A tuned ocean color algorithm for the Arctic Ocean: a solution for waters with high CDM content.

The Arctic Ocean (AO) is the most river-influenced ocean. Located at the land-sea interface wherein phytoplankton blooms are common, Arctic coastal waterbodies are among the most affected regions by climate change. Given phytoplankton are critical for energy transfer supporting marine food webs, accurate estimation of chlorophyll a concentration (Chl), which is frequently used as a proxy of phytoplankton biomass, is critical for improving our knowledge of the Arctic marine ecosystem and its response to the ongoing climate change. Due to the unique and complex bio-optical properties of the AO, efforts are still needed to obtain more accurate Chl estimates, especially for coastal waters with high colored detrital material (CDM) content. In this study, we optimized the the Garver-Siegel-Maritorena (GSM) algorithm, using an Arctic bio-optical dataset comprised of seven wavelengths (the original GSM wavelengths plus 625 nm). Results suggested that our tuned algorithm, denoted GSMA, outperformed an alternative AO GSM algorithm denoted AO.GSM, but the accuracy of Chl estimates was only improved by 8%. In addition, GSMA showed appreciable robustness when assessed using a satellite image and two non-Arctic coastal datasets.

Spectrally simplified approach for leveraging legacy geostationary oceanic observations.

The use of multispectral geostationary satellites to study aquatic ecosystems improves the temporal frequency of observations and mitigates cloud obstruction, but no operational capability presently exists for the coastal and inland waters of the United States. The Advanced Baseline Imager (ABI) on the current iteration of the Geostationary Operational Environmental Satellites, termed the R Series (GOES-R), however, provides sub-hourly imagery and the opportunity to overcome this deficit and to leverage a large repository of existing GOES-R aquatic observations. The fulfillment of this opportunity is assessed herein using a spectrally simplified, two-channel aquatic algorithm consistent with ABI wave bands to estimate the diffuse attenuation coefficient for photosynthetically available radiation, Kd(PAR). First, an in situ ABI dataset was synthesized using a globally representative dataset of above- and in-water radiometric data products. Values of Kd(PAR) were estimated by fitting the ratio of the shortest and longest visible wave bands from the in situ ABI dataset to coincident, in situKd(PAR) data products. The algorithm was evaluated based on an iterative cross-validation analysis in which 80% of the dataset was randomly partitioned for fitting and the remaining 20% was used for validation. The iteration producing the median coefficient of determination (R2) value (0.88) resulted in a root mean square difference of 0.319m-1, or 8.5% of the range in the validation dataset. Second, coincident mid-day images of central and southern California from ABI and from the Moderate Resolution Imaging Spectroradiometer (MODIS) were compared using Google Earth Engine (GEE). GEE default ABI reflectance values were adjusted based on a near infrared signal. Matchups between the ABI and MODIS imagery indicated similar spatial variability (R2=0.60) between ABI adjusted blue-to-red reflectance ratio values and MODIS default diffuse attenuation coefficient for spectral downward irradiance at 490 nm, Kd(490), values. This work demonstrates that if an operational capability to provide ABI aquatic data products was realized, the spectral configuration of ABI would potentially support a sub-hourly, visible aquatic data product that is applicable to water-mass tracing and physical oceanography research.

One- and Two-Band Sensors and Algorithms to Derive aCDOM(440) from Global Above- and In-Water Optical Observations.

The colored (or chromophoric, depending on the literature) dissolved organic matter (CDOM) spectral absorption coefficient, aCDOM(λ), is a variable of global interest that has broad application in the study of biogeochemical processes. Within the funding for scientific research, there is an overarching trend towards increasing the scale of observations both temporally and spatially, while simultaneously reducing the cost per sample, driving a systemic shift towards autonomous sensors and observations. Legacy aCDOM(λ) measurement techniques can be cost-prohibitive and do not lend themselves toward autonomous systems. Spectrally rich datasets carefully collected with advanced optical systems in diverse locations that span a global range of water bodies, in conjunction with appropriate quality assurance and processing, allow for the analysis of methods and algorithms to estimate aCDOM(440) from spectrally constrained one- and two-band subsets of the data. The resulting algorithms were evaluated with respect to established fit-for-purpose criteria as well as quality assured archival data. Existing and proposed optical sensors capable of exploiting the algorithms and intended for autonomous platforms are identified and discussed. One-band in-water algorithms and two-band above-water algorithms showed the most promise for practical use (accuracy of 3.0% and 6.5%, respectively), with the latter demonstrated for an airborne dataset.

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Extending aquatic spectral information with the first radiometric IR-B field observations.

