Anomalous trends in global ocean carbon concentrations following the 2022 eruptions of Hunga Tonga-Hunga Ha'apai.

We report on observed trend anomalies in climate-relevant global ocean biogeochemical properties, as derived from satellite ocean color measurements, that show a substantial decline in phytoplankton carbon concentrations following eruptions of the submarine volcano Hunga Tonga-Hunga Ha'apai in January 2022. The anomalies are seen in remotely-sensed ocean color data sets from multiple satellite missions, but not in situ observations, thus suggesting that the observed anomalies are a result of ocean color retrieval errors rather than indicators of a major shift in phytoplankton carbon concentrations. The enhanced concentration of aerosols in the stratosphere following the eruptions results in a violation of some fundamental assumptions in the processing algorithms used to obtain marine biogeochemical properties from satellite radiometric observations, and it is demonstrated through radiative transfer simulations that this is the likely cause of the anomalous trends. We note that any future stratospheric aerosol disturbances, either natural or geoengineered, may lead to similar artifacts in satellite ocean color and other remote-sensing measurements of the marine environment, thus confounding our ability to track the impact of such events on ocean ecosystems.

Spatial and temporal characterization of cyanobacteria blooms in the Mississippi Sound and their relationship to the Bonnet Carré Spillway openings.

During the spring and summer of 2019, an unprecedented cyanobacterial harmful algal bloom (cyanoHAB) was responsible for beach advisories on 25 beaches along the Mississippi Sound for over 3 months. Due to the preceding heavy rainfall and flooding within the Mississippi River watershed, for the first time in history, the Bonnet Carré Spillway (BCS) opened twice in one year during 2019. The coastal cyanoHAB coincided with the second BCS opening. The main objectives of this study were: (1) to investigate the potential for using the National Aeronautics and Space Administration (NASA) ocean color standard Cyanobacteria Index (CIcyano) algorithm to characterize the spatial and temporal extent of the 2019 cyanoHAB; (2) to couple the CIcyano data with river discharge, salinity, and modeled-wind data to study the conditions leading to the cyanoHAB and factors aiding the advection and persistence of the bloom within the Mississippi Sound, including a possible relationship to the BCS; (3) to further investigate the relationship with the BCS by repeating the methods using data from 2018, which was a year when the BCS was opened but no evidence of cyanoHABs was reported along the Mississippi coast. Weekly means and monthly frequency CIcyano images, river discharge, salinity, and modeled-wind data from February to September of 2018 and 2019 were analyzed, which coincide with three BCS openings. In March 2018, a cyanobacteria bloom was observed within Lake Pontchartrain coinciding with the BCS opening; however, the month-long bloom was contained to the lake. Two distinct cyanoHABs were observed in 2019 and both blooms were advected into the Mississippi Sound, and likely contributed to the 3-month-long beach water advisories of 2019 along the Mississippi coastline. From March to mid-July 2019, salinity at stations within the Mississippi Sound was consistently near zero indicating high levels of freshwater. During that time, winds were predominantly northwestward, preventing the BCS waters from flushing into the Mississippi Shelf and resulting in BCS waters remaining longer within the estuarine lakes and Mississippi Sound. Although the BCS had an undeniable impact on the presence of the coastal cyanoHAB of 2019, other variables including wind direction, water flow, mixing, and persistence of freshwater within the Sound can determine the intensity and extent of the cyanoHABs. Coupling in situ phytoplankton information from freshwater water bodies to the marine continuum along with water flow, wind data, and satellite imagery could help identify cyanoHABs at early stages and forecast their trajectory and potential impacts on coastal areas.

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 Rrs(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 Rrs(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.

Phytoplankton diversity and chemotaxonomy in contrasting North Pacific ecosystems.

Background: Phytoplankton is the base of majority of ocean ecosystems. It is responsible for half of the global primary production, and different phytoplankton taxa have a unique role in global biogeochemical cycles. In addition, phytoplankton abundance and diversity are highly susceptible to climate induced changes, hence monitoring of phytoplankton and its diversity is important and necessary. Methods: Water samples for phytoplankton and photosynthetic pigment analyses were collected in boreal winter 2017, along transect in the North Pacific Subtropical Gyre (NPSG) and the California Current System (CCS). Phytoplankton community was analyzed using light and scanning electron microscopy and photosynthetic pigments by high-performance liquid chromatography. To describe distinct ecosystems, monthly average satellite data of MODIS Aqua Sea Surface temperature and Chlorophyll a concentration, as well as Apparent Visible Wavelength were used. Results: A total of 207 taxa have been determined, mostly comprised of coccolithophores (35.5%), diatoms (25.2%) and dinoflagellates (19.5%) while cryptophytes, phytoflagellates and silicoflagellates were included in the group "others" (19.8%). Phytoplankton spatial distribution was distinct, indicating variable planktonic dispersal rates and specific adaptation to ecosystems. Dinoflagellates, and nano-scale coccolithophores dominated NPSG, while micro-scale diatoms, and cryptophytes prevailed in CCS. A clear split between CCS and NPSG is evident in dendogram visualising LINKTREE constrained binary divisive clustering analysis done on phytoplankton counts and pigment concentrations. Of all pigments determined, alloxanthin, zeaxanthin, divinyl chlorophyll b and lutein have highest correlation to phytoplankton counts. Conclusion: Combining chemotaxonomy and microscopy is an optimal method to determine phytoplankton diversity on a large-scale transect. Distinct communities between the two contrasting ecosystems of North Pacific reveal phytoplankton groups specific adaptations to trophic state, and support the hypothesis of shift from micro- to nano-scale taxa due to sea surface temperatures rising, favoring stratification and oligotrophic conditions.

