Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake

Assessing water quality restoration measures in Lake Pampulha (Brazil) through remote sensing imagery

Urban reservoirs are frequently exposed to impacts from high population density, polluting activities, and the absence of environmental control measures and monitoring. In this study, we investigated the use of satellite imagery to assess restoration measures and support decision-making in a hypereutrophic urban reservoir. Since 2016, Lake Pampulha (Brazil) has undergone restoration measures, including the application of Phoslock®, to mitigate its poor water quality conditions. Satellite images from Landsat-8 (L8) and Sentinel-2 (S2) and historical monitoring data were used to estimate total suspended matter (TSM), chlorophyll-a (Chla), and Secchi disk depth (SDD). We explored both established models from existing literature and novel alternatives, the latter employing machine learning approaches. Model performance was assessed through the coefficient of determination (R2 ) during calibration and using the root mean square error (RMSE) and its normalization by the observed mean (nRMSE) during validation. Random forest regressor presented superior performance for the three water quality parameters and both satellites. The performance metrics for Landsat-8 were TSM (n=44) R2 of 0.62; Chla (n=48) R2 of 0.74; and SDD (n=23) R2 of 0.67. For Sentinel-2, TSM (n=53) R2 of 0.72; Chla (n=54) R2 of 0.64; SDD (n=33) R2 of 0.89. The selected models were then applied to cloud-free images from 2013 to 2022 to provide modeled TSM, Chla, and SDD values. These were used for spatiotemporal analysis, encompassing cluster analysis, sample comparisons, and trend analysis. Three distinct regions within Lake Pampulha were identified, with the upstream region displaying poorer conditions for TSM, Chla, and SDD. Restoration measures have contributed positively to improving water quality in Lake Pampulha; however, rainy periods pose a challenge due to water quality deterioration associated with urban runoff. Remote sensing has proven to be a robust tool for assisting managers in assessing recovery measures in urban lakes.

Characterization of Phytoplankton Composition in Lake Maggiore: Integrated Chemotaxonomy for Enhanced Cyanobacteria Detection

Cyanobacterial blooms in lakes have increased in frequency and intensity over the past two decades, negatively affecting ecological and biogeochemical processes. This study focuses on the phytoplankton composition of Lake Maggiore, with a special emphasis on cyanobacteria detection through pigment composition. While microscopy is the standard method for phytoplankton identification, pigment-based methods provide broader spatiotemporal coverage. Between May and September 2023, five measurement campaigns were conducted in Lake Maggiore, collecting bio-geochemical and bio-optical data at 27 stations. The total Chlorophyll-a (TChl a) was measured, with concentrations ranging from 1.13 to 6.9 mg/m3. Phytoplankton pigment composition was analyzed using High-Performance Liquid Chromatography (HPLC) and the CHEMTAX approach was applied for phytoplankton classification. The results were cross-validated using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and microscopic counts. Cyanobacteria were identified based on unique pigment markers, such as carotenoids. The HPLC-derived pigment classification results aligned well with both PCA and HCA and microscopic counts verified the accuracy of the pigment-based chemotaxonomy. The study demonstrates that pigment-based classification methods, when combined with statistical analyses, offer a reliable alternative for identifying cyanobacteria and other phytoplankton groups, with potential applications in support of remote sensing algorithm development.

Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 μmol/kg to 0.99 and 4.01 μmol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.

Retrieval of suspended sediment concentration (SSC) in the Arabian Gulf water of arid region by Sentinel-2 data

