Coastal and inland waters ecosystems are ecologically, culturally, and economically important. Monitoring these environments is therefore essential to understand ecosystem functioning, how to ensure sustainable practices and assess the impact of human activities. Among the large diversity of measurement techniques, optical remote sensing presents some clear advantages. Indeed, earth observation satellites nowadays allow to monitor the spatial variability of water quality parameters over large areas and with relatively short revisiting times. In water and above water radiometers, have a great potential for ecosystem monitoring, especially if they are integrated into autonomous measurement systems providing high temporal resolution data, or if they have a high spectral resolution opening the door to new environmental products based on fine spectral features. However, retrieving relevant information on water constituents from radiometric data in optically complex waters is still challenging. Indeed, although in clear, case-1, waters most of the bio-optic parameters are dependent of the chlorophyll-a concentration, in coastal and inland waters (i.e. case-2 waters) light absorption and scattering is affected by terrestrial inputs of sediments and/or dissolved organic carbon which can make the retrieval of simple parameters such as the chlorophyll-a concentration very complicated. In addition, atmospheric correction algorithms are more challenging because of potentially extreme optical water properties and the proximity with the coast or surrounding land. This session is open to all contributions presenting novel applications of inland and coastal aquatic monitoring based on visible and NIR radiometric remote sensing data either from satellite or in situ sensors.
Lead Organizer: Héloïse Lavigne, Royal Belgium Intistute of Natural Sciences (hlavigne@naturalsciences.be)
Co-organizers:
Clémence Goyens, Royal Belgium Institute of Natural Sciences (cgoyens@naturalsciences.be)
Pierre Gernez, Nantes Université (pierre.gernez@univ-nantes.fr)
David Doxaran, Laboratoire D'Oceanographie de Villfrenche, CNRS (david.doxaran@imev-mer.fr)
Evangelos Spyrakos, University of Stirling (evangelos.spyrakos@stir.ac.uk)
Presentations
10:30 AM
CONTRASTING SUSPENDED PARTICLE CHARACTERISTICS AND OPTICAL PROPERTIES IN TWO ESTUARIES IN THE NORTHERN GULF OF MEXICO: SEASONAL TRENDS (6975)
Primary Presenter: Eurico D'Sa, Louisiana State University (ejdsa@lsu.edu)
Suspended particulate matter (SPM) plays an important role in water quality and the distribution and transport of organic carbon, pollutants and sediments in the coastal environment; its concentration and particle size distribution (PSD) influence seawater optical properties and ocean color. Seawater SPM concentrations and characteristics (e.g., PSD, size class) based on the LISST-100x are examined in conjunction with optical measurements (beam attenuation coefficient, backscattering coefficient, particle spectral absorption coefficients and remote sensing reflectance) in two estuarine systems - Barataria Bay (particle-dominated) and Apalachicola Bay (DOM-dominated), located in the northern Gulf of Mexico. Surface SPM concentrations varied both spatially and temporally in both the bays, with higher concentrations in Barataria Bay (mean: 66.36 +/- 29.17, 30.54 +/- 8.20 and 21.62 +/- 12.81 mg/L) compared to Apalachicola Bay (7.65 +/- 7.22, 8.05 +/- 6.08 and 3.43 +/- 1.73 mg/L) during spring, summer and fall, respectively. PSDs were highly variable in the two bays, with mean size fractions also varying seasonally. An examination of three particle size classes (2 – 20, 20 – 200 and >200 micrometers) indicated stronger correlations of the smaller size classes to attenuation and backscattering coefficients, suggesting the importance of these size classes on remote sensing reflectance. Further, an adaptive semi-analytical algorithm for the Sentinel 3/OLCI medium-resolution sensors will be used to examine backscattering coefficient and SPM spatiotemporal distributions in the two bays.
10:45 AM
Remote sensing of Lingulodinium polyedra harmful algal blooms: from laboratory optical measurements to satellite observation (4892)
Primary Presenter: Pierre Gernez, Nantes University (pierre.gernez@univ-nantes.fr)
Improving Harmful algal blooms (HABs) detection is crucial because massive bloom events are likely to increase in the future due to global warming and eutrophication. Remote sensing has proved useful to detect HABs, however discriminating the causative bloom species is challenging due to a lack of knowledge on the optical properties of HAB-forming species: from about 300 harmful species, only a modicum has been documented so far. We investigate the possibility of improving HAB detection using a three-step approach: 1) measurement of phytoplankton inherent optical properties (IOPs) from laboratory cultures, 2) development of specific inversion algorithm using radiative transfer (RT), and 3) detection using high resolution satellite observation. We applied this analytical approach to Lingulodinium polyedra, a dinoflagellate causing seawater discoloration worldwide. While L. polyedra has been extensively studied over the past years, its optical properties are poorly documented. Here, its IOPs were characterized from laboratory culture, in terms of attenuation, absorption, and scattering. The contribution of the particulate, phytoplankton, detrital, and dissolved constituents was measured. The characterization of the scattering properties included the measurement of the volume scattering function. The IOPs were used to parameterize a generic RT model, and develop a specific inversion algorithm. The algorithm was then applied to field and satellite reflectance data acquired during a coastal red tide to retrieve L. polyedra cell number, as well as Chl a and POC concentration.
