic processes and plan in situ observational campaigns; however, the patterns of intrinsic temporal variation in chlorophyll-a concentration have not been investigated on a global scale. Here we develop a metric to quantify time series complexity (i.e., a measure of the ups and downs of sequential observations) in chlorophyll-a concentration and show that seemingly disparate regions (e.g., Atlantic vs Indian, equatorial vs subtropical) in the global ocean can be inherently similar. These patterns can be linked to the regularity of chlorophyll-a concentration change and the likelihood of anomalous events within the satellite record. Despite distinct spatial changes in decadal chlorophyll-a concentration, changes in time series complexity have been relatively consistent. This work provides different metrics for monitoring the global ocean and suggests that the complexity of chlorophyll-a time series can be independent of its magnitude.

Inversion diffuse attenuation coefficient of photosynthetically active radiation based on deep learning

Accurate estimation of the diffuse attenuation coefficient of photosynthetically active radiation, Kd(PAR), is critical for understanding and modeling key physical, chemical, and biological processes in waters. In this study, a deep learning model (DLKPAR) was developed for remotely estimating Kd(PAR). Compared to the traditional empirical algorithms and semi-analytical algorithm, DLKPAR demonstrated an improvement in the model's stability and accuracy. By using in situ NOMAD data to evaluate the model's performance, DLKPAR had lower root mean square difference (RMSD; 0.028 vs. 0.030-0.048 m-1) and mean absolute relative difference (MARD; 0.14 vs. 0.17-0.25) and higher R2 (0.94 vs. 0.82-0.94). The statistical results of the matchup NOMAD and Argo data to the MODIS also indicated DLKPAR improves the inversion accuracy of Kd(PAR) and could be applied to remotely estimate Kd(PAR) in the global oceans. Therefore, we anticipate that DLKPAR could yield reliable Kd(PAR) values from ocean color remote sensing, providing an accurate estimation of visible light attenuation in the upper ocean and facilitating biogeochemical cycle research.

Bayesian approach to a generalized inherent optical property model

Relationships between the absorption and backscattering coefficients of marine optical constituents and ocean color, or remote sensing reflectances R_rs(λ), can be used to predict the concentrations of these constituents in the upper water column. Standard inverse modeling techniques that minimize error between the modeled and observed R_rs(λ) break down when the number of products retrieved becomes similar to, or greater than, the number of different ocean color wavelengths measured. Furthermore, most conventional ocean reflectance inversion approaches, such as the default configuration of NASA's Generalized Inherent Optical Properties algorithm framework (GIOP-DC), require a priori definitions of absorption and backscattering spectral shapes. A Bayesian approach to GIOP is implemented here to address these limitations, where the retrieval algorithm minimizes both the error in retrieved ocean color and the deviation from prior knowledge, calculated using output from a mixture of empirically-derived and best-fit values. The Bayesian approach offers potential to produce an expanded range of parameters related to the spectral shape of absorption and backscattering spectra.

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.

Winter phytoplankton size classes in the Northeastern Arabian Sea based on in-situ and remote sensing methods

Phytoplankton size classes (PSCs) are important in marine ecosystems because they organise the food chain and trophic pathways, which determine the overall biological environment. Based on three FORV Sagar Sampada cruises, the current study provides changes in PSCs in the Northeastern Arabian Sea (NEAS; north of 18 N) during different phases of the Northeast Monsoon [NEM (November-February)]. During all three phases of NEM such as early (November), peak (December), and late (February), in-situ chlorophyll-a fractionation data revealed that nanoplankton (2-20 μm) predominated, followed by microplankton (>20 μm) and picoplankton (0.2-2.0 μm). This was primarily because winter convective mixing in the NEAS maintains only a moderate level of nutrients in the surface mixed layer, which is more conducive to the dominance of nanoplankton. Brewin et al. (2012) and Sahay et al. (2017) have satellite-based PSC estimation algorithms; the former was developed for the entire Indian Ocean, while the latter is a modification of the former for the Noctiluca bloom-infested NEAS, with a claim that such blooms are typical of the NEM. When current in-situ PSCs data were compared to algorithm-based NEM data, Brewin et al. (2012) revealed a more realistic PSCs contribution pattern, especially in oceanic waters, with nanoplankton predominating except during early NEM. But the PSCs data from Sahay et al. (2017) showed a high degree of variation from the in-situ data, demonstrating the dominance of pico- and microplankton and a notably small contribution from the nano phytoplankton. The current study showed that Sahay et al. (2017) is inferior to Brewin et al. (2012) at quantifying PSCs in the NEAS without Noctiluca blooms, and provided evidence to show that Noctiluca blooms are not a typical feature of the region during the NEM.

Quantifying the Ocean's Biological Pump and Its Carbon Cycle Impacts on Global Scales

The biological pump transports organic matter, created by phytoplankton productivity in the well-lit surface ocean, to the ocean's dark interior, where it is consumed by animals and heterotrophic microbes and remineralized back to inorganic forms. This downward transport of organic matter sequesters carbon dioxide from exchange with the atmosphere on timescales of months to millennia, depending on where in the water column the respiration occurs. There are three primary export pathways that link the upper ocean to the interior: the gravitational, migrant, and mixing pumps. These pathways are regulated by vastly different mechanisms, making it challenging to quantify the impacts of the biological pump on the global carbon cycle. In this review, we assess progress toward creating a global accounting of carbon export and sequestration via the biological pump and suggest a path toward achieving this goal.

