Optical Water Quality Research Articles

In-situ optical water quality monitoring sensors—applications, challenges, and future opportunities

Water quality issues remain a major cause of global water insecurity, and real-time low-cost monitoring solutions are central to the remediation and management of water pollution. Optical sensors, based on fluorescence, absorbance, scattering and reflectance-based principles, provide effective water quality monitoring (WQM) solutions. However, substantial challenges remain to their wider adoption across scales and environments amid cost and calibration-related concerns. This review discusses the current and future challenges in optical water quality monitoring based on multi-peak fluorescence, full-spectrum absorbance, light-scattering and remotely sensed surface reflectance. We highlight that fluorescence-based sensors can detect relatively low concentrations of aromatic compounds (e.g., proteins and humic acids) and quantify and trace organic pollution (e.g., sewage or industrial effluents). Conversely, absorbance-based sensors (Ultraviolet-Visible-Infra-red, UV-VIS-IR) are suitable for monitoring a wider range of physiochemical variables (e.g., nitrate, dissolved organic carbon and turbidity). Despite being accurate under optimal conditions, measuring fluorescence and absorbance can be demanding in dynamic environments due to ambient temperature and turbidity effects. Scattering-based turbidity sensors provide a detailed understanding of sediment transport and, in conjunction, improve the accuracy of fluorescence and absorbance measurements. Recent advances in micro-sensing components such as mini-spectrometers and light emitting diodes (LEDs), and deep computing provide exciting prospects of in-situ full-spectrum analysis of fluorescence (excitation-emission matrices) and absorbance for improved understanding of interferants to reduce the signal-to-noise ratio, improve detection accuracies of existing pollutants, and enable detection of newer contaminants. We examine the applications combining in-situ spectroscopy and remotely sensed reflectance for scaling Optical WQM in large rivers, lakes and marine bodies to scale from point observations to large water bodies and monitor algal blooms, sediment load, water temperature and oil spills. Lastly, we provide an overview of future applications of optical techniques in detecting emerging contaminants in treated and natural waters. We advocate for greater synergy between industry, academia and public policy for effective pollution control and water management.

Dark waters: Evaluating seagrass community response to optical water quality and freshwater discharges in a highly managed subtropical estuary

Seagrass communities are vitally important ecosystems that support and foster organisms across trophic levels. Understanding the drivers of seagrass distribution and extent in estuary systems is valuable for conservation and restoration efforts. A critical driver of seagrass distribution is how light moves through the water column (i.e. light attenuation) and the availability of light to reach the estuary bottom. Light attenuation combined with the depth of seagrass colonization is used to estimate the percent surface irradiance and is an indicator of seagrass light requirements The objective of this study was to evaluate optical water quality parameters within the Caloosahatchee River Estuary (CRE) relative to changes in freshwater discharge conditions. During the study period, freshwater discharge significantly increased resulting in an increase of annual stressful and damaging discharge events to marine seagrass species. Concurrent with changes in freshwater discharge conditions, changes to optical water quality parameters including color, chlorophyll-a, total suspended solids, and light attenuation were detected along the estuary. Using photo-interpreted seagrass coverage data combined with bathymetric data, the depth of colonization was estimated for three survey years (2008, 2014, and 2020). This information combined with a spatial and temporal light attenuation generalized additive model percent surface irradiance (%SI) was estimated. Over the study period, %SI significantly increased across the lower CRE indicating an improvement in light attenuation. The current freshwater management plan has increased stressful and damaging discharge events, however recent changes in operations has minimized these damaging events which could be linked to the precipitous increase in %SI across the lower CRE. Seagrass density data collected from transects within the estuary suggest that prolonged stress and damaging (>5.13 hm3d−1) has significantly reduce species density. Future water management strategies are expected to reduce the frequency and volume of discharges to the CRE, effectively reducing stress and damaging freshwater discharge event.

