Water Quality Parameters In Lake Research Articles

Integrating Remote Sensing Methods for Monitoring Lake Water Quality: A Comprehensive Review

Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in water management. This review discusses the use of remote sensing methods for monitoring and assessing water quality in lakes. It explains the principles of remote sensing and the different methods used for retrieving water quality parameters in complex waterbodies. The review highlights the importance of considering the variability of optically active parameters and the need for comprehensive studies that encompass different seasons and time frames. The paper addresses the specific physical and biological parameters that can be effectively estimated using remote sensing, such as chlorophyll-α, turbidity, water transparency (Secchi disk depth), electrical conductivity, surface salinity, and water temperature. It further provides a comprehensive summary of the bands, band combinations, and band equations commonly used for remote sensing of these parameters per satellite sensor. It also discusses the limitations of remote sensing methods and the challenges associated with satellite systems. The review recommends integrating remote sensing methods using in situ measurements and computer modelling to improve the understanding of water quality. It suggests future research directions, including the importance of optimizing grid selection and time frame for in situ measurements by combining hydrodynamic models with remote sensing retrieval methods, considering variability in water quality parameters when analysing satellite imagery, the development of advanced technologies, and the integration of machine learning algorithms for effective water quality problem-solving. The review concludes with a proposed workflow for monitoring and assessing water quality parameters in lakes using remote sensing methods.

Tracking changes in chlorophyll-a concentration and turbidity in Nansi Lake using Sentinel-2 imagery: A novel machine learning approach

This study represents the first application of Sentinel-2 remote sensing imagery and model fusion techniques to assess the chlorophyll-a (Chla) concentration and turbidity in Nansi Lake, Shandong Province, China, from 2016 to 2022. First, we innovatively employed the stacking method to fuse eight fundamentally different Machine Learning (ML) models, each utilising 20 and 17 feature bands, resulting in the development of a robust algorithm for estimating the Chla concentration and turbidity in Nansi Lake. The results demonstrate that the Stacking Model has achieved outstanding theoretical generalisation capability. Second, the sensitivity of the model to extreme value data in the sample was quantified, and we found that compared with extreme gradient boosting (XGBoost), the optimal performance of the Stacking Model improved by 12%, to some extent, it solved the problem of high-value underestimation and low-value overestimation. The SHapley Additive exPlanations (SHAP) results revealed that features such as Three Bands, Enhanced Three, Rrs492/Rrs560, Rrs705/Rrs665 play a crucial role in estimating Chla concentration. For the turbidity estimation, the Normalized Difference Turbidity Index (NDTI), Rrs705+Rrs560, Rrs865-Rrs740 made significant contributions. Finally, we utilised the Stacking Model to create spatiotemporal maps of the Chla concentration and turbidity in Nansi Lake from 2016 to 2022. We analysed the causes of the water quality changes and explored the driving factors. Compared with previous studies, this paper provides a new idea for the monitoring of lake water quality parameters by using the high resolution of Sentinel-2 image and the high precision of model fusion technology, these results can provide a reference for similar water area research and decision-making support for environment-related departments.

Understanding Lake Residence Time Across Spatial and Temporal Scales: A Modeling Analysis of Lake George, New York USA

AbstractWhole lake residence time has been associated with various water quality parameters, including harmful algal blooms. Despite observations of spatial variability in commonly measured lake water quality parameters, little attention is given to the spatial variability of residence time in lakes. In this paper we use water age as a surrogate for residence time and we examine its spatial and temporal distribution in 10 bays of varying size in Lake George, New York (USA). Using a validated hydrodynamic model against observations of water temperature and water currents, and using simulated water age, we show that the average residence time in most of the bays is less than 3 days. Timeseries of bay‐average water age shows that it can sharply decrease within 1 day due to a strong wind event. The average spatial distribution is shown to be non‐uniform, with only a small section of the bottom layer of the bays having a substantially greater age, which may be more than 1 week in certain bays. Snapshots of water age transects indicate that strong wind events substantially change the vertical distribution of water age in some bays, even to the extent of inverting the distribution. The substantial decreases of water age in the bays were associated with the shallowing and deepening of the thermocline. Our results highlight how variations in water residence times within lakes could introduce substantial variation in water quality attributes. Whole lake residence times may serve as a poor proxy to understand the dynamics of water masses, especially in large and morphologically complex waterbodies.