Effects of Climate Change on Chlorophyll a in the Barents Sea: A Long-Term Assessment

The Arctic climate strongly affects phytoplankton production and biomass through several mechanisms, including warming, sea ice retreat, and global atmospheric processes. In order to detect the climatic changes in phytoplankton biomass, long-term variability of chlorophyll a (Chl-a) was estimated in situ with the changes in the surface sea temperature (SST) and salinity (SSS) in the Barents Sea and adjacent waters during the period of 1984-2021. Spatial differences were detected in SST, SSS, and Chl-a. Chl-a increased parallel to SST in the summer-autumn and spring periods, respectively. Chl-a peaks were found near the ice edge and frontal zones in the spring season, while the highest measures were observed in the coastal regions during the summer seasons. SST and Chl-a demonstrated increasing trends with greater values during 2010-2020. Generalized additive models (GAMs) revealed that SST and Chl-a were positively related with year. Climatic and oceanographic variables explained significant proportions of the Chl-a fluctuations, with six predictors (SST, annual North Atlantic Oscillation index, temperature/salinity anomalies at the Kola Section, and sea ice extent in April and September) being the most important. GAMs showed close associations between increasing Chl-a and a decline in sea ice extent and rising water temperature. Our data may be useful for monitoring the Arctic regions during the era of global changes and provide a basis for future research on factors driving phytoplankton assemblages and primary productivity in the Barents Sea.

Application and recent progress of inland water monitoring using remote sensing techniques

Hyperspectral remote sensing, which retrieves the water quality parameters by direct high-resolution analysis of the electromagnetic spectrum reflected from the water surface, has been widely applied for inland water quality detection. Such a new approach provides an opportunity to generate real-time data from water with the noncontact method, largely improving working efficiency. By summarizing the development and current applications of hyperspectral remote sensing, we compare the relative merits of varying remote sensing platforms, popular inversion models, and the application of hyperspectral monitoring of chlorophyll-a (Chl-a), transparency, total suspended solids (TSS), colored dissolved organic matter (CDOM), phycocyanin (PC), total phosphorus (TP), and total nitrogen (TN) water quality parameters. Most studies have focused on spaceborne remote sensing, which is usually used to monitor large waterbodies for Chl-a and other water quality parameters with optical properties; semiempirical, bio-optical, and semianalytical models are frequently used. With the rapid development of aerospace technology and near-surface remote sensing, the spectral resolution of remote sensing imaging technology has been dramatically improved and has begun to be applied to small waterbodies. In the future, the multiplatform linkage monitoring approach may become a new research direction. Advanced computer technology has also enabled machine learning models to be applied to water quality parameter inversion, and machine learning models have higher robustness than the three commonly used models mentioned above. Although nitrogen and phosphorus, with nonoptical properties, have also received attention and research from some scholars in recent years, the uncertainty of their mechanisms makes it necessary to maintain a cautious attitude when treating such research.

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.

A synthetic likelihood approach for intractable markov random fields

We propose a new scalable method to approximate the intractable likelihood of the Potts model. The method decomposes the original likelihood into products of many low-dimensional conditional terms, and a Monte Carlo method is then proposed to approximate each of the small terms using their corresponding (exact) Multinomial distribution. The resulting tractable synthetic likelihood then serves as an approximation to the true likelihood. The method is scalable with respect to lattice size and can also be used for problems with irregular lattices. We provide theoretical justifications for our approach, and carry out extensive simulation studies, which show that our method performs at least as well as existing methods, whilst providing significant computational savings, up to ten times faster than the current fastest method. Finally, we include three real data applications for illustration.

Subsurface phytoplankton vertical structure observations using offshore fixed platform-based lidar in the Bohai Sea for offshore responses to Typhoon Bavi

Subsurface phytoplankton vertical structure was observed using an offshore fixed platform-based lidar in the Bohai Sea for the first time. The lidar obtained two periods of continuous day-and-night measurements for a week. A hybrid retrieval method for the optical properties and chllorophyll-a concentration vertical structure of seawater using lidar data was developed. We studied offshore subsurface phytoplankton vertical variation responses to Typhoon Bavi. Significant changes in the intensity and depth of the subsurface phytoplankton maximum layer in the Bohai Sea may result from horizonal advection, light availability, and rainfall dilution following Typhoon Bavi. Preliminary results suggested that lidar measurements provide a new approach for understanding oceanic dynamics mechanisms at the submeso-mesoscale.

SOLS: An Open-Source Spaceborne Oceanic Lidar Simulator

In recent years, oceanic lidar has seen a wide range of oceanic applications, such as optical profiling and detecting bathymetry. Furthermore, spaceborne lidars, CALIOP and ICESat-2, designed for atmospheric and ice science applications, have been used for ocean backscattering retrievals, but, until now, there has been no spaceborne lidar specifically designed for ocean detection. There is a demand for an effective lidar simulator to study the detection potential capability of spaceborne oceanic lidar. In this study, an open-source spaceborne oceanic lidar simulator named SOLS was developed, which is available freely. Moreover, the maximum detectable depth and corresponding optimal wavelength for spaceborne lidar were analyzed at a global scale by using SOLS. The factors controlling detection limits of a spaceborne ocean profiling lidar in different cases were discussed. Then, the maximum detectable depths with different relative measurement errors and the influence of solar background radiance were estimated. Subsequently, the effects of laser and detector parameters on maximum detectable depths were studied. The relationship between the lidar detectable depth and the ocean mixed layer depth was also discussed. Preliminary results show that the maximum detectable depth could reach deeper than 120 m in the oligotrophic sea at low latitudes. We found that 490 nm is the optimal wavelength for most of the open seawater. For coastal water, 532 nm is a more suitable choice considering both the technical maturity and geophysical parameters. If possible, a lidar equipped with 440 nm could achieve the greatest depth in oligotrophic seawater in subtropical gyres north and south of the equator. The upper mixed layer vertical structure in most of the global open ocean is within the lidar maximum detectable depth. These results show that SOLS can help the design of future spaceborne oceanic lidar systems a lot.

Using Chlorophyll Fluorescence to Determine the Fate of Photons Absorbed by Phytoplankton in the World's Oceans

Approximately 45% of the photosynthetically fixed carbon on Earth occurs in the oceans in phytoplankton, which account for less than 1% of the world's photosynthetic biomass. This amazing empirical observation implies a very high photosynthetic energy conversion efficiency, but how efficiently is the solar energy actually used? The photon energy budget of photosynthesis can be divided into three terms: the quantum yields of photochemistry, fluorescence, and heat. Measuring two of these three processes closes the energy budget. The development of ultrasensitive, seagoing chlorophyll variable fluorescence and picosecond fluorescence lifetime instruments has allowed independent closure on the first two terms. With this closure, we can understand how phytoplankton respond to nutrient supplies on timescales of hours to months and, over longer timescales, to changes in climate.

