Chlorophyll absorption and phytoplankton size information inferred from hyperspectral particulate beam attenuation

Electromagnetic theory predicts spectral dependencies in extinction efficiency near a narrow absorption band for a particle with an index of refraction close to that of the medium in which it is immersed. These absorption band effects are anticipated in oceanographic beam-attenuation (beam-c) spectra, primarily due to the narrow red peak in absorption produced by the phytoplankton photopigment, chlorophyll a (Chl a). Here we present a method to obtain Chl a absorption and size information by analyzing an eigendecomposition of hyperspectral beam-c residuals measured in marine surface waters by an automatic underway system. We find that three principal modes capture more than 99% of the variance in beam-c residuals at wavelengths near the Chl a red absorption peak. The spectral shapes of the eigenvectors resemble extinction efficiency residuals attributed to the absorption band effects. Projection of the eigenvectors onto the beam-c residuals produces a time series of amplitude functions with absolute values that are strongly correlated to concurrent Chl a absorption line height (aLH) measurements (r values of 0.59 to 0.83) and hence provide a method to estimate Chl a absorption. Multiple linear regression of aLH on the amplitude functions enables an independent estimate of aLH, with RMSE of 3.19 · 10−3 m−1 (3.3%) or log10-RMSE of 18.6%, and a raw-scale R2 value of 0.90 based on the Tara Oceans Expedition data. Relationships between the amplitude functions and the beam-c exponential slopes are in agreement with theory relating beam-c to the particle size distribution. Compared to multispectral analysis of beam-c slope, hyperspectral analysis of absorption band effects is anticipated to be relatively insensitive to the addition of nonpigmented particles and to monodispersion

Advances in Above- and In-Water Radiometry, Volume 3: Hybridspectral Next-Generation Optical Instruments

This publication documents the scientific advances associated with new instrument systems and accessories built to improve above- and in-water observations of the apparent optical properties (AOPs) for a diversity of water masses, including optically complex waters. The principal objective is to be prepared for the launch of next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation in the shortest time possible. The technologies described herein are entirely new hybrid sampling capabilities, so as to satisfy the requirements established for next-generation missions. Both above- and in-water instruments are documented with software options for autonomous control of data collection activities as applicable. The instruments were developed for the Hybridspectral Alternative for Remote Profiling of Optical Observations for NASA Satellites (HARPOONS) vicarious calibration project. The state-of-the-art accuracy required for vicarious calibration also led to the development of laboratory instruments to ensure the field observations were within uncertainty requirements. Separate detailed presentations of the individual instruments provide the hardware designs, accompanying software for data acquisition and processing, and examples of the results achieved

Advances in Above- and In-Water Radiometry, Volume 2: Autonomous Atmospheric and Oceanic Observing Systems

This publication documents the scientific advances associated with new instrument systems and accessories built to improve above- and in-water observations of the apparent optical properties (AOPs) of optically complex waters. The principal objective is to be prepared for the launch of next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation in the shortest time possible. The Hybridspectral Alternative for Remote Profiling of Optical Observations for NASA Satellites (HARPOONS) is presented as a case example of technologies conceived, developed, and deployed operationally in support of next-generation mission requirements. The field trials, field commissioning, and operational demonstration resulted in a technology readiness level (TRL) value of 9 for a diversity of laboratory and field instrument systems. Separate detailed presentations of the individual instruments provide the hardware designs, accompanying software for data acquisition and processing, and examples of the results achieved. For the laboratory components, calibration and characterization procedures are described along with an estimation of the sources of uncertainty, which culminates in a full uncertainty budget for the radiometers deployed to the field

Large-scale shift in the structure of a kelp forest ecosystem co-occurs with an epizootic and marine heatwave

