The global coastal seascape offers a multitude of ecosystem functions and services to the natural and human-induced ecosystems. However, the current anthropogenic global warming above pre-industrial levels is inducing the degradation of seascape health with adverse impacts on biodiversity, economy, and societies. Bathymetric knowledge empowers our scientific, financial, and ecological understanding of the associated benefits, processes, and pressures to the coastal seascape. Here we leverage two commercial high-resolution multispectral satellite images of the Pleiades and two multibeam survey datasets to measure bathymetry in two zones (0–10 m and 10–30 m) in the tropical Anguilla and British Virgin Islands, northeast Caribbean. A methodological framework featuring a combination of an empirical linear transformation, cloud masking, sun-glint correction, and pseudo-invariant features allows spatially independent calibration and test of our satellite-derived bathymetry approach. The best R2 and RMSE for training and validation vary between 0.44–0.56 and 1.39–1.76 m, respectively, while minimum vertical errors are less than 1 m in the depth ranges of 7.8–10 and 11.6–18.4 m for the two explored zones. Given available field data, the present methodology could provide simple, time-efficient, and accurate spatio-temporal satellite-derived bathymetry intelligence in scientific and commercial tasks i.e., navigation, coastal habitat mapping and resource management, and reducing natural hazards.

Environmental policy involving citizen science (CS) is of growing interest. In support ofthis open data stream of information, validation or quality assessment of the CS geo-located data to their appropriate usage for evidence-based policy making needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or "pillars". Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as "two sides of the same coin". Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data product for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g., error propagation, paving the route to improvements in the approach.

27Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper 28 discusses the flexibility and usefulness of qualifying CS data, either when using an EO data for the 29 validation within the quality assurance process, or validating an EO data product that describes the 30 risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance 31 of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality 32 assurance for validation, due to the quality of secondary data used within the automatic workflow, 33 are described, e.g. error propagation, paving the route to improvements in the approach. 34

Hydrophobicity on steel-based metallic surfaces provides an advantage in limiting corrosion and debris buildup on the surface, thereby, improving the substrate performance. An experimental investigation was conducted on the development of zinc stearate and silicon dioxide coatings on the surface of hot-dipped galvanised zinc-coated steel substrates, which could be used to induce superhydrophobicity. Under optimal formulation and processing conditions, a contact angle of 146° could be produced within a 120-min processing window. This represents a reduction in processing time of 67% over previous literature using similar chemistry. In addition, we proved that costly nano silicon dioxide can be replaced by lower cost micro silicon dioxide without decreasing the performance of the coating contact angle. Under standard accelerated exposure tests, the coating was shown to reduce oxide build up by a factor of 3 compared to uncoated galvanized steel.

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