Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll- a data

In this work, trend estimates are used as indicators to compare the multi-annual variability of different satellite chlorophyll-a (Chla) data and to assess the fitness-for-purpose of multi-mission Chla products as climate data records (CDR). Under the assumption that single-mission products are free from spurious temporal artifacts and can be used as benchmark time series, multi-mission CDRs should reproduce the main trend patterns observed by single-mission series when computed over their respective periods. This study introduces and applies quantitative metrics to compare trend distributions from different data records. First, contingency matrices compare the trend diagnostics associated with two satellite products when expressed in binary categories such as existence, significance and signs of trends. Contingency matrices can be further summarized by metrics such as Cohen's κ index that rates the overall agreement between the two distributions of diagnostics. A more quantitative measure of the discrepancies between trends is provided by the distributions of differences between trend slopes. Thirdly, maps of the level of significance P of a t-test quantifying the degree to which two trend estimates differ provide a statistical, spatially-resolved, evaluation. The proposed methodology is applied to the multi-mission Ocean Colour-Climate Change Initiative (OC-CCI) Chla data. The agreement between trend distributions associated with OC-CCI data and single-mission products usually appears as good as when single-mission products are compared. As the period of analysis is extended beyond 2012 to 2015, the level of agreement tends to be degraded, which might be at least partly due to the aging of the MODIS sensor on-board Aqua. On the other hand, the trends displayed by the OC-CCI series over the short period 2012–2015 are very consistent with those observed with VIIRS. These results overall suggest that the OC-CCI Chla data can be used for multi-annual time series analysis (including trend detection), but with some caution required if recent years are included, particularly in the central tropical Pacific. The study also recalls the challenges associated with creating a multi-mission ocean color data record suitable for climate research

Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea

Over the past years, a large number of new approaches in the domain of ocean-color have been developed, leading to a variety of innovative descriptors for phytoplankton communities. One of these methods, named PHYSAT, currently allows for the qualitative detection of five main phytoplankton groups from ocean-color measurements. Even though PHYSAT products are widely used in various applications and projects, the approach is limited by the fact it identifies only dominant phytoplankton groups. This current limitation is due to the use of biomarker pigment ratios for establishing empirical relationships between in-situ information and specific ocean-color radiance anomalies in open ocean waters. However, theoretical explanations of PHYSAT suggests that it could be possible to detect more than dominance cases but move more toward phytoplanktonic assemblage detection. Thus, to evaluate the potential of PHYSAT for the detection of phytoplankton assemblages, we took advantage of the Continuous Plankton Recorder (CPR) survey, collected in both the English Channel and the North Sea. The available CPR dataset contains information on diatom abundance in two large areas of the North Sea for the period 1998-2010. Using this unique dataset, recurrent diatom assemblages were retrieved based on classification of CPR samples. Six diatom assemblages were identified in-situ, each having indicators taxa or species. Once this first step was completed, the in-situ analysis was used to empirically associate the diatom assemblages with specific PHYSAT spectral anomalies. This step was facilitated by the use of previous classifications of regional radiance anomalies in terms of shape and amplitude, coupled with phenological tools. Through a matchup exercise, three CPR assemblages were associated with specific radiance anomalies. The maps of detection of these specific radiances anomalies are in close agreement with current in-situ ecological knowledge

Internal tides off the Amazon shelf – Part 1: The importance of the structuring of ocean temperature during two contrasted seasons

The impact of internal and barotropic tides on the vertical and horizontal temperature structure off the Amazon River was investigated during two highly contrasted seasons (AMJ: April–May–June; ASO: August–September–October) over a 3-year period from 2013 to 2015. Twin regional simulations, with and without tides, were used to highlight the general effect of tides. The findings reveal that tides have a cooling effect on the ocean from the surface (∼ 0.3 ∘C) to above the thermocline (∼ 1.2 ∘C), while warming it up below the thermocline (∼ 1.2 ∘C). The heat budget analysis indicates that the vertical mixing is the dominant process driving temperature variations within the mixed layer, while it is associated with both horizontal and vertical advection to explain temperature variations below. The increased mixing in the simulations including tides is attributed to breaking of internal tides (ITs) on their generation sites over the shelf break and offshore along their propagation pathways. Over the shelf, mixing is driven by the dissipation of the barotropic tides. In addition, the vertical terms of the heat budget equation exhibit wavelength patterns typical of mode-1 IT. The study highlights the key role of tides and particularly how IT-related vertical mixing shapes the ocean temperature off the Amazon. Furthermore, we found that tides impact the interactions between the upper ocean interface and the overlying atmosphere. They contribute significantly to increasing the net heat flux between the atmosphere and the ocean, with a notable seasonal variation from 33.2 % in AMJ to 7.4 % in ASO seasons. This emphasizes the critical role of tidal dynamics in understanding regional-scale climate.</p

Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3

Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring

Monitoring marine phytoplankton seasonality from space

Remote sensing techniques are used to study the large scale patterns related to the seasonal modes of variability of the marine phytoplankton. Ten years of monthly composite maps of sea surface chlorophyll-a concentration and the PHYSAT database of four Phytoplanktonic Functional Types (PFTs), both from SeaWiFS, are used to investigate characteristics of phytoplankton seasonality in the trades and westerlies wind oceanic biomes, where data density is adequate. We use a combination of wavelet transform and statistical techniques that allow us to quantify both intensity and duration of the seasonal oscillation of chlorophyll-a concentration and PFTs relative occurrence, and to map these relationships. Next, the seasonal oscillations detected are related to four PFTs revealing six major global phytoplanktonic associations. Our results elucidate the intensity and duration of the seasonal dynamic of the chlorophyll-a concentration and of the relative occurrence of four PFTs at a global scale. Thus, the typology of the different types of seasonality is investigated. Finally, an overall agreement between the results and the biogeochemical provinces partition proposed by Longhurst is found, revealing a strong environmental control on the seasonal oscillation of primary producers and a clear latitudinal organization in the succession of the phytoplankton types. Results provided in this study quantify the seasonal oscillation of key structural parameters of the global ocean, and their potential implications for our understanding of ecosystem dynamics

Identification of dominant phytoplankton functional types in the Mediterranean Sea based on a regionalized remote sensing approach

During the last decade, the analysis of the ocean color satellite imagery has allowed determining the dominant phytoplankton groups in surface waters through the development of bio-optical models aimed at identifying the main phytoplankton functional types (PFTs) or size classes from space. One of these bio-optical model is PHYSAT, which is a global method applied for oceanic Case I water and used to identify in satellite pixels specific dominant phytoplankton groups, such as nanoeukaryotes, Prochlorococcus, Synechococcus, diatoms, Phaeocystis-like and coccolithophores. Here, we present a regionalized version of the PHYSAT method that has been specifically developed for the Mediterranean Sea due to the peculiarities of phytoplankton assemblages and succession than can be found in the basin and its particular optical properties. The updated version of the method, the so called PHYSAT-Med, has been validated successfully with large in situ datasets available for this oceanic region, mainly for nanoeukaryotes, Prochlorococcus, Synechococcus and diatoms. PHYSAT-Med allows to include a much higher number of pixels for the Mediterranean than PHYSAT does, through the use of a new Look-Up-Table created specifically for this oceanic region. Results provided by PHYSAT-Med showed the dominance of Synechococcus versus prochlorophytes throughout the year at the basin level, although nanoeukaryotes were more abundant during winter months. In addition, PHYSAT-Med data identified a rise in the eukaryote biomass (mainly diatoms) during the spring period (March to April), especially in the Ligurian and Adriatic seas. PHYSAT-Med represents a useful tool for the spatio-temporal monitoring of different dominant phytoplankton functional types in Mediterranean surface waters at a high resolution. © 2014 Elsevier Inc.This research was supported by the PERSEUS European Project and PR11-RNM-7722 Andalucía Regional Project. The authors acknowledge the NASA/GSFC/DAAC for providing access to L3 MODIS products. In situ HPLC dataset was obtained from SESAME EU Project, BOUSSOLE Project and MAREDAT data. We thank J. Ras and the members of the SAPIGH analytical platform from the Laboratoire d'Océanographie de Villefranche for the HPLC analysis. GN was supported by the program “Salvador Madariaga, Ministerio de Educación, Cultura y Deporte, Programa Nacional de Movilidad de Recursos Humanos del Plan Nacional de I-D + i 2008–2011, prorrogado por Acuerdo de Consejo de Ministros de 7 de octubre de 2011”.Peer Reviewe