Dissolved organic matter transformations in a freshwater rivermouth.

River-to-lake transitional areas are biogeochemically active ecosystems that can alter the amount and composition of dissolved organic matter (DOM) as it moves through the aquatic continuum. However, few studies have directly measured carbon processing and assessed the carbon budget of freshwater rivermouths. We compiled measurements of dissolved organic carbon (DOC) and DOM in several water column (light and dark) and sediment incubation experiments conducted in the mouth of the Fox river (Fox rivermouth) upstream from Green Bay, Lake Michigan. Despite variation in the direction of DOC fluxes from sediments, we found that the Fox rivermouth was a net sink of DOC where water column DOC mineralization outweighed the release of DOC from sediments at the rivermouth scale. Although we found DOM composition also changed during our experiments, alterations in DOM optical properties were largely independent of the direction of sediment DOC fluxes. We found a consistent decrease in humic-like and fulvic-like terrestrial DOM and a consistent increase in the overall microbial composition of rivermouth DOM during our incubations. Moreover, greater ambient total dissolved phosphorus concentrations were positively associated with the consumption of terrestrial humic-like, microbial protein-like, and more recently derived DOM but had no effect on bulk DOC in the water column. Unexplained variation indicates that other environmental controls and water column processes affect the processing of DOM in this rivermouth. Nonetheless, the Fox rivermouth appears capable of substantial DOM transformation with implications for the composition of DOM entering Lake Michigan. Supplementary Information: The online version contains supplementary material available at 10.1007/s10533-022-01000-z.

Biofouling of a unionid mussel by dreissenid mussels in nearshore zones of the Great Lakes.

In North America, native unionid mussels are imperiled due to factors such as habitat degradation, pollution, and invasive species. One of the most substantial threats is that posed by dreissenid mussels, which are invasive mussels that attach to hard substrates including unionid shells and can restrict movement and feeding of unionids. This dreissenid mussel biofouling of unionids varies spatially in large ecosystems, such as the Great Lakes, with some areas having low enough biofouling to form effective refugia where unionid mussels might persist. Here, we measured biofouling on mussels suspended in cages over the growing season (generally first week in June to last week of August) over 3 years in nearshore areas in Lake Erie (2014-2016), Lake Michigan (Grand Traverse Bay, 2015 and Green Bay, 2016), and Lake Huron (2015). Biofouling varied substantially by years within Lake Erie, with increasingly higher biofouling rates each year. Although dreissenid mussels are present throughout these lakes, we observed very low biofouling in Grand Traverse Bay (Lake Michigan) and Saginaw Bay (Lake Huron), with no dreissenid mussels in 8 of 9 sites across these two bays. Sampling in the rivermouth of the Fox River (Wisconsin) and the Maumee River (Ohio) both showed very high biofouling in areas adjacent to the outlet of these tributaries into Green Bay and Maumee Bay (Lake Erie), respectively. These watersheds are dominated by agriculture, and we would expect high growth of primary producers (i.e., mussel food) and primary consumers (unionids and zebra mussels) in these areas compared to the other sampled bays or the open waters of the Great Lakes.

Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification.

Ocean color (OC) remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance (Rrs(λ);sr-1) at the ocean surface from the top-of-atmosphere reflectance. Often the uncertainty of this process is not fully explored. Bayesian inference techniques provide a simultaneous AC and uncertainty assessment via a full posterior distribution of the relevant variables, given the prior distribution of those variables and the radiative transfer (RT) likelihood function. Given uncertainties in the algorithm inputs, the Bayesian framework enables better constraints on the AC process by using the complete spectral information compared to traditional approaches that use only a subset of bands for AC. This paper investigates a Bayesian inference research method (optimal estimation [OE]) for OC AC by simultaneously retrieving atmospheric and ocean properties using all visible and near-infrared spectral bands. The OE algorithm analytically approximates the posterior distribution of parameters based on normality assumptions and provides a potentially viable operational algorithm with a reduced computational expense. We developed a neural network RT forward model look-up table-based emulator to increase algorithm efficiency further and thus speed up the likelihood computations. We then applied the OE algorithm to synthetic data and observations from the moderate resolution imaging spectroradiometer (MODIS) on NASA's Aqua spacecraft. We compared the Rrs(λ) retrieval and its uncertainty estimates from the OE method with in-situ validation data from the SeaWiFS bio-optical archive and storage system (SeaBASS) and aerosol robotic network for ocean color (AERONET-OC) datasets. The OE algorithm improved Rrs(λ) estimates relative to the NASA standard operational algorithm by improving all statistical metrics at 443, 555, and 667 nm. Unphysical negative Rrs(λ), which often appears in complex water conditions, was reduced by a factor of 3. The OE-derived pixel-level Rrs(λ) uncertainty estimates were also assessed relative to in-situ data and were shown to have skill.