Estimation of the Biogeochemical and Physical Properties of Lakes Based on Remote Sensing and Artificial Intelligence Applications

Lakes play a crucial role in the global biogeochemical cycles through the transport, storage, and transformation of different biogeochemical compounds. Their regulatory service appears to be disproportionately important relative to their small areal extent, necessitating continuous monitoring. This study leverages the potential of optical remote sensing sensors, specifically Sentinel-2 Multispectral Imagery (MSI), to monitor and predict water quality parameters in lakes. Optically active parameters, such as chlorophyll a (CHL), total suspended matter (TSM), and colored dissolved matter (CDOM), can be directly detected using optical remote sensing sensors. However, the challenge lies in detecting non-optically active substances, which lack direct spectral characteristics. The capabilities of artificial intelligence applications can be used in the identification of optically non-active compounds from remote sensing data. This study aims to employ a machine learning approach (combining the Genetic Algorithm (GA) and Extreme Gradient Boost (XGBoost)) and in situ and Sentinel-2 Multispectral Imagery data to construct inversion models for 16 physical and biogeochemical water quality parameters including CHL, CDOM, TSM, total nitrogen (TN), total phosphorus (TP), phosphate (PO4), sulphate, ammonium nitrogen, 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and the biomasses of phytoplankton and cyanobacteria, pH, dissolved oxygen (O2), water temperature (WT) and transparency (SD). GA_XGBoost exhibited strong predictive capabilities and it was able to accurately predict 10 biogeochemical and 2 physical water quality parameters. Additionally, this study provides a practical demonstration of the developed inversion models, illustrating their applicability in estimating various water quality parameters simultaneously across multiple lakes on five different dates. The study highlights the need for ongoing research and refinement of machine learning methodologies in environmental monitoring, particularly in remote sensing applications for water quality assessment. Results emphasize the need for broader temporal scopes, longer-term datasets, and enhanced model selection strategies to improve the robustness and generalizability of these models. In general, the outcomes of this study provide the basis for a better understanding of the role of lakes in the biogeochemical cycle and will allow the formulation of reliable recommendations for various applications used in the studies of ecology, water quality, the climate, and the carbon cycle.

Spatial distribution prediction model for lake water quality parameters from ultra-sparse sampling data with recent satellite data

ABSTRACT Daily spatial distributions of water quality parameters (WQPs) are a crucial indicator in delicacy management of lake water environment in China. However, because of the high cost of construction and maintenance, there are a few automatic monitoring stations (AMSs) for continuous water sampling with only ultra-sparse spatial sampling data can be obtained. Meanwhile, although WQPs from satellite inversion are spatially continuous, they are missing on most days due to severe affected by weather and satellite revisit cycle, besides the high purchase cost. Thus, the model called MLP-GPR is formulated in this study to address the issue of predicting the spatial distribution of WQPs from ultra-sparse sampling data of AMSs combined with the spatial correlation information based on ultra-sparse satellite inversion data. This model includes the multilayer perceptron model (MLP) and Gaussian process regression model (GPR). MLP is trained to learn the interaction factors of WQPs between different locations through the satellite inversion data for predicting WQPs at more locations based on the data measured by AMSs. Then, GPR regresses the spatial distribution of WQPs at all locations on the entire lake from the predicted data by MLP. Based on the above model, we applied the satellite inversion, AMSs and artificial sampling data to predict the WQP spatial distribution of Shahu Lake in Wuhan, China. The experiment results show that the spatial resolution of WQP distribution predicted by MLP-GPR trained by satellite inversion data can reach 15 m by sampling data from artificial stations on the lake with 3.197 km2. Moreover, for each of these WQPs, compared with the satellite inversion data, the average MRE, R, and NSE of prediction results are below 3.95%, above 0.85, and above 0.69, respectively.

