the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Mar 2025
On the Challenges of Retrieving Phytoplankton Properties from Remote-Sensing Observations
Abstract. Remote-sensing satellites provide the only means to observe the entire ocean at high-temporal resolution. Optical-sensors assess ocean color through estimates of remote-sensing reflectance (Rrs(λ)). We emphasize a physical degeneracy in the radiative transfer equation that relates Rrs(λ) to absorption and backscattering coefficients (a(λ),bb(λ)) known as inherent optical properties (IOPs). This degeneracy stems from Rrs(λ) depending on the ratio bb(λ)/a(λ), preventing the independent retrieval of non-water IOPs without prior knowledge. We demonstrate that multi-spectral satellite observations lack the statistical power to recover more than three parameters describing non-water absorption and backscattering. Due to exponential-like absorption by colored dissolved organic matter and detritus at shorter wavelengths, multi-spectral Rrs(λ) data cannot detect phytoplankton absorption without strict priors, leading to biased and uncertain estimates. These results challenge decades of IOP retrieval literature, including assessments of phytoplankton growth and biomass. While hyperspectral observations hold promise to recover additional parameters, significant hurdles remain in accurately quantifying IOPs and phytoplankton biomass at a global scale.
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Status: open (until 25 Apr 2025)
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RC1: 'Comment on egusphere-2025-927', Anonymous Referee #1, 14 Apr 2025
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Summary
This contribution critically examines one of the central challenges in ocean color remote sensing: the retrieval of inherent optical properties (IOPs) from satellite-derived remote sensing reflectance Rrs(λ). The authors focus on the well-known physical degeneracy in the radiative transfer equation, namely, that Rrs depends on the ratio of backscattering to absorption coefficients, bb(λ)/a(λ), which limits the ability to independently retrieve non-water IOPs without strong prior information. It is argued that multi-spectral satellite data lack the statistical power to constrain more than three parameters, especially in optically complex waters dominated by CDOM and detritus.
To address this limitation, the authors introduce BING, an open-source Bayesian inference framework that incorporates prior knowledge and uncertainty quantification through a Markov Chain Monte Carlo (MCMC) approach. While BING builds on prior retrieval models such as GSM and GIOP, it offers a more flexible approach for assessing parameter identifiability and retrieval uncertainty. Although the paper acknowledges the added potential of hyperspectral data, it emphasizes that methodological rigor is essential, regardless of spectral resolution. The manuscript calls into question the robustness of past IOP retrievals and advocates for more statistically grounded inversion methods moving forward.
Minor Comments and Editorial Suggestions
- Given the centrality of Rrs to the analysis, the definition at Lines 26–28 would benefit from being presented as a standalone, numbered equation to highlight the role of the bb/a ratio. This would aid readers less familiar with radiative transfer.
- Paragraphs beginning at Lines 70 and 75 end with a repeated sentence, this should be revised for clarity.
- Line 211: possibly intended to read “For the principal analysis”?
- Fig. 4 caption: revise “spectrum that maintaine” to “spectrum that maintains.”
- Several paragraphs in the Results section, such as Lines 269–274 and parts of the Fig. 4 caption, describe experimental setup rather than findings. To follow standard structure, this content should be moved to the Methods section. For example, the description of parameter scaling or idealized simulation conditions belongs in Methods rather than the figure caption.
- In Fig. 6, consider increasing line thickness for the S/N 5–20 curves to improve visibility.
- Lines 339 and 349 reference “Figure 6b,” but figure panels are not labeled. Either label panels (a, b, c) or refer to them using a nomenclature (e.g., left, right) consistent with the captions.
- Spelling: “Retrieval” is misspelled in some of the Fig. 6 legends.
- For Fig. 8, using a non-monochromatic color scale would improve contrast and interpretability of parameter correlations.
Strengths and Key Contributions
The manuscript highlights a foundational issue in satellite ocean color: the physical degeneracy of the radiative transfer equation. The authors show that, without prior constraints, Rrs alone cannot separate absorption and backscattering processes, especially in the presence of strongly overlapping spectral signals such as those from CDOM, detritus, and phytoplankton. This critique of existing IOP retrieval methods is valid and important.
