Detecting and quantifying a massive invasion of floating aquatic plants in the Río de la Plata turbid waters using high spatial resolution ocean color imagery

The massive development of floating plants in floodplain lakes and wetlands in the upper Middle Paraná river in the La Plata basin is environmentally and socioeconomically important. Every year aquatic plant detachments drift downstream arriving in small amounts to the Río de la Plata, but huge temporary invasions have been observed every 10 or 15 years associated to massive floods. From late December 2015, heavy rains driven by a strong El Niño increased river levels, provoking a large temporary invasion of aquatic plants from January to May 2016. This event caused significant disruption of human activities via clogging of drinking water intakes in the estuary, blocking of ports and marinas and introducing dangerous animals from faraway wetlands into the city. In this study, we developed a scheme to map floating vegetation in turbid waters using high-resolution imagery, like Sentinel-2/SMI (MultiSpectral Imager), Landsat-8/OLI (Operational Land Imager), and Aqua/MODIS (MODerate resolution Imager Spectroradiometer)-250 m. A combination of the Floating Algal Index (that make use of the strong signal in the NIR part of the spectrum), plus conditions set on the RED band (to avoid misclassifying highly turbid waters) and on the CIE La*b* color space coordinates (to confirm the visually "green" pixels as floating vegetation) were used. A time-series of multisensor high resolution imagery was analyzed to study the temporal variability, covered area and distribution of the unusual floating macroalgae invasion that started in January 2016 in the Río de la Plata estuary.Fil: Dogliotti, Ana Inés. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Gossn, Juan Ignacio. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Vanhellemont, Quinten. Koninklijk Belgisch Instituut Voor Natuurwetenschappen; BélgicaFil: Ruddick, Kevin G.. Koninklijk Belgisch Instituut Voor Natuurwetenschappen; Bélgic

QWIP: A Quantitative Metric for Quality Control of Aquatic Reflectance Spectral Shape Using the Apparent Visible Wavelength

The colors of the ocean and inland waters span clear blue to turbid brown, and the corresponding spectral shapes of the water-leaving signal are diverse depending on the various types and concentrations of phytoplankton, sediment, detritus and colored dissolved organic matter. Here we present a simple metric developed from a global dataset spanning blue, green and brown water types to assess the quality of a measured or derived aquatic spectrum. The Quality Water Index Polynomial (QWIP) is founded on the Apparent Visible Wavelength (AVW), a one-dimensional geophysical metric of color that is inherently correlated to spectral shape calculated as a weighted harmonic mean across visible wavelengths. The QWIP represents a polynomial relationship between the hyperspectral AVW and a Normalized Difference Index (NDI) using red and green wavelengths. The QWIP score represents the difference between a spectrum’s AVW and NDI and the QWIP polynomial. The approach is tested extensively with both raw and quality controlled field data to identify spectra that fall outside the general trends observed in aquatic optics. For example, QWIP scores less than or greater than 0.2 would fail an initial screening and be subject to additional quality control. Common outliers tend to have spectral features related to: 1) incorrect removal of surface reflected skylight or 2) optically shallow water. The approach was applied to hyperspectral imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), as well as to multispectral imagery from the Visual Infrared Imaging Radiometer Suite (VIIRS) using sensor-specific extrapolations to approximate AVW. This simple approach can be rapidly implemented in ocean color processing chains to provide a level of uncertainty about a measured or retrieved spectrum and flag questionable or unusual spectra for further analysis

MICROBIAN : Microbial diversity in the Sør Rondane Mountains in a context of climate change

The Sør Rondane Mountains (SRM) represent a c. 900 km² large mountain range, encompassing a large range of terrestrial habitats differing in geology and soil characteristics, exposure time and microclimatic conditions. The objectives of the BelSPO project MICROBIAN are to (i) use a combination of remote sensing (Digital Elevation Model) and close-range field observation techniques to map physical habitat characteristics and the presence/extent of biological crust communities in the region of the Princess Elisabeth Station Antarctica (PEA), (ii) generate a comprehensive inventory of the taxonomic and functional diversity of microbial communities in these habitats by amplicon sequencing of the 16S and 18S rRNA genes and metagenomics, (iii) use mesocosm field experiments (Open Top Chambers and snow fences) to mimic the possible effects of future climate change on the taxonomic diversity of these microbial ecosystems, and (iv) conduct field experiments to inform policy-makers in view of decision making regarding environmental protection and prevention measures to reduce the introduction and spread of non-native species and to avoid cross-contamination between sites. The proposed research will provide a proof of concept to use high resolution satellite images for identifying regions of particular biological interest in East Antarctica and more broadly make a significant contribution to understanding Antarctic terrestrial microbial ecology.MICROBIA

