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At least 163 records · Page 9
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NASA NTRS2011

Long-Term Changes in Light Scattering in Chesapeake Bay Inferred from Secchi Depth, Light Attenuation, and Remote Sensing Measurements

The relationship between the Secchi depth (Z(sub SD)) and the diffuse attenuation coefficient for photosynthetically active radiation (K(sub d)(PAR)), and in particular the product of the two, Z(sub SD) X K(sub d)(PAR), is governed primarily by the ratio of light scattering to absorption. We analyzed measurements of Z(sub SD) and K(sub d)(PAR) at main stem stations in Chesapeake Bay and found that the Z(sub SD) X K(sub d)(PAR) product has declined at rates varying from 0.020 to 0.033 /yr over the 17 to 25 years of measurement, implying that there has been a long -term increase in the scattering-to-absorption ratio. Remote sensing reflectance at the green wavelength most relevant to Z(sub SD) and K(sub d)(PAR) in these waters, R(sub rs)(555), did not exhibit an increasing trend over the 10 years of available measurements. To reconcile the observations we constructed a bio-optical model to calculate Z(sub SD), K(sub d)(PAR), Z(sub SD) X K(sub d)(PAR), and R(sub rs)(555) as a function of light attenuating substances and their mass-specific absorption and scattering coefficients. When simulations were based exclusively on changes in concentrations of light attenuating substances, a declining trend in Z(sub SD) E K(sub d) entailed an increasing trend in R(sub rs)(555), contrary to observations. To simulate both decreasing Z(sub SD) X K(sub d)(PAR) and stationary R(sub rs)(555), it was necessary to allow for a declining trend in the ratio of backscattering to total scattering. Within our simulations, this was accomplished by increasing the relative proportion of organic detritus with high mass-specific scattering and low backscattering ratio. An alternative explanation not explicitly modeled is an increasing tendency for the particulate matter to occur in large aggregates. Data to discriminate between these alternatives are not available.

Gallegos, Charles L.↗
NASA NTRS2012

Satellite Ocean Biology: Past, Present, Future

Since 1978 when the first satellite ocean color proof-of-concept sensor, the Nimbus-7 Coastal Zone Color Scanner, was launched, much progress has been made in refining the basic measurement concept and expanding the research applications of global satellite time series of biological and optical properties such as chlorophyll-a concentrations. The seminar will review the fundamentals of satellite ocean color measurements (sensor design considerations, on-orbit calibration, atmospheric corrections, and bio-optical algorithms), scientific results from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate resolution Imaging Spectroradiometer (MODIS) missions, and the goals of future NASA missions such as PACE, the Aerosol, Cloud, Ecology (ACE), and Geostationary Coastal and Air Pollution Events (GeoCAPE) missions.

McClain, Charles R.↗
NASA NTRS2013

Spatially Resolving Ocean Color and Sediment Dispersion in River Plumes, Coastal Systems, and Continental Shelf Waters

Satellite remote sensing of ocean color in dynamic coastal, inland, and nearshorewaters is impeded by high variability in optical constituents, demands specialized atmospheric correction, and is limited by instrument sensitivity. To accurately detect dispersion of bio-optical properties, remote sensors require ample signal-to-noise ratio (SNR) to sense small variations in ocean color without saturating over bright pixels, an atmospheric correction that can accommodate significantwater-leaving radiance in the near infrared (NIR), and spatial and temporal resolution that coincides with the scales of variability in the environment. Several current and historic space-borne sensors have met these requirements with success in the open ocean, but are not optimized for highly red-reflective and heterogeneous waters such as those found near river outflows or in the presence of sediment resuspension. Here we apply analytical approaches for determining optimal spatial resolution, dominant spatial scales of variability ("patches"), and proportions of patch variability that can be resolved from four river plumes around the world between 2008 and 2011. An offshore region in the Sargasso Sea is analyzed for comparison. A method is presented for processing Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra imagery including cloud detection, stray lightmasking, faulty detector avoidance, and dynamic aerosol correction using short-wave- and near-infrared wavebands in extremely turbid regions which pose distinct optical and technical challenges. Results showthat a pixel size of approx. 520 mor smaller is generally required to resolve spatial heterogeneity in ocean color and total suspended materials in river plumes. Optimal pixel size increases with distance from shore to approx. 630 m in nearshore regions, approx 750 m on the continental shelf, and approx. 1350 m in the open ocean. Greater than 90% of the optical variability within plume regions is resolvable with 500 m resolution, and small, but significant, differences were found between peak and nadir river flow periods in terms of optimal resolution and resolvable proportion of variability.