Spatially Intensive Patterns of Water Clarity in Reservoirs Determined Rapidly With Sensor‐Equipped Boats and Satellites

AbstractOptical water quality affects the quantity and spectral composition of underwater light available to photoautotrophs along with light reflected off the water surface. In reservoirs, prominent gradients for optical water quality and clarity measures occur between inlets and outlets, allowing rapid surveys of ecosystem structure across diverse conditions along with tests of relationships between in situ and remotely sensed water indices. We used a sensor‐equipped boat in a clay‐rich subtropical drinking water reservoir to examine continuous spatial patterns of water clarity and the relationship between in‐lake turbidity and normalized difference turbidity index (ndti) from Sentinel 2 imagery acquired on the same date. Over the 62.3 km boat path, surface turbidity varied between 14 and 85 NTU (mean = 30), with highest values in shallower water near the main inlet. Results indicated a strong linear relationship between sensor turbidity and lab‐determined turbidity. A positive relationship between the satellite turbidity index and in‐lake turbidity, combined with a negative exponential relationship between Secchi depth and turbidity, provided a basis for predicting water clarity metrics continuously over the entire lake surface. In this turbid water body, predicted Secchi depth and turbidity over the whole lake had means of 0.22 m and 54 NTU, with 75% of the lake area <0.29 m and >26 NTU. Pursuit of general relationships involving optical properties of water from high‐speed, data‐intensive spatial surveys and remotely sensed surface reflectance facilitates further development of spatially explicit models of aquatic systems.

Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia

Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making.

Comparison of Two Water Color Algorithms: Implications for the Remote Sensing of Water Bodies with Moderate to High CDOM or Chlorophyll Levels.

The dominant wavelength and hue angle can be used to quantify the color of lake water. Understanding the water color is important because the color relates to the water quality and its related public perceptions. In this paper, we compared the accuracy levels of two methods in calculating dominant wavelength and hue angle values using simulated satellite data calculated from in situ reflectance hyperspectra for 325 lakes and rivers in Minnesota and Wisconsin. The methods developed by van der Woerd and Wernand in 2015 and Wang et al. in 2015 were applied to simulated sensor data from the Sentinel-2, Sentinel-3, and Landsat 8 satellites. Both methods performed comparably when a correction algorithm could be applied, but the correction method did not work well for the Wang method at hue angles < 75°, equivalent to levels of colored dissolved organic matter (CDOM, a440) > ~2 m-1 or chlorophyll > ~10 mg m-3. The Sentinel-3 spectral bands produced the most accurate results for the van der Woerd and Wernand method, while the Landsat 8 sensor produced the most accurate values for the Wang method. The distinct differences in the shapes of the reflectance hyperspectra were related to the dominant optical water quality constituents in the water bodies, and relationships were found between the dominant wavelength and four water quality parameters, namely the Secchi depth, CDOM, chlorophyll, and Forel-Ule color index.

A data-driven approach to flag land-affected signals in satellite derived water quality from small lakes

The land-affected signal in remotely sensed radiance from nearshore waters is a common problem for remote sensing, introducing uncertainty in atmospheric correction and subsequent water quality constituent concentration estimates. This study proposes a new method for identifying effects of land on satellite remote sensing of water quality. The new optical water types (OWT) containing the land-affected signal were derived from POLYMER-corrected imagery of the Medium Resolution Imaging Spectrometer in reduced resolution (MERIS RR) and Sentinel-3 Ocean and Land Colour Instrument (OLCI). These were then applied, as part of a larger set of existing OWTs corresponding to the variability observed in natural waters, to satellite images. The ability to identify pixels containing both water and land, and those contaminated with radiance from adjacent land, was evaluated. Our test sites include dark lakes of varying size in Sweden (Lakes Rusken, Bolmen, Ringsjön, and Ivösjön) where the classification showed high sensitivity to land near the lake shore. The land-affected signal is shown to lead to underestimations of chlorophyll-a concentration and Forel-Ule colour indices, and overestimations of turbidity in these lakes, which can be corrected after masking out the land-affected pixels. The land-affected signal is strongest in summer, both NDVI and sun zenith angle covaried with the seasonal variation of land-affected signal. Further, the results confirmed that satellite images with coarser spatial resolution are more prone to land-affected signal compared to images with finer spatial resolution, for small inland water bodies. We propose a data-driven approach for water quality processing with ‘land-affected water types’ as an effective way to improve the lake optical water quality monitoring from water colour sensors.