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.

Tracing the path of carbon export in the ocean though DNA sequencing of individual sinking particles.

Surface phytoplankton communities were linked with the carbon they export into the deep ocean by comparing 18 S rRNA gene sequence communities from surface seawater and individually isolated sinking particles. Particles were collected in sediment traps deployed at locations in the North Pacific subtropical gyre and the California Current. DNA was isolated from individual particles, bulk-collected trap particles, and the surface seawater. The relative sequence abundance of exported phytoplankton taxa in the surface water varied across functional groups and ecosystems. Of the sequences detected in sinking particles, about half were present in large (>300 µm), individually isolated particles and primarily belonged to taxa with small cell sizes (<50 µm). Exported phytoplankton taxa detected only in bulk trap samples, and thus presumably packaged in the smaller sinking size fraction, contained taxa that typically have large cell sizes (>500 µm). The effect of particle degradation on the detectable 18 S rRNA gene community differed across taxa, and differences in community composition among individual particles from the same location largely reflected differences in relative degradation state. Using these data and particle imaging, we present an approach that incorporates genetic diversity into mechanistic models of the ocean's biological carbon pump, which will lead to better quantification of the ocean's carbon cycle.

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.

A Visual Tour of Carbon Export by Sinking Particles.

To better quantify the ocean's biological carbon pump, we resolved the diversity of sinking particles that transport carbon into the ocean's interior, their contribution to carbon export, and their attenuation with depth. Sinking particles collected in sediment trap gel layers from four distinct ocean ecosystems were imaged, measured, and classified. The size and identity of particles was used to model their contribution to particulate organic carbon (POC) flux. Measured POC fluxes were reasonably predicted by particle images. Nine particle types were identified, and most of the compositional variability was driven by the relative contribution of aggregates, long cylindrical fecal pellets, and salp fecal pellets. While particle composition varied across locations and seasons, the entire range of compositions was measured at a single well-observed location in the subarctic North Pacific over one month, across 500 m of depth. The magnitude of POC flux was not consistently associated with a dominant particle class, but particle classes did influence flux attenuation. Long fecal pellets attenuated most rapidly with depth whereas certain other classes attenuated little or not at all with depth. Small particles (<100 µm) consistently contributed ∼5% to total POC flux in samples with higher magnitude fluxes. The relative importance of these small particle classes (spherical mini pellets, short oval fecal pellets, and dense detritus) increased in low flux environments (up to 46% of total POC flux). Imaging approaches that resolve large variations in particle composition across ocean basins, depth, and time will help to better parameterize biological carbon pump models.

Assessment of holographic microscopy for quantifying marine particle size and concentration.

Holographic microscopy has emerged as a tool for in situ imaging of microscopic organisms and other particles in the marine environment: appealing because of the relatively larger sampling volume and simpler optical configuration compared to other imaging systems. However, its quantitative capabilities have so far remained uncertain, in part because hologram reconstruction and image recognition have required manual operation. Here, we assess the quantitative skill of our automated hologram processing pipeline (CCV Pipeline), to evaluate the size and concentration measurements of environmental and cultured assemblages of marine plankton particles, and microspheres. Over 1 million particles, ranging from 10 to 200 µm in equivalent spherical diameter, imaged by the 4-Deep HoloSea digital inline holographic microscope (DIHM) are analyzed. These measurements were collected in parallel with a FlowCam (FC), Imaging FlowCytobot (IFCB), and manual microscope identification. Once corrections for particle location and nonuniform illumination were developed and applied, the DIHM showed an underestimate in ESD of about 3% to 10%, but successfully reproduced the size spectral slope from environmental samples, and the size distribution of cultures (Dunaliella tertiolecta, Heterosigma akashiwo, and Prorocentrum micans) and microspheres. DIHM concentrations (order 1 to 1000 particles ml-1) showed a linear agreement (r 2 = 0.73) with the other instruments, but individual comparisons at times had large uncertainty. Overall, we found the DIHM and the CCV Pipeline required extensive manual correction, but once corrected, provided concentration and size estimates comparable to the other imaging systems assessed in this study. Holographic cameras are mechanically simple, autonomous, can operate at very high pressures, and provide a larger sampling volume than comparable lens-based tools. Thus, we anticipate that these characterization efforts will be rewarded with novel discovery in new oceanic environments.

