The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Chl-a. In recent years, the Ocean and Land Color Instruments (OLCI) onboard the Sentinel 3 (S3) A and B satellites were launched, which provide data about various aquatic environments on advantageous spatial, spectral, and temporal resolutions with high SNR. Although S3 OLCI could be favorable to monitor high northern latitude waters, there have been several challenges related to Chl-a concentration retrieval in these waters due to their unique optical properties coupled with challenging environments including high sun zenith angle, presence of sea ice, and frequent cloud covers. In this work, we aim to overcome these difficulties by developing a machine-learning (ML) approach designed to estimate Chl-a concentration from S3 OLCI data in high northern latitude optically complex waters. The ML model is optimized and requires only three S3 OLCI bands, reflecting the physical characteristic of Chl-a as input in the regression process to estimate Chl-a concentration with improved accuracy in terms of the bias (five times improvements.) The ML model was optimized on data from Arctic, coastal, and open waters, and showed promising performance. Finally, we present the performance of the optimized ML approach by computing Chl-a maps and corresponding certainty maps in highly complex sub-Arctic and Arctic waters. We show how these certainty maps can be used as a support to understand possible radiometric calibration issues in the retrieval of Level 2 reflectance over these waters. This can be a useful tool in identifying erroneous Level 2 Remote sensing reflectance due to possible failure of the atmospheric correction algorithm.

Chlorophyll a concentration (Chl) is a key variable for estimating primary production (PP) through ocean-color remote sensing (OCRS). Accurate Chl estimates are crucial for better understanding of the spatio-temporal trends in PP in recent decades as a consequence of climate change. However, a number of studies have reported that currently operational chlorophyll a algorithms perform poorly in the Arctic Ocean (AO), largely due to the interference of colored and detrital material (CDM) with the phytoplankton signal in the visible part of the spectrum. To determine how and to what extent CDM biases the estimation of Chl, we evaluated the performances of eight currently available ocean-color algorithms: OC4v6, OC3Mv6, OC3V, OC4L, OC4P, AO.emp, GSM01 and AO.GSM. Our results suggest that the empirical AO.emp algorithm performs the best overall, but, for waters with high CDM a_cdm(443) > 0.067 m⁻¹), a common scenario in the Arctic, the two semi-analytical GSM models yield better performance. In addition, sensitivity analyses using a spectrally and vertically resolved Arctic primary-production model show that errors in Chl mostly propagate proportionally to PP estimates, with amplification of up to 7%. We also demonstrate that, the higher level of CDM in relation to Chl in the water column, the larger the bias in both Chl and PP estimates. Lastly, although the AO.GSM is the best overall performer among the algorithms tested, it tends to fail for a significant number of pixels (16.2% according to the present study), particularly for waters with high CDM. Our results therefore suggest the ongoing need to develop an algorithm that provides reasonable Chl estimates for a wide range of optically complex Arctic waters.

The absorption coefficient of colored dissolved organic matter (CDOM), a_y, plays a critical role in driving ocean optical properties and so light attenuation and light-dependent biogeochemical cycles. In the Southern Ocean (SO), however, a_y remains poorly documented because of the scarcity of in situ measurements and the absence of suitable bio-optical models. To address this gap, we derived a_y in surface waters from the diffuse attenuation coefficient (K_d) derived from radiometric measurements performed by Biogeochemical-Argo floats. Sensitivity analyses indicated that the uncertainty of our estimates is mainly driven by K_d, with an overall ∼ 18 % uncertainty of a_y at 380 and 412 nm based on a Monte Carlo approach. The relationships we obtained between a_y and Chl in low-latitude waters are consistent with previous studies but diverge in the SO, with a much weaker dependence on Chl and a larger relative contribution to the absorption budget for clear waters. Possible reasons for this different contribution include CDOM release by sea ice melting, CDOM enrichment of surface layers through deep winter mixing, adaptation of phytoplankton physiology to cold waters and reduced photo degradation during the polar winter.

The absorption coefficient of colored dissolved organic matter (CDOM), a_y, plays a critical role in driving ocean optical properties and so light attenuation and light-dependent biogeochemical cycles. In the Southern Ocean (SO), however, a_y remains poorly documented because of the scarcity of in situ measurements and the absence of suitable bio-optical models. To address this gap, we derived a_y in surface waters from the diffuse attenuation coefficient (K_d) derived from radiometric measurements performed by Biogeochemical-Argo floats. Sensitivity analyses indicated that the uncertainty of our estimates is mainly driven by K_d, with an overall ∼ 18 % uncertainty of a_y at 380 and 412 nm based on a Monte Carlo approach. The relationships we obtained between a_y and Chl in low-latitude waters are consistent with previous studies but diverge in the SO, with a much weaker dependence on Chl and a larger relative contribution to the absorption budget for clear waters. Possible reasons for this different contribution include CDOM release by sea ice melting, CDOM enrichment of surface layers through deep winter mixing, adaptation of phytoplankton physiology to cold waters and reduced photo degradation during the polar winter.

The absorption coefficient of colored dissolved organic matter (CDOM), a_y, plays a critical role in driving ocean optical properties and so light attenuation and light-dependent biogeochemical cycles. In the Southern Ocean (SO), however, a_y remains poorly documented because of the scarcity of in situ measurements and the absence of suitable bio-optical models. To address this gap, we derived a_y in surface waters from the diffuse attenuation coefficient (K_d) derived from radiometric measurements performed by Biogeochemical-Argo floats. Sensitivity analyses indicated that the uncertainty of our estimates is mainly driven by K_d, with an overall ∼ 18 % uncertainty of a_y at 380 and 412 nm based on a Monte Carlo approach. The relationships we obtained between a_y and Chl in low-latitude waters are consistent with previous studies but diverge in the SO, with a much weaker dependence on Chl and a larger relative contribution to the absorption budget for clear waters. Possible reasons for this different contribution include CDOM release by sea ice melting, CDOM enrichment of surface layers through deep winter mixing, adaptation of phytoplankton physiology to cold waters and reduced photo degradation during the polar winter.

The absorption coefficient of colored dissolved organic matter (CDOM), a_y, plays a critical role in driving ocean optical properties and so light attenuation and light-dependent biogeochemical cycles. In the Southern Ocean (SO), however, a_y remains poorly documented because of the scarcity of in situ measurements and the absence of suitable bio-optical models. To address this gap, we derived a_y in surface waters from the diffuse attenuation coefficient (K_d) derived from radiometric measurements performed by Biogeochemical-Argo floats. Sensitivity analyses indicated that the uncertainty of our estimates is mainly driven by K_d, with an overall ∼ 18 % uncertainty of a_y at 380 and 412 nm based on a Monte Carlo approach. The relationships we obtained between a_y and Chl in low-latitude waters are consistent with previous studies but diverge in the SO, with a much weaker dependence on Chl and a larger relative contribution to the absorption budget for clear waters. Possible reasons for this different contribution include CDOM release by sea ice melting, CDOM enrichment of surface layers through deep winter mixing, adaptation of phytoplankton physiology to cold waters and reduced photo degradation during the polar winter.