Characterization of time-varying regimes in remote sensing time series: application to the forecasting of satellite-derived suspended matter concentrations

International audienceSatellite data, with their spatial and temporal coverage, are particularly well suited for the analysis and characterization of space-time-varying relationships between geophysical processes. We investigate here the forecasting of a geophysical variable using both satellite observations and model outputs. As example we study the daily concentration of mineral suspended particulate matters estimated from satellite-derived datasets, in coastal waters adjacent to the French Gironde River mouth. We forecast this high resolution dataset using environmental data (wave height, wind strength and direction, tides and river outflow) and four multi-latent-regime models: homogeneous and non-homogeneous Markov-switching models, with and without an autoregressive term, i.e. the suspended matter concentration observed the day before. We clearly show, using a validation dataset, significant improvements with multi-regime models compared to a classical multi-regression and a state-of-the-art non-linear model (Support Vector Regression (SVR) model). The best results are reported for a mixture of 3 regimes for autoregressive model using non-homogeneous transitions. With the autoregressive models, we obtain at day+1 forecasting performances of 93% of the explained variance for the mixture model compared to 83% for a standard linear model and 85% using a SVR. These improvements are even more important for the non-autoregressive models. These results stress the potential of the identification of geophysical regimes to improve the forecasting or the inversion. We also show that for short periods of lack of observations (typically lesser than 15 days), non-homogeneous transition probabilities and estimated autoregressive term, the observation of the previous day not being available, help to enhance forecasting performances

Phytoplankton functional types observation from space in the Fram Strait (2002-2020)

Phytoplankton in the sunlit layer of the ocean act as the base of the marine food web fueling fisheries, and also regulate key biogeochemical processes. Phytoplankton composition structure varies in ocean biomes and different phytoplankton groups drive differently the marine ecosystem and biogeochemical processes. Because of this, variations in phytoplankton composition influence the entire ocean environment, specifically the ocean energy transfer and the export of organic carbon to the deep ocean. As one of the algorithms deriving phytoplankton composition from space borne data, within the framework of the EU Copernicus Marine Service (CMEMS), EOF-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021; https://marine.copernicus.eu/). By using multi-sensor merged products and Sentinel-3 OLCI data, the algorithm provides global chlorophyll a data with per-pixel uncertainty for diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today. Due to different lifespans and radiometric characteristics of the ocean color sensors, the consistency of the PFTs is evaluated to provide quality-assured data for a consistent long-term monitoring of the phytoplankton community structure. As current commonly used phytoplankton carbon estimation methods rely mostly on the backscattering property of phytoplankton, which could vary dramatically for different phytoplankton taxa, as a perspective of this study, phytoplankton carbon may be better estimated in a way that accounts for phytoplankton taxonomy

Argo Dataset production: Real‐time data‐management and delayed‐mode qualified dataset for O2, Chlorophyll‐a, backscattering and NO3

Assessment of Data flow and availability on the assembly centre. Description of the O2 delayed mode quality contro

CLUSTERING CHLOROPHYLL-A SATELLITE DATA USING QUANTILES

The use of water quality indicators is of crucial importance to identify risks to the environment, society and human health. In particular, the Chlorophyll type A (Chl-a) is a shared indicator of trophic status and for monitoring activities it may be useful to discover local dangerous behaviours (for example, the anoxic events). In this paper we consider a comprehensive data set, covering the whole Adriatic Sea, derived from Ocean Colour satellite data, during the period 2002-2012, with the aim of identifying homogeneous areas. Such zonation is becoming extremely relevant for the implementation of European policies, such the Marine Strategy Framework Directive. As an alternative to clustering based on an "average" value over the whole period, we propose a new clustering procedure for the time series. The procedure shares some similarities with the functional data clustering and combines nonparametric quantile regression with an agglomerative clustering algorithm. This approach permits to take into account some features of the time series as nonstationarity in the marginal distribution and the presence of missing data. A small simulation study is also presented for illustrating the relative merits of the procedure

20-year satellite observations of phytoplankton functional types (PFTs) in the Atlantic Ocean

Phytoplankton composition structure varies in ocean biomes. Different phytoplankton groups drive differently the marine ecosystem and biogeochemical processes. Therefore, variations in phytoplankton composition influence the entire ocean environment, specifically the ocean energy transfer, the deep ocean carbon export, water quality etc. As one of the algorithms deriving phytoplankton composition from space borne data, the EOF-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive global in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021). By using multi-sensor merged products and Sentinel-3 OLCI data, the algorithm provides global chlorophyll a (Chla) data with per-pixel uncertainty for diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today, with products available on the EU Copernicus Marine Service (CMEMS). The objectives of this study are to 1) evaluate CMEMS PFT products and improve their continuity along the products derived from different satellite sensors, and 2) 20-year satellite PFT products for time series analysis of climatology, trends, anomaly and phenology of multiple PFTs in the whole Atlantic and its different biogeochemical provinces (Longhurst, 2006)

