A Printable Device for Measuring Clarity and Colour in Lake and Nearshore Waters

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.

Automated Secchi disk depth measurement based on artificial intelligence object recognition

Water transparency affects the degree of sunlight penetration in water, which is important to many water quality processes. It can be visually measured by lowering a Secchi disk (SD) into water and recording its disappearance depth - the Secchi disk depth (SDD). High frequency SDD measurement is manpower intensive, precluding better understanding of the daily and diurnal variation of water transparency. For the first time, an artificial intelligence based object detection algorithm was employed for the automatic detection of SD from images, mimicking SDD measurement by human eyes. The trained model was validated on a large number of images (about 2000 for a single day in daytime) obtained from a remote-controlled imaging system in a fish farm in a Hong Kong embayment, demonstrating high detection accuracy of 93 %. The work opens up opportunities in the nowcast and forecast of short-term water quality changes (e.g. algal blooms) in coastal waters.

Low-Cost Microplate Reader with 3D Printed Parts for under 500 USD

A 96-well microplate reader for absorption spectroscopy was designed, constructed, and tested at a total cost of ca. 500 USD. The reduced cost of the device represents the major technical contribution of this manuscript, as costs were reduced 7 fold from previous reports. The device was able to achieve 3σ limits of detection of ca. 0.01 absorbance units (AU) over a 60 second measurement for the mid-visible wavelength range. Component parts are either commercially available, or 3D printed from plans. Analysis wavelength can be altered throughout the visible region through use of various photographic or theatrical filters. This feature allows the well plate reader to be used for typical laboratory assays such as cell population estimation by optical density (OD) at 600 nm, or enzyme-linked immunosorbent assays (ELISA) at 450 nm. This manuscript reports on the motivation and process of constructing the device, lists required parts, presents data demonstrating device function, and provides the community of scholars with plans to reproduce the work. The device can be reproduced in laboratories lacking sufficient resources to purchase commercially available options and this outcome contributes towards empowerment of individuals and equity of scientific enquiry.

Review of the Forel-Ule Index based on in situ and remote sensing methods and application in water quality assessment

Water pollution is considered an acute worldwide environmental issue. At present, the commonly adopted method of water quality characterisation involves the retrieval of optically active water quality parameters based on remote sensing reflectance (R_rs), but this method is subject to the limitation that understanding local scatter and absorption characteristics of light is essential to precisely derive these parameters. Water colour primarily depends on water constituents and is traditionally gauged with the Forel-Ule (FU) scale. In recent years, R_rs within the visible region has been considered to determine the Forel-Ule Index (FUI) for water colour measurement. The FUI exhibits the advantages of remote sensing and does not rely on local retrieval algorithms. Therefore, this index can characterise natural waters in a simple and globally effective manner. As there exists a lack of review articles on the FUI, we present a comprehensive review of this index that may help researchers progress. First, we introduce the most recent techniques for FUI measurement, especially remote sensing-deriving methods. Then, we summarise FUI applications in water quality assessment of oceans and inland waters. Finally, FUI development trends, challenges and application perspectives are examined.

Estimation of the Chlorophyll-A Concentration of Algae Species Using Electrical Impedance Spectroscopy

Algae are a significant component of a biological monitoring program in an aquatic ecosystem. They are ideally suited for water quality assessments because of their nutrient requirements, rapid reproduction rate, and very short life cycle. Algae composition and temporal variation in abundances are important in determining the trophic level of lakes, and those can be estimated by the Chlorophyll-a (Chl-a) concentration of the species. In this work, a non-destructive method was employed to estimate the Chlorophyll-a concentration of multiple algae species using electrical impedance spectroscopy (EIS). The proposed EIS method is rapid, cheaper, and suitable for in situ measurements compared with the other available non-destructive methods, such as spectrophotometry and hyperspectral or multispectral imaging. The electrical impedances in different frequencies ranging from 1 to 100 kHz were observed using an impedance converter system. Significant observations were identified within 3.5 kHz for multiple algae species and therefore reported in the results. A positive correlation was found between the Chlorophyll-a and the measured impedance of algae species at different frequencies. Later, EIS models were developed for the species in 1–3.5 kHz. A correlation of 90% was found by employing a least squares method and multiple linear regression. The corresponding coefficients of determination were obtained as 0.9, 0.885, and 0.915, respectively for 49 samples of Spirulina, 41 samples of Chlorella, and 26 samples of mixed algae species. The models were later validated using a new and separate set of samples of algae species.