Investigating machine learning models in predicting lake water quality parameters as a 3-year moving average

Lake water quality plays a vital role in the lake ecosystem, including biotic (for living creatures, such as plants, animals, and micro-organisms) and abiotic interactions. In this research, various types of machine learning (ML) methodologies, such as classification and regression tree (CART), chi-squared automatic interaction detector (CHAID), C5 tree, quick, unbiased, and efficient statistical tree (QUEST), along with multilayer perceptron (MLP) neural network, and radial basis function (RBF) neural network, are employed to predict the concentration of water quality parameters (P, EC, TDS, pH, DO, NH3, SO4, and θ). Lake Erie is situated at the international border of the USA and Canada. The C5 tree and QUEST tree are used to classify data and predict the number of groups, while the other methods are used to predict the concentration of water quality parameters in the form of a 3-year moving average. The greater matching between the observed and predicted data of dissolved oxygen (NSE = 0.978, bias = 0.126) shows that the CART decision tree has higher accuracy in correctly detecting the concentration of this parameter. The C5 tree could identify 33 groups correctly out of 36 total groups, which shows better accuracy for the C5 tree in classifying the data for this parameter.

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.

Research Progress on Remote Sensing Monitoring of Lake Water Quality Parameters

Lakes are an important component of the hydrosphere and are important freshwater resources. The water environment of lakes has become increasingly polluted, and monitoring the dynamic changes in lake water quality is of great significance for ecological environment protection. Remote sensing can provide technical support for lake water quality monitoring, overcoming the low-cost and poor timeliness characteristics of manual sampling; thus, it has been widely used in lake water quality monitoring. Remote sensing can be used to monitor many indicators, including suspended solids, chlorophyll a, soluble organic matter, dissolved oxygen, transparency, etc. Although remote sensing provides a new method for lake water quality monitoring, there are still some problems in practical application. ① It is difficult to accurately retrieve the component changes in different substances in lake water from the signals received by remote sensing, resulting in the limitation of remote sensing accuracy. Atmospheric correction can eliminate the images reflected by factors such as atmosphere and light. In order to improve the accuracy of water quality monitoring, higher-accuracy atmospheric correction algorithms are needed. However, at present the atmospheric correction algorithm is not mature enough and lacks the portability between sensors. Therefore, atmospheric correction is still a difficult problem of remote sensing retrieval in lake water quality. ② Due to seasonal and spatial constraints, there are differences in surface optical properties of different lakes and biological optical properties, resulting in changes in remote sensing reflectance. There is also a lack of portability of the model established by using limited measured data. ③ The interference of aquatic plants and external forces (wind, fish, etc.) causes the error between the measured data and the model estimation, and the synchronization of the data is difficult to guarantee, which will introduce large errors to the lake water quality model, resulting in a decrease in the reliability of the model. ④ The spatio-temporal scale of measured data and remote sensing data do not match, and it is difficult to capture dynamic and fine changes in water quality. More precise observation of water quality is needed that can capture rapid changes in lakes. However, it is still challenging to obtain real-time measured data that meet the requirements. Therefore, it is necessary to further understand the spectral characteristics of water quality parameters, combining multi-source data and other hydrological models. In the future, a retrieval algorithm with low dependence will be developed, and migrated models will be constructed to break the regional limitations of the model. Additionally, remote sensing promotes the operational development and early warning for the water quality of lakes.

