Review of algorithms estimating export production from satellite derived properties

Whereas the vertical transport of biomass from productive surface waters to the deep ocean (the biological pump) is a critical component of the global carbon cycle, its magnitude and variability is poorly understood. Global-scale estimates of ocean carbon export vary widely, ranging from ∼5 to ∼20 Gt C y – 1 due to uncertainties in methods and unclear definitions. Satellite-derived properties such as phytoplankton biomass, sea surface temperature, and light attenuation at depth provide information about the oceanic ecosystem with unprecedented coverage and resolution in time and space. These products have been the basis of an intense effort over several decades to constrain different biogeochemical production rates and fluxes in the ocean. One critical challenge in this effort has been to estimate the magnitude of the biological pump from satellite-derived properties by establishing how much of the primary production is exported out of the euphotic zone, a flux that is called export production. Here we present a review of existing algorithms for estimating export production from satellite�derived properties, available in-situ datasets that can be used for testing the algorithms, and earlier evaluations of the proposed algorithms. The satellite�derived products used in the algorithm evaluation are all based largely on the Ocean Colour Climate Change Initiative (OC-CCI) products, and carbon products derived from them. The different resources are combined in a meta-analysis

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

Addressing Climate Change Impacts on Health

Climate change is a global health emergency, with impacts felt most acutely by vulnerable populations and communities. This paper explores health risks from climate change in a global context, setting out key risks and actions towards addressing these. In the context of COP27, it draws in a focus on Egypt as a case study throughout to exemplify the risks faced by countries which are particularly vulnerable to the health impacts of climate change. This policy working paper has been produced by the Academy of Scientific Research and Technology in Egypt, with contributions from the UK Universities Climate Network, through an academic collaboration ahead of COP27 in Egypt in 2022

Ocean Biology Studied from Space

This is the final version. Available on open access from Springer via the DOI in this recordVisible spectral radiometric measurements from space, commonly referred to as ocean-colour measurements, provide a rich stream of information on ocean biota as well as on biological and ecosystem processes. The strength of the ocean-colour technology for observing marine life lies in its global reach, combined with its ability to sample the field at a variety of spatial and temporal scales that match the scales of the processes themselves. Another advantage lies in the growing length of the time series of ocean-colour-derived products, enabiling investigations into any long-term changes, if present. This paper presents an overview of the principles and applications of ocean-colour data. The concentration of chlorophyll-a, the major pigment present in phytoplankton–single-celled, free-floating plants that are present in the sunlit layers of the ocean–was the first, and remains the most common, biological variable derived from ocean-colour data. Over the years, the list of ocean-colour products have grown to encompass many measures of the marine ecosystem and its functions, including primary production, phenology and ecosystem structure. Applications that exploit the data are many and varied, and include ecosystem-based fisheries management, biogeochemical cycles in the ocean, ecosystem health and climate change. An integrated approach, incorporating other modes of ocean observations and models with satellite observations, is needed to investigate the mysteries of the marine ecosystem

Regional Satellite Algorithms to Estimate Chlorophyll-a and Total Suspended Matter Concentrations in Vembanad Lake

This is the final version. Available on open access from MDPI via the DOI in this recordData Availability Statement: Data available with the corresponding author. Would be shared on request.A growing coastal population is leading to increased anthropogenic pollution that greatly affects coastal and inland water bodies, especially in the tropics. The Sustainable Development Goal-14, ‘Life below water’ emphasises the importance of conservation and sustainable use of the ocean and its resources. Pollution management practices often include monitoring of water quality using in situ observations of chlorophyll-a (chl-a) and total suspended matter (TSM). Satellite technology, including the MultiSpectral Instrument (MSI) sensor onboard Sentinel-2, enables the continuous monitoring of these variables in inland waters at high spatial and temporal resolutions. To improve the monitoring of water quality in the tropical Vembanad-Kol-Wetland (VKW) system, situated on the southwest coast of India, we present two regionally tuned satellite algorithms developed to estimate chl-a and TSM concentrations. The new algorithms estimate the chl-a and TSM concentrations from the simulated reflectance values as a function of the inherent optical properties using a forward modelling approach. The model was parameterised using the National Aeronautics and Space Administration (NASA) bio-Optical Marine Algorithm Dataset (NOMAD) and in situ measurements collected in the VKW system. To assess model performance, results were compared with in situ measurements of chl-a and TSM and other existing satellite-based models of chl-a and TSM. For satellite application, two different atmospheric correction methods (ACOLITE and POLYMER) were tested and satellite matchups were used to validate the new chl-a and TSM algorithms following standard validation procedures. The results demonstrated that the new algorithms were in good agreement with in situ observations and outperform existing chl-a and TSM algorithms. The new regional satellite algorithms can be used to monitor water quality within the VKW system to support the sustainable management under natural (cyclones, floods, rainfall, and tsunami) and anthropogenic pressures (industrial effluents, agricultural practices, recreational activities, construction, and demolishing concrete structures) and help achieve Sustainable Development Goal 14.Natural Environment Research Council (NERC)Department of Science and Technology, IndiaEuropean Space AgencyUKR

Comparison of ocean-colour algorithms for particulate organic carbon in global ocean