Compensation of environmental parameters for optical detection of chemical oxygen demand

Chemical oxygen demand (COD) reflects the degree of water damage by organic pollutants, and is an important indicator for water environment protection and marine hydrological monitoring, so it is very important to accurately measure COD. However, changes in ambient temperature and atmospheric noise cause huge deviations in the precise measurement of COD by optical-based water quality detectors. The purpose of this research is to realize the accurate measurement of COD of the optical water quality detector by compensating the environmental parameters of the water quality detector. The compensation model established in this paper is a particle swarm optimization (PSO) back-propagation neural network (BPNN), which can compensate for temperature and filter out atmospheric noise, named back-propagation neural network of particle swarm optimization of filter out atmospheric noise (FAN-PSO-BPNN). FAN-PSO-BPNN reduced the maximum relative error by 92.51%, root mean square error by 91.64%, coefficient of variation by 91.74%, and the distance between the maximum and minimum prediction errors by 92.94% compared with BPNN in filtering out atmospheric noise interference and temperature compensation. The optimization scheme proposed in this paper for BPNN is crucial and effective, and the FAN-PSO-BPNN compensation model improves the anti-interference ability of the water quality detector, guarantees the accurate measurement of COD in seawater, and helps the water quality detector to monitor in seawater for a long time.

An optical water classification and quality control model (OC_QC model) for spectral diffuse attenuation coefficient

Spectral diffuse attenuation coefficient of down-welling irradiance (Kd(λ)) is a fundamental optical parameter for water color and of significance for heat budget calculation, photosynthesis modeling and carbon sequestration. Uncertainties exist in both in situ measured and satellite derived Kd(λ), therefore, evaluation of its data quality is essential. In addition, optical water classification of Kd(λ) is still limited. To address these issues, we proposed an optical water classification and quality control model (OC_QC model) based on a globally collected high-quality in situ Kd(λ) data. The model consists of the reference Kd(λ) spectra for 7 optical water types (OWT, ranging from clear open ocean water to highly turbid water) and a scoring system. An individual Kd(λ) spectrum can be assigned an OWT and quality control score (QC score) with this model. The performance of the OC_QC model is tested with independent in situ Kd(λ) spectra from the turbid coastal water (Bohai Sea) and clear oceanic water (Indian Ocean). Questionable or erroneous Kd(λ) spectra can be identified, suggesting the model’s robustness. The model was also evaluated with the Medium Resolution Imaging Spectrometer (MERIS) Level-2 and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 Kd(λ) data in the Bohai Sea, Yellow Sea, East China Sea and South China Sea. Satellite derived maps clearly showed the spatial and temporal variations of OWTs and QC scores, and the regions of low-quality Kd(λ) were reasonably extracted. Global maps of Kd(λ)-based optical water types and corresponding QC scores were also obtained with OC_QC model. With the quality controlled Kd(λ) data, the retrieval accuracy of Secchi disk depth was improved. The retrieval accuracies of Chlorophyll-a concentration and total seawater absorption coefficient were also improved with the Kd(λ) optical water classification based algorithms. The OC_QC model would be valuable for evaluating Kd(λ) data (especially the operational global Kd(λ) products), investigation of water optical properties, and development and merging of bio-optical algorithms.

Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3

Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (acdom(440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and acdom(440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and acdom(440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and acdom(440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model.