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a.

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

Mobile device application for monitoring cyanobacteria harmful algal blooms using Sentinel-3 satellite Ocean and Land Colour Instruments.

Cyanobacterial harmful algal blooms (cyanoHAB) cause human and ecological health problems in lakes worldwide. The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management and for targeted deployment of water quality monitoring resources. Software platforms that permit timely, useful, and cost-effective delivery of information from satellites are required to help managers respond to cyanoHABs. The Cyanobacteria Assessment Network (CyAN) mobile device application (app) uses data from the European Space Agency Copernicus Sentinel-3 satellite Ocean and Land Colour Instrument (OLCI) in near realtime to make initial water quality assessments and quickly alert managers to potential problems and emerging threats related to cyanobacteria. App functionality and satellite data were validated with 25 state health advisories issued in 2017. The CyAN app provides water quality managers with a user-friendly platform that reduces the complexities associated with accessing satellite data to allow fast, efficient, initial assessments across lakes.

On-orbit calibration of the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite for ocean color applications.

The NASA Ocean Biology Processing Group (OBPG) developed two independent calibrations of the Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate resolution reflective solar bands using solar diffuser measurements and lunar observations, and implemented a combined solar- and lunar-based calibration to track temporal changes in radiometric response of the instrument. Differences between the solar and lunar data sets have been used to identify issues and verify improvements in each. Linearization of the counts-to-radiance conversion yields a more consistent calibration at low radiance levels. Correction of a recently identified error in the VIIRS solar unit vector coordinate frame has been incorporated into the solar data and diffuser screen transmission functions. Temporal trends in the solar diffuser stability monitor data have been evaluated and addressed. Fits to the solar calibration time series show mean residuals per band of 0.067%-0.17%. Periodic residuals in the VIIRS lunar data are confirmed to arise from a wavelength-dependent libration effect for the sub-spacecraft point in the output of the U.S. Geological Survey Robotic Lunar Observatory photometric model of the Moon. Temporal variations in the relative spectral responses for each band have been assessed, and significant impact on band M1 (412 nm) lunar data has been identified and rectified. Fits to the lunar calibration time series, incorporating sub-spacecraft point libration corrections, show mean residuals per band of 0.069%-0.20%. Lunar calibrations have been used to adjust the solar-derived radiometric corrections for bands M1, M3, and M4. After all corrections, the relative differences in the solar and lunar calibrations for bands M1-M7 are 0.093%-0.22%. The OBPG has achieved a radiometric stability for the VIIRS on-orbit calibration that is commensurate with those achieved for SeaWiFS and Aqua MODIS, supporting the incorporation of VIIRS data into the long-term NASA ocean color data record.

Mercury in streams at Grand Portage National Monument (Minnesota, USA): assessment of ecosystem sensitivity and ecological risk.

Mercury (Hg) in water, sediment, soils, seston, and biota were quantified for three streams in the Grand Portage National Monument (GRPO) in far northeastern Minnesota to assess ecosystem contamination and the potential for harmful exposure of piscivorous fish, wildlife, and humans to methylmercury (MeHg). Concentrations of total Hg in water, sediment, and soil were typical of those in forest ecosystems within the region, whereas MeHg concentrations and percent MeHg in these ecosystem components were markedly higher than values reported elsewhere in the western Great Lakes Region. Soils and sediment were Hg-enriched, containing approximately 4-fold more total Hg per unit of organic matter. We hypothesized that localized Hg enrichment was due in part to anthropogenic pollution associated with historic fur-trading activity. Bottom-up forcing of bioaccumulation was evidenced by MeHg concentrations in larval dragonflies, which were near the maxima for dragonflies sampled concurrently from five other national park units in the region. Despite its semi-remote location, GRPO is a Hg-sensitive landscape in which MeHg is produced and bioaccumulated in aquatic food webs to concentrations that pose ecological risks to MeHg-sensitive piscivores, including predatory fish, belted kingfisher, and mink.

Burrowing dragonfly larvae as biosentinels of methylmercury in freshwater food webs.

We assessed the utility of larval burrowing dragonflies (Odonata: Anisoptera: Gomphidae) as biosentinels of methylmercury (MeHg) contamination. Gomphids were the most abundant family of dragonflies sampled during 2008-2010 from 17 lakes in four national parks of the northwestern Laurentian Great Lakes region. Ten species of burrowing gomphids were sampled; 13 lakes contained 3 or more species, and 2 species of Gomphus co-occurred in 12 lakes. Most of the total Hg (THg) in whole, late-instar larvae was MeHg, with mean percent MeHg exceeding 60% in 16 lakes. Mean MeHg in larvae of a given species varied greatly among lakes, ranging from 4 to 109 ng g(-1) dry weight. Methylmercury levels in larvae, however, were much less variable within a given lake and species. The mean concentration of MeHg in burrowing gomphids was positively correlated with mean MeHg concentration in unfiltered lake water. Mean concentrations of THg and MeHg in multispecies assemblages of Gomphus were also positively correlated with mean THg in coexisting prey fish and game fishes. We recommend-and provide guidance on-the application of burrowing gomphids as biosentinels of MeHg contamination, which can extend the bioassessment of MeHg to fishless fresh waters.

