Use of Sentinel-3 OLCI Images and Machine Learning to Assess the Ecological Quality of Italian Coastal Waters.

Understanding and monitoring the ecological quality of coastal waters is crucial for preserving marine ecosystems. Eutrophication is one of the major problems affecting the ecological state of coastal marine waters. For this reason, the control of the trophic conditions of aquatic ecosystems is needed for the evaluation of their ecological quality. This study leverages space-based Sentinel-3 Ocean and Land Color Instrument imagery (OLCI) to assess the ecological quality of Mediterranean coastal waters using the Trophic Index (TRIX) key indicator. In particular, we explore the feasibility of coupling remote sensing and machine learning techniques to estimate the TRIX levels in the Ligurian, Tyrrhenian, and Ionian coastal regions of Italy. Our research reveals distinct geographical patterns in TRIX values across the study area, with some regions exhibiting eutrophic conditions near estuaries and others showing oligotrophic characteristics. We employ the Random Forest Regression algorithm, optimizing calibration parameters to predict TRIX levels. Feature importance analysis highlights the significance of latitude, longitude, and specific spectral bands in TRIX prediction. A final statistical assessment validates our model's performance, demonstrating a moderate level of error (MAE of 0.51) and explanatory power (R2 of 0.37). These results highlight the potential of Sentinel-3 OLCI imagery in assessing ecological quality, contributing to our understanding of coastal water ecology. They also underscore the importance of merging remote sensing and machine learning in environmental monitoring and management. Future research should refine methodologies and expand datasets to enhance TRIX monitoring capabilities from space.

COVID-19 lockdown effects on a coastal marine environment: Disentangling perception versus reality.

COVID-19 lockdown brought to a drastic reduction of anthropic impacts on the environment worldwide, including the marine-coastal system. Earth-Observation (EO) data have the potential to monitor and diagnose the effects of the lockdown in terms of water quality. Here we connect the dots among some coastal environmental changes that occurred during the Italian COVID-19 lockdown by using EO data, also seeking to assess connectivity between inland and marine systems. We present a holistic analysis of spatial and temporal variability of environmental parameters in the North Adriatic Sea, Mediterranean basin, exploiting the synergy of different satellite sensors, as well as hydrologic data from in situ observations. Our analysis indicates a favourable interplay of environmental variability that resulted in negative anomalies of Chlorophyll-a concentration, with respect to the climatologic values. Peculiar meteo-oceanographic and hydrological conditions made hard to disentangle potential anthropogenic effects. However, a multi-year hierarchical cluster analysis of riverine remote sensing reflectances groups together the optical properties of inland waters during the lockdown. This emergent cluster highlights the possibility of a second-order, anthropogenic effect that, superimposed to the (first-order) environmental natural causes, may have enhanced water quality during the lockdown.

Correction of Biogeochemical-Argo Radiometry for Sensor Temperature-Dependence and Drift: Protocols for a Delayed-Mode Quality Control.

Measuring the underwater light field is a key mission of the international Biogeochemical-Argo program. Since 2012, 0-250 dbar profiles of downwelling irradiance at 380, 412 and 490 nm besides photosynthetically available radiation (PAR) have been acquired across the globe every 1 to 10 days. The resulting unprecedented amount of radiometric data has been previously quality-controlled for real-time distribution and ocean optics applications, yet some issues affecting the accuracy of measurements at depth have been identified such as changes in sensor dark responsiveness to ambient temperature, with time and according to the material used to build the instrument components. Here, we propose a quality-control procedure to solve these sensor issues to make Argo radiometry data available for delayed-mode distribution, with associated error estimation. The presented protocol requires the acquisition of ancillary radiometric measurements at the 1000 dbar parking depth and night-time profiles. A test on >10,000 profiles from across the world revealed a quality-control success rate >90% for each band. The procedure shows similar performance in re-qualifying low radiometry values across diverse oceanic regions. We finally recommend, for future deployments, acquiring daily 1000 dbar measurements and one night profile per year, preferably during moonless nights and when the temperature range between the surface and 1000 dbar is the largest.

Radiative Transfer Modeling With Biogeochemical-Argo Float Data in the Mediterranean Sea.

A radiative transfer model was parameterized and validated using Biogeochemical Argo float data acquired between 2012 and 2017 across the Mediterranean Sea. Fluorescence-derived chlorophyll a concentration, particulate optical backscattering at 700 nm, and fluorescence of chromophoric dissolved organic matter (CDOM) were used to parametrize the light absorption and scattering coefficients of the optically significant water constituents (such as pure water, non-algal particles, CDOM, and phytoplankton). The model was validated with in situ downwelling irradiance profiles and apparent optical properties derived both from irradiance profiles and satellite data, such as the diffuse attenuation coefficients and remote sensing reflectance. Results showed that by using regional parameterizations that are not only related to chlorophyll concentration and vertical distribution, the model was able to capture a more accurate spectral response in the examined wavelength range compared to chlorophyll-related (or Case 1) optical models. When using alternative models that incorporated also measurements of CDOM fluorescence or particulate optical backscattering, the model skill increased at all examined wavelengths. Finally, using a multi-spectral optical configuration also enabled the estimation of the relative contribution of separate water constituents in the examined spectral range. Simulations including non-algal particles and CDOM performed up to 61% and 79% better than when considering the optical properties of pure seawater alone. Moreover, a simulation including phytoplankton light absorption resulted in an error reduction of up to 42%, especially at 412 nm and with a more uniform response at the wavelengths considered.

Drivers of spectral optical scattering by particles in the upper 500 m of the Atlantic Ocean.