The estimation of total suspended matter from satellite imagery of tropical coastal water Berau Estuary, Indonesia

Water in coastal and estuary areas needs to be investigated as human activities is allegedly decrease their quality. This has led to optical remote sensing for mapping optical water quality using empirical and semi-analytical approaches. Therefore, this study aims to estimate the Total Suspended Matter (TSM) concentration from Medium Resolution Imaging Spectrometer (MERIS) data using a spectral unmixing approach combined with a radiative transfer model. This approach was implemented in the equatorial tropical coastal water, the Berau estuary, Indonesia, by generating a look-up table (LUT) of top-of-atmosphere radiance spectra through the coupled forward models, and the endmembers were selected from the LUT. The spectral unmixing algorithm was employed to the MERIS data for decomposing the image into haze and sediment components. The data were then transformed into images of a constant haze level corresponding to 50 km visibility, and the atmospheric correction was applied. Furthermore, the TMS concentration was retrieved using the inverse semi-analytical Kubelka-Munk model. The result gave overestimated TSM concentration values on clear waters. However, in turbid waters, a lower RMSE was obtained, and the coefficient of determination was higher than in clear waters.

Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs

Satellite product uncertainty estimates are critical for the further development and evaluation of remote sensing algorithms, as well as for the user community (e.g., modelers, climate scientists, and decision-makers). Optical remote sensing of water quality is affected by significant uncertainties stemming from correction for atmospheric effects as well as a lack of algorithms that can be universally applied to waterbodies spanning several orders of magnitude in non-covarying substance concentrations. We developed a method to produce estimates of Chlorophyll-a (Chla) satellite product uncertainty on a pixel-by-pixel basis within an Optical Water Type (OWT) classification scheme. This scheme helps to dynamically select the most appropriate algorithms for each satellite pixel, whereas the associated uncertainty informs downstream use of the data (e.g., for trend detection or modeling) as well as the future direction of algorithm research. Observations of Chla were related to 13 previously established OWT classes based on their corresponding water-leaving reflectance (Rw), each class corresponding to specific bio-optical characteristics. Uncertainty models corresponding to specific algorithm - OWT combinations for Chla were then expressed as a function of OWT class membership score. Embedding these uncertainty models into a fuzzy OWT classification approach for satellite imagery allows Chla and associated product uncertainty to be estimated without a priori knowledge of the biogeochemical characteristics of a water body. Following blending of Chla algorithm results according to per-pixel fuzzy OWT membership, Chla retrieval shows a generally robust response over a wide range of class memberships, indicating a wide application range (ranging from 0.01 to 362.5 mg/m3). Low OWT membership scores and high product uncertainty identify conditions where optical water types need further exploration, and where biogeochemical satellite retrieval algorithms require further improvement. The procedure is demonstrated here for the Medium Resolution Imaging Spectrometer (MERIS) but could be repeated for other sensors, atmospheric correction methods and optical water quality variables.

Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms

High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical ‘ocean colour’ blue-green band ratios and a near infra-red (NIR) band ratio algorithm, as well as a semi-analytical three-band NIR-red ratio algorithm, were included in the analysis. Six million co-registrations with MSI and OLCI spanning 24 lakes across five continents were analysed. Following atmospheric correction with POLYMER, the reflectance distributions of the red and NIR bands showed close similarity between the two sensors, whereas the distribution for blue and green bands was positively skewed in the MSI results compared to OLCI. Whilst it is not possible from this analysis to determine the accuracy of reflectance retrieved with either MSI or OLCI results, optimizing water quality algorithms for MSI against those previously derived for the Envisat Medium Resolution Imaging Spectrometer (MERIS) and its follow-on OLCI, supports the wider use of MSI for aquatic applications. Chlorophyll-a algorithms were thus tuned for MSI against concurrent OLCI observations, resulting in significant improvements against the original algorithm coefficients. The mean absolute difference (MAD) for the blue-green band ratio algorithm decreased from 1.95 mg m−3 to 1.11 mg m−3, whilst the correlation coefficient increased from 0.61 to 0.80. For the NIR-red band ratio algorithms improvements were modest, with the MAD decreasing from 4.68 to 4.64 mg m−3 for the empirical red band ratio algorithm, and 3.73 to 3.67 for the semi-analytical 3-band algorithm. Three implementations of the turbidity algorithm showed improvement after tuning with the resulting distributions having reduced bias. The MAD reduced from 0.85 to 0.72, 1.22 to 1.10 and 1.93 to 1.55 FNU for the 665, 708 and 778 nm implementations respectively. However, several sources of uncertainty remain: adjacent land showed high divergence between the sensors, suggesting that high product uncertainty near land continues to be an issue for small water bodies, while it cannot be stated at this point whether MSI or OLCI results are differentially affected. The effect of spectrally wider bands of the MSI on algorithm sensitivity to chlorophyll-a and turbidity cannot be fully established without further availability of in situ optical measurements.