Generalized ocean color inversion model for retrieving marine inherent optical properties.

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

On-orbit calibration of SeaWiFS.

Ocean color climate data records (CDRs) require water-leaving radiances with 5% absolute and 1% relative accuracies as input. Because of the amplification of any sensor calibration errors by the atmospheric correction, the 1% relative accuracy requirement translates into a 0.1% long-term radiometric stability requirement for top-of-the-atmosphere (TOA) radiances. The rigorous prelaunch and on-orbit calibration program developed and implemented for Sea-viewing Wide Field-of-view Sensor (SeaWiFS) by the NASA Ocean Biology Processing Group (OBPG) has led to the incorporation of significant changes into the on-orbit calibration methodology over the 13-year lifetime of the instrument. Evolving instrument performance and ongoing algorithm refinement have resulted in updates to approaches for the lunar, solar, and vicarious calibration of SeaWiFS. The uncertainties in the calibrated TOA radiances are addressed in terms of accuracy (biases in the measurements), precision (scatter in the measurements), and stability (repeatability of the measurements). The biases are 2%-3% from lunar calibration and 1%-2% from vicarious calibration. The precision is 0.16% from solar signal-to-noise ratios, 0.13% from lunar residuals, and 0.10% from vicarious gains. The long-term stability of the TOA radiances, derived from the lunar time series, is 0.13%. The stability of the vicariously calibrated TOA radiances, incorporating the uncertainties of the in situ measurements and the atmospheric correction, is 0.30%. This stability of the radiometric calibration of SeaWiFS over its 13-year on-orbit lifetime has allowed the OBPG to produce CDRs from the ocean color data set.

Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing.

The atmospheric correction algorithm employed by the NASA Ocean Biology Processing Group requires an assumption of negligible water-leaving reflectance in the near-infrared region of the spectrum. For waters where this assumption is not valid, an optical model is used to estimate near-infrared water-leaving reflectance. We describe this optical model as implemented for the sixth reprocessing of the SeaWiFS mission-long time-series (September 2009). Application of the optical model resulted in significant reductions in the number of negative water-leaving reflectance retrievals in turbid and optically complex waters, and improved agreement with in situ chlorophyll-a observations. The incidence of negative water-leaving reflectance retrievals at 412 nm was reduced by 40%, while negative reflectance at 490 nm was nearly eliminated.

Sources and assumptions for the vicarious calibration of ocean color satellite observations.

Spaceborne ocean color sensors require vicarious calibration to sea-truth data to achieve accurate water-leaving radiance retrievals. The assumed requirements of an in situ data set necessary to achieve accurate vicarious calibration were set forth in a series of papers and reports developed nearly a decade ago, which were embodied in the development and site location of the Marine Optical BuoY (MOBY). Since that time, NASA has successfully used data collected by MOBY as the sole source of sea-truth data for vicarious calibration of the Sea-viewing Wide field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer instruments. In this paper, we make use of the 10-year, global time series of SeaWiFS measurements to test the sensitivity of vicarious calibration to the assumptions inherent in the in situ requirements (e.g., very low chlorophyll waters, hyperspectral measurements). Our study utilized field measurements from a variety of sources with sufficient diversity in data collection methods and geophysical variability to challenge those in situ restrictions. We found that some requirements could be relaxed without compromising the ability to vicariously calibrate to the level required for accurate water-leaving radiance retrievals from satellite-based sensors.

Analysis of fin clips as a nonlethal method for monitoring mercury in fish.