Satellite monitoring of surface phytoplankton functional types in the Atlantic Ocean over 20 years (2002–2021)

An analysis of multi-satellite-derived products of four major phytoplankton functional types (PFTs – diatoms, haptophytes, prokaryotes and dinoflagellates) was carried out to investigate the PFT time series in the Atlantic Ocean between 2002 and 2021. The investigation includes the 2-decade trends, climatology, phenology and anomaly of PFTs for the whole Atlantic Ocean and its different biogeochemical provinces in the surface layer that optical satellite signals can reach. The PFT time series over the whole Atlantic region showed mostly no clear trend over the last 2 decades, except for a small decline in prokaryotes and an abrupt increase in diatoms during 2018–2019, which is mainly observed in the northern Longhurst provinces. The phenology of diatoms, haptophytes and dinoflagellates is very similar: at higher latitudes bloom maxima are reached in spring (April in the Northern Hemisphere and October in the Southern Hemisphere), in the oligotrophic regions in winter time and in the tropical regions during May to September. In general, prokaryotes show opposite annual cycles to the other three PFTs and present more spatial complexity. The PFT anomaly (in percent) of 2021 compared to the 20-year mean reveals mostly a slight decrease in diatoms and a prominent increase in haptophytes in most areas of the high latitudes. Both diatoms and prokaryotes show a mild decrease along coastlines and an increase in the gyres, while prokaryotes show a clear decrease at mid-latitudes to low latitudes and an increase on the western African coast (Canary Current Coastal Province, CNRY and Guinea Current Coastal Province, GUIN) and southwestern corner of North Atlantic Tropical Gyral Province (NATR). Dinoflagellates, as a minor contributor to the total biomass, are relatively stable in the whole Atlantic region. This study illustrated the past and current PFT state in the Atlantic Ocean and acted as the first step to promote long-term consistent PFT observations that enable time series analyses of PFT trends and interannual variability to reveal potential climate-induced changes in phytoplankton composition on multiple temporal and spatial scales

Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data

This study presents an algorithm for globally retrieving chlorophyll a (Chl-a) concentrations of phytoplankton functional types (PFTs) from multi-sensor merged ocean color (OC) products or Sentinel-3A (S3) Ocean and Land Color Instrument (OLCI) data from the GlobColour archive in the frame of the Copernicus Marine Environmental Monitoring Service (CMEMS). The retrieved PFTs include diatoms, haptophytes, dinoflagellates, green algae and prokaryotic phytoplankton. A previously proposed method to retrieve various phytoplankton pigments, based on empirical orthogonal functions (EOF), is investigated and adapted to retrieve Chl-a concentrations of multiple PFTs using extensive global data sets of in situ pigment measurements and matchups with satellite OC products. The performance of the EOF-based approach is assessed and cross-validated statistically. The retrieved PFTs are compared with those derived from diagnostic pigment analysis (DPA) based on in situ pigment measurements. Results show that the approach predicts well Chl-a concentrations of most of the mentioned PFTs. The performance of the approach is, however, less accurate for prokaryotes, possibly due to their general low variability and small concentration range resulting in a weak signal which is extracted from the reflectance data and corresponding EOF modes. As a demonstration of the approach utilization, the EOF-based fitted models based on satellite reflectance products at nine bands are applied to the monthly GlobColour merged products. Climatological characteristics of the PFTs are also evaluated based on ten years of merged products (2002−2012) through inter-comparisons with other existing satellite derived products on phytoplankton composition including phytoplankton size class (PSC), SynSenPFT, OC-PFT and PHYSAT. Inter-comparisons indicate that most PFTs retrieved by our study agree well with previous corresponding PFT/PSC products, except that prokaryotes show higher Chl-a concentration in low latitudes. PFT dominance derived from our products is in general well consistent with the PHYSAT product. A preliminary experiment of the retrieval algorithm using eleven OLCI bands is applied to monthly OLCI products, showing comparable PFT distributions with those from the merged products, though the matchup data for OLCI are limited both in number and coverage. This study is to ultimately deliver satellite global PFT products for long-term continuous observation, which will be updated timely with upcoming OC data, for a comprehensive understanding of the variability of phytoplankton composition structure at a global or regional scale

BioGeoChemical‐Argo floats reveal stark latitudinal gradient in the Southern Ocean deep carbon flux driven by phytoplankton community composition

