Dariusz Stramski’s research while affiliated with University of California, San Diego and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (158)


GEOGRAPHIC PROVINCES OF PHYTOPLANKTON ALONG LATITUDINAL TRANSECT IN THE EASTERN ATLANTIC OCEAN FROM THE CONCEPT THAT PIGMENT RATIOS DRIVE THE SPECTRAL ABSORPTION SHAPES
  • Poster
  • File available

November 2024

·

4 Reads

·

Dariusz Stramski

·

Rick A. Reynolds

·

Oceanographic cruise with 59 stations in the eastern Atlantic Ocean (EAO) from October 15 to November 14, 2005. Data were collected on phytoplankton bio-optical variability of suspended particulate matter (ap) and photosynthetic pigment analysis at two depth levels (surface and 10 m), where values of light absorption coefficient by phytoplankton (aph) and non-pigmented material (ad) were determined, as well as the pigment composition of phytoplankton through the HPLC technique. In general, the Atlantic Ocean region showed oligotrophic characteristics, except in the northern Bay of Biscay and Cape Town region. In these two regions, high values of aph440 and chlorophyll-carotenoids were observed. The most relevant chlorophylls/carotenoids were: divinyl chlorophyll a (Dvchla), zeaxanthin (Zea), hexanoyloxyfucoxanthin (Hex-fuco), fucoxanthin (Fuco); so we assume that the different phytoplankton assemblages were: Prochlorococcus, Synechococcus, prymnesiophytes, diatoms, and dinoflagellates that were present during

Download



Figure 2. (a) Scatter plot of bbp(555) as a function of SPM for the CWD dataset where bbp(555) is estimated from the LS model (green circles). The green line refers to the Model-II best linear fit using the log-transformed variables. For comparison, the bbp(555) vs. SPM relationship of Neukermans et al. (2012) originally developed at 660 nm and recalculated for 555 nm assuming that bbp() has 225 a mean spectral dependency of  -0.5 (Babin et al., 2003a) is also shown (black line). (b) Same as (a) but for SPM as a function of bbp(555) for the CWD dataset. For comparison, the SPM vs. bbp(555) relationship of Stramski et al. (2023) is also shown (black line).
Figure 11. Radar plots summarizing the performance of the IOP-based algorithms for deriving PON. The IOPs(λ) considered are 645
Relationships between the concentration of particulate organic nitrogen and the inherent optical properties of seawater in oceanic surface waters

July 2024

·

91 Reads

The concentration of particulate organic nitrogen (PON) in seawater plays a central role in ocean biogeochemistry. Limited availability of PON data obtained directly from in situ sampling methods hinders development of thorough understanding and characterization of spatio-temporal variability of PON and associated source and sink processes within the global ocean. Measurements of seawater inherent optical properties (IOPs) that can be performed over extended temporal and spatial scales from various in situ and remote-sensing platforms represent a valuable approach to address this gap. We present the analysis of relationships between PON and particulate IOPs including the absorption coefficients of total particulate matter, ap(λ), phytoplankton, aph(λ), and non-algal particles, ad(λ), as well as the particulate backscattering coefficient, bbp(λ). This analysis is based on an extensive field dataset of concurrent measurements of PON and particulate IOPs in the near-surface oceanic waters and shows that reasonably strong relationships hold across a range of diverse oceanic and coastal marine environments. The coefficient ap(λ) and aph(λ) show the best ability to serve as PON proxies over a broad range of PON from open ocean oligotrophic to coastal waters. The particulate backscattering coefficient can also provide a good proxy of PON in open ocean environments. The presented relationships demonstrate a promising means to assess PON from optical measurements conducted from spaceborne and airborne remote-sensing platforms and in situ autonomous platforms. In support of this potential application, we provide the relationships between PON and spectral IOPs at light wavelengths consistent with those used by satellite ocean color sensors.