Consistency between Satellite Ocean Colour Products under High Coloured Dissolved Organic Matter Absorption in the Baltic Sea

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua OC radiometric products were assessed using Baltic Sea in situ remote sensing reflectance (Rrs) from ferry tracks (Alg@line) and at two Aerosol Robotic Network for Ocean Colour (AERONET-OC) sites from April 2016 to September 2018. A range of atmospheric correction (AC) processors for OLCI-A were evaluated. POLYMER performed best with <23 relative % difference at 443, 490 and 560 nm compared to in situ Rrs and 28% at 665 nm, suggesting that using this AC for deriving Chl a will be the most accurate. Suomi-VIIRS and MODIS-Aqua underestimated Rrs by 35, 29, 22 and 39% and 34, 22, 17 and 33% at 442, 486, 560 and 671 nm, respectively. The consistency between different AC processors for OLCI-A and MODIS-Aqua and VIIRS products was relatively poor. Applying the POLYMER AC to OLCI-A, MODIS-Aqua and VIIRS may produce the most accurate Rrs and Chl a products and OC time series for the Baltic Sea.

Variability in spring phytoplankton blooms associated with ice retreat timing in the Pacific Arctic from 2003-2019

The Arctic is experiencing rapid changes in sea-ice seasonality and extent, with significant consequences for primary production. With the importance of accurate monitoring of spring phytoplankton dynamics in a changing Arctic, this study further examines the previously established critical relationship between spring phytoplankton bloom types and timing of the sea-ice retreat for broader temporal and spatial coverages, with a particular focus on the Pacific Arctic for 2003–2019. To this end, time-series of satellite-retrieved phytoplankton biomass were modeled using a parametric Gaussian function, as an effective approach to capture the development and decay of phytoplankton blooms. Our sensitivity analysis demonstrated accurate estimates of timing and presence/absence of peaks in phytoplankton biomass even with some missing values, suggesting the parametric Gaussian function is a powerful tool for capturing the development and decay of phytoplankton blooms. Based on the timing and presence/absence of a peak in phytoplankton biomass and following the classification developed by the previous exploratory work, spring bloom types are classified into three groups (under-ice blooms, probable under-ice blooms, and marginal ice zone blooms). Our results showed that the proportion of under-ice blooms was higher in the Chukchi Sea than in the Bering Sea. The probable under-ice blooms registered as the dominant bloom types in a wide area of the Pacific Arctic, whereas the marginal ice zone bloom was a relatively minor bloom type across the Pacific Arctic. Associated with a shift of sea-ice retreat timing toward earlier dates, we confirmed previous findings from the Chukchi Sea of recent shifts in phytoplankton bloom types from under-ice blooms to marginal ice zone blooms and demonstrated that this pattern holds for the broader Pacific Arctic sector for the time period 2003–2019. Overall, the present study provided additional evidence of the changing sea-ice retreat timing that can drive variations in phytoplankton bloom dynamics, which contributes to addressing the detection and consistent monitoring of the biophysical responses to the changing environments in the Pacific Arctic.

LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah

Harmful algal bloom (HAB) events have alarmed authorities of human health that have caused severe illness and fatalities, death of marine organisms, and massive fish killings. This work aimed to perform the long short-term memory (LSTM) method and convolution neural network (CNN) method to predict the HAB events in the West Coast of Sabah. The results showed that this method could be used to predict satellite time series data in which previous studies only used vector data. This paper also could identify and predict whether there is HAB occurrence in the region. A chlorophyll a concentration (Chl-a; mg/L) variable was used as an HAB indicator, where the data were obtained from MODIS and GEBCO bathymetry. The eight-day dataset interval was from January 2003 to December 2018. The results obtained showed that the LSTM model outperformed the CNN model in terms of accuracy using RMSE and the correlation coefficient r as the statistical criteria.

Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the Particulate Backscattering Coefficient

We explored how algorithm (model) and in situ measurement (observation) uncertainties can effectively be incorporated into empirical ocean color model development and assessment. In this study we focused on methods for deriving the particulate backscattering coefficient at 555 nm, b bp (555) (m-1). We developed a simple empirical algorithm for deriving b bp (555) as a function of a remote sensing reflectance line height (LH) metric. Model training was performed using a high-quality bio-optical dataset that contains coincident in situ measurements of the spectral remote sensing reflectances, R rs (λ) (sr-1), and the spectral particulate backscattering coefficients, b bp (λ). The LH metric used is defined as the magnitude of R rs (555) relative to a linear baseline drawn between R rs (490) and R rs (670). Using an independent validation dataset, we compared the skill of the LH-based model with two other models. We used contemporary validation metrics, including bias and mean absolute error (MAE), that were corrected for model and observation uncertainties. The results demonstrated that measurement uncertainties do indeed impact contemporary validation metrics such as mean bias and MAE. Zeta-scores and z-tests for overlapping confidence intervals were also explored as potential methods for assessing model skill.

Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure.

Assessing Summertime Primary Production Required in Changed Marine Environments in Upwelling Ecosystems Around the Taiwan Bank

The Taiwan Bank (TB) is located in the southern Taiwan Strait, where the marine environments are affected by South China Sea Warm Current and Kuroshio Branch Current in summer. The bottom water flows upward along the edge of the continental shelf, forming an upwelling region that is an essential high-productivity fishing ground. Using trophic dynamic theory, fishery resources can be converted into primary production required (PPR) by primary production, which indicates the environmental tolerance of marine ecosystems. This study calculated the PPR of benthic and pelagic species, sea surface temperature (SST), upwelling size, and net primary production (NPP) to analyze fishery resource structure and the spatial distribution of PPR in upwelling, non-upwelling, and thermal front (frontal) areas of the TB in summer. Pelagic species, predominated by those in the Scombridae, Carangidae families and Trachurus japonicus, accounted for 77% of PPR (67% of the total catch). The benthic species were dominated by Mene maculata and members of the Loliginidae family. The upwelling intensity was the strongest in June and weakest in August. Generalized additive models revealed that the benthic species PPR in frontal habitats had the highest deviance explained (28.5%). Moreover, frontal habitats were influenced by NPP, which was also the main factor affecting the PPR of benthic species in all three habitats. Pelagic species were affected by high NPP, as well as low SST and negative values of the multivariate El Niño–Southern Oscillation (ENSO) index in upwelling habitats (16.9%) and non-upwelling habitats (11.5%). The composition of pelagic species varied by habitat; this variation can be ascribed to impacts from the ENSO. No significant differences were noted in benthic species composition. Overall, pelagic species resources are susceptible to climate change, whereas benthic species are mostly insensitive to climatic factors and are more affected by NPP.

