Novel ocean color information from Sentinel-5P’s high spectral resolution
Bibliographic record
Abstract
High spectrally resolved satellite data are a source of the top of the atmosphere radiance signal which can be used for novel algorithms aimed for observations of phytoplankton groups biomass and the spectral composition of the light-lit ocean. Atmospheric sensors such as SCIAMACHY, GOME-2 and OMI have proven in the past to yield valuable information on phytoplankton diversity, sun-induced marine fluorescence, and the underwater light field. However, the use of these data sets was limited by their temporal and spatial resolution mostly not meeting requirements for time series studies. Within the ESA Sentinel-5p+ Innovation project S5POC, we explore Sentinel-5P instrument TROPOMI's potential for deriving the diffuse attenuation coefficient and the quantification of different phytoplankton groups. As commonly used for the retrieval of atmospheric trace gases, we apply the differential optical absorption spectroscopy combined with radiative transfer modeling (RTM) to infer these oceanic parameters. We present results on a measure describing the diminishing of incoming radiation in the ocean with depth, the diffuse attenuation coefficient Kd. Kd is derived by the retrieval of the vibrational Raman scattering signal in backscattered radiances measured by TROPOMI in the UV and blue spectral range which then is further converted to the associated Kd using RTM. The final TROMPOMI KD data sets resolved for three spectral regions (UV-B+short wave UV-A, UV-A and short blue) agree well with in situ data sampled during an expedition with RV Polarstern in 2018 in the tropical, temperate and polar Atlantic Ocean. Further, Kd-blue compared to wavelength-converted Kd(490 nm) products (OLCI-A and the merged OC-CCI) from common, multispectral, ocean color sensors, show that differences between the three data sets are within uncertainties given for the OC-CCI product. TROPOMI’s potential for retrieving phytoplankton groups is also explored for the Atlantic open ocean and, additionally, for the Portuguese coast and coast and British Columbia, Canada coast. Comparison to independent phytoplankton groups biomass data derived from in-situ pigment data and similar satellite products (CMEMS global PFT product based on Xi et al. 2021 and OCPFT algorithm following Losa et al. 2017 applied to OLCI-Chla and OC-CCI data sets) show reasonable agreement for most groups. Having established these new TROPOMI products, the next steps are to investigate global products over the full operating period of TROPOMI to assess the temporal and spatial stability of the products. Perspectively, these data products delivering information on the spectral underwater light and phytoplankton composition can be used as auxiliary information for modeling marine ecosystem/biogeochemical functioning or photochemical reaction rates of climatically important compounds and inhibition of primary productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".