Planetary radiometric observations enable remote sensing of biogeochemical parameters to describe spatiotemporal variability in aquatic ecosystems. For approximately the last half century, the science of aquatic radiometry has established a knowledge base using primarily, but not exclusively, visible wavelengths. Scientific subdisciplines supporting aquatic radiometry have evolved hardware, software, and procedures to maximize competency for exploiting visible wavelength information. This perspective culminates with the science requirement that visible spectral resolution must be continually increased to extract more information. Other sources of information, meanwhile, remain underexploited, particularly information from nonvisible wavelengths. Herein, absolute radiometry is used to evaluate spectral limits for deriving and exploiting aquatic data products, specifically the normalized water-leaving radiance, Γ(λ), and its derivative products. Radiometric observations presented herein are quality assured for individual wavebands, and spectral verification is conducted by analyzing celestial radiometric results, comparing agreement of above- and in-water observations at applicable wavelengths, and evaluating consistency with bio-optical models and optical theory. The results presented include the first absolute radiometric field observations of Γ(λ) within the IR-B spectral domain (i.e. spanning 1400-3000 nm), which indicate that IR-B signals confer greater and more variable flux than formerly ascribed. Black-pixel processing, a routine correction in satellite and in situ aquatic radiometry wherein a spectrum is offset corrected relative to a nonvisible waveband (often IR-B or a shorter legacy waveband) set to a null value, is shown to degrade aquatic spectra and derived biogeochemical parameters.

Community composition and photosynthetic physiology of phytoplankton in the western subarctic Pacific near the Kuril Islands with special reference to iron availability.

The western subarctic Pacific (WSP) is known as one of the most productive regions among the world's oceans in spring. However, its oceanic waters are also known as a High Nutrient, Low Chlorophyll (HNLC) region during summer due to low iron (Fe) availability in seawater. Indeed, recent studies have demonstrated that the distribution of Fe in the WSP is complex and heterogeneous. This study thus investigated the effects of Fe availability on the community composition and photophysiology of surface phytoplankton from coastal to offshore waters in the WSP in the summer of 2014. Although relatively high concentrations (>2 mg m-3) of chlorophyll (chl) a were found in the Sea of Okhotsk and some coastal waters, low chl a concentrations (<1 mg m-3) were commonly observed in offshore waters. Based on dissolved Fe and macronutrient concentrations, we deduced that low Fe availability limited phytoplankton growth in offshore waters, whereas low silicate and/or nitrate levels limited growth in the shelf areas. Scanning electron microscopy also revealed that the centric diatom Chaetoceros exclusively dominated the diatom assemblages in the shelf and coexisted with pennate diatoms in offshore waters, respectively. Primary productivity in surface waters was negatively correlated with the bottom of the euphotic layer or the light saturation index of the photosynthesis-irradiance curve, which indicates that the phytoplankton assemblages were well acclimated to in situ light conditions regardless of the water masses.

Pan-Arctic distributions of continental runoff in the Arctic Ocean.

Continental runoff is a major source of freshwater, nutrients and terrigenous material to the Arctic Ocean. As such, it influences water column stratification, light attenuation, surface heating, gas exchange, biological productivity and carbon sequestration. Increasing river discharge and thawing permafrost suggest that the impacts of continental runoff on these processes are changing. Here, a new optical proxy was developed and implemented with remote sensing to determine the first pan-Arctic distribution of terrigenous dissolved organic matter (tDOM) and continental runoff in the surface Arctic Ocean. Retrospective analyses revealed connections between the routing of North American runoff and the recent freshening of the Canada Basin, and indicated a correspondence between climate-driven changes in river discharge and tDOM inventories in the Kara Sea. By facilitating the real-time, synoptic monitoring of tDOM and freshwater runoff in surface polar waters, this novel approach will help understand the manifestations of climate change in this remote region.

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.

Platform perturbations in above-water radiometry.

A comparison of above- and in-water spectral measurements in coastal (but predominantly Case-1) conditions has shown that the uncertainty in above-water determinations of water-leaving radiances made from an offshore tower depends on the proximity of the above-water measurement with respect to the side of the platform. For purposes of this study the proximity of the sampling platform is parameterized as the perpendicular distance (denoted x) from the side of the sampling platform to the center of the area on the sea surface observed by the sea-viewing sensor, the so-called surface spot, which is set by the field of view of the radiometer (or the overlapping fields of view of a multiaperture sensor). Two above-water data processing methods were used to create a diagnostic variable (formulated for Case-1 waters only but also applicable to Case-2 conditions over short time scales) to quantify the presence of superstructure reflections. Based on the height of the tower, H, the analyses were partitioned into near- and far-field data sets (x < H and x > H, respectively). The primary conclusions of the radiometric intercomparisons are as follows: (a) the maximum perturbations occur very close to the tower ( x/H << 1), and, as x/H increases and approaches 1 (i.e., as the surface spot becomes as far away as the platform is high), the platform perturbations converge toward smaller and smaller values, and (b) within the far field (x > H) the platform perturbation is negligible, and a remote sensing 5% absolute accuracy objective can be satisfied.

Above-water radiometry in shallow coastal waters.

Above- and in-water radiometric data were collected from two coastal platforms: a small boat and an oceanographic tower. The above-water data were processed with and without a correction for bidirectional effects (Q02 and S95, respectively). An intercomparison of water-leaving radiances over a wide range of environmental conditions showed (a) total uncertainties across the blue-green domain were to within 4%, (b) a convergence of the Q02 method with the in-water method (average Q02 intercomparisons were to within 4%), and (c) chlorophyll a concentrations derived from Q02 reflectances and the OC4V4 (Ocean Color 4 Version 4) algorithm agreed with independent high-performance liquid-chromatography determinations to within approximately 32%.