Suspended sediment concentration (SSC) in water increases temperature and turbidity, limits the photosynthesis of aquatic plants, and reduces biologically available oxygen. It is important to study SSC in the coastal waters of the Arabian Gulf. Thus, this study mapped the SSC of coastal water between Al Arish and Al Ghariyah in northern Qatar using the spectral bands of the MultiSpectral Imager (MSI) of Sentinel-2 by calculating the Normalized Difference Suspended Sediment Index and Normalized Suspended Material Index. The results are studied using the Normalized Difference Turbidity Index and Modified Normalized Difference Water Index. The mapping of SSC in the water using NDSSI showed the presence of a high concentration of suspended sediments between Al Arish and Al Mafjar and a low concentration between Al Mafjar and Al Ghariyah. The mapping of NSMI showed values between 0.012 (clear water) and 0.430 (more suspended material) for the occurrence of suspended materials and supported the results of NDSSI. The study of turbidity using an NDTI image showed turbidity index values ranging from -0.44 (clear water) to 0.12 (high turbidity) and confirmed the occurrence and distribution of suspended sediments and materials in the water. The MNDWI image was able to discriminate clear water with bright pixels from silty sand and mud flats. The relationships between NDSSI, NSMI, and NDTI were correlated with in-situ measurements and studied to find suitable indices to map SSC. Regression analyses showed the strongest relationship between NSMI and NDTI (R2 = 0.95) next to NDSSI and NDTI, where NDTI had the strongest effect on NDSSI (R2 = 0.86). The satellite data results were evaluated by studying the physical parameters and spatial distribution of suspended sediments in the surface and bottom waters. In addition, the grain size distributions, mineral identification, and chemical element concentrations in the bottom sediment samples were studied.

Towards the Combination of C2RCC Processors for Improving Water Quality Retrieval in Inland and Coastal Areas

Sentinel-2 offers great potential for monitoring water quality in inland and coastal waters. However, atmospheric correction in these waters is challenging, and there is no standardized approach yet, but different methods coexist under constant development. The atmospheric correction Case 2 Regional Coast Colour (C2RCC) processor has been recently updated with the C2X-COMPLEX (C2XC). This study is one of the first attempts at exploring its performance, in comparison with C2RCC and C2X, in inland and coastal waters in the east of the Iberian Peninsula, in retrieving water surface reflectance and estimating chlorophyll-a ([Chl-a]), total suspended matter ([TSM]), and Secchi disk depth (ZSD). The relationship between in situ ZSD and Kd_z90max product (i.e., the depth of the water column from which 90% of the water-leaving irradiance is derived) of the C2RCC processors demonstrated the potential of this product for estimating water clarity (r > 0.75). However, [TSM] and [Chl-a] derived from the different processors with default calibration factors were not suitable within the targeted scenarios, requiring recalibration based on optical water types or a shift to dynamic algorithm blending approaches. This would benefit from switching between C2RCC and C2XC, which extends the potential for improving surface reflectance estimates to a wide range of scenarios and suggests a promising future for C2-Nets in operational monitoring of water quality.

Localized Surface Plasmon Resonance Decorated with Carbon Quantum Dots and Triangular Ag Nanoparticles for Chlorophyll Detection

This paper demonstrates carbon quantum dots (CQDs) with triangular silver nanoparticles (AgNPs) as the sensing materials of localized surface plasmon resonance (LSPR) sensors for chlorophyll detection. The CQDs and AgNPs were prepared by a one-step hydrothermal process and a direct chemical reduction process, respectively. FTIR analysis shows that a CQD consists of NH₂, OH, and COOH functional groups. The appearance of C=O and NH₂ at 399.5 eV and 529.6 eV in XPS analysis indicates that functional groups are available for adsorption sites for chlorophyll interaction. A AgNP-CQD composite was coated on the glass slide surface using (3-aminopropyl) triethoxysilane (APTES) as a coupling agent and acted as the active sensing layer for chlorophyll detection. In LSPR sensing, the linear response detection for AgNP-CQD demonstrates R² = 0.9581 and a sensitivity of 0.80 nm ppm^-1, with a detection limit of 4.71 ppm ranging from 0.2 to 10.0 ppm. Meanwhile, a AgNP shows a linear response of R² = 0.1541 and a sensitivity of 0.25 nm ppm^-1, with the detection limit of 52.76 ppm upon exposure to chlorophyll. Based on these results, the AgNP-CQD composite shows a better linearity response and a higher sensitivity than bare AgNPs when exposed to chlorophyll, highlighting the potential of AgNP-CQD as a sensing material in this study.

Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll-a content: A case study in the Tri An Reservoir, Vietnam

Chlorophyll-a (Chl-a) is one of the most important indicators of the trophic status of inland waters, and its continued monitoring is essential. Recently, the operated Sentinel-2 MSI satellite offers high spatial resolution images for remote water quality monitoring. In this study, we tested the performance of the three well-known machine learning (ML) (random forest [RF], support vector machine [SVM], and Gaussian process [GP]) and the two novel ML (extreme gradient boost (XGB) and CatBoost [CB]) models for estimation a wide range of Chl-a concentration (10.1-798.7 μg/L) using the Sentinel-2 MSI data and in situ water quality measurement in the Tri An Reservoir (TAR), Vietnam. GP indicated the most reliable model for predicting Chl-a from water quality parameters (R²  = 0.85, root-mean-square error [RMSE] = 56.65 μg/L, Akaike's information criterion [AIC] = 575.10, and Bayesian information criterion [BIC] = 595.24). Regarding input model as water surface reflectance, CB was the superior model for Chl-a retrieval (R²  = 0.84, RMSE = 46.28 μg/L, AIC = 229.18, and BIC = 238.50). Our results indicated that GP and CB are the two best models for the prediction of Chl-a in TAR. Overall, the Sentinel-2 MSI coupled with ML algorithms is a reliable, inexpensive, and accurate instrument for monitoring Chl-a in inland waters. PRACTITIONER POINTS: Machine learning algorithms were used for both remote sensing data and in situ water quality measurements. The performance of five well-known machine learning models was tested Gaussian process was the most reliable model for predicting Chl-a from water quality parameters CatBoost was the best model for Chl-a retrieval from water surface reflectance.

A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective

Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.

Individual-based modelling of cyanobacteria blooms: Physical and physiological processes

Lakes and reservoirs throughout the world are increasingly adversely affected by cyanobacterial harmful algal blooms (CyanoHABs). The development and spatiotemporal distributions of blooms are governed by complex physical mixing and transport processes that interact with physiological processes affecting the growth and loss of bloom-forming species. Individual-based models (IBMs) can provide a valuable tool for exploring and integrating some of these processes. Here we contend that the advantages of IBMs have not been fully exploited. The main reasons for the lack of progress in mainstreaming IBMs in numerical modelling are their complexity and high computational demand. In this review, we identify gaps and challenges in the use of IBMs for modelling CyanoHABs and provide an overview of the processes that should be considered for simulating the spatial and temporal distributions of cyanobacteria. Notably, important processes affecting cyanobacteria distributions, in particular their vertical passive movement, have not been considered in many existing lake ecosystem models. We identify the following research gaps that should be addressed in future studies that use IBMs: 1) effects of vertical movement and physiological processes relevant to cyanobacteria growth and accumulations, 2) effects and feedbacks of CyanoHABs on their environment; 3) inter and intra-specific competition of cyanobacteria species for nutrients and light; 4) use of high resolved temporal-spatial data for calibration and verification targets for IBMs; and 5) climate change impacts on the frequency, intensity and duration of CyanoHABs. IBMs are well adapted to incorporate these processes and should be considered as the next generation of models for simulating CyanoHABs.

Detecting Climate Driven Changes in Chlorophyll-a Using High Frequency Monitoring: The Impact of the 2019 European Heatwave in Three Contrasting Aquatic Systems

The frequency of heatwave events in Europe is increasing as a result of climate change. This can have implications for the water quality and ecological functioning of aquatic systems. We deployed three spectroradiometer WISPstations at three sites in Europe (Italy, Estonia, and Lithuania/Russia) to measure chlorophyll-a at high frequency. A heatwave in July 2019 occurred with record daily maximum temperatures over 40 °C in parts of Europe. The effects of the resulting storm that ended the heatwave were more discernable than the heatwave itself. Following the storm, chlorophyll-a concentrations increased markedly in two of the lakes and remained high for the duration of the summer while at one site concentrations increased linearly. Heatwaves and subsequent storms appeared to play an important role in structuring the phenology of the primary producers, with wider implications for lake functioning. Chlorophyll-a peaked in early September, after which a wind event dissipated concentrations until calmer conditions returned. Synoptic coordinated high frequency monitoring needs to be advanced in Europe as part of water management policy and to improve knowledge on the implications of climate change. Lakes, as dynamic ecosystems with fast moving species-succession, provide a prism to observe the scale of future change.