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in McPherson, M. L., Finger, D. J., I., Houskeeper, H. F., Bell, T. W., Carr, M. H., Rogers-Bennett, L., & Kudela, R. M. Large-scale shift in the structure of a kelp forest ecosystem co-occurs with an epizootic and marine heatwave. Communications Biology, 4(1), (2021): 298, https://doi.org/10.1038/s42003-021-01827-6.Climate change is responsible for increased frequency, intensity, and duration of extreme events, such as marine heatwaves (MHWs). Within eastern boundary current systems, MHWs have profound impacts on temperature-nutrient dynamics that drive primary productivity. Bull kelp (Nereocystis luetkeana) forests, a vital nearshore habitat, experienced unprecedented losses along 350 km of coastline in northern California beginning in 2014 and continuing through 2019. These losses have had devastating consequences to northern California communities, economies, and fisheries. Using a suite of in situ and satellite-derived data, we demonstrate that the abrupt ecosystem shift initiated by a multi-year MHW was preceded by declines in keystone predator population densities. We show strong evidence that northern California kelp forests, while temporally dynamic, were historically resilient to fluctuating environmental conditions, even in the absence of key top predators, but that a series of coupled environmental and biological shifts between 2014 and 2016 resulted in the formation of a persistent, altered ecosystem state with low primary productivity. Based on our findings, we recommend the implementation of ecosystem-based and adaptive management strategies, such as (1) monitoring the status of key ecosystem attributes: kelp distribution and abundance, and densities of sea urchins and their predators, (2) developing management responses to threshold levels of these attributes, and (3) creating quantitative restoration suitability indices for informing kelp restoration efforts.M.H.C. received support from the National Science Foundation (OCE‐1538582)

Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas)

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Houskeeper, H. F., Rosenthal, I. S., Cavanaugh, K. C., Pawlak, C., Trouille, L., Byrnes, J. E. K., Bell, T. W., & Cavanaugh, K. C. Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas). Plos One, 17(1), (2022): e0257933, https://doi.org/10.1371/journal.pone.0257933.Giant kelp populations that support productive and diverse coastal ecosystems at temperate and subpolar latitudes of both hemispheres are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally uneven, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp using satellite imagery requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery (8 images spanning the majority of our continuous timeseries, cumulatively covering over 2,700 km of coastline, and including all relevant sensors). Using the remote sensing approaches evaluated herein, we present the first continuous timeseries of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017–2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.This work was funded by the National Aeronautics and Space Administration as part of the Citizen Science for Earth Systems Program (https://earthdata.nasa.gov/esds/competitive-programs/csesp) with grant #80NSSC18M0103 (awarded to JEKB), which also provided salary to HFH, and by the National Science Foundation through the Santa Barbara Coastal Long-Term Environmental Research (https://sbclter.msi.ucsb.edu) program with grants #OCE 0620276 and 1232779. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Advances in Above- and In-Water Radiometry, Volume 1: Enhanced Legacy and State-of-the-Art Instrument Suites

This publication documents the scientific advances associated with new instrument systems and accessories built to improve above- and in-water observations of the apparent optical properties (AOPs) of aquatic ecosystems. The perspective is to obtain high quality data in offshore, nearshore, and inland waters with equal efficacy. The principal objective is to be prepared for the launch of the next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation in the shortest time possible. The technologies described herein are designed to either improve legacy radiometric systems or to provide entirely new hybrid sampling capabilities, so as to satisfy the requirements established for diverse remote sensing requirements. Both above- and in-water instrument suites are documented with software options for autonomous control of data collection activities. The latter includes an airborne instrument system plus unmanned surface vessel (USV) and buoy concepts

From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model

Many scientific domains gather sufficient labels to train machine algorithms through human-in-the-loop techniques provided by the Zooniverse.org citizen science platform. As the range of projects, task types and data rates increase, acceleration of model training is of paramount concern to focus volunteer effort where most needed. The application of Transfer Learning (TL) between Zooniverse projects holds promise as a solution. However, understanding the effectiveness of TL approaches that pretrain on large-scale generic image sets vs. images with similar characteristics possibly from similar tasks is an open challenge. We apply a generative segmentation model on two Zooniverse project-based data sets: (1) to identify fat droplets in liver cells (FatChecker; FC) and (2) the identification of kelp beds in satellite images (Floating Forests; FF) through transfer learning from the first project. We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts. We find that both the FC and COCO TL models perform better than the baseline cases when using >75% of the original training sample size. The COCO-based TL model generally performs better than the FC-based one, likely due to its generalized features. Our investigations provide important insights into usage of TL approaches on multi-domain data hosted across different Zooniverse projects, enabling future projects to accelerate task completion.Comment: 5 pages, 4 figures, accepted for publication at the Proceedings of the ACM/CIKM 2022 (Human-in-the-loop Data Curation Workshop