MODIS↗
NASA NTRS2014

Locating Noctiluca Miliaris in the Arabian Sea: An Optical Proxy Approach

Coincident with shifting monsoon weather patterns over India, the phytoplankter Noctiluca miliaris has recently been observed to be dominating phytoplankton blooms in the northeastern Arabian Sea during the winter monsoons. Identifying the exact environmental and/or ecological conditions that favor this species has been hampered by the lack of concurrent environmental and biological observations on time and space scales relevant to ecologic and physiologic processes. We present a bio-optical proxy for N. miliaris measured on highly resolved depth scales coincident with hydrographic observations with the goal to identify conducive hydrographic conditions for the bloom. The proxy is derived from multichannel excitation chlorophyll a fluorescence and is validated with microscopy, pigment composition, and spectral absorption. Phytoplankton populations dominated by either diatoms or other dinoflagellates were additionally discerned. N. miliaris populations in full bloom were identified offshore in low-nutrient and low-N : P ratio surface waters within a narrow temperature and salinity range. These populations transitioned to high-biomass diatom-dominated coastal upwelling populations. A week later, the N. miliaris blooms were observed in declining phase, transitioning to very-low-biomass populations of non-N. miliaris dinoflagellates. There were no clear hydrographic conditions uniquely associated with the N. miliaris populations, although N. miliaris was not found in the upwelling or extremely oligotrophic waters. Taxonomic transitions were not discernible in the spatial structure of the bloom as identified by the ocean color Chl imagery, indicating that in situ observations may be necessary to resolve community structure, particularly for populations below the surface.

Arabian Sea↗
NASA NTRS2013

Regional to Global Assessments of Phytoplankton Dynamics From The SeaWiFS Mission

Photosynthetic production of organic matter by microscopic oceanic phytoplankton fuels ocean ecosystems and contributes roughly half of the Earth's net primary production. For 13 years, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission provided the first consistent, synoptic observations of global ocean ecosystems. Changes in the surface chlorophyll concentration, the primary biological property retrieved from SeaWiFS, have traditionally been used as a metric for phytoplankton abundance and its distribution largely reflects patterns in vertical nutrient transport. On regional to global scales, chlorophyll concentrations covary with sea surface temperature (SST) because SST changes reflect light and nutrient conditions. However, the oceanmay be too complex to be well characterized using a single index such as the chlorophyll concentration. A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans. The SeaWiFS data set demonstrates complexity in the interpretation of changes in regional to global phytoplankton distributions and illustrates limitations for the assessment of phytoplankton dynamics using chlorophyll retrievals alone.

phytoplankton↗
NASA NTRS2016

Thinking Outside of the Blue Marble: Novel Ocean Applications Using the VIIRS Sensor

While planning for future space-borne sensors will increase the quality, quantity, and duration of ocean observations in the years to come, efforts to extend the limits of sensors currently in orbit can help shed light on future scientific gains as well as associated uncertainties. Here, we present several applications that are unique to the polar orbiting Visual Infrared Imaging Radiometer Suite (VIIRS), each of which challenge the threshold capabilities of the sensor and provide lessons for future missions. For instance, while moderate resolution polar orbiters typically have a one day revisit time, we are able to obtain multiple looks of the same area by focusing on the extreme zenith angles where orbital views overlap, and pair these observations with those from other sensors to create pseudo-geostationary data sets. Or, by exploiting high spatial resolution (imaging) channels and analyzing patterns of synoptic covariance across the visible spectrum, we can obtain higher spatial resolution bio-optical products. Alternatively, non-traditional products can illuminate important biological interactions in the ocean, such as the use of the Day-Night-Band to provide some quantification of phototactic behavior of marine life along light polluted beaches, as well as track the location of marine fishing vessel fleets along ocean fronts. In this talk, we explore ways to take full advantage of the capabilities of existing sensors in order to maximize insights for future missions.

ocean color↗
NASA NTRS2016

Coastal High-resolution Observations and Remote Sensing of Ecosystems (C-HORSE)