Using MODIS data to track the long-term variations of dissolved oxygen in Lake Taihu

Dissolved oxygen (DO) is crucial for the health of aquatic ecosystems, and plays an essential role in regulating biogeochemical processes in inland lakes. Traditional measurements of DO using the probe or analysis in a laboratory are time-consuming and cannot obtain data with high frequency and broad coverage. Satellites can provide daily/hourly observations within a broad scale and have been used as an important technique for aquatic environments monitoring. However, satellite-derived DO in waters is challenging due to its non-optically active property. Here, we developed a two-step model for retrieving DO concentration in Lake Taihu from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua images. A machine learning model (eXtreme gradient boosting) was developed to estimate DO from field water temperature, water clarity, and chlorophyll-a (Chla) (root-mean-square error (RMSE) = 0.98 mg L−1, mean absolute percentage error (MAPE) = 7.9%) and subsequently was validated on MODIS-derived water temperature, water clarity, and Chla matchups with a satisfactory accuracy (RMSE = 1.28 mg L−1, MAPE = 9.9%). MODIS-derived DO in Lake Taihu from 2002 to 2021 demonstrated that DO ranged from 7.2 mg L−1 to 14.2 mg L−1, with a mean value of 9.3 mg L−1. DO in the northern region was higher than in the central and southern regions, and higher in winter than in summer. We revealed that DO in this decade (2010–2021) was considerably lower than that in the last decade (2002–2009). Meanwhile, annual mean of DO increased in 2002–2009 and decreased from 2010 to 2021. The spatial distribution of DO in Lake Taihu was related to Chla and water clarity, while seasonal and interannual variations in DO resulted from air temperature primarily. This research enhances the potential use of machine learning approaches in monitoring non-optically active constituents from satellite imagery and indicates the possibility of long-term and high-range variations in more water quality parameters in lakes.

Assessing temporal variability of lake turbidity and trophic state of European lakes using open data repositories

Water turbidity is one of the more important water quality parameters that is strictly linked with the productivity of the lake and is commonly used as an indicator of the trophic state. However, limited field data availability across wide geographic gradients may hinder the conduction of large scale longitudinal studies. In this study, time series of lake turbidity and trophic state index (TSI) between 2002 and 2012 were obtained from the Copernicus Lake Water products to create a large longitudinal dataset of lake variables for 22 European lakes. The dataset was combined with estimates of nutrient concentrations and surface water temperature obtained from the Hydrological Predictions for the Environment (HYPE) and ERA5-Land data repositories, that were used as environmental predictors. Hence, the validity of the lake water quality parameters was tested by a) exploring their spatial and temporal variability and b) identifying associations with the environmental predictors. For this purpose, seasonal Mann-Kendall tests were applied to find significant inter-annual trends of turbidity and TSI for each lake, and generalized additive models (GAMs) were employed to identify the main parameters that shape their temporal dynamics. Although we did not find significant inter-annual changes, our findings highlighted the strong influence of seasonality and surface water temperature in defining the temporal variability patterns in most of the lakes. In addition, the importance of nutrients varied among lakes as several lakes exhibited narrow nutrient gradients reflecting relatively stable nutrient conditions during the examined period. Other lake intrinsic factors, such as local climate and biotic interactions, are important drivers of shaping turbidity and nutrient dynamics. This study highlighted the usefulness of combining lake data from large repositories in conducting large scale spatial studies as a valuable asset for future lake research and management purposes.

The Estimation of Water Quality Parameters in Lentic Environments Through Remote Sensing Technologies: a Review of the Past Two Decades

The use of remote sensing technology applied to measure the quality of continental waters has grown exponentially since the turn of the century. Using different sensors on board satellites or airborne platforms, the estimation of water quality parameters has been carried out through both empirical and analytical approaches. This work aims to review the specific scientific production of the last two decades to assess how the evolution of the sensors and platforms have affected the potential and the limitations of remote sensing technologies to estimate water quality parameters in lakes and reservoirs. The study also focuses on the accuracy of remote sensing techniques for the major optically active parameters: chlorophyll-a and phycocyanin, Secchi disk depth and turbidity. The article is subdivided by sections dedicated to each of these parameters. A review of remote sensing platforms and sensors precedes the parameters sections. The past 20 years have brought a large body of articles on how remote sensing data can be used to estimate these parameters. Empirical methods dominate overwhelmingly with a four to one proportion over analytical approaches. Environmental factors such as season, complexity of water and concentration loads appear to exert a strong control over the quality of the results. Recent platforms and sensors have brought noticeable improvements over results achieved in this period.

Utilizing neural networks for image downscaling and water quality monitoring

Remotely sensed images are becoming highly required for various applications, especially those related to natural resource management. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has the advantages of its high spectral and temporal resolutions but remains inadequate in providing the required high spatial resolution. On the other hand, Sentinel-2 is more advantageous in spatial and temporal resolution but lacks a solid historical database. In this study, four MODIS bands in the visible and near-infrared spectral regions of the electromagnetic spectrum and their matching Sentinel-2 bands were used to monitor the turbidity in Lake Nasser, Egypt. The MODIS data were downscaled to Sentinel-2, which enhanced its spatial resolution from 250 and 500m to 10m.Furthermore, it provided a historical database that was used to monitor the changes in lake turbidity. Spatial approach based on neural networks was presented to downscale MODIS bands to the spatial resolution of the Sentinel-2 bands. The correlation coefficient between the predicted and actual images exceeded 0.70 for the four bands. Applying this approach, the downscaled MODIS images were developed and the neural networks were further employed to these images to develop a model for predicting the turbidity in the lake. The correlation coefficient between the predicted and actual measurements reached 0.83. The study suggests neural networks as a comparatively simplified and accurate method for image downscaling compared to other methods. It also demonstrated the possibility of utilizing neural networks to accurately predict lake water quality parameters such as turbidity from remote sensing data compared to statistical methods.

Research on Baiyangdian Lake Water Body Changes and Water Quality Parameters Inversion Based on Landsat Dense Time Series Data

Baiyangdian Lake is the largest wetland ecosystem in North China, which has a special strategic position, so it is of great significance to study its temporal and spatial changes. Based on the 112 Landsat satellite remote sensing data from 1984 to 2020, this paper extracted the thematic information of Baiyangdian Lake open water body, carried out the study on the temporal and spatial evolution of the Baiyangdian Wetland ecosystem, and analyzed its area change trend as “increase-decrease-increase-decrease again-increase again ”. Among them, the Landsat7 ETM SLC-off satellite digital product with band noise during 2003-2012 was repaired, so that it can better reflect the mutation characteristics and overall laws of Baiyangdian Lake water body in the past 30 years. On this basis, the Baiyangdian Lake water quality parameters were inverted, and the conclusions drawn have certain reference significance for maintaining the ecological environment of Baiyangdian Lake.

CDOM Optical Characteristics and Related Environmental Factors of High-turbidity Waters on the Loess Plateau

Chromophoric dissolved organic matter (CDOM) optical absorption characteristics, CDOM spectral slopes (S275-295), contribution of each component to water absorption, and the effects of environmental factors on them were analyzed to determine the sources and composition of CDOM in high-turbidity waters on the Loess Plateau. Samples in rivers and lakes (saline and freshwater) in the Loess Plateau area of Shaanxi and Inner Mongolia were collected in May 2018. The results demonstrated significant differences in CDOM absorption optical characteristics between rivers and lakes. The average aCDOM(440) (CDOM concentration, 8.45 m-1) in lakes was higher than that of rivers (2.70 m-1), and the saline lakes showed a higher CDOM concentration (13.52 m-1) than the freshwater lakes (3.38 m-1). Moreover, the light utilization efficiency of freshwater lakes is higher than that of saline lakes and turbid rivers. Great differences in pH and dissolved organic carbon (DOC) were observed between different types of water (P<0.01). The differences in electrical conductivity (EC), turbidity (Tur), and total suspended matter concentration (TSM) were significant with no statistical significances (P>0.1). The chlorophyll a concentration (Chla) in saline lakes was close to the Chla concentration in freshwater lakes if extreme values were excluded. In addition, the CDOM molecular weights of lake water were lower than those in the rivers, while CDOM molecular weights in saline lakes were lower than those in freshwater lakes based on the S275-295. Analyses of specific ultraviolet absorbance (SUVA254) were also conducted to determine the sources of CDOM in different water types, and the results showed that the more terrigenous humus were brought into the rivers and saline lakes compared with the freshwater lakes. Redundancy analysis (RDA) indicated that river and lake water quality parameters of the cumulative variance explained rates were 35.2% for river samples and 61.4% for lake samples, and 100% for samples in the saline and freshwater lakes individually. The results of RDA showed that dissolved oxygen (DO), water temperature, and EC exerted significant effects on CDOM optical properties of the river (P<0.01), while DOC, TSM, and Tur had a great influence on the CDOM optical properties of lakes (P<0.01). There was a strong correlation between pH and CDOM in the saline lakes, while DOC was significantly correlated with CDOM in the freshwater lakes (P<0.05).

Remote sensing inversion of lake water quality parameters based on ensemble modelling

In this paper, combined with water quality sampling data and Landsat8 satellite remote sensing image data, the inversion model of Chl-a and TN water quality parameter concentration was constructed based on machine learning algorithm. After the verification and evaluation of the inversion results of the test samples, Chl-a TN inversion model with high correlation between model test results and measured data was selected to participate in remote sensing inversion ensemble modelling of water quality parameters. Then, the ensemble remote sensing inversion model of water quality parameters was established based on entropy weight method and error analysis. By applying the idea of ensemble modelling to remote sensing inversion of water quality parameters, the advantages of different models can be integrated and the precision of water quality parameters inversion can be improved. Through the evaluation and comparative analysis of the model results, the entropy weight method can improve the inversion accuracy to some extent, but the improvement space is limited. In the verification of the two methods of ensemble modelling based on error analysis, compared with the optimal results of a single model, the determination coefficient (R2) of Chlorophyll a and TN concentration inversion results was increased from 0.9288 to 0.9313 and from 0.8339 to 0.8838, and the root mean square error was decreased from 14.2615 μ/L to 10.4194 μ/L and from1.1002mg/L to 0.8621mg/L. At the same time, with the increase of the number of models involved in the set modelling, the inversion accuracy is higher.

Physico-chemical Characterization of Kaptai Lake and Foy’s Lake Water Quality Parameters in Chittagong, Bangladesh

In order to protect the quality of the environment and human health, freshwater assets are tremendously important in various ways. To ensure the freshwater resources in the Chittagong region of Bangladesh, we have studied the water quality parameters of Kaptai and Foy’s Lake. This research has done based on the essential surface water standard parameters such as pH, temperature, DO, BOD, COD, TDS, TSS, TS, EC, hardness, turbidity, salinity, total alkalinity, total acidity, SO42-, PO43-, NO3--N, NO2-, CO2, and most of the heavy and toxic metals (As, Cd, Cr, Cu, Co, Fe, Pb, Mn, Ni, and Zn) of two lakes namely Foy’s (Chittagong) and Kaptai (Rangamati) Lakes in Chittagong , Bangladesh. The statistical approaches to sampling were utilized for collecting samples. The samples were assembled from ten different locations of each lake. Samples were conserved using a satisfactory preservation procedure. Water samples from the surface-water assets were collected from various locations, and tide conditions and at various seasons for continual monitoring during the hydrological years 2014-2015. The results showed that Kaptai Lake and Foy’s Lake all physicochemical parameters are within the permissible limit of WHO guidelines. The results also supplied data to view, and quantify the enemy of the impact of climate alter on freshwater resources of this region. The outcomes further showed data for water quality of surface-water resources of greater Chittagong zone to match national and international quality for drinking, agricultural, manufacture and livestock requirements. A strategic water quality management plan has been proposed.

