Phytoplankton community structure determined through remote sensing and in situ optical measurements

Includes bibliographical references.Linking variability in optical signals with phytoplankton community characteristics is important to extend the use of the vast resource that is the satellite ocean colour archive. Detection of species, functional types or size classes has been addressed through a spectrum of empirical to analytical approaches. A key step in developing these techniques is quantifying the sensitivity in reflectance, which can be attributed to phytoplankton characteristics (e.g cell size) under different optical regimes. Ultimately, highly spatially and temporally resolved information on phytoplankton characteristics can help the global scientific community to answer important questions relating to primary ecosystem variability. In the southern Benguela, Harmful Algal Blooms threaten public health and the economic viability of fishery and aquaculture industries in the region. Concurrently, the dominance of phytoplankton biomass amongst optically significant constituents in the southern Benguela makes the region ideal for assessing the extent to which phytoplankton characteristics beyond biomass can influence the ocean colour signal. A forward and inverse approach is presented. Phytoplankton absorption and back scattering are generated from a phytoplankton particle population model coupled to two radiative transfer approaches: a reflectance approximation and the radiative transfer model, EcoLight-S. Non-linear optimisation inversion schemes are then implemented. A simulated dataset is created to investigate how much variability in reflectance can be associated with changes in phytoplankton cell size in different bio-optical water types. This dataset is inverted to investigate the errors inherent in the inversion process as a result of ambiguity. Comparison of the two radiative transfer techniques allows for consideration of the suitability of approximations for bidirection-ality and subsurface propagation. The inversion algorithm is then applied to hyperspectral in situ radiometric data to provide validation and further assessment of errors from all sources. Results indicate that size related sensitivity in reflectance is highly dependent on phytoplank-ton biomass, as determined by the relative phytoplankton contribution to the Inherent Optical Property budget. The algorithm is finally applied to ten years of MERIS data covering the southern Benguela. A time series of biomass and cell size is presented and metrics developed to demonstrate the utility of this approach for identifying previously unobserved interannual variability in Harmful Algal Blooms

Using Multi-Spectral Remote Sensing for Flood Mapping: A Case Study in Lake Vembanad, India

Water is an essential natural resource, but increasingly water also forms a threat to the human population, with floods being the most common natural disaster worldwide. Earth Observation has the potential for developing cost-effective methods to monitor risk, with free and open data available at the global scale. In this study, we present the application of remote sensing observations to map flooded areas, using the Vembanad-Kol-Wetland system in the southwest of India as a case study. In August 2018, this region experienced an extremely heavy monsoon season, which caused once-in-a-century floods that led to nearly 500 deaths and the displacement of over a million people. We review the use of existing algorithms to map flooded areas in the Lake Vembanad region using the spectral reflectances of the green, red and near-infrared bands from the MSI sensor on board Sentinel-2. Although the MSI sensor has no cloud-penetrating capability, we show that the Modified Normalised Difference Water Index and the Automated Water Extraction Index can be used to generate flood maps from multi-spectral visible remote sensing observations to complement commonly used SAR-based techniques to enhance temporal coverage (from 12 to 5 days). We also show that local knowledge of paddy cultivation practices can be used to map the manoeuvring of water levels and exclude inundated paddy fields to improve the accuracy of flood maps in the study region. The flood mapping addressed here has the potential to become part of a solution package based on multi-spectral visible remote sensing with capabilities to simultaneously monitor water quality and risk of human pathogens in the environment, providing additional important services during natural disasters

Satellite remote sensing of surface winds, waves, and currents: Where are we now?

This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters

Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (>560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay

Building Capacity and Resilience Against Diseases Transmitted via Water Under Climate Perturbations and Extreme Weather Stress

It is now generally accepted that climate variability and change, occurrences of extreme weather events, urbanisation and human pressures on the environment, and high mobility of human populations, all contribute to the spread of pathogens and to outbreaks of water-borne and vector-borne diseases such as cholera and malaria. The threats are heightened by natural disasters such as floods, droughts, earth-quakes that disrupt sanitation facilities. Aligned against these risks are the laudable Sustainable Development Goals of the United Nations dealing with health, climate, life below water, and reduced inequalities. Rising to the challenges posed by these goals requires an integrated approach bringing together various scientific disciplines that deal with parts of the problem, and also the various stakeholders including the populations at risk, local governing bodies, health workers, medical professionals, international organisations, charities, and non-governmental organisations. Satellite-based instruments capable of monitoring various properties of the aquatic ecosystems and the environs have important contributions to make in this context. In this chapter, we present two case studies—the Ganga Delta region and the Vembanad Lake region in south-western India—to illustrate some of the benefits that remote sensing can bring to address the problem of global health, and use these examples to identify the capacity building that is essential to maximise the exploitation of the remote sensing potential in this context

Multiplatform analysis of a large anticyclonic eddy in the Algero-Provencal basin in 2019

A large anticyclonic eddy formed in April 2019 in the Algero-Provencal basin between Mallorca and Sardinia, and lasted until November 2019. While mesoscale activity is usually high in this part of the Mediterranean basin, the formation of such large (about 150 km in diameter) and long-lived eddies is not common. The eddy formed from a filament originated in the Algerian coast and was visible in multiple sources of satellite data, including sea surface temperature and ocean colour from Sentinel-3, until summer. Because of the warming of the surface layer, during summer months the eddy remained as a subsurface structure, evidenced by the sea level anomaly derived from altimetry data. A surface signal developed again in November, and the eddy finally dissipated in December 2019. According to CMEMS model data, in its strongest period the eddy reached about 300 m in depth, and during its sub-surface period the center was located at about 100 m depth. While at the surface the temperature signal was very clear, model data suggest the salinity anomaly was stronger than temperature, especially at depth. Such large and long-lived eddies have an impact in the basin currents, specifically in the transport of cold water from the northern to the southern part of the western Mediterranean basin, influencing the ecosystem there. The impact of the presence of this eddy, its long duration and the additional mesoscale and submesoscale activity that originated in its surroundings are investigated using a combination of remote sensing data, in situ data and model data