11:00 AM
IN SITU AND SATELLITE-DERIVED RADIOMETRIC SPECTRAL FEATURES OF CYANOBACTERIA BLOOMS IN THE BALTIC SEA (5422)
Primary Presenter: Ilaria Cazzaniga, Joint Research Centre - European Commission (ilaria.cazzaniga@ec.europa.eu)
Cyanobacteria blooms are recurrent in the Baltic Sea with increasing frequency and intensity, but their accurate quantification through satellite imagery is challenged by many factors, including satellite coarse spatial resolution, atmospheric correction issues, cyanobacteria irregular vertical and horizontal spatial distribution. This study investigates the temporal evolution of the in situ remote sensing reflectance RRS during these blooms, benefitting from multispectral measurements from the Ocean Colour component of the Aerosol Robotic Network (AERONET-OC). Spectral features that may show potential to identify cyanobacteria and their development stages are investigated. The capability and uncertainties in reproducing these features by the Ocean Colour products of Copernicus Sentinel-3 Ocean and Land Colour Instrument (OLCI) are also assessed. Satellite derived RRS values are compared to AERONET-OC radiometric data, showing high uncertainties particularly pronounced at the blue centre-wavelengths. On the other hand, band-differences in the green-red spectral region exhibit less dependence on atmospheric correction issues with mean absolute relative differences comprised between 8.3% and 9.6%, when considering data affected by the presence of cyanobacteria. A first comparison with in situ data of phytoplankton biovolume shows the potential for these band differences to identify cyanobacteria near-surface presence also at lower concentrations respect to other existing algorithms, being at the same time not sensitive to algal blooms usually occurring in spring.
11:15 AM
Using Artificial Intelligence to Detect Phytoplankton Community Composition in the Great Lakes from Space (5269)
Primary Presenter: Guangming Zheng, NOAA and UMCP (guangming.zheng@noaa.gov)
We present a study on using artificial intelligence (AI) to detect phytoplankton community composition in the Great Lakes from space. The AI model was trained using a dataset of EPA field-measured phytoplankton groups collected from 2012 to 2019. The input features for the model include VIIRS remote-sensing reflectance, surface temperature, as well as geospatial and temporal information. The outputs of the model are the total cell volume concentration and volume fractions of eight different phytoplankton groups, including diatoms, Chlorophytes, Chrysophytes, Cryptophytes, Cyanophytes, Euglenoids, Pyrrhophytes, and others. The testing results show a significant relationship between the model-derived phytoplankton groups and the measured phytoplankton groups. This study highlights the potential of AI in learning patterns from historical data and reconstructing the history of desired water quality parameters in large aquatic systems, such as the Great Lakes.
11:30 AM
PHYBOM: A PHYTOPLANKTON-BASED MODEL FOR THE REMOTE SENSING SIGNAL SIMULATION OF OPTICALLY COMPLEX WATERS. (6708)
Primary Presenter: Antonio Ruiz-Verdú, University of Valencia (antonio.ruiz@uv.es)
PHYBOM (PHYtoplankton-Based Optical Model) is a bio-optical model that simulates the bulk Inherent Optical Properties (IOP) of a water body, based on the taxonomic and/or pigment composition of phytoplankton populations. The absorption spectra of phytoplankton are constructed from the concentration of chlorophylls, carotenoids and phycobilins, accounting for the package effect, whereas the average cell size (Equivalent Spherical Diameter) is used to simulate the scattering properties from the Equivalent Algal Population (EAP) specific coefficients, distinguishing between Eukaryote or Cyanobacteria cell types. If the cell sizes or pigment composition are not known, the simulation can start by simply defining the total Chlorophyll-a concentration (Chl-a) and any combination of the main freshwater phytoplankton classes, expressed as percentage of the total Chl-a. The model also computes the IOP of Chl-a-correlated organic detritus, different types of Non-Algal Particles (NAP) and Colored Dissolved Organic Matter (CDOM). Linked to the Radiative Transfer Code Hydrolight (HL), PHYBOM can simulate the Remote Sensing Reflectance of optically complex water bodies for a large variety of environmental conditions. The model was calibrated and validated with an extensive database of limnological, bio-optical and radiometric in situ data, acquired in lakes and reservoirs in Spain. It is being used for the development of inversion algorithms for the remote sensing of Phytoplankton Functional Types (PFT) in the RESSBIO project (REmote Sensing Spectroscopy for wetlands BIOdiversity).
11:45 AM
MAPPING UNDERWATER ECOSYSTEMS THROUGH REMOTE SENSING: NOVEL APPROACHES TO EXPAND THE SCALES OF GLOBAL BIODIVERSITY TRACKING (7099)
Primary Presenter: Fernando Garcia-Gonzalez, CEAB-CSIC (f.garcia@ceab.csic.es)
Ocean environmental conditions are changing at unprecedented rates causing profound ecosystem transformations. These changes are especially important along the coast where intensifying human activity tends to concentrate. As a result, underwater vegetated ecosystems are severely degrading and substantial areas of both seagrass meadows and macroalgal forests have already been lost. Preserving these crucial foundation species is an imminent societal challenge, but to accomplish this, we first need to be able to robustly survey coastal waters at a broad spatial and temporal scales. In this context, satellite remote sensing has emerged as a magnificent approach to expand the scope of in situ observations in a cost-effective way. Here, we test whether Sentinel-2 will be useful to monitor underwater habitats in the “Freus de Ibiza y Formentera” Marine Protected Area. We use training data to calibrate machine learning algorithms that classify satellite imagery into four broad habitat types: sand, macroalgae, Posidonia oceanica and Cymodocea nodosa. The resulting distribution map reaches 20 m deep and shows an overall accuracy of 78% when compared with available ground truth data. In conclusion, this work shows that Sentinel-2 is a useful tool for coastal habitat mapping which can be relevant to monitor Essential Biodiversity Variables, such as the ecosystem distribution of seagrass meadows and macroalgal forests at large spatial and temporal scales.
SS102B Inland and Coastal Aquatic Ecosystems Monitoring from In Situ and Satellite Radiometric Measurements
Description
Time: 10:30 AM
Date: 9/6/2023
Room: Sala Menorca A