Synergy between Satellite Altimetry and Optical Water Quality Data towards Improved Estimation of Lakes Ecological Status

European countries are obligated to monitor and estimate ecological status of lakes under European Union Water Framework Directive (2000/60/EC) for sustainable lakes’ ecosystems in the future. In large and shallow lakes, physical, chemical, and biological water quality parameters are influenced by the high natural variability of water level, exceeding anthropogenic variability, and causing large uncertainty to the assessment of ecological status. Correction of metric values used for the assessment of ecological status for the effect of natural water level fluctuation reduces the signal-to-noise ratio in data and decreases the uncertainty of the status estimate. Here we have explored the potential to create synergy between optical and altimetry data for more accurate estimation of ecological status class of lakes. We have combined data from Sentinel-3 Synthetic Aperture Radar Altimeter and Cryosat-2 SAR Interferometric Radar Altimeter to derive water level estimations in order to apply corrections for chlorophyll a, phytoplankton biomass, and Secchi disc depth estimations from Sentinel-3 Ocean and Land Color Instrument data. Long-term in situ data was used to develop the methodology for the correction of water quality data for the effects of water level applicable on the satellite data. The study shows suitability and potential to combine optical and altimetry data to support in situ measurements and thereby support lake monitoring and management. Combination of two different types of satellite data from the continuous Copernicus program will advance the monitoring of lakes and improves the estimation of ecological status under European Union Water Framework Directive.

Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

Submerged Vegetation Responses to Climate Variation and Altered Hydrology in a Subtropical Estuary: Interpreting 33 Years of Change.

Links between hydrologic modifications, flow and salinity regimes, and submerged aquatic vegetation (SAV) species composition and abundance were assessed with an empirical analysis of 33 years of monitoring data collected at nine sites in Florida's Caloosahatchee River Estuary (CRE). Freshwater inflows to the estuary (30-day means) were often outside the previously recommended envelope of 12.74 to 79.29 m3 s-1. Discharges from Lake Okeechobee through a synthetic hydrologic link were responsible for 43% of the above-envelope flows, but reduced the incidence of below-envelope flows by 30%. A salinity model and salinity stress indices developed for each SAV species indicated that the observed flows generated variable salinity conditions likely to harm both seagrasses and freshwater SAV in the estuary. Regression modeling of SAV abundance generally confirmed the flow and salinity responses expected for each species: Halodule wrightii and Thalassia testudinum in the lower estuary were both harmed by high-flow, low-salinity conditions, while Vallisneria americana in the upper estuary was decimated by low-flow, high-salinity conditions. There was a species-specific effect of the seasonal timing of high flows-T. testudinum was more negatively correlated with high flows in the dry season; H. wrightii in the wet season. The regression analyses also highlighted strong, year-to-year autocorrelations in SAV abundance, indicating reduced resilience after severe losses, particularly for V. americana. Large residual variation in some regression models suggested that factors other than salinity (e.g., optical water quality or grazing impacts) may also influence the system dynamics and should be incorporated in continuing research. This analysis suggests that use of artificial water management infrastructure to reduce extreme high and low flows to the Caloosahatchee and other estuaries could help maintain SAV health in light of intensifying climate variability and degraded watershed flow regulation capacity.