Model for partitioning the non-phytoplankton absorption coefficient of seawater in the ultraviolet and visible spectral range into the contributions of non-algal particulate and dissolved organic matter

May 2024

·

50 Reads

Non-algal particles and chromophoric dissolved organic matter (CDOM) are two major classes of seawater constituents that contribute substantially to light absorption in the ocean within the ultraviolet (UV) and visible (VIS) spectral regions. The similarities in the spectral shape of these two constituent absorption coefficients, ad(λ){a_{\rm d}}(\lambda) a d ( λ ) and ag(λ){a_{\rm g}}(\lambda) a g ( λ ) , respectively, have led to their common estimation as a single combined non-phytoplankton absorption coefficient, adg(λ){a_{\rm{dg}}}(\lambda) a d g ( λ ) , in optical remote-sensing applications. Given the different biogeochemical and ecological roles of non-algal particles and CDOM in the ocean, it is important to determine and characterize the absorption coefficient of each of these constituents separately. We describe an ADG model that partitions adg(λ){a_{\rm{dg}}}(\lambda) a d g ( λ ) into ad(λ){a_{\rm d}}(\lambda) a d ( λ ) and ag(λ){a_{\rm g}}(\lambda) a g ( λ ) . This model improves upon a recently published model [Appl. Opt. 58, 3790 (2019)APOPAI0003-693510.1364/AO.58.003790] through implementation of a newly assembled dataset of hyperspectral measurements of ad(λ){a_{\rm d}}(\lambda) a d ( λ ) and ag(λ){a_{\rm g}}(\lambda) a g ( λ ) from diverse oceanic environments to create the spectral shape function libraries of these coefficients, a better characterization of variability in spectral shape of ad(λ){a_{\rm d}}(\lambda) a d ( λ ) and ag(λ){a_{\rm g}}(\lambda) a g ( λ ) , and a spectral extension of model output to include the near-UV (350–400 nm) in addition to the VIS (400–700 nm) part of the spectrum. We developed and tested two variants of the ADG model: the ADG_UV-VIS model, which determines solutions over the spectral range from 350 to 700 nm, and the ADG_VIS model, which determines solutions in the VIS but can also be coupled with an independent extrapolation model to extend output to the near-UV. This specific model variant is referred to as ADG_VIS-UVExt{{\rm ADG}\_{{\rm VIS}}} \text{-} {{\rm UV}_{\rm{Ext}}} A D G _ V I S - U V E x t . Evaluation of the model with development and independent datasets demonstrates good performance of both ADG_UV-VIS and ADG_VIS-UVExt{{\rm ADG}\_{{\rm VIS}}} \text{-} {{\rm UV}_{\rm{Ext}}} A D G _ V I S - U V E x t . Comparative analysis of model-derived and measured values of ad(λ){a_{\rm d}}(\lambda) a d ( λ ) and ag(λ){a_{\rm g}}(\lambda) a g ( λ ) indicates negligible or small median bias, generally within ±5%\pm {5}\% ± 5 % over the majority of the 350–700 nm spectral range but extending to or above 10% near the ends of the spectrum, and the median percent difference generally below 20% with a maximum reaching about 30%. The presented ADG models are suitable for implementation as a component of algorithms in support of satellite ocean color missions, especially the NASA PACE mission.


Comparison of ocean-colour algorithms for particulate organic carbon in global ocean

April 2024

·

199 Reads

In the oceanic surface layer, particulate organic carbon (POC) constitutes the biggest pool of particulate material of biological origin, encompassing phytoplankton, zooplankton, bacteria, and organic detritus. POC is of general interest in studies of biologically-mediated fluxes of carbon in the ocean, and over the years, several empirical algorithms have been proposed to retrieve POC concentrations from satellite products. These algorithms can be categorised into those that make use of remote-sensing-reflectance data directly, and those that are dependent on chlorophyll concentration and particle backscattering coefficient derived from reflectance values. In this study, a global database of in situ measurements of POC is assembled, against which these different types of algorithms are tested using daily matchup data extracted from the Ocean Colour Climate Change Initiative (OC-CCI; version 5). Through analyses of residuals, pixel-by-pixel uncertainties, and validation based on optical water types, areas for POC algorithm improvement are identified, particularly in regions underrepresented in the in situ POC data sets, such as coastal and high-latitude waters. We conclude that POC algorithms have reached a state of maturity and further improvements can be sought in blending algorithms for different optical water types when the required in situ data becomes available. The best performing band ratio algorithm was tuned to the OC-CCI version 5 product and used to produce a global time series of POC between 1997–2020 that is freely available.