Quantification of Phycocyanin in Inland Waters through Remote Measurement of Ratios and Shifts in Reflection Spectral Peaks

This study introduces a semi-empirical algorithm to estimate the extent of the phycocyanin (PC) concentration in eutrophic freshwater bodies; this is achieved by studying the reflectance characteristics of the red and near-red spectral regions, especially the shifting of the peak near 700 nm towards longer wavelengths. Spectral measurements in a darkroom environment over the pure-cultured cyanobacteria Microcystis showed that the shift is proportional to the algal biomass. A similar proportional trend was found from extensive field measurement data. The data also showed that the correlation of the magnitude of the shift with the PC concentration was greater than that with chlorophyll-a. This indicates that the characteristic can be a useful index to quantify cyanobacterial biomass. Based on these observations, a new PC algorithm was proposed that uses the remote sensing reflectance of the peak band around 700 nm and the trough band around 620 nm, and the magnitude of the peak shift near 700 nm. The efficacy of the algorithm was tested with 300 sets of field data, and the results were compared to select algorithms for the PC concentration prediction. The new algorithm performed better than the other algorithms with respect to most error indices, especially the mean relative error, indicating that the algorithm can reduce errors when PC concentrations are low. The algorithm was also applied to a hyperspectral dataset obtained through aerial imaging, in order to predict the spatial distribution of the PC concentration in an approximately 86 km long reach of the Nakdong River.

Environmental factors associated with cyanobacterial assemblages in a mesotrophic subtropical plateau lake: A focus on bloom toxicity

Harmful algal blooms caused by cyanobacteria have been increasing in frequency worldwide. However, the main environmental drivers of this change are often difficult to identify because of the effects of the interaction between eutrophication and climate change. Recently, filamentous N₂-fixing cyanobacteria and non-diazotrophic Microcystis have been observed to be co-existing and undergoing succession in some eutrophic lakes. However, the succession patterns of dominant cyanobacteria and the factors driving this in mesotrophic lakes are not well understood. We hypothesized that the changes in cyanobacterial assemblages in mesotrophic lakes could result in a relatively high risks of toxic blooms, and that these changes are associated with the global climatic changes. We tested these hypotheses using data from the subtropical mesotrophic Lake Erhai. We found that the high spatiotemporal variability in the cyanobacterial community, and the increase in biomass were driven primarily by the growth of bloom-forming cyanobacterial taxa. Species-specific biomasses were related to a different environmental stressor; increases in dissolved organic carbon (DOC) concentrations were statistically associated with an increase of Microcystis biomass, whereas increases in surface water temperature favored higher biomass of Pseudanabaena at low transparency and high concentration of phosphorus. In addition, low nitrogen- to- phosphorus ratios were identified as potential determinants of the abundance of N₂-fixing Dolichospermum. Furthermore, changes in the concentration of DOC, total nitrogen, pH and water transparency levels were found to affect the composition of Microcystis morphotypes and genotypes mostly. This study highlights that the toxic to non-toxic Microcystis ratio might increase with the water darkening and browning (which occurs in many subtropical plateau lakes). Lake management strategies, therefore, need to consider the toxicity of cyanobacterial assemblages in mesotrophic lakes over the intensity of the cyanobacterial blooms.

Water Mixing Conditions Influence Sentinel-2 Monitoring of Chlorophyll Content in Monomictic Lakes

Prompt estimation of phytoplankton biomass is critical in determining the ecological quality of freshwaters. Remote Sensing (RS) may provide new opportunities to integrate with situ traditional monitoring techniques. Nonetheless, wide regional and temporal variability in freshwater optical constituents makes it difficult to design universally applicable RS protocols. Here, we assessed the potential of two neural networks-based models, namely the Case 2 Regional CoastColour (C2RCC) processor and the Mixture Density Network (MDN), applied to MSI Sentinel-2 data for monitoring Chlorophyll (Chl) content in three monomictic volcanic lakes while accounting for the effect of their specific water circulation pattern on the remotely-sensed and in situ data relation. Linear mixed models were used to test the relationship between the remote sensing indices calculated through C2RCC (INN) and MDN (IMDN), and in situ Chl concentration. Both indices proved to explain a large portion of the variability in the field data and exhibited a positive and significant relationship between Chl concentration and satellite data, but only during the mixing phase. The significant effect of the water circulation period can be explained by the low responsiveness of the RS approaches applied here to the low phytoplankton biomass, typical of the stratification phase. Sentinel-2 data proved their valuable potential for the remote sensing of phytoplankton in small inland water bodies, otherwise challenging with previous sensors. However, caution should be taken, since the applicability of such an approach on certain water bodies may depend on hydrological and ecological parameters (e.g., thermal stratification and seasonal nutrient availability) potentially altering RS chlorophyll detection by neural networks-based models, despite their alleged global validity.

