Satellite Estimation of Chlorophyll-\u3ci\u3ea\u3c/i\u3e Concentration Using the Red and NIR Bands of MERIS—The Azov Sea Case Study

We present here the results of calibrating and validating a three-band model and, its special case, a two-band model, which use MEdium Resolution Imaging Spectrometer (MERIS) reflectances in the red and near-infrared spectral regions for estimating chlorophyll-a (chl-a) concentration in inland, estuarine, and coastal turbid productive waters. During four data collection campaigns in 2008 and one campaign in 2009 in the Taganrog Bay and the Azov Sea, Russia, water samples were collected, and concentrations of chl-a and total suspended solids were measured in the laboratory. The data collected in 2008 were used for model calibration, and the data collected in 2009 were used for model validation. The models were applied to MERIS images acquired within two days from the date of in situ data collection. Two different atmospheric correction procedures were considered for processing the MERIS images. The results illustrate the high potential of the models to estimate chl-a concentration in turbid productive (Case II) waters in real time from satellite data, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland and coastal aquatic ecosystems

NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study

A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water. But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or time of the year. In this study we tested the performance of NIR-red models in estimating Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical for coastal and mesotrophic inland waters. The models with the same spectral bands as MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chla concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations ranged from 4 to 21 mgm-3 during four field campaigns. A two-band model without reparameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5 mgm-3. Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes

Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands

Remote sensing algorithms that use red and NIR bands for the estimation of chlorophyll-a concentration [Chl] can be more effective in inland and coastal waters than algorithms that use blue and green bands. We tested such two-band and three-band red-NIR algorithms using comprehensive synthetic data sets of reflectance spectra and inherent optical properties related to various water parameters and a very consistent in situ data set from several lakes in Nebraska, USA. The two-band algorithms tested with MERIS bands were Rrs(708)/Rrs(665) and Rrs(753)/Rrs(665). The three-band algorithm with MERIS bands was in the form R3 = [Rrs−1(665) − Rrs−1(708)] × Rrs(753). It is shown that the relationships of both Rrs(708)/Rrs(665) and R3 with [Chl] do not depend much on the absorption by CDOM and non-algal particles, or the backscattering properties of water constituents, and can be defined in terms of water absorption coefficients at the respective bands as well as the phytoplankton specific absorption coefficient at 665 nm. The relationship of the latter with [Chl] was established for [Chl] \u3e 1 mg/m3 and then further used to develop algorithms which showed a very good match with field data and should not require regional tuning

Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N \u3e 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (Rrs), (b) retrieval of particulate backscattering (bbp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from bbp via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m3], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to \u3e100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to \u3c20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives

Integrated dataset of screening hits against multiple neglected disease pathogens.

New chemical entities are desperately needed that overcome the limitations of existing drugs for neglected diseases. Screening a diverse library of 10,000 drug-like compounds against 7 neglected disease pathogens resulted in an integrated dataset of 744 hits. We discuss the prioritization of these hits for each pathogen and the strong correlation observed between compounds active against more than two pathogens and mammalian cell toxicity. Our work suggests that the efficiency of early drug discovery for neglected diseases can be enhanced through a collaborative, multi-pathogen approach

SATELLITE-BASED ESTIMATION OF CHLOROPHYLL-a CONCENTRATION IN TURBID PRODUCTIVE WATERS

Inland, coastal, and estuarine waters, which are often turbid and biologically productive, play a crucial role in maintaining global bio-diversity and are of immense value to aquatic life as well as human-beings. Concentration of chlorophyll-a (chl-a) is a key indicator of the trophic status of these waters, which should be regularly monitored to ensure that their ecological balance is not disturbed. Remote sensing is a powerful tool for this. Due to the optical complexity of turbid productive waters, standard algorithms that use blue and green reflectances are unreliable for estimating chl-a concentration. Algorithms based on red and near-infrared (NIR) reflectances are preferable. Three-band and two-band NIR-red models based on the spectral channels of MODIS and MERIS satellites have been tested for numerous datasets collected with field spectrometers from inland, coastal, and estuarine waters. The NIR-red models, especially the two-band model with MERIS wavebands, gave consistently highly accurate estimates of chl-a concentration in waters from different geographic locations with widely varying biophysical characteristics, without the need to re-parameterize the algorithms for each different water body. The MODIS NIR-red model can be used to estimate moderate-to-high chl-a concentrations. The NIR-red models were applied to airborne AISA data acquired over several lakes in Nebraska on different days with non-uniform atmospheric conditions. Without atmospheric correction, the NIR-red models showed a close correlation with chl-a concentration for each image. With an effective relative correction for the non-uniform atmospheric effects on the multi-temporal images, the NIR-red models were shown to have a close correlation with chl-a concentration, with uniform slope and offset, for the whole dataset. The models were also applied to MODIS and MERIS images. Reliable results were obtained from the MERIS NIR-red models. Calibrated MERIS NIR-red algorithms were validated using data from the Taganrog Bay and Azov Sea (Russia) and lakes in Nebraska. The calibrated NIR-red algorithms have the potential for universal application to estimate chl-a concentration from satellite data routinely acquired over turbid and productive waters from around the globe

Research & Implementation of a Node.js Email Framework for RecRight's Video Interview Tool

RecRight is a product of MobileCV Oy and is a human resources platform that utilizes video interview services for companies’ recruitment processes. Companies use this service to post job openings, and then invite potential candidates to video interviews. Currently, RecRight has an email system that uses Amazon Simple Email Service (Amazon SES) as a cloud-based service to send notification and transactional emails. This system is also used by companies using RecRight's service to notify and contact candidates during the interview process. This thesis describes and outlines the research and implementation of a new email and email templating framework to be used by RecRight and its customers. The new framework will include functionality for composing and customizing notification and transactional emails and custom email templates in RecRight's service. RecRight's service is created with primarily JavaScript (ECMAScript 6), using React & Redux on the front-end, and Node.js with MongoDB on the back-end. Amazon SES is the cloud service used for sending and delivering emails. The new framework outlined in this thesis was designed to be interoperable with all of the aforementioned technologies. For communication and project management, the development team at RecRight uses Git and GitHub for version control and development. Slack is used for communication across all departments. The need for this research and implementation arose from the customers’ need to send transactional and notification emails using their company’s custom styles, colours, and tone of voice. Customers of RecRight receive the practical benefits of this thesis, as they are be able to email candidates who apply for their job openings using their own company branding and styles. RecRight also receives benefits, as this implementation further develops and improves upon their platform