Challenges and opportunities of Earth observation for the prediction of water quality in inland waters

Water quality in lakes and river systems has deteriorated worldwide due to intensification in land use and associated nutrient loading or changes in natural flow regimes. The most obvious impacts are increase in the frequency of harmful algal blooms caused by potentially toxic cyanobacteria and fish kills due to hypoxia. Other problems are not immediately visible or have indirect impacts like contamination by metals and pathogens, or vector borne diseases depending on wetting and increased temperature. To reduce health and economic risks posed by such water quality issues, there is an increased need for early warning systems. While Earth observation of inland aquatic systems can give an account of historic conditions and current state, integrating hydrodynamic and hydrologic modelling tools with predictive capabilities allow for timely intervention and optimised management options. On a local scale Earth observation can be used to drive hydrodynamic simulations for short term prediction of harmful algal blooms in specific water bodies allowing for early warning and providing operating strategies for risk minimisation for, e.g., water treatment plants or reservoirs (case studies shown here). Combined with local hyperspectral sensors it is even possible to discriminate cyanobacteria species based on their pigments and thus infer potential toxicity. A generalisation of these methods on a regional or continental scale not only yields an early warning account for a larger region, e.g. state wide, but can yield a risk estimation based on weather forecast. In combination with hydrologic modelling tools EO is applied in ecological impact studies of flood inundation, e.g., the generation of hypoxic conditions in lowland rivers, or the spread of a carp virus for pest eradication in a large basin. Although there is a large spectrum of water quality issues where EO can lead to better insight, spatial and temporal resolution of satellite sensors limits their application. Other techniques of remote sensing are necessary to fill these gaps

Intercomparison of shallow water bathymetry, hydro-optics, and benthos mapping techniques in Australian and Caribbean coastal environments

Science, resource management, and defense need algorithms capable of using airborne or satellite imagery to accurately map bathymetry, water quality, and substrate composition in optically shallow waters. Although a variety of inversion algorithms are available, there has been limited assessment of performance and no work has been published comparing their accuracy and efficiency. This paper compares the absolute and relative accuracies and computational efficiencies of one empirical and five radiative-transfer-based published approaches applied to coastal sites at Lee Stocking Island in the Bahamas and Moreton Bay in eastern Australia. These sites have published airborne hyperspectral data and field data. The assessment showed that (1) radiative-transfer-based methods were more accurate than the empirical approach for bathymetric retrieval, and the accuracies and processing times were inversely related to the complexity of the models used; (2) all inversion methods provided moderately accurate retrievals of bathymetry, water column inherent optical properties, and benthic reflectance in waters less than 13 m deep with homogeneous to heterogeneous benthic/substrate covers; (3) slightly higher accuracy retrievals were obtained from locally parameterized methods; and (4) no method compared here can be considered optimal for all situations. The results provide a guide to the conditions where each approach may be used (available image and field data and processing capability). A re-analysis of these same or additional sites with satellite hyperspectral data with lower spatial and radiometric resolution, but higher temporal resolution would be instructive to establish guidelines for repeatable regional to global scale shallow water mapping approaches

Integration of near-surface and satellite observations for algal bloom detection

Retrieval of water quality information from satellite imagery can provide resource managers with an improved understanding into the spatial variability of the water body. In light of the increasing availability of ‘analysis ready data’ (ARD) satellite imagery in open datacubes*, either on cloud-based services or on high performance computing environments, development of operational monitoring systems is becoming feasible. Near-surface sensors can assist in more rapid and widespread algal bloom monitoring at a much higher temporal resolution. Remote sensing imagery, whilst cost effective, may not be optimal in terms of spatial or spectral resolution and can be greatly enhanced with the integration of near-surface observations. We describe pathways to use field-based near-surface sensors to calibrate and validate satellite remote sensing. These methods allow early detection of algal blooms and assist in the early warning for management intervention. We have designed and deployed several low-cost, near-surface sensors at several inland water sites around eastern Australia. The data is transferred using mobile networks where it is processed into spectral information. From this data and coincident field bio-optical measurements, we have developed algorithms for quantitative estimation of blue-green algal-specific pigments (phycocyanin) and chlorophyll concentrations. We have tested these algorithms for detection using a number of existing satellite sensors and report on results here. These methods have applied next-generation monitoring technology and when combined with hydrologic modelling will provide aquatic observations and forecasts. These will lead to improved management preparedness to respond to environmental challenges, e.g., a harmful algal blooms