Coastal benthic marine ecosystems, such as coral reefs, seagrass beds, and kelp forests are highly productive as well as ecologically and commercially important resources. These systems are vulnerable to degraded water quality due to coastal development, terrestrial run-off, and harmful algal blooms. Measurements of these features are important for understanding linkages with land-based sources of pollution and impacts to coastal ecosystems. Challenges for accurate remote sensing of coastal benthic (shallow water) ecosystems and water quality are complicated by atmospheric scattering/absorption (approximately 80+% of the signal), sun glint from the sea surface, and water column scattering (e.g., turbidity). Further, sensor challenges related to signal to noise (SNR) over optically dark targets as well as insufficient radiometric calibration thwart the value of coastal remotely-sensed data. Atmospheric correction of satellite and airborne remotely-sensed radiance data is crucial for deriving accurate water-leaving radiance in coastal waters. C-HORSE seeks to optimize coastal remote sensing measurements by using a novel airborne instrument suite that will bridge calibration, validation, and research capabilities of bio-optical measurements from the sea to the high altitude remote sensing platform. The primary goal of C-HORSE is to facilitate enhanced optical observations of coastal ecosystems using state of the art portable microradiometers with 19 targeted spectral channels and flight planning to optimize measurements further supporting current and future remote sensing missions.

Coasts↗
NASA NTRS2016

Linking Remotely Sensed Aerosol Types to Their Chemical Composition

Aerosol types measured during the Ship-Aircraft Bio-Optical Research (SABOR) experiment are related to GEOS-Chem model chemical composition. The application for this procedure to link model chemical components to aerosol type is desirable for understanding aerosol evolution over time. The Mahalanobis distance (DM) statistic is used to cluster model groupings of five chemical components (organic carbon, black carbon, sea salt, dust and sulfate) in a way analogous to the methods used by Burton et al. [2012] and Russell et al. [2014]. First, model-to-measurement evaluation is performed by collocating vertically resolved aerosol extinction from SABOR High Spectral Resolution LiDAR (HSRL) to the GEOS-Chem nested high-resolution data. Comparisons of modeled-to-measured aerosol extinction are shown to be within 35% +/- 14%. Second, the model chemical components are calculation into five variables to calculate the DM and cluster means and covariances for each HSRL-retrieved aerosol type. The layer variables from the model are aerosol optical depth (AOD) ratios of (i) sea salt and (ii) dust to total AOD, mass ratios of (iii) total carbon (i.e. sum of organic and black carbon) to the sum of total carbon and sulfate (iv) organic carbon to black carbon, and (v) the natural log of the aerosol-to-molecular extinction ratio. Third, the layer variables and at most five out of twenty SABOR flights are used to form the pre-specified clusters for calculating DM and to assign an aerosol type. After determining the pre-specified clusters, model aerosol types are produced for the entire vertically resolved GEOS-Chem nested domain over the United States and the model chemical component distributions relating to each type are recorded. Resulting aerosol types are Dust/Dusty Mix, Maritime, Smoke, Urban and Fresh Smoke (separated into 'dark' and 'light' by a threshold of the organic to black carbon ratio). Model-calculated DM not belonging to a specific type (i.e. not meeting a threshold probability) is termed an outlier and those DM values that can belong to multiple types (i.e. showing weak probability of belonging to a specific cluster) are termed as Overlap. MODIS active fires are overlaid on the model domain to qualitatively evaluate the model-predicted Smoke aerosol types.

Aerosol Types↗
NASA NTRS2017

Updated MISR Dark Water Research Aerosol Retrieval Algorithm - Part 1: Coupled 1.1 km Ocean Surface Chlorophyll a Retrievals with Empirical Calibration Corrections

As aerosol amount and type are key factors in the 'atmospheric correction' required for remote-sensing chlorophyll alpha concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chl(sub in situ) less than 1.5 mg m(exp -3), the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-mean-square error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample Kolmogorov- Smirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p greater than 0.1). We also compare MODIS-Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR-MODIS collocations having MODIS Chl less than 1.5 mg m(exp -3), MISR and MODIS show very good agreement: r = 0.96, MAE = 0.09, and RMSE = 0.15. The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS-Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.