Uncertainty assessment of unattended above-water radiometric data collection from research vessels with the Dynamic Above-water Radiance (L) and Irradiance (E) Collector (DALEC)

We used above- and below-water radiometry measurements collected during a research voyage in the eastern Indian Ocean to assess uncertainties in deriving the remote sensing reflectance, R_rs, from unattended above-water radiometric data collection with the In-Situ Marine Optics Pty. Ltd. (IMO) Dynamic Above-water Radiance (L) and Irradiance (E) Collector (DALEC). To achieve this, the R_rs values derived from using the latest version of this hyperspectral radiometer were compared to values obtained from two in-water profiling radiometer systems of rather general use in the ocean optics research community, i.e., the Biospherical Instruments Inc. Compact Optical Profiling System (C-OPS) and the Seabird HyperPro II. Our results show that unattended, carefully quality-controlled, DALEC measurements provide R_rs for wavelengths < 600 nm that match those derived from the in-water systems with no bias and a dispersion of about 8%, provided that the appropriate technique is used to quantify the contribution of sky light reflection to the measured signal. The dispersion is larger (25-50%) for red bands, which is expected for clear oligotrophic waters as encountered during the voyage, where ∼2 10^-5 < R_rs < ∼2 10^-4 sr^-1. For comparison, the two in-water systems provided R_rs in agreement within 4% for wavelengths < 600 nm.

Appraisal of sedimentary alkenones for the quantitative reconstruction of phytoplankton biomass

Marine primary productivity (PP) is the driving factor in the global marine carbon cycle. Its reconstruction in past climates relies on biogeochemical proxies that are not considered to provide an unequivocal signal. These are often based on the water column flux of biogenic components to sediments (organic carbon, biogenic opal, biomarkers), although other factors than productivity are posited to control the sedimentary contents of the components, and their flux is related to the fraction of export production buried in sediments. Moreover, most flux proxies have not been globally appraised. Here, we assess a proxy to quantify past phytoplankton biomass by correlating the concentration of C37 alkenones in a global suite of core-top sediments with sea surface chlorophyll-a (SSchla) estimates over the last 20 y. SSchla is the central metric to calculate phytoplankton biomass and is directly related to PP. We show that the global spatial distribution of sedimentary alkenones is primarily correlated to SSchla rather than diagenetic factors such as the oxygen concentration in bottom waters, which challenges previous assumptions on the role of preservation on driving concentrations of sedimentary organic compounds. Moreover, our results suggest that the rate of global carbon export to sediments is not regionally constrained, and that alkenones producers play a dominant role in the global export of carbon buried in the seafloor. This study shows the potential of using sedimentary alkenones to estimate past phytoplankton biomass, which in turn can be used to infer past PP in the global ocean.

Skin Sea-Surface Temperature from VIIRS on Suomi-NPP—NASA Continuity Retrievals

Retrievals of skin Sea-Surface Temperature (SSTskin) from the measurements of the Visible Infrared Imaging Radiometer Suite on the Suomi-National Polar-orbiting Partnership satellite are presented and discussed. The algorithms used to derive the SSTskin from the radiometric measurements are given in detail. A number of approaches to assess the accuracy and stability of the Visible Infrared Imaging Radiometer Suite (VIIRS) SSTskin retrievals are reported, and factors including latitude and season, and physical processes in the atmosphere and at the surface are discussed. We conclude that the Suomi National Polar-orbiting Partnership (S-NPP) VIIRS is capable of matching and improving upon the accuracies of SSTskin from the MODISs on Terra and Aqua, and that the VIIRS SSTskin fields have the potential to contribute to the extension of the satellite-derived Climate Data Records of SST into the future.

Comparison of Cloud-Filling Algorithms for Marine Satellite Data

Marine remote sensing provides comprehensive characterizations of the ocean surface across space and time. However, cloud cover is a significant challenge in marine satellite monitoring. Researchers have proposed various algorithms to fill data gaps “below the clouds”, but a comparison of algorithm performance across several geographic regions has not yet been conducted. We compared ten basic algorithms, including data-interpolating empirical orthogonal functions (DINEOF), geostatistical interpolation, and supervised learning methods, in two gap-filling tasks: the reconstruction of chlorophyll a in pixels covered by clouds, and the correction of regional mean chlorophyll a concentrations. For this purpose, we combined tens of cloud-free images with hundreds of cloud masks in four study areas, creating thousands of situations in which to test the algorithms. The best algorithm depended on the study area and task, and differences between the best algorithms were small. Ordinary Kriging, spatiotemporal Kriging, and DINEOF worked well across study areas and tasks. Random forests reconstructed individual pixels most accurately. We also found that high levels of cloud cover led to considerable errors in estimated regional mean chlorophyll a concentration. These errors could, however, be reduced by about 50% to 80% (depending on the study area) with prior cloud-filling.