Phytoplankton composition from sPACE: Requirements, opportunities, and challenges

January 2024

·

495 Reads

·

14 Citations

Remote Sensing of Environment

Ocean color satellites have provided a synoptic view of global phytoplankton for over 25 years through near surface measurements of the concentration of chlorophyll a. While remote sensing of ocean color has revolutionized our understanding of phytoplankton and their role in the oceanic and freshwater ecosystems, it is important to consider both total phytoplankton biomass and changes in phytoplankton community composition in order to fully understand the dynamics of the aquatic ecosystems. With the upcoming launch of NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission, we will be entering into a new era of global hyperspectral data, and with it, increased capabilities to monitor phytoplankton diversity from space. In this paper, we analyze the needs of the user community, review existing approaches for detecting phytoplankton community composition in situ and from space, and highlight the benefits that the PACE mission will bring. Using this three-pronged approach, we highlight the challenges and gaps to be addressed by the community


Improved multivariable algorithms for estimating oceanic particulate organic carbon concentration from optical backscattering and chlorophyll-a measurements

January 2024

·

115 Reads

·

1 Citation

The capability to estimate the oceanic particulate organic carbon concentration (POC) from optical measurements is crucial for assessing the dynamics of this carbon reservoir and the capacity of the biological pump to sequester atmospheric carbon dioxide in the deep ocean. Optical approaches are routinely used to estimate oceanic POC from the spectral particulate backscattering coefficient bbp , either directly (e.g., with backscattering sensors on underwater platforms like BGC-Argo floats) or indirectly (e.g., with satellite remote sensing). However, the reliability of algorithms which relate POC to bbp is typically limited due to the complexity of interactions between light and natural assemblages of marine particles, which depend on variations in particle concentration, composition, and size distribution. This study expands on our previous work by analysis of an extended field dataset created with judicious data inclusion criteria with the aim to provide POC algorithms for multiple light wavelengths of measured bbp , which can be useful for applications with in situ optical sensors as well as above-water active or passive measurement systems. We describe an improved empirical multivariable approach to estimate POC from simultaneous measurements of bbp and chlorophyll-a concentration (Chla) to better account for the effects of variable particle composition on the relationship between POC and bbp . The multivariable regression models are formulated using a relatively large dataset of coincident measurements of POC, bbp , and Chla, including surface and subsurface data from the Atlantic, Pacific, Arctic, and Southern Oceans. We show that the multivariable algorithm provides reduced uncertainty of estimated POC across diverse marine environments when compared with a traditional univariate algorithm based on only bbp . We also propose an improved formulation of univariate algorithm based on bbp alone. Finally, we examine performance of several algorithms to estimate POC using our dataset as well as a dataset consisting of optical measurements from BGC-Argo floats and traditional POC measurements collected during a coincident research cruise in the Atlantic Ocean.


A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean

August 2023

·

206 Reads

·

4 Citations

Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the probability distributions of IOPs that are consistent with global distributions representative of vast areas of open-ocean pelagic environments and coastal regions, covering a broad range of optical water types. The generation of synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs, assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0, 30, and 60∘ assuming clear skies, standard atmosphere, and a wind speed of 5 m s-1. Thus, overall 29 880 RT simulations were performed. The output results of these simulations include radiance distributions, plane and scalar irradiances, and a whole set of AOPs, including remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines, where all optical variables are reported in the spectral range of 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving IOPs and AOPs. The database is available at the Dryad open-access repository of research data (10.6076/D1630T, Loisel et al., 2023).



Citations (78)


... Due to these contributions to ecosystem function and differing roles, assessing phytoplankton abundances and disentangling phytoplankton community composition (PCC) is necessary to understand marine environments (Cetinić et al., 2024). For example, current Earth systems models (ESMs) are unable to confidently project if primary productivity will increase or decrease under future climate scenarios (Kwiatkowski et al., 2020). ...

Reference:

Relationships between phytoplankton pigments and DNA- or RNA-based abundances support ecological applications
Phytoplankton composition from sPACE: Requirements, opportunities, and challenges

Remote Sensing of Environment

... For the development and testing of BING, we have leveraged a large set of a(λ),b b (λ) spectra made public by Loisel et al. (2023) (hereafter L23). We use their X = 4, Y = 0 model which includes inelastic scattering (not relevant here) and the Sun at the zenith. ...