A key contribution is the structured evaluation of a hierarchy of IOP models with increasing complexity. These range from simple constant-parameter models to physically informed formulations that include exponential CDOM absorption and power-law particle backscattering. This approach helps quantify how many parameters can be meaningfully constrained by reflectance data, especially when comparing multi-spectral and hyperspectral observations.
The introduction of BING, a Bayesian inference framework, adds value by allowing for rigorous uncertainty quantification and the exploration of model identifiability. BING facilitates sensitivity analysis and the testing of retrieval fidelity across different oceanic regimes. Though Bayesian approaches are not new to the field, BING's open-source implementation, built around MCMC sampling, provides a platform for evaluating retrieval scenarios.
Critical Assessment of Hyperspectral Results and Retrieval Limits
The manuscript’s analysis of simulated PACE observations offers a demonstration of the retrieval capabilities and limitations of hyperspectral instruments. In high-chlorophyll waters, the [k = 5] model yields absorption and backscattering estimates that closely match known values. However, the analysis also shows that in oligotrophic waters, retrieval of phytoplankton absorption in parts of the spectrum (e.g., aph(440)) becomes much more uncertain. For example, in the experiment shown in Figure 7, a substantial portion of the posterior distribution includes vanishingly small aph values, highlighting the limited ability of Rrs alone to isolate phytoplankton absorption without additional constraints. The authors interpret this as evidence that aph is not being retrieved in a statistically robust manner.
The comparison between BING, GIOP, and GSM reinforces the point: even when the models achieve statistically acceptable fits to Rrs, they yield IOP estimates that differ by large factors. This divergence, especially under low-Chla conditions, illustrates the sensitivity of retrievals to prior assumptions and model structure. The analysis supports the broader conclusion that hyperspectral data are a significant advancement but do not, on their own, resolve the underlying inversion ambiguity.
The authors offer several practical strategies for improving retrieval accuracy, such as incorporating near-UV bands to better constrain CDOM, refining priors on the spectral slope and adopting adaptive regularization techniques. These are sensible and actionable, though the paper would benefit from a more specific discussion of how these approaches might be implemented in operational contexts.
Discussion and Broader Implications
The discussion does a good job framing the limitations of current and future IOP retrievals considering the limitations. The authors acknowledge that their simulations are idealized, assuming perfect knowledge of water IOPs, no vertical variability, and uncorrelated noise. Even under these favorable conditions, the conclusion remains that only four or five IOP parameters can be meaningfully constrained from hyperspectral reflectance. The implication is clear: improving retrieval accuracy will require better priors, not just better sensors.
The authors note that while their findings do not invalidate historical satellite products, they do suggest that uncertainties have likely been underestimated. Their call for geographically and temporally adaptive priors, particularly for CDOM slope is well-founded and practical. The proposal to formalize a community-driven Bayesian framework, supported by initiatives like IHOP, is a logical and constructive path forward.
The manuscript also discusses alternative approaches, including fluorescence signals, lidar, and polarization, and rightly treats these as promising but still emerging. The comments on machine learning are appropriately cautious, especially given the risks of overfitting and hidden priors in purely data-driven approaches.
Conclusion
This manuscript presents a timely and methodologically sound re-examination of the assumptions underpinning satellite-based ocean color IOP retrieval. It challenges prevailing optimism about hyperspectral retrievals by rigorously quantifying the information content of Rrs and by demonstrating that key biogeochemical properties, particularly phytoplankton absorption, cannot be reliably retrieved without strong, context-dependent priors.
While the novelty of the approach is incremental considering prior Bayesian work, the paper’s systematic model hierarchy, open-source implementation, and discussion of retrieval challenges make it a meaningful contribution. Its conclusions are both technically sound and of high relevance to the ocean color community, particularly in the context of PACE and future mission planning.
With minor editorial revisions and some structure reorganization around model implementation, this manuscript will serve as a valuable reference for researchers and developers seeking to advance ocean color retrieval methodologies.
Citation: https://doi.org/10.5194/egusphere-2025-927-RC1
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