VNREDSat-1 is the first Vietnamese satellite enabling the survey of environmental parameters, such as vegetation and water coverages or surface water quality at medium spatial resolution (from 2.5 to 10 m depending on the considered channel). The New AstroSat Optical Modular Instrument (NAOMI) sensor on board VNREDSat-1 has the required spectral bands to assess the suspended particulate matter (SPM) concentration. Because recent studies have shown that the remote sensing reflectance, Rrs(λ), at the blue (450–520 nm), green (530–600 nm), and red (620–690 nm) spectral bands can be assessed using NAOMI with good accuracy, the present study is dedicated to the development and validation of an algorithm (hereafter referred to as V1SPM) to assess SPM from Rrs(λ) over inland and coastal waters of Vietnam. For that purpose, an in-situ data set of hyper-spectral Rrs(λ) and SPM (from 0.47 to 240.14 g·m−3) measurements collected at 205 coastal and inland stations has been gathered. Among the different approaches, including four historical algorithms, the polynomial algorithms involving the red-to-green reflectance ratio presents the best performance on the validation data set (mean absolute percent difference (MAPD) of 18.7%). Compared to the use of a single spectral band, the band ratio reduces the scatter around the polynomial fit, as well as the impact of imperfect atmospheric corrections. Due to the lack of matchup data points with VNREDSat-1, the full VNREDSat-1 processing chain (atmospheric correction (RED-NIR) and V1SPM), aiming at estimating SPM from the top-of-atmosphere signal, was applied to the Landsat-8/OLI match-up data points with relatively low to moderate SPM concentration (3.33–15.25 g·m−3), yielding a MAPD of 15.8%. An illustration of the use of this VNREDSat-1 processing chain during a flooding event occurring in Vietnam is provided.
Coloured dissolved organic matter (CDOM) is one of the major contributors to the absorption budget of most freshwaters and can be used as a proxy to assess non-optical carbon fractions such as dissolved organic carbon (DOC) and the partial pressure of carbon dioxide (pCO2). Nevertheless, riverine studies that explore the former relationships are still relatively scarce, especially within tropical regions. Here we document the spatial-seasonal variability of CDOM, DOC and pCO2, and assess the potential of CDOM absorption coefficient (aCDOM(412)) for estimating DOC concentration and pCO2 along the Lower Amazon River. Our results revealed differences in the dissolved organic matter (DOM) quality between clearwater (CW) tributaries and the Amazon River mainstream. A linear relationship between DOC and CDOM was observed when tributaries and mainstream are evaluated separately (Amazon River: N = 42, R2 = 0.74, p<0.05; CW: N = 13, R2 = 0.57, p<0.05). However, this linear relationship was not observed during periods of higher rainfall and river discharge, requiring a specific model for these time periods to be developed (N = 25, R2 = 0.58, p<0.05). A strong linear positive relation was found between aCDOM(412) and pCO2(N = 69, R2 = 0.65, p<0.05) along the lower river. pCO2 was less affected by the optical difference between tributaries and mainstream waters or by the discharge conditions when compared to CDOM to DOC relationships. Including the river water temperature in the model improves our ability to estimate pCO2 (N = 69; R2 = 0.80, p<0.05). The ability to assess both DOC and pCO2 from CDOM optical properties opens further perspectives on the use of ocean colour remote sensing data for monitoring carbon dynamics in large running water systems worldwide.
Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, Rrs(λ), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m−3. Different empirical and semi-analytical approaches developed to assess SPM from Rrs(λ) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.
Internal tides (ITs) in the Indonesian seas were largely investigated and hotspots of intensified mixing identified in the straits in regional models and observations. Both of them indicate strong mixing up to 10⁻⁴cm/s even close to the surface and show that tides at spring-neap cycle cool by 0.2°C the surface water at ITs’ generation sites.These findings supported the idea of strong and surfaced mixing capable of providing cold and nutrient-rich water favorable for the whole ecosystem. However, it has never been assessed through an ad-hoc study. Our aim is to provide a quantification of ITs impact on chlorophyll-a through a coupled model, whose physical part was validated against the INDOMIX data in precedent studies and the biogeochemical part is compared to in-situ samples and satellite products. In particular, explicit tides’ inclusion within the model improves the representation of chlorophyll and of the analyzed nutrients. Results from harmonic analysis of chlorophyll-a demonstrate that tidal forcing modify spring/neap tides’ variability on the regions of maximum concentration in correspondence to ITs’ génération areas and to plateau sites where barotropic tides produce large friction reaching the surface. The adoption of measured vertical diffusivities explains the biogéochemical tracers’ transformation within the Halmahera Sea and used to estimate the nutrients’ turbulent flux, with an associated increase in new production of ~25% of the total and a growth in mean chlorophyll of ~30%. Hence, we confirm the key role of ITs in shaping vertical distribution and variability of chlorophyll as well as nutrients in the maritime continent.