PANTHYR hyperspectral water reflectance - O1BE

<p><strong>Introduction</strong> </p><p>This dataset contains water-leaving radiance reflectance (, variable names reflectance and reflectance_nosc) measurements made by an autonomous Pan and Tilt Hyperspectral Radiometer (PANTHYR, Vansteenwegen et al. 2019) installed at site O1BE. Data are provided in NetCDF format with information on processing settings provided in the NetCDF global attributes.  This dataset contains measurements from the first two deployments (Dec. 2019—Aug. 2020 and Feb. 2022—Nov. 2022) that pass quality control, and have bounding calibration information, and ancillary wind speed available. For this site, the use of reflectance without the Similarity Spectrum offset correction (Ruddick et al. 2005, 2006) is recommended.</p><p><strong>Methods</strong>  </p><p>A PANTHYR was deployed at RT1 Blue Accelerator Platform (Oostende, Belgium), O1BE, located at 51.2464°N, 2.9193°E for two deployments Dec. 2019—Aug. 2020 and Feb. 2022—Nov. 2022. </p><p>PANTHYR consists of a pair of TriOS RAMSES radiometers, one for measurement of radiance, and one with a cosine collector for measurement of irradiance, with custom control hard- and software, mounted on a pan and tilt head.  The RAMSES spectral range is about 350—950 nm in 190 channels. The pan and tilt head allows the orientation of each radiometer in a specific direction. Using the standard protocol, a PANTHYR cycle consists of sequential measurements of downwelling irradiance (, 6 replicates), downwelling (sky) radiance (, 6 replicates), and upwelling radiance (, 11 replicates). Three and measurements are performed each before and after the measurements. Measurement cycles are performed every 20 minutes during daytime, at 90, 135, 225, and/or 270 degrees relative azimuth to the sun to minimize air-water interface reflectance (Mobley 1999, Ruddick et al. 2006). Platform pointing conditions are skipped by the definition of an absolute pointing azimuth keep-out zone. Each cycle takes around a minute to complete.</p><p>Measurements are converted from digital counts to (ir)radiance using two laboratory instrument characterisations performed by Tartu Observatory (Estonia) before and after each deployment period. Calibration data for a specific scan are obtained from linear interpolation in time between pre-deployment and post-deployment instrument characterisation. The calibrated scan data are linearly interpolated from the instrument specific wavelengths to a common wavelength grid (355—900 nm, every 2.5 nm). Individual calibrated scans are subjected to quality control as in Ruddick et al. (2006), i.e. scans differing > 25% at 550 nm from their neighbouring scans are rejected. For the Ed measurements, this quality control step takes the change in sun zenith angle between the measurements into account.</p><p>If sufficient calibrated scans are available in the cycle, i.e. >=5/6 , >=5/6 , >=9/11 , the scans are mean averaged and the standard deviation is computed. The water-leaving radiance reflectance (, variable name reflectance_nosc) is then computed according to:</p><p>reflectance_nosc = / × ( - × )</p><p>where , , and are the mean averaged values, and the effective Fresnel correction factor as determined from lookup tables provided by Mobley (1999). Ancillary wind speed is obtained fromthe GDAS1 0.25 degree global model 6 hourly nowcast archive, by interpolation of the model grid in time and space to the measurement average time, and site position. The used wind speed and are provided in the global attributes of each file.</p><p>In the present dataset, reflectance data are provided with and without a "nosc" suffix, indicating whether a residual correction for the air-water interface reflectance (Ruddick et al. 2005) is performed. The reflectance without the "nosc" suffix uses the Similarity Spectrum (Ruddick et al. 2006) to estimate a spectrally flat residual air-water interface reflectance error () using the 720 and 780 nm combination:</p><p>reflectance = / × ( - × ) - </p><p> = ( × 780 – 720) / ( -1),</p><p>where is the Similarity Spectrum ratio between the two used wavelengths, i.e. 2.35 for 720:780 nm. The value is provided in the global attributes of each file. For this O1BE dataset, the use of reflectance without Similarity Spectrum correction is recommended.</p><p>The reflectance products are further quality controlled using the following criteria:</p><p>1) / at 750 nm < 5%, removing non-clear sky conditions</p><p>2) Variability (coefficient of variation) of water reflectance at 780 nm < 10%, removing highly variable water conditions</p><p>3) Water reflectance > 0 for 350—900 nm, removing spectra with negative reflectance retrievals</p><p>4) NIR water reflectance (840—900 nm) is assumed to be decreasing with wavelength, removing potentially contaminated spectra</p><p>5) Bright water spectra (average VIS reflectance 400—700 nm > 0.07 or average NIR reflectance 780—950 nm > 0.01) have a local maximum at around 810 nm (805—815 nm) due to the local minimum in pure water absorption, removing potentially contaminated spectra</p><p>6) Irradiance measurements in the range 860—885 nm are within 20% of the Gregg and Carder (1990) clear sky model with an aerosol optical depth of 0.1 at normal pressure, removing cloudy, shadowed, or very hazy conditions</p><p><strong>Acknowledgements</strong></p><p>The Flanders Marine Institute (VLIZ) and POM West-Vlaanderen are thanked for access to the RT1 Blue Accelerator Platform (Oostende, Belgium) and installation support.</p><p><strong>References</strong></p><p>Gregg, W.W. and Carder, K.L., 1990. A simple spectral solar irradiance model for cloudless maritime atmospheres. Limnology and oceanography, 35(8), pp.1657-1675.</p><p>Mobley, C.D., 1999. Estimation of the remote-sensing reflectance from above-surface measurements. Applied optics, 38(36), pp.7442-7455.</p><p>Ruddick, K., De Cauwer, V. and Van Mol, B., 2005, August. Use of the near infrared similarity reflectance spectrum for the quality control of remote sensing data. In Remote Sensing of the Coastal Oceanic Environment (Vol. 5885, p. 588501). SPIE.</p><p>Ruddick, K.G., De Cauwer, V., Park, Y.J. and Moore, G., 2006. Seaborne measurements of near infrared water‐leaving reflectance: The similarity spectrum for turbid waters. Limnology and Oceanography, 51(2), pp.1167-1179.</p><p>Vansteenwegen, D., Ruddick, K., Cattrijsse, A., Vanhellemont, Q. and Beck, M., 2019. The pan-and-tilt hyperspectral radiometer system (PANTHYR) for autonomous satellite validation measurements—Prototype design and testing. Remote Sensing, 11(11), p.1360.</p&gt