A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean

... The PACE payload also includes two multi-angle polarimeters to measure the polarization state of the reflected light, namely the Hyper Angular Rainbow Polarimeter (HARP2) built by the University of Maryland Baltimore County and the Spectropolarimeter for Planetary Exploration (SPEXone) contributed by a consortium of organizations in the Netherlands. Together, these sensors will provide insights into the functioning and interactions of atmospheric aerosols, clouds, and ocean biology and are poised to make significant breakthroughs on phytoplankton dynamics (Cetinić et al., 2023) and ocean-atmosphere exchange (Remer et al., 2019). ...

Phytoplankton composition from sPACE: requirements, opportunities, and challenges

... This assumption, however, is not satisfied across diverse natural assemblages of marine particles [68,70,71,[76][77][78]. In addition to the direct effect on the spectral values of a d (λ), the null-point correction produces a biasing effect on the spectral shape with a steeper spectral slope [79]. Third, to construct the spectral shape library the spectra of a d (λ) and a g (λ) were characterized by a single spectral shape parameter across the spectral region from 440 to 550 nm. ...

Estimation of chromophoric dissolved organic matter and non-algal particulate absorption coefficients of seawater in the ultraviolet by extrapolation from the visible spectral region

... However, the spatial sampling of medium-resolution sensors (250-1,000 m) is generally too coarse for application in most marsh-influenced estuaries. Over the past decade, HSRRS has become increasingly popular for studying nearshore and inland water quality (Brewin et al., 2023) and has recently been used to assess the drivers of DOC dynamics and other water constituents (e.g., total suspended solids (TSS)) in tidal marsh-influenced estuaries (Cao & Tzortziou, 2021;Zhang et al., 2020). However, the sporadic temporal coverage of these sensors (5 days at best) continues to limit the applicability of HSRRS imagery for calculating accurate DOC fluxes in these systems (Fichot et al., 2023). ...

Ocean carbon from space: Current status and priorities for the next decade

Earth-Science Reviews

... Hence, a radiative transfer modeling framework specific to algorithm development, such as the ONNS in-water algorithm , is necessary. Given that, previous forward models based on deterministic functions, e.g., fixed parameters, may not capture all the variability, and utilizing reasonable random values can improve flexibility in simulations (IOCCG, 2006;Zheng et al., 2015;Loisel et al., 2023). ...

A synthetic database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean

... On the other hand, R rs at 551 and 671 nm for SNPP-VIIRS-S were also obviously lower than those of SNPP-VIIRS (see Fig. K2 in Appendix K). The discrepancies in R rs might be the main factor in the underestimation of PON concentrations by SNPP-VIIRS, which is presumably due to the inherent differences in their radiometric and spectral characteristics, as well as the inevitable difference in space and time for the matchups [43]. ...

Performance assessment and validation of ocean color sensor-specific algorithms for estimating the concentration of particulate organic carbon in oceanic surface waters from satellite observations

Remote Sensing of Environment

... In contrast, satellite remote sensing offers advantages such as rapid observation, broad spatial coverage, and frequent intervals, providing new technical support for estimating TSM. In the past few decades, researchers have conducted extensive studies on satellite remote sensing retrieval of TSM, focusing on its optical properties and the development of remote sensing algorithms [28][29][30][31][32]. These efforts have led to the proposal of empirical algorithms and analytical/semi-analytical algorithms, successfully enabling the retrieval of TSM concentration in various waters of the world ocean [4,18,[33][34][35][36]. ...

Adaptive optical algorithms with differentiation of water bodies based on varying composition of suspended particulate matter: A case study for estimating the particulate organic carbon concentration in the western Arctic seas

Remote Sensing of Environment

... POC was calculated from BGC-Argo measured and quality-controlled profiles of b bp (700) and Chla following the multivariable empirical algorithm described by Model B parametrized for wavelength of 700 nm in Koestner et al. (2022). Then, carbon stocks were computed by integrating each POC profile within the mixed layer. ...

A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements

... Integration of these algorithms to achieve optimal performance has attracted much attention from researchers. A combined algorithm was developed by Stramski et al. based on the Band Ratio Difference Index (BRDI) and the MBR-OC4 algorithm for POC retrieval [6]. It maintains high accuracy in both Type I and Type II waters. ...

Ocean color algorithms to estimate the concentration of particulate organic carbon in surface waters of the global ocean in support of a long-term data record from multiple satellite missions

Remote Sensing of Environment