The gravitational sinking of particles in the mesopelagic layer (∼200–1,000 m) transfers to the deep ocean a part of atmospheric carbon fixed by phytoplankton. This process, called the gravitational pump, exerts an important control on atmospheric CO2 levels but remains poorly characterized given the limited spatio-temporal coverage of ship-based flux measurements. Here, we examined the gravitational pump with BioGeoChemical-Argo floats in the Southern Ocean, a critically under-sampled area. Using time-series of bio-optical measurements, we characterized the concentration of particles in the productive zone, their export and transfer efficiency in the underlying mesopelagic zone, and the magnitude of sinking flux at 1,000 m. We separated float observations into six environments delineated by latitudinal fronts, sea-ice coverage, and natural iron fertilization. Results show a significant increase in the sinking-particle flux at 1,000 m with increasing latitude, despite comparable particle concentrations in the productive layer. The variability in deep flux was driven by changes in the transfer efficiency of the flux, related to the composition of the phytoplanktonic community and the size of particles, with intense flux associated with the predominance of micro-phytoplankton and large particles at the surface. We quantified the relationships between the nature of surface particles and the flux at 1,000 m and used these results to upscale our flux survey across the whole Southern Ocean using surface observations by floats and satellites. We then estimated the basin-wide Spring-Summer flux of sinking particles at 1,000 m over the Southern Ocean (0.054 ± 0.021 Pg C)

Phytoplankton functional types from multi-sensor satellite observations – towards a long-term monitoring (2002-2020)

Phytoplankton in the sunlit layer of the ocean act as the base of the marine food web fueling fisheries, and also regulate key biogeochemical processes such as exporting carbon to the deep ocean. Phytoplankton composition structure varies in ocean biomes and different phytoplankton groups drive differently the marine ecosystem. As one of the algorithms deriving phytoplankton composition from space borne data, within the framework of the EU Copernicus Marine Service (CMEMS), OLCI-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021). It provides global PFT retrievals including chlorophyll a estimations of diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today, by using multi-sensor merged products and OLCI data. These PFT products with per-pixel uncertainty are publicly available on the CMEMS. Due to different lifespans and radiometric characteristics of the ocean color sensors, it is crucial to evaluate the CMEMS PFT products to provide quality-assured data for a consistent long-term monitoring of the phytoplankton community structure. In this study, using in-situ phytoplankton data (HPLC pigment data further evaluated with microscopic, flow cytometry, molecular and hyperspectral optical data) collected from expeditions since 2009 in the tropical, temperate and polar (mainly Fram Strait within the PEBCAO network) regions, we aim to 1) validate the CMEMS PFT products and investigate the continuity of the PFTs data derived from different satellites, and 2) deliver two-decade consistent PFT products for times series analysis. For the latter we determine inter-annual trends and variation of the surface phytoplankton community structure targeting some key sub-regions (e.g.,east Fram Strait) that have been observed being influenced by the changing marine environment

Global Retrieval Algorithms for Phytoplankton Functional Types (PFTs): toward the Applications to OLCI and GlobColour Merged Products

With integrated use of extensive in situ measurements from various cruises in different regions, this study focuses on PFT retrieval algorithms that are then applied to Sentinel-3 (S3) OLCI data and merged ocean colour (OC) products from CMEMS GlobColour archive. The main retrieved PFTs include diatoms, haptophytes, prokaryotic phytoplankton (cyanobacteria). Previously investigated retrieval methods, empirical orthogonal functions (EOF) for pigment concentrations estimation (Bracher et al. 2015) and generalized IOP (GIOP) (Werdell et al. 2013, 2014) for PFT discrimination, are tested and adapted potentially with full use of our current available in situ measurements from various campaigns worldwide, in which we have a number of collocated remote sensing reflectance spectra (Rrs) and PFT data based on HPLC pigments in addition to other bio-optical measurements. Algorithms are tested and compared by both taking hyperspectral and multispectral in situ Rrs spectra as input data, and the multispectral based approach is later on applied to the above mentioned satellite data. Performances of both EOF- and GIOP-based approaches are assessed statistically and cross-validated, with results showing that both could well predict chlorophyll-a concentrations for diatoms and haptophytes but less good for prokaryotes. In a next step these algorithms are adapted to satellite OC data collocated to an even larger in-situ PFT database derived from HPLC phytoplankton pigments. This is to eventually develop the global satellite PFT products for long-term observation, updated timely with more available OLCI data in the future, and intercompared to the results with other existing PFT products (e.g. PhytoDOAS, OC-PFT, SynSenPFT)