An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

Chlorophyll-a (Chl-a) is an objective biological indicator, which reflects the nutritional status of coastal waters. However, the turbid coastal waters pose challenges to the application of existing Chl-a remote sensing models of case II waters. Based on the bio-optical models, we analyzed the suppression of coastal total suspended matter (TSM) on the Chl-a optical characteristics and developed an improved model using the imagery from a hyper-spectrometer mounted on an unmanned aerial vehicle (UAV). The new model was applied to estimate the spatiotemporal distribution of Chl-a concentration in coastal waters of Qingdao on 17 December 2018, 22 March 2019, and 20 July 2019. Compared with the previous models, the correlation coefficients (R2) of Chl-a concentrations retrieved by the new model and in situ measurements were greatly improved, proving that the new model shows a better performance in retrieving coastal Chl-a concentration. On this basis, the spatiotemporal variations of Chl-a in Qingdao coastal waters were analyzed, showing that the spatial variation is mainly related to the TSM concentration, wind waves, and aquaculture, and the temporal variation is mainly influenced by the sea surface temperature (SST), sea surface salinity (SSS), and human activities.

Regional surface chlorophyll trends and uncertainties in the global ocean

Changes in marine primary productivity are key to determine how climate change might impact marine ecosystems and fisheries. Satellite ocean color sensors provide coverage of global ocean chlorophyll with a combined record length of ~ 20 years. Coupled physical–biogeochemical models can inform on expected changes and are used here to constrain observational trend estimates and their uncertainty. We produce estimates of ocean surface chlorophyll trends, by using Coupled Model Intercomparison Project (CMIP5) models to form priors as a “first guess”, which are then updated using satellite observations in a Bayesian spatio-temporal model. Regional chlorophyll trends are found to be significantly different from zero in 18/23 regions, in the range ± 1.8% year⁻¹. A global average of these regional trends shows a net positive trend of 0.08 ± 0.35% year⁻¹, highlighting the importance of considering chlorophyll changes at a regional level. We compare these results with estimates obtained with the commonly used “vague” prior, representing no independent knowledge; coupled model priors are shown to slightly reduce trend magnitude and uncertainties in most regions. The statistical model used here provides a robust framework for making best use of all available information and can be applied to improve understanding of global change.

A Dual-Wavelength Ocean Lidar for Vertical Profiling of Oceanic Backscatter and Attenuation

Ocean water column information profiles are essential for ocean research. Currently, water column profiles are typically obtained by ocean lidar instruments, including spaceborne, airborne and shipborne lidar, most of which are equipped with a 532 nm laser; however, blue wavelength penetrates more for open ocean detection. In this paper, we present a novel airborne dual-wavelength ocean lidar (DWOL), equipped with a 532 and 486 nm laser that can operate simultaneously. This instrument was designed to compare the performance of 486 and 532 nm lasers in a single detection area and to provide a reference for future spaceborne oceanic lidar (SBOL) design. Airborne and shipborne experiments were conducted in the South China Sea. Results show that—for a 500-frame accumulation—the 486 nm channel obtained volume profiles from a depth of approximately 100 m. In contrast, the vertical profiles obtained by the 532 nm channel only reached in a depth of 75 m, which was approximately 25% less than that of 486 m channel in the same detection area. Results from the inverse lidar attenuation coefficient α(z) for the DWOL show that the maximum value of α(z) ranged from 40 to 80 m, which was consistent with the chlorophyll-scattering layer (CSL) distribution measured by the shipborne instrument. Additionally, α486(z) decreased for depth beyond 80 m, indicating that the 486 nm laser can potentially penetrate the entire CSL.

A Semianalytic Monte Carlo Simulator for Spaceborne Oceanic Lidar: Framework and Preliminary Results

Spaceborne lidar (light detection and ranging) is a very promising tool for the optical properties of global atmosphere and ocean detection. Although some studies have shown spaceborne lidar’s potential in ocean application, there is no spaceborne lidar specifically designed for ocean studies at present. In order to investigate the detection mechanism of the spaceborne lidar and analyze its detection performance, a spaceborne oceanic lidar simulator is established based on the semianalytic Monte Carlo (MC) method. The basic principle, the main framework, and the preliminary results of the simulator are presented. The whole process of the laser emitting, transmitting, and receiving is executed by the simulator with specific atmosphere–ocean optical properties and lidar system parameters. It is the first spaceborne oceanic lidar simulator for both atmosphere and ocean. The abilities of this simulator to characterize the effect of multiple scattering on the lidar signals of different aerosols, clouds, and seawaters with different scattering phase functions are presented. Some of the results of this simulator are verified by the lidar equation. It is confirmed that the simulator is beneficial to study the principle of spaceborne oceanic lidar and it can help develop a high-precision retrieval algorithm for the inherent optical properties (IOPs) of seawater.

Bayesian retrieval of optically relevant properties from hyperspectral water-leaving reflectances

Current methods to retrieve optically relevant properties from ocean color observations do not explicitly make use of prior knowledge about property distributions. Here we implement a simplified Bayesian approach that takes into account prior probability distributions on two sets of five optically relevant parameters, and conduct a retrieval of these parameters using hyperspectral simulated water-leaving reflectances. We focus specifically on the ability of the model to distinguish between two optically similar phytoplankton taxa, diatoms and Noctiluca scintillans. The inversion retrieval gives most-likely concentrations and uncertainty estimates, and we find that the model is able to probabilistically predict the occurrence of Noctiluca scintillans blooms using these metrics. We discuss how this method can be expanded to include a priori covariances between different parameters, and show the effect of varying measurement uncertainty and spectral resolution on Noctiluca scintillans bloom predictions.