Limbacher, James A.↗
NASA NTRS2017

Atmospheric Correction for Hyperspectral Ocean Color Retrieval with Application to the Hyperspectral Imager for the Coastal Ocean (HICO)

The classical multi-spectral Atmospheric Correction (AC) algorithm is inadequate for the new generation of spaceborne hyperspectral sensors such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and scattering by aerosols from the measured top-of-atmosphere (TOA) radiance, compensate for the absorption by atmospheric gases, and correct for reflection and refraction of the air-sea interface. In this work, we present and evaluate an improved AC for hyperspectral sensors developed within NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System software package (SeaDAS). The improvement is based on combining the classical AC approach of multi-spectral capabilities to correct for the atmospheric path radiance, extended to hyperspectral, with a gas correction algorithm to compensate for absorbing gases in the atmosphere, including water vapor. The SeaDAS-hyperspectral version is capable of operationally processing the AC of any hyperspectral airborne or spaceborne sensor. The new algorithm development was evaluated and assessed using the Hyperspectral Imager for Coastal Ocean (HICO) scenes collected at the Marine Optical BuoY (MOBY) site, and other SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and AERosol Robotic NETwork - Ocean Color (AERONET-OC) coastal sites. A hyperspectral vicarious calibration was applied to HICO, showing the validity and consistency of HICO's ocean color products. The hyperspectral AC capability is currently available in SeaDAS to the scientific community at https://oceancolor.gsfc.nasa.gov/.

Ocean Color↗
NASA NTRS2018

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.

Matteo Ottaviani↗
NASA NTRS2017

Creating Aerosol Types Form CHemistry (CATCH): a New Algorithm to Extend the Link Between Remote Sensing and Models

Current remote sensing methods can identify aerosol types within an atmospheric column, presenting an opportunity to incrementally bridge the gap between remote sensing and models. Here a new algorithm was designed for Creating Aerosol Types from CHemistry (CATCH). CATCH-derived aerosol types—dusty mix, maritime, urban, smoke, and fresh smoke—are based on first-generation airborne High Spectral Resolution Lidar (HSRL-1) retrievals during the Ship-Aircraft Bio-Optical Research (SABOR) campaign, July/August 2014. CATCH is designed to derive aerosol types from model output of chemical composition. CATCH-derived aerosol types are determined by multivariate clustering of model-calculated variables that have been trained using retrievals of aerosol types from HSRL-1. CATCH-derived aerosol types (with the exception of smoke) compare well with HSRL-1 retrievals during SABOR with an average difference in aerosol optical depth (AOD) <0.03. Data analysis shows that episodic free tropospheric transport of smoke is underpredicted by the Goddard Earth Observing System- with Chemistry (GEOS-Chem) model. Spatial distributions of CATCH-derived aerosol types for the North American model domain during July/August 2014 show that aerosol type-specific AOD values occurred over representative locations: urban over areas with large population, maritime over oceans, smoke, and fresh smoke over typical biomass burning regions. This study demonstrates that model-generated information on aerosol chemical composition can be translated into aerosol types analogous to those retrieved from remote sensing methods. In the future, spaceborne HSRL-1 and CATCH can be used to gain insight into chemical composition of aerosol types, reducing uncertainties in estimates of aerosol radiative forcing.

K W Dawson↗
NASA NTRS2019

First Steps in the Creation of a Joint MISR/MODIS Ocean Color Atmospheric Correction Algorithm

We are creating a new algorithm that combines observations from MISR and MODIS (both on the NASA Terra spacecraft) to improve atmospheric correction and coverage for ocean color data products. The algorithm utilizes information rich, multi-angle MISR observations for atmospheric correction, applied to MODIS. Our goal is to produce atmospherically corrected Remote Sensing Reflectance from MODIS with enhanced coverage and accuracy, for input to downstream bio-optical ocean parameter retrieval algorithms.An important aspect of this work is the utilization of multi-angle views of the reflected ocean surface sun glint. Usually, such observations are avoided, since the intensity of the glint overwhelms any contribution from the ocean body. However, MISR's multi-angle observations see varying degrees of glint, which means they can be used to better determine aerosol optical properties (Kaufman et al., 2002, Ottaviani et al., 2013), and to identify surface wind speeds that govern the glint pattern. The latter could be utilized to replace the wind speeds taken from ancillary sources that are currently used to conservatively mask potential glint contamination in MODIS observations.To assess this capability, and to identify the appropriate parameterization, we present an analysis using the Generalized Nonlinear Retrieval Analysis (GENRA, Vukicevic et al., 2009) information content assessment. This technique is also easily modified to act as a Bayesian retrieval algorithm, for which initial results are discussed. Finally, we describe the status of integrating MISR data into the processing capabilities of the Ocean Biology Processing Group (OBPG) at NASA, and show the first ocean color vicarious calibration (Franz et al., 2007) of the MISR instrument.