Citizen Science Tools Reveal Changes in Estuarine Water Quality Following Demolition of Buildings

Turbidity and water colour are two easily measurable properties used to monitor pollution. Here, we highlight the utility of a low-cost device—3D printed, hand-held Mini Secchi disk (3DMSD) with Forel-Ule (FU) colour scale sticker on its outer casing—in combination with a mobile phone application (‘TurbAqua’) that was provided to laymen for assessing the water quality of a shallow lake region after demolition of four high-rise buildings on the shores of the lake. The demolition of the buildings in January 2020 on the banks of a tropical estuary—Vembanad Lake (a Ramsar site) in southern India—for violation of Indian Coastal Regulation Zone norms created public uproar, owing to the consequences of subsequent air and water pollution. Measurements of Secchi depth and water colour using the 3DMSD along with measurements of other important water quality variables such as temperature, salinity, pH, and dissolved oxygen (DO) using portable instruments were taken for a duration of five weeks after the demolition to assess the changes in water quality. Paired t-test analyses of variations in water quality variables between the second week of demolition and consecutive weeks up to the fifth week showed that there were significant increases in pH, dissolved oxygen, and Secchi depth over time, i.e., the impact of demolition waste on the Vembanad Lake water quality was found to be relatively short-lived, with water clarity, colour, and DO returning to levels typical of that period of year within 4–5 weeks. With increasing duration after demolition, there was a general decrease in the FU colour index to 17 at most stations, but it did not drop to 15 or below, i.e., towards green or blue colour indicating clearer waters, during the sampling period. There was no significant change in salinity from the second week to the fifth week after demolition, suggesting little influence of other factors (e.g., precipitation or changes in tidal currents) on the inferred impact of demolition waste. Comparison with pre-demolition conditions in the previous year (2019) showed that the relative changes in DO, Secchi depth, and pH were very high in 2020, clearly depicting the impact of demolition waste on the water quality of the lake. Match-ups of the turbidity of the water column immediately before and after the demolition using Sentinel 2 data were in good agreement with the in situ data collected. Our study highlights the power of citizen science tools in monitoring lakes and managing water resources and articulates how these activities provide support to Sustainable Development Goal (SDG) targets on Health (Goal 3), Water quality (Goal 6), and Life under the water (Goal 14).

Effect of Reduced Anthropogenic Activities on Water Quality in Lake Vembanad, India

The United Nation’s Sustainable Development Goal Life Below Water (SDG-14) aims to “conserve and sustainably use the oceans, seas, and marine resources for sustainable development”. Within SDG-14, targets 14.1 and 14.2 deal with marine pollution and the adverse impacts of human activities on aquatic systems. Here, we present a remote-sensing-based analysis of short-term changes in the Vembanad-Kol wetland system in the southwest of India. The region has experienced high levels of anthropogenic pressures, including from agriculture, industry, and tourism, leading to adverse ecological and socioeconomic impacts with consequences not only for achieving the targets set out in SDG-14, but also those related to water quality (SDG-6) and health (SDG-3). To move towards the sustainable management of coastal and aquatic ecosystems such as Lake Vembanad, it is important to understand how both natural and anthropogenic processes affect water quality. In 2020, a unique opportunity arose to study water quality in Lake Vembanad during a period when anthropogenic pressures were reduced due to a nationwide lockdown in response to the global pandemic caused by SARS-CoV-2 (25 March–31 May 2020). Using Sentinel-2 and Landsat-8 multi-spectral remote sensing and in situ observations to analyse changes in five different water quality indicators, we show that water quality improved in large areas of Lake Vembanad during the lockdown in 2020, especially in the more central and southern regions, as evidenced by a decrease in total suspended matter, turbidity, and the absorption by coloured dissolved organic matter, all leading to clearer waters as indicated by the Forel-Ule classification of water colour. Further analysis of longer term trends (2013–2020) showed that water quality has been improving over time in the more northern regions of Lake Vembanad independent of the lockdown. The improvement in water quality during the lockdown in April–May 2020 illustrates the importance of addressing anthropogenic activities for the sustainable management of coastal ecosystems and water resources.