Assessing the Effect of Tandem Phase Sentinel-3 OLCI Sensor Uncertainty on the Estimation of Potential Ocean Chlorophyll-a Trends

The Sentinel-3 tandem project represents the first time that two ocean colour satellites have been flown in the same orbit with minimal temporal separation (~30 s), thus allowing them to have virtually identical views of the ocean. This offers an opportunity for understanding how differences in individual sensor uncertainty can affect conclusions drawn from the data. Here, we specifically focus on trend estimation. Observational chlorophyll-a uncertainty is assessed from the Sentinel-3A Ocean and Land Colour Imager (OLCI-A) and Sentinel-3B OLCI (OLCI-B) sensors using a bootstrapping approach. Realistic trends are then imposed on a synthetic chlorophyll-a time series to understand how sensor uncertainty could affect potential long-term trends in Sentinel-3 OLCI data. We find that OLCI-A and OLCI-B both show very similar trends, with the OLCI-B trend estimates tending to have a slightly wider distribution, although not statistically different from the OLCI-A distribution. The spatial pattern of trend estimates is also assessed, showing that the probability distributions of trend estimates in OLCI-A and OLCI-B are most similar in open ocean regions, and least similar in coastal regions and at high northern latitudes. This analysis shows that the two sensors should provide consistent trends between the two satellites, provided future ageing is well quantified and mitigated. The Sentinel-3 programme offers a strong baseline for estimating long-term chlorophyll-a trends by offering a series of satellites (starting with Sentinel-3A and Sentinel-3B) that use the same sensor design, reducing potential issues with cross-calibration between sensors. This analysis contributes an important understanding of the reliability of the two current Sentinel-3 OLCI sensors for future studies of climate change driven chlorophyll-a trends.

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

Satellite-measured water properties in high altitude Lake Tahoe

It has been difficult in satellite remote sensing to derive accurate water optical, biological, and biogeochemical products over high-altitude inland waters due to issues in satellite data processing (i.e., atmospheric correction). In this study, we demonstrate that accurate normalized water-leaving radiance spectra nL_w(λ) can be derived for a high-altitude lake, Lake Tahoe, using improved Rayleigh radiance computations (Wang, M., Opt. Express, 24, 12414-12429, 2016) which accurately account for water surface altitude effects in the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) between 2012 and 2018 are used to evaluate and validate satellite-derived nL_w(λ) spectra, and to quantitatively characterize water properties in the lake. Results show that VIIRS-derived nL_w(λ) spectra are quite comparable with those from the in situ measurements. Subsequent retrievals of water biological and biogeochemical products show that chlorophyll-a (Chl-a) concentration and Secchi depth (SD) are reasonably well-estimated, and captured normal seasonal variations in the lake, e.g., the annual highest Chl-a and SD normally occur in the winter while the lowest occur in the summer, which is consistent with in situ measurements. Interannual variability of these water quality parameters is also observed. In particular, Lake Tahoe experienced a significant environmental anomaly associated with an extreme weather condition event in 2017, showing considerably decreased nL_w(λ) at the spectral bands of 410, 443, and 486 nm, and significantly reduced SD values in the entire lake. The low SD measurements from VIIRS are consistent with in situ observations. Following the event in the 2017-2018 winter, Lake Tahoe recovered and returned to its typical conditions in spring 2018.

Assessment of the Number of Valid Observations and Diurnal Changes in Chl-a for GOCI: Highlights for Geostationary Ocean Color Missions

The first geostationary ocean color satellite mission (geostationary ocean color imager, or GOCI) has provided eight hourly observations per day over the western Pacific region since June 2010. GOCI imagery has been widely used to track the short-term dynamics of coastal and inland waters. Few studies have been performed to comprehensively assess the advantages of GOCI images in obtaining valid observations and estimating diurnal changes within the water column. Using the entire mission dataset between 2011 and 2017, these knowledge gaps were filled by comparing the daily percentages of valid observations (DPVOs) between GOCI and MODIS Aqua (MODISA) and by examining the diurnal changes in Chl-a over the East China Sea. The mean DPVOs of GOCI was 152.6% over the clear open ocean, suggesting that a daily valid coverage could be expected with GOCI. The GOCI DPVOs were ~26 times greater than the MODISA DPVOs; this pronounced difference was caused by the combined effects of their different observational frequencies and the more conservative quality flag system for MODISA. Diurnal changes in the GOCI-derived Chl-a were also found, with generally higher Chl-a in the afternoon than the morning and pronounced heterogeneities in the temporal and spatial domains. However, whether such diurnal changes are due to the real dynamics of the oceanic waters or artifacts of the satellite retrievals remains to be determined. This study provides the first comprehensive quantification of the unparalleled advantages of geostationary ocean color missions over polar orbiters, and the results highlights the importance of geostationary ocean color missions in studying coastal and inland waters.

A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications

Light emerging from natural water bodies and measured by radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities for characterizing aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; the NASA Surface Biology and Geology designated observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP-AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries, and oceans. Specific in situ measurements include remote-sensing reflectance, irradiance reflectance, and coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.902230 (Casey et al., 2019).

Impact of ship on radiometric measurements in the field: a reappraisal via Monte Carlo simulations

The presence of a ship in water disturbs the ambient light field and propagates errors to radiometric measurements. This study investigated the ship perturbation via Monte Carlo simulations with a reflective 3D ship. It is found that the height of ship could cause significant perturbation. However, these perturbations could be compensated by the reflection of the ship's hull, where such compensations vary from sun angle to hull's reflectance. Further, as a rule of thumb, to keep the perturbation on water-leaving radiance under ∼3% from an operating ship, a look-up table is generated with the requirements of viewing angle for the radiometers operated at the deck and for the deployment distance of floating and profiling instruments.

Phytoplankton in the Tara Ocean

Photosynthesis evolved in the ocean more than 2 billion years ago and is now performed by a wide range of evolutionarily distinct organisms, including both prokaryotes and eukaryotes. Our appreciation of their abundance, distributions, and contributions to primary production in the ocean has been increasing since they were first discovered in the seventeenth century and has now been enhanced by data emerging from the Tara Oceans project, which performed a comprehensive worldwide sampling of plankton in the upper layers of the ocean between 2009 and 2013. Largely using recent data from Tara Oceans, here we review the geographic distributions of phytoplankton in the global ocean and their diversity, abundance, and standing stock biomass. We also discuss how omics-based information can be incorporated into studies of photosynthesis in the ocean and show the likely importance of mixotrophs and photosymbionts.