Advances in Bio-Optics for Observing Aquatic Ecosystems

The generalized bio-optical transition from oligotrophic blue to mesotrophic green waters has enabled remote measurement of oceanic ecosystems under conditions in which the optically relevant water mass constituents covary with the primary phytoplankton photopigment, chlorophyll a (Chla). This assumption, known as case-1, is generally incorrect within coastal and inland water bodies, where external factors, such as riverine discharge and sediment resuspension, also modify the concentrations of organic and inorganic constituents beyond the ranges anticipated for natural oceanic ecosystems. New approaches to characterize aquatic ecosystems without relying on case-1 criteria are proposed here using measurements of apparent and inherent optical properties. The methods applied in this work include bio-optical modeling and analyses of in situ, airborne, and satellite radiometric measurements of oceanic, coastal, and inland water ecosystems. Key findings are that: satellite phytoplankton datasets underestimate phytoplankton biomass in coastal ocean regions when default atmospheric correction methods are applied (Chapter 1); the expansion of spectral range for above-water radiometric measurements improves the accuracy of colored dissolved organic matter algorithms (Chapter 2); and hyperspectral beam-attenuation meters can accurately predict Chla without relying on empirical relationships between organic and inorganic particle populations (Chapter 3). The bio-optical advances presented here improve measurement capabilities for optically complex waters and are relevant to global (i.e., oceanic, coastal, and inland) water bodies. Finally, this work supports the perspective that bio-optical approaches should be consistent with electromagnetic theory or other fundamental principles - rather than requiring a partition between waters that satisfy versus fail empirical open ocean criteria - to incorporate a greater diversity of global aquatic ecosystems

Advances in Bio-Optics for Observing Aquatic Ecosystems

The generalized bio-optical transition from oligotrophic blue to mesotrophic green waters has enabled remote measurement of oceanic ecosystems under conditions in which the optically relevant water mass constituents covary with the primary phytoplankton photopigment, chlorophyll a (Chla). This assumption, known as case-1, is generally incorrect within coastal and inland water bodies, where external factors, such as riverine discharge and sediment resuspension, also modify the concentrations of organic and inorganic constituents beyond the ranges anticipated for natural oceanic ecosystems. New approaches to characterize aquatic ecosystems without relying on case-1 criteria are proposed here using measurements of apparent and inherent optical properties. The methods applied in this work include bio-optical modeling and analyses of in situ, airborne, and satellite radiometric measurements of oceanic, coastal, and inland water ecosystems. Key findings are that: satellite phytoplankton datasets underestimate phytoplankton biomass in coastal ocean regions when default atmospheric correction methods are applied (Chapter 1); the expansion of spectral range for above-water radiometric measurements improves the accuracy of colored dissolved organic matter algorithms (Chapter 2); and hyperspectral beam-attenuation meters can accurately predict Chla without relying on empirical relationships between organic and inorganic particle populations (Chapter 3). The bio-optical advances presented here improve measurement capabilities for optically complex waters and are relevant to global (i.e., oceanic, coastal, and inland) water bodies. Finally, this work supports the perspective that bio-optical approaches should be consistent with electromagnetic theory or other fundamental principles - rather than requiring a partition between waters that satisfy versus fail empirical open ocean criteria - to incorporate a greater diversity of global aquatic ecosystems

Ocean Color Quality Control Masks Contain the High Phytoplankton Fraction of Coastal Ocean Observations

Satellite estimation of oceanic chlorophyll-a content has enabled characterization of global phytoplankton stocks, but the quality of retrieval for many ocean color products (including chlorophyll-a) degrades with increasing phytoplankton biomass in eutrophic waters. Quality control of ocean color products is achieved primarily through the application of masks based on standard thresholds designed to identify suspect or low-quality retrievals. This study compares the masked and unmasked fractions of ocean color datasets from two Eastern Boundary Current upwelling ecosystems (the California and Benguela Current Systems) using satellite proxies for phytoplankton biomass that are applicable to satellite imagery without correction for atmospheric aerosols. Evaluation of the differences between the masked and unmasked fractions indicates that high biomass observations are preferentially masked in National Aeronautics and Space Administration (NASA) ocean color datasets as a result of decreased retrieval quality for waters with high concentrations of phytoplankton. This study tests whether dataset modification persists into the default composite data tier commonly disseminated to science end users. Further, this study suggests that statistics describing a dataset’s masked fraction can be helpful in assessing the quality of a composite dataset and in determining the extent to which retrieval quality is linked to biological processes in a given study region