Radiometric approach for the detection of picophytoplankton assemblages across oceanic fronts.

Cell abundances of Prochlorococcus, Synechococcus, and autotrophic picoeukaryotes were estimated in surface waters using principal component analysis (PCA) of hyperspectral and multispectral remote-sensing reflectance data. This involved the development of models that employed multilinear correlations between cell abundances across the Atlantic Ocean and a combination of PCA scores and sea surface temperatures. The models retrieve high Prochlorococcus abundances in the Equatorial Convergence Zone and show their numerical dominance in oceanic gyres, with decreases in Prochlorococcus abundances towards temperate waters where Synechococcus flourishes, and an emergence of picoeukaryotes in temperate waters. Fine-scale in-situ sampling across ocean fronts provided a large dynamic range of measurements for the training dataset, which resulted in the successful detection of fine-scale Synechococcus patches. Satellite implementation of the models showed good performance (R2 > 0.50) when validated against in-situ data from six Atlantic Meridional Transect cruises. The improved relative performance of the hyperspectral models highlights the importance of future high spectral resolution satellite instruments, such as the NASA PACE mission's Ocean Color Instrument, to extend our spatiotemporal knowledge about ecologically relevant phytoplankton assemblages.

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.

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.

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.

NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton.

Nitrogen (N) is a limiting nutrient in vast regions of the world's oceans, yet the sources of N available to various phytoplankton groups remain poorly understood. In this study, we investigated inorganic carbon (C) fixation rates and nitrate (NO3-), ammonium (NH4+) and urea uptake rates at the single cell level in photosynthetic pico-eukaryotes (PPE) and the cyanobacteria Prochlorococcus and Synechococcus. To that end, we used dual 15N and 13C-labeled incubation assays coupled to flow cytometry cell sorting and nanoSIMS analysis on samples collected in the North Pacific Subtropical Gyre (NPSG) and in the California Current System (CCS). Based on these analyses, we found that photosynthetic growth rates (based on C fixation) of PPE were higher in the CCS than in the NSPG, while the opposite was observed for Prochlorococcus. Reduced forms of N (NH4+ and urea) accounted for the majority of N acquisition for all the groups studied. NO3- represented a reduced fraction of total N uptake in all groups but was higher in PPE (17.4 ± 11.2% on average) than in Prochlorococcus and Synechococcus (4.5 ± 6.5 and 2.9 ± 2.1% on average, respectively). This may in part explain the contrasting biogeography of these picoplankton groups. Moreover, single cell analyses reveal that cell-to-cell heterogeneity within picoplankton groups was significantly greater for NO3- uptake than for C fixation and NH4+ uptake. We hypothesize that cellular heterogeneity in NO3- uptake within groups facilitates adaptation to the fluctuating availability of NO3- in the environment.

The adsorption of dissolved organic carbon onto glass fiber filters and its effect on the measurement of particulate organic carbon: A laboratory and modeling exercise.

Particulate organic carbon (POC) represents a small portion of total carbon in the ocean. However, it plays a large role in the turnover of organic matter through the biological pump and other processes. Early on since the development of the POC measurement technique in the 1960s, it was known that dissolved organic carbon (DOC) adsorbs and is retained both on and in the filter. That retained DOC is measured as if it was part of the particulate fraction, an artifact that can cause significant overestimates of POC concentration. We set out to address the long-standing question of whether the magnitude of the DOC adsorption is affected by the quantity and quality of the dissolved organic matter in the sample. However, our results precluded an unequivocal answer to that question; nevertheless, the experimental data generated did allow us to develop and test predictive models that relate the mass of carbon adsorbed to the volume of sample filtered. The results indicate that the uptake of DOC can be predicted using an exponential model and that a saturation point is approached when approximately a half-liter of water is filtered. This model can be a valuable tool for correcting existing POC data sets that did not account for DOC adsorption. Nonetheless, this approach should not be regarded as a substitute for collecting in situ filter blanks in parallel with POC samples to prop-erly correct for this artifact.

A multi-method autonomous assessment of primary productivity and export efficiency in the springtime North Atlantic.