Inter-Comparison of Methods for Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis in Italian Lakes

Different methods are available for retrieving chlorophyll-a (Chl-a) in inland waters from optical imagery, but there is still a need for an inter-comparison among the products. Such analysis can provide insights into the method selection, integration of products, and algorithm development. This work aims at inter-comparison and consistency analyses among the Chl-a products derived from publicly available methods consisting of Case-2 Regional/Coast Colour (C2RCC), Water Color Simulator (WASI), and OC3 (3-band Ocean Color algorithm). C2RCC and WASI are physics-based processors enabling the retrieval of not only Chl-a but also total suspended matter (TSM) and colored dissolved organic matter (CDOM), whereas OC3 is a broadly used semi-empirical approach for Chl-a estimation. To pursue the inter-comparison analysis, we demonstrate the application of Sentinel-2 imagery in the context of multitemporal retrieval of constituents in some Italian lakes. The analysis is performed for different bio-optical conditions including subalpine lakes in Northern Italy (Garda, Idro, and Ledro) and a turbid lake in Central Italy (Lake Trasimeno). The Chl-a retrievals are assessed versus in situ matchups that indicate the better performance of WASI. Moreover, relative consistency analyses are performed among the products (Chl-a, TSM, and CDOM) derived from different methods. In the subalpine lakes, the results indicate a high consistency between C2RCC and WASI when aCDOM(440) < 0.5 m−1, whereas the retrieval of constituents, particularly Chl-a, is problematic based on C2RCC for high-CDOM cases. In the turbid Lake Trasimeno, the extreme neural network of C2RCC provided more consistent products with WASI than the normal network. OC3 overestimates the Chl-a concentration. The flexibility of WASI in the parametrization of inversion allows for the adaptation of the method for different optical conditions. The implementation of WASI requires more experience, and processing is time demanding for large lakes. This study elaborates on the pros and cons of each method, providing guidelines and criteria on their use.

Sensor-based detection of algal blooms for public health advisories and long-term monitoring

Throughout the United States, many eutrophic freshwater bodies experience seasonal blooms of toxic cyanobacteria. These blooms limit recreational uses and pose a threat to both human and ecological health. Traditional bi-weekly chlorophyll-based sampling programs designed to assess overall algal biomass fail to capture important bloom parameters such as bloom timing, duration, and peak intensity. In-situ optical and fluorometric measurements have the potential to fill this gap. However, relating in-situ measurements to relevant water quality measures (e.g. cyanobacterial cell density or chlorophyll concentration) is a challenge that limits the implementation of probe-based monitoring strategies. This study, of Aphanizomenon dominated blooms in Boston's Charles River, combines five years of cyanobacterial cell counts with high resolution insitu sensor measurements to relate turbidity and fluorometric readings to cyanobacterial cell density. Our work compares probe and lab-based estimates of summer-mean chlorophyll concentration and highlights the challenges of working with raw fluorescence in cyanobacteria dominated waterbodies. A strong correlation between turbidity and cyanobacterial cell density (R 2 = 0.84) is used to construct a simple cell-density-estimation-model suitable for triggering rapid bloom-responsesampling and classifying bloom events with a true positive rate of 95%. The approach described in this study is potentially applicable to many cyanobacteria dominated freshwater bodies.

