Sustainable marine ecosystems: deep learning for water quality assessment and forecasting

An appropriate management of the available resources within oceans and coastal regions is vital to guarantee their sustainable development and preservation, where water quality is a key element. Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim. In this paper, we review methodologies and technologies for water quality assessment that contribute to a sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for water quality estimation and forecasting. The analyzed literature is classified depending on the type of task, scenario and architecture. Moreover, several applications including coastal management and aquaculture are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies are expected to be the main involved agents.Postprint (published version

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Recently, the marine habitat has been under pollution threat, which impacts many human activities as well as human life. Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Derivative analysis of hyperspectral oceanographic data

22 pages, 12 figuresThis study was supported by the projects HIDRA (PIE06-301102) and ANERIS (PIF08-015) funded by the Spanish Ministry of Science and InnovationPeer Reviewe

Performance of the MODIS FLH algorithm in estuarine waters: a multi-year (2003–2010) analysis from Tampa Bay, Florida (USA)

Although satellite technology promises great usefulness for the consistent monitoring of chlorophyll-α concentration in estuarine and coastal waters, the complex optical properties commonly found in these types of waters seriously challenge the application of this technology. Blue–green ratio algorithms are susceptible to interference from water constituents, different from phytoplankton, which dominate the remote-sensing signal. Alternatively, modelling and laboratory studies have not shown a decisive position on the use of near-infrared (NIR) algorithms based on the sun-induced chlorophyll fluorescence signal. In an analysis of a multi-year (2003–2010) in situ monitoring data set from Tampa Bay, Florida (USA), as a case, this study assesses the relationship between the fluorescence line height (FLH) product from the Moderate Resolution Imaging Spectrometer (MODIS) and chlorophyll-α

Optical remote sensing (Sentinel-3 OLCI) used to monitor dissolved organic carbon in the Lena River, Russia

In the past decades the Arctic has experienced stronger temperature increases than any other region globally. Shifts in hydrological regimes and accelerated permafrost thawing have been observed and are likely to increase mobilization of organic carbon and its transport through rivers into the Arctic Ocean. In order to better quantify changes to the carbon cycle, Arctic rivers such as the Lena River in Siberia need to be monitored closely. Since 2018, a sampling program provides frequent in situ observations of dissolved organic carbon (DOC) and colored dissolved organic matter (CDOM) of the Lena River. Here, we utilize this ground truth dataset and aim to test the potential of frequent satellite observations to spatially and temporally complement and expand these observations. We explored all available overpasses (~3250) of the Ocean and Land Colour Instrument (OLCI) on Sentinel-3 within the ice-free periods (May – October) for four years (2018 to 2021) to develop a new retrieval scheme to derive concentrations of DOC. OLCI observations with a spatial resolution of ~300 m were corrected for atmospheric effects using the Polymer algorithm. The results of this study show that using this new retrieval, remotely sensed DOC concentrations agree well with in situ DOC concentrations (MAPD=10.89%, RMSE=1.55 mg L−1, r²=0.92, n=489). The high revisit frequency and wide swath of OLCI allow it to capture the entire range of DOC concentrations and their seasonal variability. Estimated satellite-derived DOC export fluxes integrated over the ice-free periods of 2018 to 2021 show a high interannual variability and agree well with flux estimates from in situ data (RMSD=0.186 Tg C, MAPD=4.05%). In addition, 10-day OLCI composites covering the entire Lena River catchment revealed increasing DOC concentration and local sources of DOC along the Lena from south to north. We conclude that moderate resolution satellite imagers such as OLCI are very capable of observing DOC concentrations in large/wide rivers such as the Lena River despite the relatively coarse spatial resolution. The global coverage of remote sensing offers the expansion to more rivers in order to improve our understanding of the land-ocean carbon fluxes in a changing climate

Optical remote sensing (Sentinel-3 OLCI) used to monitor dissolved organic carbon in the Lena River, Russia

In the past decades the Arctic has experienced stronger temperature increases than any other region globally. Shifts in hydrological regimes and accelerated permafrost thawing have been observed and are likely to increase mobilization of organic carbon and its transport through rivers into the Arctic Ocean. In order to better quantify changes to the carbon cycle, Arctic rivers such as the Lena River in Siberia need to be monitored closely. Since 2018, a sampling program provides frequent in situ observations of dissolved organic carbon (DOC) and colored dissolved organic matter (CDOM) of the Lena River. Here, we utilize this ground truth dataset and aim to test the potential of frequent satellite observations to spatially and temporally complement and expand these observations. We explored all available overpasses (~3250) of the Ocean and Land Colour Instrument (OLCI) on Sentinel-3 within the ice-free periods (May – October) for four years (2018 to 2021) to develop a new retrieval scheme to derive concentrations of DOC. OLCI observations with a spatial resolution of ~300 m were corrected for atmospheric effects using the Polymer algorithm. The results of this study show that using this new retrieval, remotely sensed DOC concentrations agree well with in situ DOC concentrations (MAPD=10.89%, RMSE=1.55 mg L−1, r²=0.92, n=489). The high revisit frequency and wide swath of OLCI allow it to capture the entire range of DOC concentrations and their seasonal variability. Estimated satellite-derived DOC export fluxes integrated over the ice-free periods of 2018 to 2021 show a high interannual variability and agree well with flux estimates from in situ data (RMSD=0.186 Tg C, MAPD=4.05%). In addition, 10-day OLCI composites covering the entire Lena River catchment revealed increasing DOC concentration and local sources of DOC along the Lena from south to north. We conclude that moderate resolution satellite imagers such as OLCI are very capable of observing DOC concentrations in large/wide rivers such as the Lena River despite the relatively coarse spatial resolution. The global coverage of remote sensing offers the expansion to more rivers in order to improve our understanding of the land-ocean carbon fluxes in a changing climate

A Reconstruction Method for Hyperspectral Remote Sensing Reflectance in the Visible Domain and Applications

A reconstruction method was developed for hyperspectral remote sensing reflectance (Rrs)data in the visible domain (400–700 nm) based on in situ observations. A total of 2,647 Rrs spectra were collected over a wide variety of water environments including open ocean, coastal and inland waters. Ten schemes with different band numbers (6 to 15) were tested based on a nonlinear model. It was found that the accuracy of the reconstruction increased with the increase of input band numbers. Eight of these schemes met the accuracy criterion with the mean absolute error (MAE) and mean relative error (MRE)values between reconstructed and in situ Rrs less than 0.00025 sr-1 and 5%, respectively. We chose the eight-band scheme for further evaluation because of its decent performance. The results revealed that the parameterization derived by the eight-band scheme was efficient for restoring Rrs spectra from different water bodies. In contrast to the previous studies that used a linear model with 15 spectral bands, the nonlinear model with the eight-band scheme yielded a comparable reconstruction performance. The MAE andMRE values were generally less than 0.00016 sr-1 and 3% respectively; much lower than the uncertainties in satellite-derived Rrs products. Furthermore, a preliminary experiment of this method on the data from the Hyperspectral Imager for the Coastal Ocean (HICO) showed high potential in the future applications for reconstructing Rrs spectra from space-borne optical sensors. Overall, the eight-band scheme with our non-linear model was proven to be optimal for hyperspectral Rrs reconstruction in the visible domain