Fixation of organic carbon by phytoplankton is the foundation of nearly all open-ocean ecosystems and a critical part of the global carbon cycle. But quantification and validation of ocean primary productivity at large scale remains a major challenge, due to limited coverage of ship-based measurements and the difficulty of validating diverse measurement techniques. Accurate primary productivity measurements from autonomous platforms would be highly desirable, due to much greater potential coverage. In pursuit of this goal we estimate gross primary productivity over two months in the springtime North Atlantic from an autonomous Lagrangian float using diel cycles of particulate organic carbon derived from optical beam attenuation. We test method precision and accuracy by comparison against entirely independent estimates from a locally parameterized model based on chlorophyll a and light measurements from the same float. During nutrient replete conditions (80% of the study period), we obtain strong relative agreement between the independent methods across an order of magnitude of productivities (r2=0.97), with slight under-estimation by the diel cycles method (-19±5 %). At the end of the diatom bloom, this relative difference increases to -58 % for a six-day period, likely a response to SiO4 limitation, which is not included in the model. In addition, we estimate gross oxygen productivity from O2 diel cycles and find strong correlation with diel cycles-based gross primary productivity over the entire deployment, providing further qualitative support to both methods. Finally, simultaneous estimates of net community productivity, carbon export and particle size suggest that bloom growth is halted by a combination of reduced productivity due to SiO4 limitation and increased export efficiency due to rapid aggregation. After the diatom bloom, high chlorophyll a normalized productivity indicates that low net growth during this period is due to increased heterotrophic respiration and not nutrient limitation. These findings represent a significant advance in the accuracy and completeness of upper ocean carbon cycle measurements from an autonomous platform.

Airborne and shipborne polarimetric measurements over open ocean and coastal waters: intercomparisons and implications for spaceborne observations.

Comprehensive polarimetric closure is demonstrated using observations from two in-situ polarimeters and Vector Radiative Transfer (VRT) modeling. During the Ship-Aircraft Bio-Optical Research (SABOR) campaign, the novel CCNY HyperSAS-POL polarimeter was mounted on the bow of the R/V Endeavor and acquired hyperspectral measurements from just above the surface of the ocean, while the NASA GISS Research Scanning Polarimeter was deployed onboard the NASA LaRC's King Air UC-12B aircraft. State-of-the-art, ancillary measurements were used to characterize the atmospheric and marine contributions in the VRT model, including those of the High Spectral Resolution Lidar (HSRL), the AErosol RObotic NETwork for Ocean Color (AERONET-OC), a profiling WETLabs ac-9 spectrometer and the Multi-spectral Volume Scattering Meter (MVSM). An open-ocean and a coastal scene are analyzed, both affected by complex aerosol conditions. In each of the two cases, it is found that the model is able to accurately reproduce the Stokes components measured simultaneously by each polarimeter at different geometries and viewing altitudes. These results are mostly encouraging, considering the different deployment strategies of RSP and HyperSAS-POL, which imply very different sensitivities to the atmospheric and ocean contributions, and open new opportunities in above-water polarimetric measurements. Furthermore, the signal originating from each scene was propagated to the top of the atmosphere to explore the sensitivity of polarimetric spaceborne observations to changes in the water type. As expected, adding polarization as a measurement capability benefits the detection of such changes, reinforcing the merits of the full-Stokes treatment in modeling the impact of atmospheric and oceanic constituents on remote sensing observations.

From toes to top-of-atmosphere: Fowler's Sneaker Depth index of water clarity for the Chesapeake Bay.

Fowler's Sneaker Depth (FSD), analogous to the well known Secchi disk depth (Zsd), is a visually discerned citizen scientist metric used to assess water clarity in the Patuxent River estuary. In this study, a simple remote sensing algorithm was developed to derive FSD from space-borne spectroradiometric imagery. An empirical model was formed that estimates FSD from red-end remote sensing reflectances at 645 nm, Rrs(645). The model is based on a hyperbolic function relating water clarity to Rrs(645) that was established using radiative transfer modeling and fine tuned using in-water FSD measurements and coincident Rrs(645) data observed by NASA's Moderate Resolution Imaging Spectroradiometer aboard the Aqua spacecraft (MODISA). The resultant FSD algorithm was applied to Landsat-8 Operational Land Imager data to derive a short time-series for the Patuxent River estuary from January 2015 to June 2016. Satellite-derived FSD had an inverse, statistically significant relationship (p<0.005) with total suspended sediment concentration (TSS). Further, a distinct negative relationship between FSD and chlorophyll concentration was discerned during periods of high biomass (> 4 µg L-1). The complex nature of water quality in the mid-to-upper Chesapeake Bay was captured using a MODISA-based FSD time series (2002-2016). This study demonstrates how a citizen scientist-conceived observation can be coupled with remote sensing. With further refinement and validation, the FSD may be a useful tool for delivering scientifically relevant results and for informing and engaging local stakeholders and policy makers.