Citizen science with colour blindness: A case study on the Forel-Ule scale

Many citizen science projects depend on colour vision. Examples include classification of soil or water types and biological monitoring. However, up to 1 in 11 participants are colour blind. We simulate the impact of various forms of colour blindness on measurements with the Forel-Ule scale, which is used to measure water colour by eye with a 21-colour scale. Colour blindness decreases the median discriminability between Forel-Ule colours by up to 33% and makes several colour pairs essentially indistinguishable. This reduces the precision and accuracy of citizen science data and the motivation of participants. These issues can be addressed by including uncertainty estimates in data entry forms and discussing colour blindness in training materials. These conclusions and recommendations apply to colour-based citizen science in general, including other classification and monitoring activities. Being inclusive of the colour blind increases both the social and scientific impact of citizen science.

Comparison of a Smartfin with an Infrared Sea Surface Temperature Radiometer in the Atlantic Ocean

The accuracy and precision of satellite sea surface temperature (SST) products in nearshore coastal waters are not well known, owing to a lack of in-situ data available for validation. It has been suggested that recreational watersports enthusiasts, who immerse themselves in nearshore coastal waters, be used as a platform to improve sampling and fill this gap. One tool that has been used worldwide by surfers is the Smartfin, which contains a temperature sensor integrated into a surfboard fin. If tools such as the Smartfin are to be considered for satellite validation work, they must be carefully evaluated against state-of-the-art techniques to quantify data quality. In this study, we developed a Simple Oceanographic floating Device (SOD), designed to float on the ocean surface, and deployed it during the 28th Atlantic Meridional Transect (AMT28) research cruise (September and October 2018). We attached a Smartfin to the underside of the SOD, which measured temperature at a depth of ∼0.1 m, in a manner consistent with how it collects data on a surfboard. Additional temperature sensors (an iButton and a TidbiT v2), shaded and positioned a depth of ∼1 m, were also attached to the SOD at some of the stations. Four laboratory comparisons of the SOD sensors (Smartfin, iButton and TidbiT v2) with an accurate temperature probe (±0.0043 K over a range of 273.15 to 323.15 K) were also conducted during the AMT28 voyage, over a temperature range of 290–309 K in a recirculating water bath. Mean differences (δ), referenced to the temperature probe, were removed from the iButton (δ=0.292 K) and a TidbiT v2 sensors (δ=0.089 K), but not from the Smartfin, as it was found to be in excellent agreement with the temperature probe (δ=0.005 K). The SOD was deployed for 20 min periods at 62 stations (predawn and noon) spanning 100 degrees latitude and a gradient in SST of 19 K. Simultaneous measurements of skin SST were collected using an Infrared Sea surface temperature Autonomous Radiometer (ISAR), a state-of-the-art instrument used for satellite validation. Additionally, we extracted simultaneous SST measurements, collected at slightly different depths, from an underway conductivity, temperature and depth (CTD) system. Over all 62 stations, the mean difference (δ) and mean absolute difference (ϵ) between Smartfin and the underway CTD were −0.01 and 0.06 K respectively (similar results obtained from comparisons between Smartfin and iButton and Smartfin and TidbiT v2), and the δ and ϵ between Smartfin and ISAR were 0.09 and 0.12 K respectively. In both comparisons, statistics varied between noon and predawn stations, with differences related to environmental variability (wind speed and sea-air temperature differences) and depth of sampling. Our results add confidence to the use of Smartfin as a citizen science tool for evaluating satellite SST data, and data collected using the SOD and ISAR were shown to be useful for quantifying near-surface temperature gradients.

Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.

Special Issue on Remote Sensing of Ocean Color: Theory and Applications

The editorial team are delighted to present this Special Issue of Sensors focused on Remote Sensing of Ocean Color: Theory and Applications. We believe that this is a timely opportunity to showcase current developments across a broad range of topics in ocean color remote sensing (OCRS). Although the field is well-established, in this Special Issue we are able to highlight advances in the applications of the technology, our understanding of the underpinning science, and its relevance in the context of monitoring climate change and engaging public participation.

