Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Curation Notes
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

See the "Related Datasets" section for other datasets from the same profiles.

Acronyms: 

DESAFÍO = DisEntangling Seasonality of Active Flux in the Ocean (expedition name)
FoSI = Focused Shadowgraph Imaging
LODP = Low Optical-Density Particles
HODP = High Optical-Density Particles
OD = Optical Density
ESD = equivalent spherical diameter
AZ = Azores
CI = Canary Islands
OO = Open Ocean
RGF = Relative Gel Fraction
ART ANOVA = Aligned Rank Transform (ART) Analysis of Variance (ANOVA)
ANCOVA = Analysis of Covariance

Particle and hydrographic profile data were collected during the DESAFÍO expedition (DisEntangling Seasonality of Active Flux in the Ocean; PID2020-118118RB-100) in the Northeastern subtropical Atlantic Ocean, from 31 January to 2 March 2023, along a transect between the Azores (AZ; 41°5'32.39"N, 24°58'41.00"W) and the Canary Islands (CI; 28°41'00.00"N, 13°12'2.40"W). Twenty-two CTD casts were grouped into three geographic regions — CI (casts 1–6), the open-ocean station OO (casts 7–14, west of CI and south of the Azores), and AZ (casts 15–22) — and profiles were further partitioned into three depth strata: epipelagic (≤200 m), mesopelagic (200–1,000 m), and bathypelagic (>1,000 m). Because the particle imaging system was mounted directly on the CTD rosette, particle and hydrographic data were collected simultaneously on the same vertical casts, allowing direct depth-matching between the two profile types. Four casts (5, 6, 18, and 20) have incomplete particle profiles due to unexpected camera shutdowns, likely from low battery voltage.

CTD-derived temperature, salinity, dissolved oxygen, fluorescence, and turbidity profiles were binned to 1 dbar. Particle images were processed following the pipeline of Huang and Bochdansky, restricting analysis to particles ≥23.5 μm to exclude single-pixel noise, and classifying particles by optical density into low-OD (LODP; threshold 11, OD = 0.1466) and high-OD (HODP; threshold 29, OD = 0.2246) pools. Particle abundance and total particle volume (TPV) were resolved to 1-m depth bins to match the vertical resolution of the CTD data, and the Relative Gel Fraction (RGF) was calculated at each depth bin from the ratio of LODP to HODP abundance. Depth-resolved trends in particle metrics were evaluated using the Theil–Sen estimator with Mann–Kendall significance testing.

Huang H and Bochdansky AB (2025) Optimizing an image analysis protocol for ocean particles in focused shadowgraph imaging systems. Front. Mar. Sci. 12:1539828. doi: 10.3389/fmars.2025.1539828

- Loaded sum_allcasts_twopools.csv from source submission files, naming resulting table 1003447_v1_fosi-particle-profiles
- Configured CSV parsing with comma delimiter, header row 1, and treated empty string, "NaT", and "NA" as missing values
- Retained blank-header columns and empty rows were removed as part of load
- Updated column metadata with BCO-DMO standard name IDs, descriptions, and supplied units for Conductivity, Date, Density, Depth, Fluorescence, Latitude, Longitude, Oxygen, PAR, Part_No, ParticlePool, PotentialTemperature, SalinityPractical, TotalParticleArea, TotalParticleVolume, and Turbidity columns
- Flagged Depth, Latitude, and Longitude columns as primary parameters
- Converted Date column from string format %m/%d/%Y to date type with output format %Y-%m-%d, preserving column metadata
- Set column data types: Conductivity, Density, Fluorescence, Latitude, Longitude, Oxygen, PAR, PotentialTemperature, SalinityPractical, and Turbidity set to number type; Depth set to integer type; Date set to date type with format %Y-%m-%d; Part_No, TotalParticleArea, and TotalParticleVolume set to numeric float, ParticlePool to string.
- Dumped final table to CSV output as 1003447_v1_fosi-particle-profiles.csv, including generation of a unique latitude/longitude file and saving of the pipeline specification

NSF Award Abstract

Globally, the ocean removes more carbon dioxide than it releases into the atmosphere storing a portion of the excess carbon in the deep sea. Sinking particles, both living plankton and non-living detritus, are major contributors to this flux of carbon. Modern camera systems and image analysis techniques have made it possible to count, measure and classify these particles, thus providing oceanographers with a tool to estimate carbon transfers to the deep ocean at high resolution in space and time. Unfortunately, it is not enough to know the sizes of particles to estimate how fast these particles sink because shape and particle density also influence the sinking velocity. This project examines the velocities of individual particles as they sink into the deep ocean using a camera attached to a particle trap. For each of these particles, classification criteria, such as size, shape factors, optical density, and in the case of plankton, taxonomic identification, is determined and compared to their individual sinking velocities. This information serves to calculate overall sinking velocities from surveys of particles in the water column and thereby produce more reliable estimates of carbon fluxes from camera images. This project supports technology development in underwater imaging systems, graduate and undergraduate student education, and science literacy initiatives for middle-school students and their mentors through public outreach programs.

Shipboard and autonomous vehicle surveys of oceanic particle inventories hold great promise for estimating carbon fluxes at high temporal and spatial resolutions. However, while the sinking velocities of larger particles such as foraminifera shells and fecal pellets of salps, krill, and larger copepods are relatively well constrained, the dynamics of the smaller particle size pool (50–500 micrometers) remain more elusive. Despite their size and presumed slow sinking velocities, small particles occur in large numbers in the mesopelagic layer and sediment-trap material. Their abundance in the mesopelagic could be the result of deep mixing, or small particles could be remnants of digested larger particles, particles with a high excess density such as lithogenic dust particles, minipellets egested by protists, protist spores, or the result of fragmentation at depth due to the activity of flux feeders, among other possibilities. This project addresses some unanswered questions about the small particle pool by linking individually-resolved optical features with sinking velocities. Using Stokes’ law, excess density is being estimated from size and sinking velocity and then assigned to particles from optical surveys. A horizontally installed camera system records sinking velocities, sizes, and features of particles in a sediment trap attached to the Oceanic Flux Program mooring array. The recorded particles are being characterized using 1) classic image analysis, taking various shape factors into account; 2) opacity of individual particles; and 3) image classification with supervised and unsupervised deep learning using convolutional neural networks. A second identical camera surveys the particle inventory at the same station and time in the water column to integrate flux estimates over the existing and undisturbed particle pool. Niskin bottle samples and microscopic examination of particles augment the interpretation of image data. The results of this project contribute to the overarching goal of achieving higher predictive power for carbon flux models based on optical particle surveys.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.