Impacts of Physical and Biological Processes on Spatial and Temporal Variability of Particulate Organic Carbon in the North Pacific Ocean during 2003-2017

The North Pacific Ocean is a significant carbon sink region, but little is known about the dynamics of particulate organic carbon (POC) and the influences of physical and biological processes in this region at the basin scale. Here, we analysed high-resolution surface POC data derived from MODIS-Aqua during 2003-2017, together with satellite-derived sea surface chlorophyll and temperature (SST). There are large spatial and temporal variations in surface POC in the North Pacific. Surface POC is much lower in the subtropical region (<50 mg m-3) than in the subarctic region (>100 mg m-3), primarily resulting from the south-to-north variability in biological production. Our analyses show significant seasonal and interannual variability in surface POC. In particular, there is one peak in winter-spring in the western subtropical region and two peaks in late spring and fall in the western subarctic region. Surface POC is positively correlated with chlorophyll (r = ~1) and negatively correlated with SST (r = ~-0.45, P < 0.001) south of 45°N, indicating the strong influence of physically driven biological activity on the temporal variability of POC in the subtropical region. There is a significantly positive but relatively lower correlation coefficient (0.6-0.8) between POC and chlorophyll and an overall non-significantly positive correlation between POC and SST north of 45°N, reflecting the reduction in the POC standing stock due to the fast sinking of large particles. The climate modes of the Pacific Decadal Oscillation, El Niño-Southern Oscillation and North Pacific Gyre Oscillation have large impacts on POC in various seasons in the subtropical region and weak influences in the subarctic region. Surface POC was anomalously high after 2013 (increased by ~15%) across the basin, which might be the result of complex interactions of physical and biological processes associated with an anomalous warming event (the Blob).

Comparing level-2 and level-3 satellite ocean color retrieval validation methodologies

Many ocean color data applications leverage global spatially composited level-3 (L3) satellite data because of their regular Earth-grid frame of reference. However, ocean color satellite retrieval performance is routinely evaluated on level-2 (L2) data at the native satellite swath resolution and geometries. This study assesses how accurately binned and gridded L3 data represent L2 satellite data products via satellite-to-in situ match-up activities. L2 and L3 satellite data retrievals of the photosynthetic pigment chlorophyll-a are compared with a common in situ dataset, revealing similar L2 and L3 satellite-to-in situ performance for both MODIS-Aqua and VIIRS-SNPP. This agreement implies that L2 validation results are generally applicable to L3 data. However, uncertainties are introduced during the generation of L3 data from L2 data. L3 data comparisons introduce a wider temporal window between the time of in situ measurement and the time of the satellite observation, which can unintentionally reflect on the quality of the satellite retrieval or algorithm performance. The choice of L3 map projection may introduce additional uncertainty by spatially distorting the true location of the satellite retrievals. Each manipulation of satellite data beyond the instrument's native spatiotemporal reference (L2) reduces the applicability of L2 validation results to higher data processing levels.

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.

Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms

Spectroradiometric satellite observations of the ocean are commonly referred to as "ocean color" remote sensing. NASA has continuously collected, processed, and distributed ocean color datasets since the launch of the Sea-viewing Wide-field-of-view Sensor (SeaWiFS) in 1997. While numerous ocean color algorithms have been developed in the past two decades that derive geophysical data products from sensor-observed radiometry, few papers have clearly demonstrated how to estimate measurement uncertainty in derived data products. As the uptake of ocean color data products continues to grow with the launch of new and advanced sensors, it is critical that pixel-by-pixel data product uncertainties are estimated during routine data processing. Knowledge of uncertainties can be used when studying long-term climate records, or to assist in the development and performance appraisal of bio-optical algorithms. In this methods paper we provide a comprehensive overview of how to formulate first-order first-moment (FOFM) calculus for propagating radiometric uncertainties through a selection of bio-optical models. We demonstrate FOFM uncertainty formulations for the following NASA ocean color data products: chlorophyll-a pigment concentration (Chl), the diffuse attenuation coefficient at 490 nm (K _d,490), particulate organic carbon (POC), normalized fluorescent line height (nflh), and inherent optical properties (IOPs). Using a quality-controlled in situ hyperspectral remote sensing reflectance (R _rs,i ) dataset, we show how computationally inexpensive, yet algebraically complex, FOFM calculations may be evaluated for correctness using the more computationally expensive Monte Carlo approach. We compare bio-optical product uncertainties derived using our test R _rs dataset assuming spectrally-flat, uncorrelated relative uncertainties of 1, 5, and 10%. We also consider spectrally dependent, uncorrelated relative uncertainties in R _rs . The importance of considering spectral covariances in R _rs , where practicable, in the FOFM methodology is highlighted with an example SeaWiFS image. We also present a brief case study of two POC algorithms to illustrate how FOFM formulations may be used to construct measurement uncertainty budgets for ecologically-relevant data products. Such knowledge, even if rudimentary, may provide useful information to end-users when selecting data products or when developing their own algorithms.

Regional Vicarious Calibration of the SWIR-Based Atmospheric Correction Approach for MODIS-Aqua Measurements of Highly Turbid Inland Water

Water color remote sensing requires accurate atmospheric correction but this remains a significant challenge in highly turbid waters. In this respect, the shortwave infrared (SWIR) band-based atmospheric correction approach has proven advantageous when applied to the moderate resolution imaging spectroradiometer (MODIS) onboard the Aqua satellite. However, even so, uncertainties affect its accuracy. We performed a regional vicarious calibration of the MODIS-Aqua SWIR (1240, 2130)-based atmospheric correction using in situ water surface reflectance data measured during different seasons in Lake Taihu, a highly turbid lake. We then verified the accuracy of the (1240, 2130)-based atmospheric correction approach using these results; good results were obtained for the remote sensing reflectance retrievals at the 555, 645, and 859 nm, with average relative errors of 15%, 14%, and 22%, respectively, and no significant bias. Comparisons with the (1240, 2130)-based iterative approach and (1640, 2130)-based approach showed that the vicarious calibrated (1240, 2130)-based approach has the best accuracy and robustness. Thus, it is applicable to the highly turbid Lake Taihu. It may also be applicable to other highly turbid inland waters with similar optical and aerosol optical properties above water, but such applications will require further validation.