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

Consistency of Radiometric Satellite Data over Lakes and Coastal Waters with Local Field Measurements

The Sentinel-3 mission launched its first satellite Sentinel-3A in 2016 to be followed by Sentinel-3B and Sentinel-3C to provide long-term operational measurements over Earth. Sentinel-3A and 3B are in full operational status, allowing global coverage in less than two days, usable to monitor optical water quality and provide data for environmental studies. However, due to limited ground truth data, the product quality has not yet been analyzed in detail with the fiducial reference measurement (FRM) dataset. Here, we use the fully characterized ground truth FRM dataset for validating Sentinel-3A Ocean and Land Colour Instrument (OLCI) radiometric products over optically complex Estonian inland waters and Baltic Sea coastal areas. As consistency between satellite and local data depends on uncertainty in field measurements, filtering of the in situ data has been made based on the uncertainty for the final comparison. We have compared various atmospheric correction methods and found POLYMER (POLYnomial-based algorithm applied to MERIS) to be most suitable for optically complex waters under study in terms of product accuracy, amount of usable data and also being least influenced by the adjacency effect.

Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data.

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84–0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

Heightened levels and seasonal inversion of riverine suspended sediment in a tropical biodiversity hot spot due to artisanal gold mining

In recent years, rising gold prices have exacerbated the global proliferation of artisanal-scale gold mining (ASGM), with catastrophic consequences for human and ecological health. Much of this burgeoning industry has occurred in biodiversity hot spots, notably in the tropical forests of South America. While the loss of tropical forests and floodplains as a result of ASGM has been well characterized, ASGM impacts on riverine hydrological properties are less understood. Previous fieldwork on ASGM-affected and gully-eroded tropical streams and rivers has demonstrated that increases in suspended-sediment concentration (SSC) can substantially impact fish diversity and aquatic community structure, yet our understanding of the timing and scope of impacts of such increases is limited by the lack of long-term records of SSC. To address this challenge, we present a 34-y analysis of the direct effect of ASGM on 32 river reaches in the Madre de Dios region of Peru, which has been heavily impacted by ASGM since the 1980s. We evaluate spatial and temporal patterns of impacts using estimated SSC derived from Landsat satellite imagery. We find that 16 of 18 stretches of river impacted by ASGM show significant increasing trends in SSC (P < 0.05), while only 5 of 14 unaffected sites do so. Additionally, ASGM appears to reverse natural seasonal cycles of SSC, which may imperil aquatic species. Overall, our findings indicate that ASGM is fundamentally altering optical water quality dynamics of a critical tropical biodiversity hot spot and provide guidance for future regulation of these activities.

Management Options to Improve Water Quality in Lake Peipsi: Insights from Large Scale Models and Remote Sensing

Nutrient pollution causes frequent blooms of potentially harmful cyanobacteria in Lake Peipsi (Estonia/Russia). Although external nutrient loading has reduced since the 1990s, lake water quality has barely improved, and eutrophication is still considered a threat to lake biota and water usage. To understand the recovery dynamics of the lake it is necessary to analyse the effects of land use and lake management on water quality to develop mitigation strategies. Comprehensive analysis has thus far failed due to information gaps inherent to conventional monitoring strategies. We show how two large-scale hydrological models using Earth observation data provide spatial information on pollution and can help explain the causes of past and current lake eutrophication. WaterGAP3.2 provides valid estimates of present and probable future phosphorus concentration in the lake water, based on past hydrological conditions. WaterWorld models spatial potential water quality and a scenario of optimal pollution reduction. Remotely sensed optical water quality data can be used to analyse recent, spatial water quality dynamics. The spatial and temporal algae distributions and can help explain eutrophication causes at Lake Peipsi and its catchment, adding value to in situ monitoring and supporting river basin management with large scale data.