Optical models have been proposed to relate spectral variations in the beam attenuation (cp) and optical backscattering (bbp) coefficients to marine particle size distributions (PSDs). However, due to limited PSD data, particularly in the open ocean, optically derived PSDs suffer from large uncertainties and we have a poor empirical understanding of the drivers of spectral cp and bbp coefficients. Here we evaluated PSD optical proxies and investigated their drivers by analyzing an unprecedented dataset of co-located PSDs, phytoplankton abundances and optical measurements collected across the upper 500 m of the Atlantic Ocean. The spectral slope of cp was correlated (r>0.59) with the slope of the PSD only for particles with diameters >1 µm and also with eukaryotic phytoplankton concentrations. No significant relationships between PSDs and the spectral slope of bbp were observed. In the upper 200 m, the bbp spectral slope was correlated to the light absorption by particles (ap; r<-0.54) and to the ratio of cyanobacteria to eukaryotic phytoplankton. This latter correlation was likely the consequence of the strong relationship we observed between ap and the concentration of eukaryotic phytoplankton (r=0.83).

Small Phytoplankton Shapes Colored Dissolved Organic Matter Dynamics in the North Atlantic Subtropical Gyre.

The North Atlantic subtropical gyre (NASTG) is a model of the future ocean under climate change. Ocean warming signals are hidden within the blue color of these clear waters and can be tracked by understanding the dynamics among phytoplankton chlorophyll ([Chl]) and colored dissolved organic matter (CDOM). In NASTG, [Chl] and CDOM are strongly correlated. Yet, this unusual correlation for open oceans remains unexplained. Here, we test main hypotheses by analyzing high spatiotemporal resolution data collected by Biogeochemical-Argo floats between 2012 and 2018. The direct production of CDOM via phytoplankton metabolism is the main occurring mechanism. More importantly, CDOM dynamics strongly depend on the abundance of picophytoplankton. Our findings thus highlight the critical role of these small organisms under the ocean warming scenario. Picophytoplankton will enhance the production of colored dissolved compounds and, ultimately, impact on the ocean carbon cycle.

The open-ocean missing backscattering is in the structural complexity of particles.

Marine microscopic particles profoundly impact global biogeochemical cycles, but our understanding of their dynamics is hindered by lack of observations. To fill this gap, optical backscattering measured by satellite sensors and in-situ autonomous platforms can be exploited. Unfortunately, these observations remain critically limited by an incomplete mechanistic understanding of what particles generate the backscattering signal. To achieve this understanding, optical models are employed. The simplest of these models-the homogeneous sphere-severely underestimates the measured backscattering and the missing signal has been attributed to submicron particles. This issue is known as the missing backscattering enigma. Here we show that a slightly more complex optical model-the coated sphere-can predict the measured backscattering and suggests that most of the signal comes from particles >1 µm. These findings were confirmed by independent size-fractionation experiments. Our results demonstrate that the structural complexity of particles is critical to understand open-ocean backscattering and contribute to solving the enigma.

Determination of the absorption coefficient of chromophoric dissolved organic matter from underway spectrophotometry.

Measurements of the absorption coefficient of chromophoric dissolved organic matter (ay) are needed to validate existing ocean-color algorithms. In the surface open ocean, these measurements are challenging because of low ay values. Yet, existing global datasets demonstrate that ay could contribute between 30% to 50% of the total absorption budget in the 400-450 nm spectral range, thus making accurate measurement of ay essential to constrain these uncertainties. In this study, we present a simple way of determining ay using a commercially-available in-situ spectrophotometer operated in underway mode. The obtained ay values were validated using independent collocated measurements. The method is simple to implement, can provide measurements with very high spatio-temporal resolution, and has an accuracy of about 0.0004 m-1 and a precision of about 0.0025 m-1 when compared to independent data (at 440 nm). The only limitation for using this method at sea is that it relies on the availability of relatively large volumes of ultrapure water. Despite this limitation, the method can deliver the ay data needed for validating and assessing uncertainties in ocean-colour algorithms.

On the discrimination of multiple phytoplankton groups from light absorption spectra of assemblages with mixed taxonomic composition and variable light conditions.

According to recommendations of the international community of phytoplankton functional type algorithm developers, a set of experiments on marine algal cultures was conducted to (1) investigate uncertainties and limits in phytoplankton group discrimination from hyperspectral light absorption properties of assemblages with mixed taxonomic composition, and (2) evaluate the extent to which modifications of the absorption spectral features due to variable light conditions affect the optical discrimination of phytoplankton. Results showed that spectral absorption signatures of multiple species can be extracted from mixed assemblages, even at low relative contributions. Errors in retrieved pigment abundances are, however, influenced by the co-occurrence of species with similar spectral features. Plasticity of absorption spectra due to changes in light conditions weakly affects interspecific differences, with errors <21% for retrievals of pigment concentrations from mixed assemblages.

Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site).

Models based on the multivariate partial least squares (PLS) regression technique are developed for the retrieval of phytoplankton size structure from measured light absorption spectra (BOUSSOLE site, northwestern Mediterranean Sea). PLS-models trained with data from the Mediterranean Sea showed good accuracy in retrieving, over the nine-year BOUSSOLE time series, the concentrations of total chlorophyll a [Tchl a], of the sum of seven diagnostic pigments and of pigments associated with micro, nano, and picophytoplankton size classes separately. PLS-models trained using either total particle or phytoplankton absorption spectra performed similarly, and both reproduced seasonal variations of biomass and size classes derived by high performance liquid chromatography. Satisfactory retrievals were also obtained using PLS-models trained with a data set including various locations of the world's oceans, with however a lower accuracy. These results open the way to an application of this method to absorption spectra derived from hyperspectral and field satellite radiance measurements.