Hotspot analysis of spatial distribution of algae blooms in small and medium water bodies

Intensive land use favors eutrophication processes and algae bloom proliferation in freshwaters, which is considered to be one of the main environmental issues worldwide. In general, and particularly in South America, inland water monitoring only covers the main water bodies due to the high costs and efforts involved. In order to improve the coverage of spatial and temporal of algae bloom monitoring, remote sensing serves as an alternative tool. Thereby, the analysis of significant spatial clusters of high values (hotspots) and low values (coldspots) of chlorophyll-a has been applied in coastal studies; however, at present, there are no studies in freshwaters. In this study, Getis-Ord Gi* hotspot analysis was applied to detect spatial distribution patterns of algae bloom dynamics in small- and medium-sized freshwater bodies. Four in situ samplings were carried out in five suburban lakes of Uruguay, in agreement with the satellite capture. Total and cyanobacterial chlorophyll-a concentration, and suspended solids were evaluated. Linear models were developed by combining pre-established indexes with additional Sentinel-2 spectral bands and in situ data. The relationship between red and red edge regions allowed mapping the chlorophyll-a in the study lakes with an adjustment of R² = 0.83. Hotspot analysis was performed with the selected linear model, and significant chlorophyll-a variability within each lake was successfully detected. The novel application of hotspots analyses presented in this work represents a contribution to advance knowledge in the remote detection of algae bloom dynamics and improve monitoring capabilities of inland water bodies.

Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

The recent PlanetScope constellation (130+ satellites currently in orbit) has shifted the high spatial resolution imaging into a new era by capturing the Earth’s landmass including inland waters on a daily basis. However, studies on the aquatic-oriented applications of PlanetScope imagery are very sparse, and extensive research is still required to unlock the potentials of this new source of data. As a first fully physics-based investigation, we aim to assess the feasibility of retrieving bathymetric and water quality information from the PlanetScope imagery. The analyses are performed based on Water Color Simulator (WASI) processor in the context of a multitemporal analysis. The WASI-based radiative transfer inversion is adapted to process the PlanetScope imagery dealing with the low spectral resolution and atmospheric artifacts. The bathymetry and total suspended matter (TSM) are mapped in the relatively complex environment of Venice lagoon during two benchmark events: The coronavirus disease 2019 (COVID-19) lockdown and an extreme flood occurred in November 2019. The retrievals of TSM imply a remarkable reduction of the turbidity during the lockdown, due to the COVID-19 pandemic and capture the high values of TSM during the flood condition. The results suggest that sizable atmospheric and sun-glint artifacts should be mitigated through the physics-based inversion using the surface reflectance products of PlanetScope imagery. The physics-based inversion demonstrated high potentials in retrieving both bathymetry and TSM using the PlanetScope imagery.

Eutrophication Monitoring for Lake Pamvotis, Greece, Using Sentinel-2 Data

The use of remote sensing to monitor inland waters and their current state is of high importance, as fresh waters are the habitat of many species of flora and fauna, and are also important for anthropogenic activities. Water quality can be monitored by many parameters, including dissolved suspended matter, phytoplankton, turbidity, and dissolved organic matter, while the concentration of chlorophyll-a (chl-a) is a representative indicator for detecting phytoplankton and monitoring water quality. The detection of phytoplankton in water layers, through chl-a indicators, is an effective method for displaying eutrophication. Numerous scientific publications and studies have shown that remote sensing data and techniques are capable of monitoring the temporal and spatial distribution and variation of this phenomenon. This study aimed to investigate the eutrophication in Pamvotis Lake, in Ioannina, Greece with the application of chl-a detection algorithms, by using Sentinel-2 satellite imagery data for the time period of 2016–2018. The maximum chlorophyll index (MCI) and maximum peak-height (MPH) algorithms have been applied to top of atmosphere (TOA) reflectance data, to detect chl-a and monitor the trophic range of the water body. Both algorithms were correlated and resulted in Pearson’s r values up to 0.95. Finally, the chl-a concentration was estimated by applying an empirical equation that correlates the MPH and chl-a concentration developed within previous studies. Those results were further analyzed and interpreted with spatial statistical methods, to understand the spatial distribution pattern of the eutrophication in our study area. Our results demonstrated that Pamvotis Lake is a eutrophic lake, and the highest chl-a concentration was located in the east and south-east of the lake during the study period. Sentinel-2 data can be a useful tool for lake managers, in order to estimate the spatial distribution of the chl-a concentration and identify areas prone to eutrophication, as well as the coastal zones that may influence the lake through water canals.