Knobelspiesse, Kirk↗
NASA NTRS2020

Robust Algorithm for Estimating Total Suspended Solids (TSS) in Inland and Nearshore Coastal Waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N > 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (R(sub rs)), (b) retrieval of particulate backscattering (b(sub bp)) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from b(sub bp) via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m (exp 3)], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e., from 10 to > 100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to < 20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads.

Total suspended solids↗
NASA NTRSDate not supplied

The PACE-MAPP Algorithm: Coupled Ocean/Aerosol Products

The PACE-MAPP algorithm, under development for combined SPEXone, HARP2 and OCI observations from NASA’s future Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission, directly inverts the coupled atmosphere-ocean system to retrieve aerosol optical and microphysical properties and ocean optical properties simultaneously. PACE-MAPP thus retrieves the spectrally-resolved inherent optical properties of the Earth’s waters: the spectral particulate scattering coefficient, b_p (λ), the spectral absorption coefficient for particulates and color-dissolved matter, a_tot (λ), and the spectral particulate backscatter efficiency, b ̃_"bp". From these three spectrally-resolved components, we can derive the spectral particulate hemispherical backscattering coefficient b_bp (λ) and the spectral diffuse attenuation coefficient K_"d" (λ). Direct comparisons of b_"bp" (532) and K_"d" (532) are made to collocated High-Spectral Resolution Lidar (HSRL) in-water measurements. The PACE-MAPP algorithm was tested using PACE-analog datasets collected by Research Scanning Polarimeter (RSP) observations during the NASA NAAMES (The North Atlantic Aerosols and Marine Ecosystems Study) and SABOR (Ship-Aircraft Bio-Optical Research) airborne campaigns, both of which also include ship-based in situ measurements.

Snorre Alfred Moen Stamnes↗
NASA NTRS2021

Advances in the Ocean Color Component of the Aerosol Robotic Network (AERONET-OC)

The Ocean Color Component of the Aerosol Robotic Network(AERONET-OC) supports activities related to ocean color such as validation of satellite data products, assessment of atmospheric correction schemes, and evaluation of bio-optical models through globally distributed standardized measurements of water-leaving radiance and aerosol optical depth. In view of duly assisting the AERONET-OC data user community, this work (i) summarizes the latest investigations on a number of scientific issues related to above-water radiometry, (ii) emphasizes the network expansion that from 2002 until the end of 2020 integrated 31 effective measurement sites, (iii) shows the equivalence of data product accuracy across sites and time for measurements performed with different instrument series, (iv) illustrates the variety of water types represented by the network sites ensuring validation activities across a diversity of observation conditions, and (v) documents the availability of water-leaving radiance data corrected for bidirectional effects by applying a method specifically developed for chlorophyll-a-dominated waters and an alternative one that is likely suitable for any water type.

In situ oceanic observations↗
NASA NTRS2018

An Overview of Approaches and Challenges for Retrieving Marine Inherent Optical Properties from Ocean Color Remote Sensing

Ocean color measured from satellites provides daily global, synoptic views of spectral water-leaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents. Because of their dependence on the concentration and composition of marine constituents, IOPs can be used to describe the contents of the upper ocean mixed layer. This information is critical to further our scientific understanding of biogeochemical oceanic processes, such as organic carbon production and export, phytoplankton dynamics, and responses to climatic disturbances. Given their importance, the international ocean color community has invested significant effort in improving the quality of satellite-derived IOP products, both regionally and globally. Recognizing the current influx of data products into the community and the need to improve current algorithms in anticipation of new satellite instruments (e.g., the global, hyperspectral spectroradiometer of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission), we present a synopsis of the current state of the art in the retrieval of these core optical properties. Contemporary approaches for obtaining IOPs from satellite ocean color are reviewed and, for clarity, separated based their inversion methodology or the type of IOPs sought. Summaries of known uncertainties associated with each approach are provided, as well as common performance metrics used to evaluate them. We discuss current knowledge gaps and make recommendations for future investment for upcoming missions whose instrument characteristics diverge sufficiently from heritage and existing sensors to warrant reassessing current approaches.

Bio-optics↗