A benchmark dataset for the validation of MERIS and MODIS ocean colour turbidity and PAR attenuation algorithms using autonomous buoy data

We present a dataset that combines marine reflectance spectra and several standard L2 products from MERIS and MODIS, with turbidity (T), Photosynthetically Available Radiation (PAR) at different depths, and fluorescence (F) from three autonomous buoys (CEFAS Smartbuoys) located in turbid coastal waters of the North Sea and the Irish Sea. Our dataset contains several hundreds of matchups between in situ and satellite, and is a powerful benchmarking tool for validating satellite products and retrieval algorithms for turbidity and PAR attenuation

Retrieval and Validation of Water Turbidity at Metre-Scale Using Pléiades Satellite Data: A Case Study in the Gironde Estuary

International audienceThis study investigated the use of frequent metre-scale resolution Pléiades satellite imagery to monitor water quality parameters in the highly turbid Gironde Estuary (GE, SW France). Pléiades satellite data were processed and analyzed in two representative test sites of the GE: 1) the maximum turbidity zone and 2) the mouth of the estuary. The main objectives of this study were to: (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction developed by Vanhellemont and Ruddick (2018) applied to Pléiades satellite data recorded over the GE; (ii) highlight the benefits of frequent metre-scale Pléiades observations in highly turbid estuaries by comparing them to previously validated satellite observations made at medium (250/300 m for MODIS, MERIS, OLCI data) and high (20/30 m for SPOT, OLI and MSI data) spatial resolutions. The results show that the DSF allows for an accurate retrieval of water turbidity by inversion of the water reflectance in the near-infrared (NIR) and red wavebands. The difference between Pléiades-derived turbidity and field measurements was proven to be in the order of 10%. To evaluate the spatial variability of water turbidity at metre scale, Pléiades data at 2 m resolution were resampled to 20 m and 250 m to simulate typical coarser resolution sensors. On average, the derived spatial variability in the GE is lower than or equal to 10% and 26%, respectively, in 20-m and 250-m aggregated pixels. Pléiades products not only show, in great detail, the turbidity features in the estuary and river plume, they also allow to map the turbidity inside ports and capture the complex spatial variations of turbidity along the shores of the estuary. Furthermore, the daily acquisition capabilities may provide additional advantages over other satellite constellations when monitoring highly dynamic estuarine systems