Mapping hydroxyl variability throughout the global remote troposphere via synthesis of airborne and satellite formaldehyde observations

The hydroxyl radical (OH) fuels tropospheric ozone production and governs the lifetime of methane and many other gases. Existing methods to quantify global OH are limited to annual and global-to-hemispheric averages. Finer resolution is essential for isolating model deficiencies and building process-level understanding. In situ observations from the Atmospheric Tomography (ATom) mission demonstrate that remote tropospheric OH is tightly coupled to the production and loss of formaldehyde (HCHO), a major hydrocarbon oxidation product. Synthesis of this relationship with satellite-based HCHO retrievals and model-derived HCHO loss frequencies yields a map of total-column OH abundance throughout the remote troposphere (up to 70% of tropospheric mass) over the first two ATom missions (August 2016 and February 2017). This dataset offers unique insights on near-global oxidizing capacity. OH exhibits significant seasonality within individual hemispheres, but the domain mean concentration is nearly identical for both seasons (1.03 ± 0.25 × 106 cm-3), and the biseasonal average North/South Hemisphere ratio is 0.89 ± 0.06, consistent with a balance of OH sources and sinks across the remote troposphere. Regional phenomena are also highlighted, such as a 10-fold OH depression in the Tropical West Pacific and enhancements in the East Pacific and South Atlantic. This method is complementary to budget-based global OH constraints and can help elucidate the spatial and temporal variability of OH production and methane loss.

Optimal satellite orbit configuration for global ocean color product coverage

We develop a methodology to evaluate the current orbital configuration of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites and to study various orbital configurations for the next VIIRS in the Joint Polar Satellite System (JPSS) series from the perspective of maximizing the global daily ocean color retrievals. We focus on the coverage losses due to high sensor-zenith angle and high sun glint contamination and find that two sensors cannot avoid gaps in daily coverage. If JPSS-2 shares the same orbit with SNPP and NOAA-20, then phase shift of around 90° relative to SNPP and NOAA-20 would maximize daily ocean color retrievals.

Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas

Large-scale spatial synchrony is ubiquitous in ecology. We examined 56 years of data representing chlorophyll density in 26 areas in British seas monitored by the Continuous Plankton Recorder survey. We used wavelet methods to disaggregate synchronous fluctuations by timescale and determine that drivers of synchrony include both biotic and abiotic variables. We tested these drivers for statistical significance by comparison with spatially synchronous surrogate data. Identification of causes of synchrony is distinct from, and goes beyond, determining drivers of local population dynamics. We generated timescale-specific models, accounting for 61% of long-timescale (> 4yrs) synchrony in a chlorophyll density index, but only 3% of observed short-timescale (< 4yrs) synchrony. Thus synchrony and its causes are timescale-specific. The dominant source of long-timescale chlorophyll synchrony was closely related to sea surface temperature, through a climatic Moran effect, though likely via complex oceanographic mechanisms. The top-down action of Calanus finmarchicus predation enhances this environmental synchronising mechanism and interacts with it non-additively to produce more long-timescale synchrony than top-down and climatic drivers would produce independently. Our principal result is therefore a demonstration of interaction effects between Moran drivers of synchrony, a new mechanism for synchrony that may influence many ecosystems at large spatial scales.

The size of the annual bloom in phytoplankton can vary similarly from year to year in different parts of the same oceanic region, a phenomenon called spatial synchrony. The growth of phytoplankton near the ocean surface is the foundation of marine food webs, which include numerous commercially exploited species. And spatial synchrony in phytoplankton abundance time series can have consequences for the total production of marine ecosystems. Therefore we studied the spatial synchrony of fluctuations in green phytoplankton abundance in 26 areas in seas around the British Isles. Variation and synchrony can occur differently on long and short timescales. We used a novel wavelet-based approach to examine long- and short-timescale fluctuations separately, and we thereby show that slow synchronous fluctuations in phytoplankton can be explained by the effects of slow synchronous fluctuations in sea surface temperature and related oceanographic phenomena, and by the effects of synchronous fluctuations in a zooplankton predator. Crucially, these drivers reinforce one another in a super-additive way, the interaction constituting a new mechanism of synchrony. Future changes in the climate or changes in predation are likely to influence phytoplankton synchrony via this mechanism and hence may influence the aggregate productivity of British seas.

Earth observations and integrative models in support of food and water security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.

Ocean colour signature of climate change

Monitoring changes in marine phytoplankton is important as they form the foundation of the marine food web and are crucial in the carbon cycle. Often Chlorophyll-a (Chl-a) is used to track changes in phytoplankton, since there are global, regular satellite-derived estimates. However, satellite sensors do not measure Chl-a directly. Instead, Chl-a is estimated from remote sensing reflectance (R_RS): the ratio of upwelling radiance to the downwelling irradiance at the ocean’s surface. Using a model, we show that R_RS in the blue-green spectrum is likely to have a stronger and earlier climate-change-driven signal than Chl-a. This is because R_RS has lower natural variability and integrates not only changes to in-water Chl-a, but also alterations in other optically important constituents. Phytoplankton community structure, which strongly affects ocean optics, is likely to show one of the clearest and most rapid signatures of changes to the base of the marine ecosystem.

Changes in chlorophyll-a are used as an indirect proxy for monitoring global changes in marine phytoplankton. Here the authors show that remote sensing reflectance (R_RS), such as the ratio of upwelling versus downwelling light at the ocean’s surface, has a stronger and earlier climate-change-driven signal over the 21st century.

Uncertainties in the Geostationary Ocean Color Imager (GOCI) Remote Sensing Reflectance for Assessing Diurnal Variability of Biogeochemical Processes

Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor’s field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA’s SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving “remote-sensing” reflectance (Rrs) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 × 10−4, 5.49 × 10−4, 4.48 × 10−4, 2.51 × 10−4, 8.83 × 10−5, and 1.36 × 10−4 sr−1, respectively, and 1.09 × 10−2 mg m−3 for the chlorophyll-a concentration (Chl-a), 2.09 × 10−3 m−1 for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (ag (412)), and 3.7 mg m−3 for particulate organic carbon (POC). These Rrs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI.