Inland Water Quality Research Articles

Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics. Data synchronization and fusion are achieved through recursive KF methods, enabling real-time adaptive management in response to environmental fluctuations such as seasonal changes, precipitation (6–18%), and evaporation rates (1.2–11.9 mm/day). Over a 30-day evaluation period, the model accurately predicted chlorophyll concentrations, reaching 128 mg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mg}/\\hbox {m}^{3}$$\\end{document} in mid-level inflow regions (3.6 m water elevation) compared to 86 mg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mg}/\\hbox {m}^{3}$$\\end{document} in extreme inflow areas (5.5 m). The integration of Markov feature extraction and eigenvalue estimation enhanced prediction stability and sensitivity, with the KF maintaining computational efficiency at 7.8 ms per computation cycle. The model’s accuracy was validated by achieving a residual error of less than 0.05 with minimal noise interference. Overall, the system provides a resilient and precise framework for real-time lake water quality assessment, capable of handling multi-parameter uncertainties and dynamic environmental changes, thereby supporting informed decision-making for aquatic ecosystem management.

The impact of spatial resolution on inland water quality monitoring from space

Abstract Remote sensing of inland waters can provide timely and global water quality information to a wide variety of stakeholders. One of the parameters that determines the feasibility of using optical space-based instruments for monitoring inland waters is the ground sampling distance (GSD), defined as the width of a pixel projected on the Earth’s surface. We assume that to analyze a body of water with optical imagery, its characteristic width must be larger than 3 times the GSD to obtain an ‘unmixed’ pixel that doesn’t contain signal from the adjacent land. Here we obtain the size distribution of river lengths, river areas, and lake areas—as a function of width—for rivers and lakes in the Western United States (US) and in Australia. We base this analysis on the Surface Water and Ocean Topography River Database (SWORD) and HydroLAKES databases, extrapolated to 5 m-wide features. We show that the fraction of river length and river area larger than a certain width increases sharply as the width decreases, indicating that even small decreases in the GSD result in significant increases in the number of bodies that can be surveyed. On the other hand, the distribution of lake areas shows a ‘knee’ at around 400 m, indicating that gains from GSDs smaller than 130 m will be modest. We found that a satellite instrument with a GSD capability of 18 m can provide coverage of 4.4% of total river lengths, 38% of total river area, and 94% of total lake area within the study areas. We argue that decreasing the GSD incurs penalties associated with loss of signal-to-noise, larger instrument, smaller swath, and longer revisit times.

Effects of residential self-taxing districts on urban surface water quality

An extensive literature documents the economic benefits associated with improving the quality of inland surface waters, including lakes, streams, and rivers. Efforts to do so in urban U.S. landscapes are often limited due to laws restricting the use of property taxes for surface water management. However, programs in several states allow homeowners to establish residential self-taxing districts through referendum to fund management and improvement activities. This study investigates the effectiveness of self-taxing districts for improving lake water quality, focusing upon a long-standing program in Orange County, Florida. Results from dynamic panel data models estimated with annual measures of total nitrogen and phosphorus concentrations, secchi disk depth, and trophic state from >250 lakes over 1970–2018 indicate significant differences in mean annual water quality emerge over time between taxing district lakes and un-managed lakes and that particular district objectives and best management practices are associated with significant reductions in nitrogen and phosphorus concentrations.

Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Water quality index (WQI) is a well-established tool for assessing the overall quality of fresh inland-waters. However, the effectiveness of real-time assessment of aquatic ecosystems using the WQI is usually impacted by the absence of some water quality parameters in which their accurately in-situ measurements are impossible and face difficulties. Using a rich water quality dataset spanned from 1980 to 2023, we employed four machine learning-based models to estimate the British Colombia WQI (BCWQI) in the Lake Päijänne, Finland, without parameters like chemical oxygen demand (COD) and total phosphorus (TP). Measurement of both COD and TP is time-consuming, needs laboratory equipment and labor costs, and faces sampling-related difficulties. Our results suggest the machine learning-based models successfully estimate the BCWQI in Lake Päijänne when TP and COD are omitted from the dataset. The long-short term memory model is the least sensitive model to exclusion of COD and TP from inputs. This model with the coefficient of determination and root-mean squared error of 0.91 and 0.11, respectively, outperforms the support vector regression, random forest, and neural network models in real-time estimation of the BCWQI in Lake Päijänne. Incorporation of BCWQI with the machine learning-based models could enhance assessment of overall quality of inland-waters with a limited database in a more economical and time-saving way. Our proposed method is an effort to replace the traditional offline water quality assessment tools with a real-time model and improve understanding of decision-makers on the effectiveness of management practices on the changes in lake water quality.

Bridging the divide between inland water quantity and quality with satellite remote sensing: An interdisciplinary review

AbstractThe quantity and quality of surface water are inherently connected yet are overwhelmingly studied separately in the field of remote sensing. Remotely observable water quantity (e.g., water extent, water elevation, lake/reservoir volume, and river discharge) and water quality (e.g., color, turbidity, total suspended solids, chlorophyll a, colored dissolved organic matter, and temperature) parameters of inland waterbodies interact through a series of hydrological and biogeochemical processes. In this review, we analyzed trends in remote sensing publications to understand the prevalence of studies on the quantity versus quality of open‐surface inland waterbodies (rivers, streams, lakes, and reservoirs) as well as identified opportunities for integrating both water quality and quantity sensing in future work. Our bibliometric analysis found that despite the increasing number of publications using remote sensing for inland waterbodies, few studies to date have used remote sensing tools or approaches to simultaneously study water quantity and quality. Ultimately, by providing insights into potential integration of the water quality and quantity studies, we aim to identify a pathway to advance the understanding of inland water dynamics and freshwater resources through remote sensing.This article is categorized under: Water and Life > Methods Science of Water > Water Quality Water and Life > Nature of Freshwater Ecosystems Science of Water > Methods

UAV and satellite remote sensing for inland water quality assessments: a literature review.

High spatial and temporal resolution data is crucial to comprehend the dynamics of water quality fully, support informed decision-making, and allow efficient management and protection of water resources. Traditional in situ water quality measurement techniques are both time-consuming and labor-intensive, resulting in databases with limited spatial and temporal frequency. To address these challenges, satellite-driven water quality assessment has emerged as an efficient and effective solution, offering comprehensive data on larger-scale water bodies. Numerous studies have utilized multispectral and hyperspectral remote sensing data from various sensors to assess water quality, yielding promising results. However, the recent popularity of unmanned aerial vehicle (UAV) remote sensing can be attributed to its high spatial and temporal resolution, flexibility, ability to capture data at different times of day, and relatively low cost compared to traditional platforms. This study presents a comprehensive review of the current state of the art in monitoring water quality in small inland water bodies using satellite and UAV remote sensing data. It encompasses an overview of atmospheric correction algorithms and the assessment of different water quality parameters. Furthermore, the review addresses the challenges associated with monitoring water quality in these bodies of water and emphasizes the potential of UAVs to overcome these challenges by providing accurate and reliable data.

A Critical Review of Remote Sensing Methods for Inland Water Quality Monitoring: Progress, Limitations, and Future Perspectives

A Critical Review of Remote Sensing Methods for Inland Water Quality Monitoring: Progress, Limitations, and Future Perspectives

Long-Term Monitoring of Inland Water Quality Parameters Using Landsat Time-Series and Back-Propagated ANN: Assessment and Usability in a Real-Case Scenario

Water scarcity and quality deterioration, driven by rapid population growth, urbanization, and intensive industrial and agricultural activities, emphasize the urgency for effective water management. This study aims to develop a model to comprehensively monitor various water quality parameters (WQP) and evaluate the feasibility of implementing this model in real-world scenarios, addressing the limitations of conventional in-situ sampling. Thus, a comprehensive model for monitoring WQP was developed using a 38-year dataset of Landsat imagery and in-situ data from the Water Information System of Europe (WISE), employing Back-Propagated Artificial Neural Networks (ANN). Correlation analyses revealed strong associations between remote sensing data and various WQPs, including Total Suspended Solids (TSS), chlorophyll-a (chl-a), Dissolved Oxygen (DO), Total Nitrogen (TN), and Total Phosphorus (TP). Optimal band combinations for each parameter were identified, enhancing the accuracy of the WQP estimation. The ANN-based model exhibited very high accuracy, particularly for chl-a and TSS (R2 > 0.90, NRMSE < 0.79%), surpassing previous studies. The independent validation showcased accurate classification for TSS and TN, while DO estimation faced challenges during high variation periods, highlighting the complexity of DO dynamics. The usability of the developed model was successfully tested in a real-case scenario, proving to be an operational tool for water management. Future research avenues include exploring additional data sources for improved model accuracy, potentially enhancing predictions and expanding the model’s utility in diverse environmental contexts.

A2DWQPE: Adaptive and automated data-driven water quality parameter estimation

Accurate remote sensing estimation of inland water quality parameters (WQPs) plays a crucial role in guiding water resource management. To achieve this, researchers have explored various data-driven approaches utilizing machine learning (ML) techniques. However, there are two major challenges in WQPs estimation for inland waters. Firstly, current data-driven approaches focus on building a unified estimation model for an entire study area, which underestimates the complex dynamics of water constituents and optical properties. Secondly, ML models, particularly neural networks, require extensive hyperparameter tuning and are not user-friendly for researchers lacking relevant background and experience. In this paper, we propose an innovative method called adaptive and automated data-driven water quality parameter estimation (A2DWQPE) to address both challenges. Our method operates under the assumption that water bodies with similar spectral characteristics should share the same WQP estimation model. A2DWQPE is composed of three phases. Firstly, water types are automatedly classified by unsupervised hierarchical clustering according to spectral similarity. Then, optimal Deep Neural Network (DNN) models for estimating WQPs from multi-spectral satellite images are customized for each water type utilizing Bayesian optimization (BO). Finally, the target WQP is estimated based on the type-specific estimates and degree of membership of each water type. To evaluate the effectiveness of A2DWQPE, we applied it to estimate Secchi disk depth (SDD) in Lake Erie with in situ measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The results demonstrate that A2DWQPE outperforms the traditional approaches of developing a unified model for the entire study area. A2DWQPE achieved high accuracy with coefficient of determination (R2) over 0.72 and root mean square error (RMSE) below 1.4 m. Our method also outperforms the methods that applied Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) instead of BO, and several traditional ML algorithms. We firmly believe that A2DWQPE holds great potential for accurate inland water quality estimation and will contribute significantly to various applications in water quality monitoring and pollution prevention.

Seawater intrusion assessment along the Volturno River (Italy) via numerical modeling and spectral analysis

Surface and groundwater salinization are becoming a significant challenge to inland water quality, negatively affecting people and ecosystems in coastal areas. Even if rivers provide critical pathways for seawater intrusion, this salinization phenomenon has received relatively little attention compared to other salinization mechanisms. To assess the distribution of salinity along the final reach of the Volturno River (Italy), an entire hydrologic year was modeled using the HEC-RAS software. The model was fed with high resolution time-series measurements (time interval of 10 min) of water surface elevations at both river mouth and Cancello Arnone (a hydrometric station located 13 km inland). Field observations and remote sensed data were used to perform the hydrodynamic analysis. The model showed good performance indicators (R2 = 0.878, NSE = 0.870, and MAE = 0.037 m) and well caught hydrometric variation over the simulation period. The tidal component was affected by dissipation moving upstream and showed the capability to shape the salinity profile during dry periods. Whereas during wet periods, even if a strong tidal component is present, the profile is totally regulated by the river discharge. The analysis of the salinity distribution, modelled via the Water Quality module, revealed the massive contribution of the river discharge in limiting seawater intrusion. A correlation between intrusion events and hydrometric stages was established over twenty years (2002–2022), showing a consistent trend between intrusion occurrence and the surface water storage anomaly in the lower Volturno River calculated by Global Land Data Assimilation System (GLDAS) model. Although the 1D approach here used may lead to uncertainties in the reproduction of the involved hydrodynamic and salinization processes, the results are useful for the understanding of seawater intrusion in rivers, and may be utilized to study seawater intrusion in aquifers.

Challenges and Advances of Inland Waters Monitoring: A Case Study Highlighting the Use of Satellite Imagery and Cloud Computing in Brazil

AbstractMonitoring of the quality of inland waters is important to maintain the aquatic ecosystems health and to ensure water quality standards established by environmental laws in each country. Our goal here is to present the possibilities and challenges of water quality mapping based on Sentinel 2 and Landsat 8 satellite images in geographically distinct Brazilian reservoirs as a study case. The satellite images were able to show chlorophyll a, turbidity, and Secchi data trends over 8 years of remote sensing monitoring. The time savings in the analysis of a large dataset and the availability to a wide range of stakeholders make remote sensing a good tool for monitoring inland waters. However, water quality parameters were highly influenced by cloud cover and the accuracy was low at the margin of tributary rivers. The integration of remote sensing and in situ data are needed to increase the effectiveness and accuracy of the remote sensing tools.

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.

Design and Performance Analysis of a Constellation of Nanosatellites to Monitor Water Quality in the Southern Catchment of the Baltic Sea.

The quality of inland waters has a significant influence on human life and the functioning of the environment. The disasters that result from water pollution may cause major financial losses and lead to irreversible changes in the ecosystem, such as the dying out of endemic species of plants and animals. Quick detection of pollution sources may minimise those negative effects and reduce the costs of their elimination. The study presents a constellation design that provides imagery in the optic range and that might supplement the point water quality measurements that are conducted in situ. The area of interest was the southern catchment of the Baltic Sea and the main rivers in the region. The requirements for the designed mission were defined in reference to the remote sensing needs concerning the monitoring of water quality, the characteristics of the analysed area, and weather conditions. Based on these requirements, the Simera Sense MultiScape100 CIS sensor and the M6P nanosatellite manufactured by NanoAvionics were selected. The authors proposed a process for selecting the optimum orbit, taking into account the area of interest, the possibilities of the satellite platform, and of the sensor's optics. As a result of the analyses, four concepts of creating a constellation were presented. Each constellation consisted of four nanosatellites. The designs were then subjected to performance analysis, considering the lighting limitations. Among the proposed systems, the constellation designed by the authors was distinguished; it used four orbital planes and achieved the coverage and availability of imagery in the time that was best suited to monitoring the waters. Thanks to a small number of platforms, the costs of the mission are relatively low, and it might significantly improve awareness of the current state of surface waters in the southern catchment of the Baltic Sea.

Modeling the drivers of eutrophication in Finland with a machine learning approach

AbstractAnthropogenic eutrophication is one of the most common threats to inland water quality, often causing toxic algal blooms and loss of aquatic biodiversity. Mitigating the harmful impacts of eutrophication requires managing nutrient inputs from the catchment focusing on the major local drivers of eutrophication. These drivers can be identified using models that predict lake trophic state based on characteristics of the lake and its catchment. In this study, we aimed to extend the spatial scope of these models by identifying drivers of eutrophication in a large sample of lakes (1547) distributed across Finland. Moreover, we used satellite‐observed chlorophyll a (chl a) concentration as trophic state indicator, instead of site‐sampled data, which is commonly used in existing research. We identified major drivers of eutrophication on river basin district to country scale based on 11 catchment and lake characteristics, applying the random forest algorithm. On country scale, the catchment and lake characteristics explained 41% of the variation in lake chl a concentrations, and on river basin district scale, 20%–44%. Catchment and lake hydromorphology were the most important explanatory characteristics. Especially, high natural eutrophication level, shallow mean depth of lake, and small share of lake area in the catchment were related to increased lake chl a concentration. Moreover, depending on the dominant land use type in the model area, share of agricultural area and share of peatland area in the catchment were ranked among the most important drivers of increased lake chl a concentration. The results suggest that trophic state predictive models utilizing satellite‐observed chl a concentration could provide an additional, cost‐effective tool for addressing lake eutrophication, especially in areas without and extensive on‐site monitoring network.

An enhanced deep learning approach to assessing inland lake water quality and its response to climate and anthropogenic factors

Remote sensing has long been used for inland water quality monitoring. However, due to the complex correlation between water quality parameters (WQPs) and water optical properties, the interactions of WQPs, and the impacts of climate, using remote sensing reflectance (Rrs) to adequately estimate WQPs is still a grand challenge. Deep learning has the potential in capturing the correlation among Rrs, optically active constituents (OACs), and non-OACs, and is progressively used in remote sensing retrieval of inland water quality. In this study, the enhanced multimodal deep learning (EMDL) models were proposed for Chlorophyll-a, total phosphorous, total nitrogen, Secchi disk depth, dissolved organic carbon, and dissolved oxygen retrieval in Lake Simcoe (80 km north of Toronto, Canada). The EMDL models were developed and validated using the Rrs data derived from the harmonized Landsat and Sentinel-2 images, synchronized water quality measurements, water surface temperature, and climate data (N = 1173). The performance of the EMDL models was compared to that of several other machine learning, deep learning, and empirical models. Using the developed EMDL models, the spatial distributions and long-term variations of the WQPs in Lake Simcoe from 2013 to 2019 were reconstructed. The impacts of 12 potential natural and anthropogenic factors on the water quality of the entire Lake Simcoe and its two most concerned estuaries were also quantitatively discussed. The results showed that the EMDL models produced satisfactory performance in estimation of the six WQPs, with the Slope being close to 1 (0.84–0.95), normalized mean absolute error ≤20.17%, and Bias ≤14.68%. The EMDL models had the potential to reconstruct the spatial patterns and time-series dynamics of water quality and effectively detect the gradients of spatial patterns. This study provides a novel approach to supporting the environmental management and identification of the affecting factors for the Lake Simcoe watershed.

Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.

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.

Multitype inland water atmospheric correction and water quality estimation based on HY-1C CZI images

Multitype inland water atmospheric correction and water quality estimation based on HY-1C CZI images

Assessing the impacts of industrial wastewater on the inland surface water quality: An application of analytic hierarchy process (AHP) model-based water quality index and GIS techniques

The main objective of the present work is to provide a clear insight into receiving inland surface water quality in Kashipur city of Uttarakhand state, India, due to the impact of industrial discharges using a novel Water Quality Index method based on the Analytic Hierarchy Process (AHP) approach. Surface water samples were collected monthly from 12 locations between September 2020 and March 2021. The locations were chosen based on their potential to acquire the most contaminants from surrounding industries and analyzed for their physicochemical characteristics. Geostatistical analysis results represented the spatial variation of the parameter concentrations in the study area. Statistical analyses were performed using Principal Component Analysis and Hierarchical Cluster Analysis. PCA results revealed that two factor loadings impact the surface water chemistry in the study region, where factor 1 represents 50.051% of the variance with six dominant water parameters, and factor 2 represents 33.222% of the total variance. HCA classified the sampling locations randomly into two major clusters. Analytic Hierarchy Process results in establishing the weights of the parameters revealed that dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, and total dissolved solids were the dominant parameters for assessing water quality with a consistency ratio of 0.097. The WQI results showed that 8% of the water samples depict “very poor” inland surface water quality. The anthropogenic sources might be the reason for such poor water quality. This model can be effectively applied at any location and with as many parameters as required to assess surface water quality.

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.