Remote sensing represent a powerful tool to estimate chla concentrations and to predict primary production in coastal waters. However, in order to use this tool efficiently in such variable ecosystems we need to increase our knowledge concerning bio-optical properties, phytoplankton community and photosynthetic parameters variability in order to develop adapted regional algorithms. Spatial and temporal variations of these key parameters were investigated during spring 2000 in the English Channel across five mesoscale cruises (BIOPTEL) realised between February and October 2000. From this in situ data set (phytoplankton pigments obtained by HPLC, nutrients concentration, phytoplankton productivity estimated by variable fluorescence technique (Phyto-Pam), yellow substance absorption, particulate matter absorption), we assessed the scales of variation of primary production model inputs in the several identified biological provinces and throughout spring. These in situ observations were used to proceed to sensitivity analyses of a local primary production model. Further, from this local modelisation we discussed on (i) the biases induced by the using of existing algorithms developed to obtain primary production directly from satellite data in such ecosystems and (ii) of the improvements which should be done concerning such general models.
Tides and internal tides (IT) in the ocean can significantly affect local to regional ocean temperature and even sea surface temperature (SST), via processes such as vertical mixing, vertical advection and transport of water masses. Offshore of the Amazon River, IT have already been detected and studied; however, their impact on temperature, SST and associated processes are not known in this region. In this work, we use high resolution (1/36°) numerical simulations with and without the tides from an ocean circulation model (NEMO) which explicitly resolves the internal tides (IT), to assess how they can affect ocean temperature in the studied area. We distinguish the analysis for two contrasted seasons, from April to June (AMJ) and from August to October (ASO), since the seasonal stratification off the Amazon River modulates the IT’s response and their influence in temperature.  The IT are well reproduced by the model, and are in good agreement with observations, for both their generation and their propagation. The simulation with tides is in better agreement with satellite SST data compared to the simulation without tides. During ASO season, stronger meso-scale currents, deeper and weaker pycnocline are observed in contrast to the AMJ season. Results show that the observed coastal upwelling during ASO season is well reproduced by the model including tides, whereas the no-tide simulation is too warm by +0.3 °C at sea surface. In the subsurface above the thermocline, the tide simulation is cooler by -1.2 °C, and warmer below the thermocline by +1.2 °C compared to the simulation without the tides. The study further highlights that the IT induce vertical mixing on their generation site along the shelf break and on their propagation pathways towards the open ocean. This process explains the cooler temperature at the ocean surface and in the subsurface water above the thermocline and a warming in the deeper layers (below the thermocline). The surface cooling induced in turn an increase of the net heat flux from the atmosphere to the ocean surface, which could induce significant changes in the local and even for the regional tropical Atlantic atmospheric circulation and precipitation. We therefore demonstrate that IT, mainly via vertical diffusivity along their propagation pathways of approximately 700 km offshore, and tides over the continental shelf, play a key role on the temperature structure off the Amazon River mouth, particularly in the coastal cooling enhanced by IT.  
La mise en place de plusieurs reseaux nationaux d’observation in situ (e.g. SOMLIT, REPHY, COAST-HF,..) ainsi que les recents developpements methodologiques en terme d’observation spatiale dite « couleur de l’eau » a l’echelle nationale permettent desormais de suivre la dynamique des eaux cotieres francaises a de multiples echelles spatiales ou temporelles. L’exploitation de ces informations terrain ou satellite, generalement effectuee de maniere independante, doit cependant faire face a differentes limitations propres aux observations in situ (e.g. faible emprise spatiale : donnees localisees et nombre de stations limite au sein d’un meme site ou le long du littoral) ou aux donnees satellitaires (e.g. incertitudes sur les produits, frequence d’acquisition, couverture temporelle reduite). Le projet OSYNICO (TOSCA/CNES) a ete defini dans ce contexte et a pour objectif general de demontrer l’avantage de la complementarite des observations in situ et satellite pour 1) decrire les evolutions a long terme (evolution des signaux moyens et des oscillations saisonnieres) des caracteristiques biogeochimiques des eaux cotieres francaises (de l’echelle locale a l’echelle synoptique) 2) d’apprecier l’impact des evenements climatiques extremes sur ces ecosystemes cotiers. Les bases de donnees et outils/metriques mis en place dans le cadre du projet pour la comparaison des dynamiques observees pour des variables cles (e.g. Chla, MES, POC) via les observations in situ et couleur seront presentes. Un focus sera effectue sur les premiers resultats obtenus aux echelles saisonnieres et interannuelles.
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.