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData availability statement: The data sets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at https://www.bicep-project.org/DeliverablesIn the oceanic surface layer, particulate organic carbon (POC) constitutes the biggest pool of particulate material of biological origin, encompassing phytoplankton, zooplankton, bacteria, and organic detritus. POC is of general interest in studies of biologically-mediated fluxes of carbon in the ocean, and over the years, several empirical algorithms have been proposed to retrieve POC concentrations from satellite products. These algorithms can be categorised into those that make use of remote-sensing-reflectance data directly, and those that are dependent on chlorophyll concentration and particle backscattering coefficient derived from reflectance values. In this study, a global database of in situ measurements of POC is assembled, against which these different types of algorithms are tested using daily matchup data extracted from the Ocean Colour Climate Change Initiative (OC-CCI; version 5). Through analyses of residuals, pixel-by-pixel uncertainties, and validation based on optical water types, areas for POC algorithm improvement are identified, particularly in regions underrepresented in the in situ POC data sets, such as coastal and high-latitude waters. We conclude that POC algorithms have reached a state of maturity and further improvements can be sought in blending algorithms for different optical water types when the required in situ data becomes available. The best performing band ratio algorithm was tuned to the OC-CCI version 5 product and used to produce a global time series of POC between 1997–2020 that is freely available.European Space AgencySimons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES)National Centre for Earth Observations (NCEO)OPERA projec

Comparison of ocean-colour algorithms for particulate organic carbon in global ocean

In the oceanic surface layer, particulate organic carbon (POC) constitutes the biggest pool of particulate material of biological origin, encompassing phytoplankton, zooplankton, bacteria, and organic detritus. POC is of general interest in studies of biologically-mediated fluxes of carbon in the ocean, and over the years, several empirical algorithms have been proposed to retrieve POC concentrations from satellite products. These algorithms can be categorised into those that make use of remote-sensing-reflectance data directly, and those that are dependent on chlorophyll concentration and particle backscattering coefficient derived from reflectance values. In this study, a global database of in situ measurements of POC is assembled, against which these different types of algorithms are tested using daily matchup data extracted from the Ocean Colour Climate Change Initiative (OC-CCI; version 5). Through analyses of residuals, pixel-by-pixel uncertainties, and validation based on optical water types, areas for POC algorithm improvement are identified, particularly in regions underrepresented in the in situ POC data sets, such as coastal and highlatitude waters. We conclude that POC algorithms have reached a state of maturity and further improvements can be sought in blending algorithms for different optical water types when the required in situ data becomes available. The best performing band ratio algorithm was tuned to the OC-CCI version 5 product and used to produce a global time series of POC between 1997–2020 that is freely available

Affine modifications and affine hypersurfaces with a very transitive automorphism group

We study a kind of modification of an affine domain which produces another affine domain. First appeared in passing in the basic paper of O. Zariski (1942), it was further considered by E.D. Davis (1967). The first named author applied its geometric counterpart to construct contractible smooth affine varieties non-isomorphic to Euclidean spaces. Here we provide certain conditions which guarantee preservation of the topology under a modification. As an application, we show that the group of biregular automorphisms of the affine hypersurface $X \subset C^{k+2}$ given by the equation $uv=p(x_1,...,x_k)$ where $p \in C[x_1,...,x_k],$ acts $m-$transitively on the smooth part reg$X$ of $X$ for any $m \in N.$ We present examples of such hypersurfaces diffeomorphic to Euclidean spaces.Comment: 39 Pages, LaTeX; a revised version with minor changes and correction

Reconciling models of primary production and photoacclimation

This is the final version. Available on open access from the Optical Society of America via the DOI in this recordPrimary production and photoacclimation models are two important classes of physiological models that find applications in remote sensing of pools and fluxes of carbon associated with phytoplankton in the ocean. They are also key components of ecosystem models designed to study biogeochemical cycles in the ocean. So far, these two classes of models have evolved in parallel, somewhat independently of each other. Here we examine how they are coupled to each other through the intermediary of the photosynthesis–irradiance parameters. We extend the photoacclimation model to accommodate the spectral effects of light penetration in the ocean and the spectral sensitivity of the initial slope of the photosynthesis–irradiance curve, making the photoacclimation model fully compatible with spectrally resolved models of photosynthesis in the ocean. The photoacclimation model contains a parameter , which is the maximum chlorophyll-to-carbon ratio that phytoplankton can attain when available light tends to zero. We explore how size-class-dependent values of could be inferred from field data on chlorophyll and carbon content in phytoplankton, and show that the results are generally consistent with lower bounds estimated from satellite-based primary production calculations. This was accomplished using empirical models linking phytoplankton carbon and chlorophyll concentration, and the range of values obtained in culture measurements. We study the equivalence between different classes of primary production models at the functional level, and show that the availability of a chlorophyll-to-carbon ratio facilitates the translation between these classes. We discuss the importance of the better assignment of parameters in primary production models as an important avenue to reduce model uncertainties and to improve the usefulness of satellite-based primary production calculations in climate research.Simons FoundationEuropean Space AgencyNational Centre for Earth ObservationNational Science Foundatio

The Distribution of Fecal Contamination in an Urbanized Tropical Lake and Incidence of Acute Diarrheal Disease

Aquatic ecosystems of tropical countries are vulnerable to fecal contamination that could cause spikes in the incidences of acute diarrheal disease (ADD) and challenge public health management systems. Vembanad lake, situated along the southwest coast of India, was monitored for one year (2018−2019). Escherichia coli, an indicator of fecal contamination, was prevalent in the lake throughout the year. Multiple antibiotic resistance among more than 50% of the E. coli isolates adds urgency to the need to control this contamination. The high abundance of E. coli and incidence of ADD were recorded during the early phase of the southwest monsoon (June−July), prior to the once-in-a-century floods that affected the region in the later phase (August). The extent of inundation in the low-lying areas peaked in August, but E. coli in the water peaked in July, suggesting that contamination occurred even prior to extreme flooding. During the COVID-19-related lockdown in March−May 2021, fecal contamination in the lake and incidence of ADD reached minimum values. These results indicate the need for improving sewage treatment facilities and city planning in flood-prone areas to avoid the mixing of septic sewage with natural waters during extreme climate events or even during the normal monsoon