Environmental Reservoirs of Vibrio cholerae: Challenges and Opportunities for Ocean-Color Remote Sensing

The World Health Organization has estimated the burden of the on-going pandemic of cholera at 1.3 to 4 million cases per year worldwide in 2016, and a doubling of case-fatality-rate to 1.8% in 2016 from 0.8% in 2015. The disease cholera is caused by the bacterium Vibrio cholerae that can be found in environmental reservoirs, living either in free planktonic form or in association with host organisms, non-living particulate matter or in the sediment, and participating in various biogeochemical cycles. An increasing number of epidemiological studies are using land- and water-based remote-sensing observations for monitoring, surveillance, or risk mapping of Vibrio pathogens and cholera outbreaks. Although the Vibrio pathogens cannot be sensed directly by satellite sensors, remotely-sensed data can be used to infer their presence. Here, we review the use of ocean-color remote-sensing data, in conjunction with information on the ecology of the pathogen, to map its distribution and forecast risk of disease occurrence. Finally, we assess how satellite-based information on cholera may help support the Sustainable Development Goals and targets on Health (Goal 3), Water Quality (Goal 6), Climate (Goal 13), and Life Below Water (Goal 14).

Empirical Formulas for Estimating Backscattering and Absorption Coefficients in Complex Waters from Remote-Sensing Reflectance Spectra and Examples of Their Application

Many standard methods used for the remote sensing of ocean colour have been developed, though mainly for clean, open ocean waters. This means that they may not always be effective in complex waters potentially containing high concentrations of optically significant constituents. This paper presents new empirical formulas for estimating selected inherent optical properties of water from remote-sensing reflectance spectra R_rs(λ), derived, among other things, for waters with high concentrations of dissolved and suspended substances. These formulas include one for estimating the backscattering coefficient b_b(620) directly from the magnitude of R_rs in the red part of the spectrum, and another for estimating the absorption coefficient a(440) from the hue angle α. The latter quantity represents the water's colour as it might be perceived by the human eye (trichromatic colour vision); it is easily calculated from the shape of the R_rs spectrum. These new formulas are based on a combined dataset. Most of the data were obtained in the specific, optically complex environment of the Baltic Sea. Additional data, taken from the NASA bio-Optical Marine Algorithm Dataset (NOMAD) and representing various regions of the global oceans, were used to widen the potential applicability of the new formulas. We indicate the reasons why these simple empirical relationships can be derived and compare them with the results of straightforward modelling; possible applications are also described. We present, among other things, an example of a simple semi-analytical algorithm using both new empirical formulas. This algorithm is a modified version of the well-known quasi-analytical algorithm (QAA), and it can improve the results obtained in optically complex waters. This algorithm allows one to estimate the full spectra of the backscattering and absorption coefficients, without the need for any additional a priori assumptions regarding the spectral shape of absorption by dissolved and suspended seawater constituents.

Observing the Ocean Submesoscale with Enhanced-Color GOES-ABI Visible Band Data

Ocean color remote sensing has long been utilized as a fundamental research tool in the oceanographic investigations of coupled biological-physical processes. Despite numerous technical advances in the application of space borne ocean-viewing radiometers, host satellite platforms in a polar-orbiting configuration often render the temporal frequency of sensor data acquisition insufficient for studies of ocean processes that occur within increasingly smaller space-time scales. Whereas geostationary ocean color missions are presently the exception (GOCI) rather than the rule, this paper presents a method to convolve ocean reflectance data obtained from contemporary ocean-viewing multispectral radiometers (VIIRS, OLCI) with spectrally-limited Advanced Baseline Imager (ABI) data obtained from the GOES-R meteorological satellites. The method, Chromatic Domain Mapping (CDM), employs a colorimetry approach to visible range ocean reflectance data. The true color space is used as a frame-of-reference that is mapped by the dedicated yet temporally sparse ocean color sensors; coincident and spectrally coarse information from ABI is then used to estimate the evolution of the true color scene. The procedure results in very high resolution (~5 min) true color image sequences. Herein, example CDM applications of rapid frontal boundary evolution and feature displacement in the Gulf of Mexico are presented and future applications of this technique are discussed.

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and interannual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.