Monitoring of mercury in fish typically involves removal of individuals from the sampled population and subsequent analysis of fillets. This study assessed whether the analysis of fins, structures routinely clipped to mark released fish in population studies, could provide a nonlethal approach for estimating mercury concentrations in axial muscle (fillets). We analyzed fillets and selected fins from 401 northern pike (Esox lucius) and 79 walleye (Sander vitreus) from 21 lakes in Minnesota and Wisconsin, 19 Arctic grayling (Thymallus arcticus) from Toolik Lake, Alaska, and 14 winter flounder (Pseudopleuronectes americanus) from Long Island Sound, New York. On average, 83% of mercury in fins was methylmercury, and concentrations in the proximal portion were about half-those in the distal portion in pelvic fins. Mean concentrations of mercury in fins, averaged by species and fin type, ranged from 2.7 to 8.9% of those in fillets. Coefficients of determination (r2) for linear regressions of mercury concentrations in fillets against those in fins of individual fish ranged from 0.04 to 0.96 among species and water bodies (median r2 = 0.52). The concentration in fin clips was a better predictor of mercury in fillets for individual Arctic grayling (r2 = 0.65, n = 12 and r2 = 0.84, n = 8) and winter flounder (r2 = 0.94, n = 14) than for individual northern pike (median r2 = 0.56) or walleye (median r2 = 0.22) from a given lake. In northern pike in the 400-500 mm total-length interval, the mean concentrations of total mercury in caudal fins and fillets, averaged by lake (n = 12), were strongly correlated (r2 = 0.95). The analysis of composited samples of fin clips from fish within a restricted length interval could, therefore, be a useful screening tool for assessing the relative mercury contamination of fish among different water bodies.

SeaWiFS on-orbit gain and detector calibrations: effect on ocean products.

The NASA Ocean Biology Processing Group's Calibration and Validation Team has analyzed the mission-long Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) on-orbit gain and detector calibration time series to verify that lunar calibrations, obtained at nonstandard gains and radiance ranges, are valid for Earth data collected at standard gains and typical ocean, cloud, and land radiances. For gain calibrations, a constant voltage injected into the postdetector electronics allows gain ratios to be computed for all four detectors in each band. The on-orbit lunar gain ratio time series show small drifts for the near infrared bands. These drifts are propagated into the ocean color data through the atmospheric correction parameter epsilon, which uses the 765/865 nm band ratio. An anomaly analysis of global mean normalized water-leaving radiances at 510 nm shows a small decrease over the mission, while an analysis of epsilon shows a corresponding increase. The drifts in the lunar time series for the 765 and 865 nm bands were corrected. An analysis of the revised water-leaving radiances at 510 nm shows the drift has been eliminated, while an analysis of epsilon shows a reduced drift. For detector calibrations, solar diffuser observations made by the individual detectors in each band allows the response of the detectors to be monitored separately. The mission-long time series of detector calibration data show that the variations in the response of the individual detectors are less than 0.5% over the mission for all bands except the 865 nm band, where the variations are less than 1%.

On-orbit vicarious calibration of ocean color sensors using an ocean surface reflectance model.

Recent advances in global biogeochemical research demonstrate a critical need for long-term ocean color satellite data records of consistent high quality. To achieve that quality, spaceborne instruments require on-orbit vicarious calibration, where the integrated instrument and atmospheric correction system is adjusted using in situ normalized water-leaving radiances, such as those collected by the marine optical buoy (MOBY). Unfortunately, well-characterized time-series of in situ data are scarce for many historical satellite missions, in particular, the NASA coastal zone color scanner (CZCS) and the ocean color and temperature scanner (OCTS). Ocean surface reflectance models (ORMs) accurately reproduce spectra observed in clear marine waters, using only chlorophyll a (C(a)) as input, a measurement for which long-term in situ time series exist. Before recalibrating CZCS and OCTS using modeled radiances, however, we evaluate the approach with the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS). Using annual C(a) climatologies as input into an ORM, we derive SeaWiFS vicarious gains that differ from the operational MOBY gains by less than +/-0.9% spectrally. In the context of generating decadal C(a) climate data records, we quantify the downstream effects of using these modeled gains by generating satellite-to-in situ data product validation statistics for comparison with the operational SeaWiFS results. Finally, we apply these methods to the CZCS and OCTS ocean color time series.

Sensor-independent approach to the vicarious calibration of satellite ocean color radiometry.

The retrieval of ocean color radiometry from space-based sensors requires on-orbit vicarious calibration to achieve the level of accuracy desired for quantitative oceanographic applications. The approach developed by the NASA Ocean Biology Processing Group (OBPG) adjusts the integrated instrument and atmospheric correction system to retrieve normalized water-leaving radiances that are in agreement with ground truth measurements. The method is independent of the satellite sensor or the source of the ground truth data, but it is specific to the atmospheric correction algorithm. The OBPG vicarious calibration approach is described in detail, and results are presented for the operational calibration of SeaWiFS using data from the Marine Optical Buoy (MOBY) and observations of clear-water sites in the South Pacific and southern Indian Ocean. It is shown that the vicarious calibration allows SeaWiFS to reproduce the MOBY radiances and achieve good agreement with radiometric and chlorophyll a measurements from independent in situ sources. We also find that the derived vicarious gains show no significant temporal or geometric dependencies, and that the mission-average calibration reaches stability after approximately 20-40 high-quality calibration samples. Finally, we demonstrate that the performance of the vicariously calibrated retrieval system is relatively insensitive to the assumptions inherent in our approach.