The Use of Multisource Optical Sensors to Study Phytoplankton Spatio-Temporal Variation in a Shallow Turbid Lake

Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing knowledge of phytoplankton dynamics at different temporal and spatial scales. High frequency chlorophyll-a data from the WISPstation was modeled using non-parametric multiplicative regression. The ‘day of year’ was the most important factor, reflecting the seasonal progression of a phytoplankton bloom from July to September. In addition, weather factors such as the east–west wind component were also significant in predicting phytoplankton seasonal and diurnal patterns. Sentinel 3-OLCI and Sentinel 2-MSI satellites delivered 42 images in 2018 that successfully mapped the spatial and seasonal change in chlorophyll-a. The potential influence of localized inflows in contributing to increased chlorophyll-a in mid-summer was visualized. The satellite data also allowed an estimation of quality status at a much finer scale than traditional manual methods. Good correspondence was found with manually collected field data but more significantly, the greatly increased spatial and temporal resolution provided by satellite and WISPstation sensors clearly offers an unprecedented resource in the research and management of aquatic resources.

Monitoring Coastal Lagoon Water Quality Through Remote Sensing: The Mar Menor as a Case Study

The Mar Menor is a hypersaline coastal lagoon located in the southeast of Spain. This fragile ecosystem is suffering several human pressures, such as nutrient and sediment inputs from agriculture and other activities and decreases in salinity. Therefore, the development of an operational system to monitor its evolution is crucial to know the cause-effect relationships and preserve the natural system. The evolution and variability of the turbidity and chlorophyll-a levels in the Mar Menor water body were studied here through the joint use of remote sensing techniques and in situ data. The research was undertaken using Operational Land Imager (OLI) images on Landsat 8 and two SPOT images, because cloudy weather prevented the use of OLI images alone. This provided the information needed to perform a time series analysis of the lagoon. We also analyzed the processes that occur in the salt lagoon, characterizing the different spatio-temporal patterns of biophysical parameters. Special attention was given to the role of turbidity and chlorophyll-a levels in the Mar Menor ecosystem with regard to the programs of integral management of this natural space that receives maximum environmental protection. The objective of the work has been fulfilled by answering the questions of the managers: when did the water quality in the Mar Menor begin to change? What is happening in the lagoon? Is remote sensing useful for monitoring the water quality in the Mar Menor? The answers to these questions have allowed the generation of a methodology and monitoring system to track the water quality in the Mar Menor in real-time and space. The tracking system using satellite images is open to the incorporation of images provided by new multispectral sensors.

Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and east China sea

Marine phytoplankton accounts for roughly half the planetary primary production, and plays significant roles in marine ecosystem functioning, physical and biogeochemical processes, and climate changes. Documenting phytoplankton assemblages' dynamics, particularly their community structure properties, is thus a crucial and also challenging task. A large number of in situ and space-borne observation datasets are collected that cover the marginal seas in the west Pacific, including Bohai Sea, Yellow Sea, and East China Sea. Here, a customized region-specific semi-analytical model is developed in order to detect phytoplankton community structure properties (using phytoplankton size classes, PSCs, as its first-order delegate), and repeatedly tested to assure its reliable performance. Independent in situ validation datasets generate relatively low and acceptable predictive errors (e.g., mean absolute percentage errors, MAPE, are 38.4%, 22.7%, and 34.4% for micro-, nano-, and picophytoplankton estimations, respectively). Satellite synchronization verification also produces comparative predictive errors. By applying this model to long time-series of satellite data, we document the past two-decadal (namely from 1997 to 2017) variation on the PSCs. Satellite-derived records reveal a general spatial distribution rule, namely microphytoplankton accounts for most variation in nearshore regions, when nanophytoplankton dominates offshore water areas, together with a certain high contribution from picophytoplankton. Long time-series of data records indicate a roughly stable tendency during the period of the past twenty years, while there exist periodical changes in a short-term one-year scale. High covariation between marine environment factors and PSCs are further found, with results that underwater light field and sea surface temperature are the two dominant climate variables which exhibit a good ability to multivariate statistically model the PSCs changes in these marginal seas. Specifically, three types of influence induced by underwater light field and sea surface temperature can be generalized to cover different water conditions and regions, and meanwhile a swift response time (approximately < 1 month) of phytoplankton to the changing external environment conditions is found by the wavelet analysis. This study concludes that phytoplankton community structures in the marginal seas remain stable and are year-independent over the past two decades, together with a short-term in-year cycle; this change rule need to be considered in future oceanographic studies.

Quantifying the trophic status of lakes using total light absorption of optically active components

Eutrophication of lakes has become one of the world's most serious environmental problems, resulting in an urgent need to monitor and provide safeguards to control water quality. Results from analysis of lake trophic status based on calculated throphic state index (TSI) showed that 69.5% of the surveyed 277 lakes were in a state of eutrophication. Significant logarithmic relationships between light absorption of optically active components (a_OACs) and TSI (R² = 0.78) existed: TSI = 13.64 × ln(a_OACs)+43.24, and the regression relationship between a_OACs and TSI had a better degree of fit (R²) than the currently used reflectance-TSI relationship. a_OACs appeared to be a good predictor of TSI estimation in lake ecosystems. The relationship coefficient (a_OACs-TSI) slightly varied with lake type, and relationships in saline lakes and phy-type lakes were shown to be more robust than the relationship with the total lake data. This study highlights the quantification of the trophic status in lakes using a_OACs, which realized the monitoring of trophic status in lakes using inherent optical properties on a large-scale. To our knowledge this is the first investigation to assess the variability of trophic status in lakes across China. The assessment trophic state of lakes based on a_OACs provides a new way to monitor the trophic status of lakes, and findings may have applications for monitoring large-scale and long-term trophic patterns in lakes using remote sensing techniques.

On the minimization of adjacency effects in SeaWiFS primary data products from coastal areas

The minimization of adjacency effects (AE) in SeaWiFS primary products at the Aqua Alta Oceanographic Tower (AAOT) was investigated using sample images concurrent with in situ measurements. The validation exercise was performed with the NASA SeaDAS processing scheme ingesting original SeaWiFS data and alternatively SeaWiFS top-of-atmosphere data corrected for AE, and additionally including and excluding the default turbid water (TW) correction algorithm. Results show overestimates of the TW contributions partially compensating for AE. The analysis also suggests that intra-annual biases observed in SeaWiFS radiometric products at the AAOT may result from a misinterpretation of the NIR atmospheric signal as water contribution in data acquired in winter, and from uncompensated AE in data acquired in summer.

Quantification of dissolved organic carbon (DOC) storage in lakes and reservoirs of mainland China

As a major fraction of carbon in inland waters, dissolved organic carbon (DOC) plays a crucial role in carbon cycling on a global scale. However, the quantity of DOC stored in lakes and reservoirs was not clear to date. In an attempt to examine the factors that determine the DOC storage in lakes and reservoirs across China, we assembled a large database (measured 367 lakes, and meta-analyzed 102 lakes from five limnetic regions; measured 144 reservoirs, and meta-analyzed 272 reservoirs from 31 provincial units) of DOC concentrations and water storages for lakes and reservoirs that are used to determine DOC storage in static inland waters. We found that DOC concentrations in saline waters (Mean/median ± S.D: 50.5/30.0 ± 55.97 mg/L) are much higher than those in fresh waters (8.1/5.9 ± 6.8 mg/L), while lake DOC concentrations (25.9/11.5 ± 42.04 mg/L) are much higher than those in reservoirs (5.0/3.8 ± 4.5 mg/L). In terms of lake water volume and DOC storage, the Tibet-Qinghai lake region has the largest water volume (552.8 km³), 92% of which is saline waters, thus the largest DOC (13.39 Tg) is stored in these alpine lake region; followed by the Mengxin lake region, having a water volume of 99.4 km³ in which 1.75 Tg DOC was stored. Compared to Mengxin lake region, almost the same amount of water was stored in East China lake region (91.9 km³), however, much less DOC was stored in this region (0.43 Tg) due to the lower DOC concentration (Ave: 3.45 ± 2.68 mg/L). According to our investigation, Yungui and Northeast lake regions had water storages of 32.14 km³ and 19.44 km³ respectively, but relatively less DOC was stored in Yungui (0.13 Tg) than in Northeast lake region (0.19 Tg). Due to low DOC concentration in reservoirs, especially these large reservoirs having lower DOC concentration (V > 1.0 km³: 2.31 ± 1.48 mg/L), only 1.54 Tg was stored in a 485.1 km³ volume of water contained in reservoirs across the entire country.