The impact of COVID-19 on nurses (ICON) survey : nurses' accounts of what would have helped to improve their working lives

Aims To use nurses' descriptions of what would have improved their working lives during the first peak of the COVID-19 pandemic in the UK. Design Analysis of free-text responses from a cross-sectional survey of the UK nursing and midwifery workforce. Methods Between 2 and 14 April 2020, 3299 nurses and midwives completed an online survey, as part of the ‘Impact of COVID-19 on Nurses’ (ICON) study. 2205 (67%) gave answers to a question asking for the top three things that the government or their employer could do to improve their working lives. Each participants' response was coded using thematic and content analysis. Multiple response analysis quantified the frequency of different issues and themes and examined variation by employer. Results Most (77%) were employed by the National Health Service (77%) and worked at staff or senior staff nurse levels (55%). 5938 codable responses were generated. Personal protective equipment/staff safety (60.0%), support to workforce (28.6%) and better communication (21.9%) were the most cited themes. Within ‘personal protective equipment’, responses focussed most on available supply. Only 2.8% stated that nothing further could be done. Patterns were similar in both NHS and non-NHS settings. Conclusions The analysis provided valuable insight into key changes required to improve the work lives of nurses during a pandemic. Urgent improvements in provision and quality of personal protective equipment were needed for the safety of both workforce and patients. Impact Failure to meet nurses needs to be safe at work appears to have damaged morale in this vital workforce. We identified key strategies that, if implemented by the Government and employers, could have improved the working lives of the nursing and midwifery workforce during the early stages of the COVID-19 pandemic and could prevent the pandemic from having a longer-term negative impact on the retention of this vital workforce. Patient or Public Contribution No Patient or Public Contribution, due to the COVID-19 Pandemic, urgency of the work and the target population being health and social care staff

Citizen science and the United Nations Sustainable Development Goals

Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.Additional co-authors: Courtney Di Vittorio, Nathan Drayson, Reagan M. Errera, Virginia Fernandez, Dariusz Ficek, Cédric G. Fichot, Peter Gege, Claudia Giardino, Anatoly A. Gitelson, Steven R. Greb, Hayden Henderson, Hiroto Higa, Abolfazl Irani Rahaghi, Cédric Jamet, Thomas Jordan, Kersti Kangro, Jeremy A. Kravitz, Arne S. Kristoffersen, Raphael Kudela, Lin Li, Martin Ligi, Hubert Loisel, Steven Lohrenz, Ronghua Ma, Daniel A. Maciel, Tim J. Malthus, Bunkei Matsushita, Mark Matthews, Camille Minaudo, Deepak R. Mishra, Sachidananda Mishra, Tim Moore, Wesley J. Moses, Hà Nguyễn, Evlyn M. L. M. Novo, Stéfani Novoa, Daniel Odermatt, David M. O’Donnell, Leif G. Olmanson, Michael Ondrusek, Natascha Oppelt, Sylvain Ouillon, Waterloo Pereira Filho, Stefan Plattner, Antonio Ruiz Verdú, Salem I. Salem, John F. Schalles, Stefan G. H. Simis, Eko Siswanto, Brandon Smith, Ian Somlai-Schweiger, Mariana A. Soppa, Elinor Tessin, Hendrik J. van der Woerd, Andrea Vander Woude, Ryan A. Vandermeulen, Vincent Vantrepotte, Marcel R. Wernand, Kyana Young & Linwei Yu

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring