Inland Water Monitoring Research Articles

Modeling- and Simulation-Driven Methodology for the Deployment of an Inland Water Monitoring System

In response to the challenges introduced by global warming and increased eutrophication, this paper presents an innovative modeling and simulation (M&S)-driven model for developing an automated inland water monitoring system. This system is grounded in a layered Internet of Things (IoT) architecture and seamlessly integrates cloud, fog, and edge computing to enable sophisticated, real-time environmental surveillance and prediction of harmful algal and cyanobacterial blooms (HACBs). Utilizing autonomous boats as mobile data collection units within the edge layer, the system efficiently tracks algae and cyanobacteria proliferation and relays critical data upward through the architecture. These data feed into advanced inference models within the cloud layer, which inform predictive algorithms in the fog layer, orchestrating subsequent data-gathering missions. This paper also details a complete development environment that facilitates the system lifecycle from concept to deployment. The modular design is powered by Discrete Event System Specification (DEVS) and offers unparalleled adaptability, allowing developers to simulate, validate, and deploy modules incrementally and cutting across traditional developmental phases.

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

Beyond Exact Repeat Missions: Embracing Geodetic Altimetry for Inland Water Monitoring and Modeling

Satellite radar altimetry has become an essential tool for hydrological research over the past 2 decades. Unlike imagery that quantifies the water dynamics from the horizontal dimension, altimetry observations deliver vertical information, which is crucial for hydrological research. Geodetic mission (GM) altimetry data, characterized by long repeat periods compared to exact repeat missions, have been underutilized in hydrology. This paper aims to advocate for integrating geodetic altimetry data into inland water research and bring awareness to its potential applications. We demonstrated the values of GM data from CryoSat-2, Saral/AltiKa, Jason-1, and Jason-2 through 3 applications: (a) large-scale lake monitoring for improved understanding of regional and global hydrological cycles, (b) retrieval of local geoid undulations over large lakes to enhance the precision of individual water level observations, and (c) mapping river longitudinal profiles and construction of time series of river levels. We believe that the findings of this study will pave the way for more comprehensive utilization of geodetic altimetry data in diverse hydrological applications, ultimately advancing inland water altimetry.

Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

ABSTRACTInland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has been applied to effectively detect the algal spatiotemporal behaviors in expensive water bodies. However, image sensor resolution limitation can render the understanding of spatiotemporal features of relatively small water bodies challenging. In addition, few studies have improved the resolution of remote sensing images to investigate inland water quality, owing to the image sensor resolution limitations. Therefore, this study applied deep learning-based Super-resolution for transforming satellite imagery of 20 m to airborne imagery of 5 m. After performing atmospheric correction for the acquired images, we adopted super-resolution (SR) methodologies using a super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial networks (SRGAN) to estimate the Chlorophyll-a (Chl-a) concentration in the Geum River of South Korea. Both methods generated SR images with water reflectance at 665, 705, and 740 nm. Then, two band-ratio algorithms at 665 and 740 nm wavelengths were applied to the reflectance images to estimate the Chl-a concentration maps. The SRCNN model outperformed SRGAN and bicubic interpolation with peak signal-to-noise ratios (PSNR), mean square errors (MSE), and structural similarity index measures (SSIM) for the validation dataset of 24.47 (dB), 0.0074, and 0.74, respectively. SR maps from the SRCNN provided more detailed spatial information on Chl-a in the Geum River compared to the information obtained from satellite images. Therefore, these findings showed the potential of deep learning-based SR algorithms by providing further information according to the algal dynamics for inland water management with remote sensing images.

Monitoring Inland Water Quantity Variations: A Comprehensive Analysis of Multi-Source Satellite Observation Technology Applications

The advancement of multi-source Earth observation technology has led to a substantial body of literature on inland water monitoring. This has resulted in the emergence of a distinct interdisciplinary field encompassing the application of multi-source Earth observation techniques in inland water monitoring. Despite this growth, few systematic reviews of this field exist. Therefore, in this paper, we offer a comprehensive analysis based on 30,212 publications spanning the years 1990 to 2022, providing valuable insights. We collected and analyzed fundamental information such as publication year, country, affiliation, journal, and author details. Through co-occurrence analysis, we identified country and author partnerships, while co-citation analysis revealed the influence of journals, authors, and documents. We employed keywords to explore the evolution of hydrological phenomena and study areas, using burst analysis to predict trends and frontiers. We discovered exponential growth in this field with a closer integration of hydrological phenomena and Earth observation techniques. The research focus has shifted from large glaciers to encompass large river basins and the Tibetan Plateau. Long-term research attention has been dedicated to optical properties, sea level, and satellite gravity. The adoption of automatic image recognition and processing, enabled by deep learning and artificial intelligence, has opened new interdisciplinary avenues. The results of the study emphasize the significance of long-term, stable, and accurate global observation and monitoring of inland water, particularly in the context of cloud computing and big data.

Global path planning and waypoint following for heterogeneous unmanned surface vehicles assisting inland water monitoring

The idea of dispatching multiple unmanned surface vehicles (USVs) to undertake marine missions has ignited a burgeoning enthusiasm on a global scale. Embarking on a quest to facilitate inland water monitoring, this paper presents a systematical approach concerning global path planning and path following for heterogeneous USVs. Specifically, by capturing the heterogeneous nature, an extended multiple travelling salesman problem (EMTSP) model, which seamlessly bridges the gap between various disparate constraints and optimization objectives, is formulated for the first time. Then, a novel Greedy Partheno Genetic Algorithm (GPGA) is devised to consistently address the problem from two aspects: (1) Incorporating the greedy randomized initialization and local exploration strategy, GPGA merits strong global and local searching ability, providing high-quality solutions for EMTSP. (2) A novel mutation strategy which not only inherits all advantages of PGA but also maintains the best individual in the offspring is devised, contributing to the local escaping efficiently. Finally, to track the waypoint permutations generated by GPGA, control input is generated by the nonlinear model predictive controller (NMPC), ensuring the USV corresponds with the reference path and smoothen the motion under constrained dynamics. Simulations and comparisons in various scenarios demonstrated the effectiveness and superiority of the proposed scheme.

Improvements in mountain lake monitoring from satellite altimetry over the past 30 years – lessons learned from Tibetan lakes

Satellite altimetry has been widely used for inland water monitoring and modeling in the present decade. In this paper, the availability, data quality, and quantity of a number of spaceborne radar altimetry missions (including Topex/Poseidon (TP), ERS-2, Envisat, Jason-1/−2/−3, CryoSat-2, Saral, and Sentinel-3) are investigated. This is, to the best of our knowledge, the first paper to investigate the influences of onboard trackers, satellite flying directions, and land contamination on Level-1 waveforms and Level-2 measurements over large mountainous lakes. The analyses were conducted over eight Tibetan lakes, which are of varying sizes from ca. 93 to 1046 km2 and are sampled by multiple altimeters.From the results presented in this paper, it is demonstrated that the previous generation of altimeters, such as TP, Jason-1, ERS-2, and Envisat, shows a higher percentage of data unavailability and lower valid observation rates. Investigations of Level-1 waveforms show onboard tracker failing in locking on the lake surface, resulting in data loss or invalid measurements. In addition, the onboard tracking systems are sometimes unstable, resulting in larger along-track standard deviations (SD) (SD > 15 cm). In contrast, the recent generation of altimeters, i.e., Jason-3 and Sentinel-3 with open-loop tracking mode, significantly increases the data availability (> 90% passes and > 60% individual observations) and delivers higher precision measurements (SD around 10 cm). Moreover, the waveform leading edge is very consistent, indicating more stable onboard tracking. Higher quality Jason-3 and Sentinel-3 data allow monitoring of sub- and quasi-monthly lake variations, which are not revealed by e.g., TP and ERS-2 data due to poorer signal-to-noise ratios. Exceptionally, Saral Ka-band altimeter, operated in closed-loop tracking mode, is less affected by flying directions and land contamination (topography and landscapes), delivering high quality measurements (SD around 7 cm for 40 Hz).This study provides a comprehensive comparison of multiple altimeters in monitoring lakes in mountainous areas, and demonstrates the substantial improvements achieved in the past decades. However, care has to be taken when merging data from old generation altimetry missions over mountainous lakes. The lessons learned from these Tibetan lakes can be taken across to other mountain lakes.

Bioavailability-based Environmental Quality Standards for metals under the Water Framework Directive

Metals are naturally present in surface waters. By considering the bioavailability of a metal in an aquatic environment, we can more precisely predict the effect of a selected metal on aquatic organisms. This enables us to make a more reliable assessment of the compliance of the metal's concentration in inland surface waters with the environmental quality standard for that metal. In addition to complex-forming inorganic and organic ligands, the bioavailability of metals in water is also influenced by physicochemical conditions in the water. Metals occur in different concentrations and in different forms in aquatic environments. This is referred to as metal speciation. The bioavailability of a metal in water depends mainly on its speciation. Normally, a dissolved free metal ion is more bioavailable than a metal ion bound in a complex. The risks posed by given metals to aquatic environments are addressed under EU legislation in the Water Framework Directive (Council of the European Union, 2000) (WFD). The WFD’s purpose is to direct Member States to protect and improve the status of all water bodies throughout the European Union. Directive 2013/39/EC (Council of the European Union, 2013), which refers to priority substances as regards water policy, supplements the WFD and the Directive on environmental quality standards (Council of the European Union, 2008). For nickel and lead, Directive 2013/39/EC sets the annual average environmental quality standards (AA-EQS) for inland surface waters, which refer to biologically available concentrations of metals (AA-EQSbioavailable). In this way, uniform environmental quality standards for the entire European Union are determined for metals, allowing for a compliance assessment of metals for surface waters. In the article, we have presented an analysis of inland surface water monitoring data for nickel and lead, examining how the introduction of AA-EQSbioavailable affects the consistency of monitoring data for these metals. We used monitoring data from 2018 to 2021. To calculate bioavailable concentrations for nickel and lead, we used the bioavailability assessment model bio-met bioavailability tool v.2.3 (Bio-met, 2017). Additionally, for lead, we used equations (1), (2), and (3) to calculate the bioavailable concentration (European Commission, 2019). Based on the analysis conducted, we recommend the utilization of the bio-met model instead of the equations for calculating the concentration of bioavailable lead in the context of the annual compliance assessment of inland surface water monitoring data carried out by ARSO. Using the mentioned bioavailability assessment model, we have provided an upgrade in the methods used to assess metal’s concentration compliance with the EQS for that metal for surface waters. As a result, the requirements of Directive 2013/39/EC relating to biologically available concentrations of metals could be transferred to the Slovenian legal system.

Application of UAV Push-Broom Hyperspectral Images in Water Quality Assessments for Inland Water Protection: A Case Study of Zhang Wei Xin River in Dezhou Distinct, China

A water quality parameter retrieval scheme based on the UAV push-broom hyperspectral images was designed and validated for assessing the ecological health of Zhang Wei Xin River in Dezhou distinct, China. First, a UAV carrying a push-broom hyperspectral imager that is lightweight and has a small size was used to acquire high spatial and hyperspectral resolution images. Then, the mosaicked reflectance data of the whole river were produced by a seamless image mosaicking method with high geometrical accuracy and spectral fidelity. Next, the in-field measurements of different parameters and the corresponding spectral reflectance from the mosaicked images at the sampling points were used to build the water quality parameter retrieval models for total phosphorus (TP), chlorophyll a (Chla), and total suspended solids (TSS). To validate the model, the retrieval results of the testing sampling points were compared with the measured parameters. The coefficients of determination R2 of TP, Chla, and TSS were 0.886, 0.918, and 0.968, respectively. The retrieved TP, Chla, and TSS maps showed that the water pollution of Zhang Wei Xin River is serious, the total phosphorus exceeds the standard, and the water body is in a state of eutrophication. The UAV-based hyperspectral remote sensing technique provides a cost-effective method for inland water monitoring at a local scale with high accuracy.

GEDI DATA WITHIN GOOGLE EARTH ENGINE: PRELIMINARY ANALYSIS OF A RESOURCE FOR INLAND SURFACE WATER MONITORING

Abstract. Freshwater is one the most important renewable water resources of the planet but, due to climate change, surface freshwater available in the form of lakes, rivers, reservoirs, snow, and glaciers is becoming significantly threatened. As a result, surface water level monitoring is fundamental for understanding climatic changes and their impact on humans and biodiversity.This study evaluates the accuracy of the Global Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection And Ranging) instrument for monitoring inland water levels. Four lakes in northern Italy were selected for comparison with gauge station measurements. To evaluate the accuracy of GEDI altimetric data, two steps of outlier removal are proposed. The first stage employs GEDI metadata to filter out footprints with very low accuracy. Then, a robust version of the standard 3σ test using a 3NMAD (Normalized Median Absolute Deviation) test is iteratively applied.After the outlier removal, which led to the elimination of between 80% to 87% of the data, the remaining footprints show an average standard deviation of 0.36 m, a mean NMAD of 0.38 m, and a Root Mean Square Error (RMSE) of 0.44 m, proving the promising potentialities of GEDI L2A altimetric data for inland water monitoring.

Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China

Water quality monitoring of medium-sized inland water is important for water environment protection given the large number of small-to-medium size water bodies in China. A case study was conducted on Yuandang Lake in the Yangtze Delta region, with a surface area of 13 km2. This study proposed utilising a multispectral uncrewed aerial vehicle (UAV) to collect large-scale data and retrieve multiple water quality parameters using machine learning algorithms. An alternate processing method is proposed to process large and repetitive lake surface images for mapping the water quality data to the image. Machine learning regression methods (Random Forest, Gradient Boosting, Backpropagation Neural Network, and Convolutional Neural Network) were used to construct separate water quality inversion models for ten water parameters. The results showed that several water quality parameters (CODMn, temperature, pH, DO, and NC) can be retrieved with reasonable accuracy (R2 = 0.77, 0.75, 0.73, 0.67, and 0.64, respectively), although others (NH3-N, BGA, TP, Turbidity, and Chl-a) have a determination coefficient (R2) less than 0.6. This work demonstrated the tremendous potential of employing multispectral data in conjunction with machine learning algorithms to retrieve multiple water quality parameters for monitoring medium-sized bodies of water.

Long-term water quality monitoring using Sentinel-2 data, Głuszyńskie Lake case study

This study shows the results of long-term inland water monitoring using Sentinel-2 data for Głuszyńskie Lake in the years 2015–2022. Four water quality parameters: biological oxygen demand (BOD), dissolved organic carbon (DOC), chlorophyll concentration (CHL) and electrical conductivity (EC) were calculated according to formulas found in the literature. The results were validated based on measurements conducted in 2021 and 2022, where for BOD, DOC and CHL high determination coefficients (0.77 and 0.79) were observed, and the EC determination coefficient was equal to 0.45. The results show that empirical formulas can be used for qualitative analyses of inland water quality, while for quantitative analyses more extensive field work needs to be performed.

Application and recent progress of inland water monitoring using remote sensing techniques.

Hyperspectral remote sensing, which retrieves the water quality parameters by direct high-resolution analysis of the electromagnetic spectrum reflected from the water surface, has been widely applied for inland water quality detection. Such a new approach provides an opportunity to generate real-time data from water with the noncontact method, largely improving working efficiency. By summarizing the development and current applications of hyperspectral remote sensing, we compare the relative merits of varying remote sensing platforms, popular inversion models, and the application of hyperspectral monitoring of chlorophyll-a (Chl-a), transparency, total suspended solids (TSS), colored dissolved organic matter (CDOM), phycocyanin (PC), total phosphorus (TP), and total nitrogen (TN) water quality parameters. Most studies have focused on spaceborne remote sensing, which is usually used to monitor large waterbodies for Chl-a and other water quality parameters with optical properties; semiempirical, bio-optical, and semianalytical models are frequently used. With the rapid development of aerospace technology and near-surface remote sensing, the spectral resolution of remote sensing imaging technology has been dramatically improved and has begun to be applied to small waterbodies. In the future, the multiplatform linkage monitoring approach may become a new research direction. Advanced computer technology has also enabled machine learning models to be applied to water quality parameter inversion, and machine learning models have higher robustness than the three commonly used models mentioned above. Although nitrogen and phosphorus, with nonoptical properties, have also received attention and research from some scholars in recent years, the uncertainty of their mechanisms makes it necessary to maintain a cautious attitude when treating such research.

Water Quality and Water Hyacinth Monitoring with the Sentinel-2A/B Satellites in Lake Tana (Ethiopia)

Human activities coupled with climate change impacts are becoming the main factors in decreasing inland surface water quantity and quality, leading to the disturbance of the aquatic ecological balance. Under such conditions, the introduction and proliferation of aquatic invasive alien species are more likely to occur. Hence, frequent surface water quality monitoring is required for aquatic ecosystem sustainability. The main objectives of the present study are to analyze the seasonal variation in the invasive plant species water hyacinth (Pontederia crassipes) and biogeochemical water quality parameters, i.e., chlorophyll-a (Chl-a) and total suspended matter (TSM), and to examine their relationship in Lake Tana (Ethiopia) during a one-year study period (2020). Sentinel-2A/B satellite images are used to monitor water hyacinth expansion and Chl-a and TSM concentrations in the water. The Case 2 Regional Coast Colour processor (C2RCC) is used for atmospheric and sunglint correction over inland waters, while the Sen2Cor atmospheric processor is used to calculate the normalized difference vegetation index (NDVI) for water hyacinth mapping. The water hyacinth cover and biomass are determined by NDVI values ranging from 0.60 to 0.95. A peak in cover and biomass is observed in October 2020, just a month after the peak of Chl-a (25.2 mg m−3) and TSM (62.5 g m−3) concentrations observed in September 2020 (end of the main rainy season). The influx of sediment and nutrient load from the upper catchment area during the rainy season could be most likely responsible for both Chl-a and TSM increased concentrations. This, in turn, created a fertile situation for water hyacinth proliferation in Lake Tana. Overall, the freely available Sentinel-2 satellite imagery and appropriate atmospheric correction processors are an emerging potent tool for inland water monitoring and management in large-scale regions under a global change scenario.

Making waves: Time for chemical surface water quality monitoring to catch up with its technical potential

A comprehensive real-time evaluation of the chemical status of surface water bodies is still utopian, but in our opinion, it is time to use the momentum delivered by recent advanced technical, infrastructural, and societal developments to get significantly closer. Procedures like inline and online analysis (in situ or in a bypass) with close to real-time analysis and data provision are already available in several industrial sectors. In contrast, atline and offline analysis involving manual sampling and time-decoupled analysis in the laboratory is still common practice in aqueous environmental monitoring. Automated tools for data analysis, verification, and evaluation are changing significantly, becoming more powerful with increasing degrees of automation and the introduction of self-learning systems. In addition, the amount of available data will most likely in near future be increased by societal awareness for water quality and by citizen science. In this analysis, we highlight the significant potential of surface water monitoring techniques, showcase “lighthouse” projects from different sectors, and pin-point gaps we must overcome to strike a path to the future of chemical monitoring of inland surface waters.

Bibliometric Analysis of Remote Sensing of Inland Waters Publications from 1985 to 2020

Over the past few decades, there has been a significant increase in the number of studies about the use of remote sensing techniques for the monitoring of inland waters. Since these aquatic environments have multiple uses for mankind, monitoring them is essential for the environment, society, and the economy. The use of Earth Observations data for the monitoring of inland waters is important for water quantity and quality management. Because of this, the goal of the present study is to systematically analyze and visualize the evolution of publications in this field. This study will not focus on algorithm comparisons or descriptions; instead, it will use a co-citation analysis to map the relationship among publications. Using the Web of Science database, publications related to the terms, “remote sensing” + “inland waters”, were analyzed using the entire database. The bibliometric analysis showed how research topics evolved from measuring optical properties and understanding their signal to the development of remote sensing algorithms and their applications to satellite imagery. This study provides the historical development of the scientific publications in this field and points out what could be the direction for future research.

Proof of Concept Study of an Electrochemical Sensor for Inland Water Monitoring with a Network Approach

The technologies most suitable for monitoring the ecosystem of inland waters are image spectrometry and electrochemical sensors. The reason is that these instruments are able to ensure accuracy in the surveillance of very large areas through reliable and frequent measurements performed remotely. Electrochemical systems provide low-cost, miniaturized, reliable sensors that can be organized, when equipped with commercial on the shelf (COTS) low-power radio components implementing LoRaWAN, Sigfox or NB-IoT communications, in a dense network of sensors achieving the aforementioned requirements. In this work, a low-cost, low-size and low-noise electrochemical sensor endowed with protocols for network configuration, management and monitoring is presented. The electronic interface of the sensor allows high reproducible responses. As proof of concept for its utilization in inland water monitoring, the device has been tested for water composition analysis, bacteria identification and frequent pollutant detection: atrazine, dichloromethane and tetrachloroethene. The results are promising, and future investigations will be oriented to unlock the true potential of a general-purpose approach exploiting the continuous fusion of distributed data in each of the three considered application scenarios. A new device, with reduced power consumption and size, has been also developed and tested; this new device should be a node of a large network for inland water monitoring.

Inland and Coastal Bathing Water Quality in the Last Decade (2011–2020): Croatia vs. Region vs. EU

Europe is one of the leading tourist destinations where tourism is one of the key economic sectors. The quality of bathing waters is a very important factor when choosing a vacation destination. Croatia recognized this early and was one of the first Mediterranean countries to start systematic monitoring of bathing waters. On the other hand, monitoring of inland bathing waters is relatively new and includes a much smaller number of sites (41) compared to coastal waters (894). The aim of this paper was to summarize and analyze the water quality of inland and coastal bathing sites of Croatia, closer regions (non-EU Member States) and in the EU for the last decade. The share of excellent water quality in EU Member States increased by 10.1% and 6.6% for inland and coastal waters, respectively (2011–2020). Germany recorded the highest proportion of excellent water quality for inland waters (92.2%) and Cyprus for coastal waters (99.3%). Looking at the 10-year average of the proportion of bathing waters with excellent quality, the proportion of coastal bathing sites exceeds that of inland waters by 7.1%. It is clear that additional efforts should be made to improve the management and monitoring of inland waters.

Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.

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.