Assessment of Surface Water Quality by Using Satellite Images Fusion Based on PCA Method in the Lake Gala, Turkey

Monitoring water quality with classical methods is not an easy task. Remote sensing with wide coverage and multiple temporal monitoring is the best solution for surface water quality monitoring. This paper demonstrates the determination of surface water quality parameters by using principal component analysis (PCA) data fusion and mining techniques with the aid of Landsat 8 OLI (L8 OLI), Sentinel 2A (S2A), and Gokturk-2 (GK2) satellite sensors. Chlorophyll-a, dissolved oxygen, total suspended solids, Secchi disk depth, total dissolved substance, and pH were the parameters selected for surface water quality analysis. High spectral resolution of L8 OLI/S2A images and the high spatial resolution of GK2 images were fused and analyzed by a suite of data mining models to provide more reliable images with both high spatial and temporal resolutions. Surface water quality parameters calculated by PCA-based response surface regression (RSR) method were compared with results obtained from multiple linear regression (MLR), artificial neural network (ANN), and support vector machines (SVMs) data mining methods. The performance of the data mining models derived using only multispectral band data and PCA fused data were quantified using four statistical indices; such as mean-square error (MSE), root MSE, mean absolute error, and coefficient of determination (R2). The analysis confirmed that the PCA-based RSR method is superior to MLR, ANN, and SVM data mining models to accurately estimate water quality parameters in lakes.

Bio-optical retrieval algorithm for the optically shallow waters of lAKE MICHIGAN. II. efficiency assessment

With the exception of a few areas, Lake Michigan (LM) is an oligotrophic clear water body. It is predominantly in its littoral zone where ecology-relevant processes unfold due to a variety of natural and anthropogenic forcings arising from the watershed. However, the bottom influence there is strong enough to contaminate the at-satellite signal, thus impeding the remote sensing of water quality parameters within the coastal zone. A new bio-optical retrieval algorithm, based on a forward radiation transfer model, LM specific hydro-optical model and the multivariate optimization technique is developed for operational retrieval from satellite data of water quality parameters in lakes optically shallow areas. Application of the developed operational tool to processing MODIS-Aqua data (matching up the location and timing of in situ CPA and radiometric measurements) has convincingly shown its advantage over the OC4 performance in lacustrine optically shallow waters at all sampling stations.

Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8

For effective lakes’ management, high-frequent water quality data on a synoptic scale are essential. The aim of this study is to test the suitability of the latest generation of satellite sensors to provide information on lake water quality parameters for the five largest Italian subalpine lakes. In situ data of phytoplankton composition, chlorophyll-a (chl-a) concentration and water reflectance were used in synergy with satellite observations to map some algal blooms in 2016. Chl-a concentration maps were derived from satellite data by applying a bio-optical model to satellite data, previously corrected for atmospheric effects. Results were compared with in situ data, showing good agreement. The shape and magnitude of water reflectance from different satellite data were consistent. Output chl-a concentration maps, show the distribution within each lake during blooming events, suggesting a synoptic view is required for these events monitoring. Maps show the dynamic of bloom events with concentration increasing from 2 up to 7 mg m−3 and dropping again to initial value in less than 20 days. Latest generation sensors were shown to be valuable tools for lakes monitoring, thanks to frequent, free of charge data availability over long time periods.

Hyper-spectral Remote Sensing of Water Quality Parameters in Lakes: A Case Study of Hyderabad City, Telangana State, India

This paper is a research work intended to present a comprehensive water quality modeling for predicting three water quality parameters (Chlorophyll (a), Turbidity and Secchi Depth) in typical Inland lake environments (Hussain sagar and Umda sagar) using Hyperspectral Remote sensing technique. They are estimated through regression models by combining the field Spectro-radiometer reflectance values with concurrent in situ ground data (Analytical) collected in the study area and correlated and validated with the available Hyperspectral data (Hyperion). A total of 180 in situ water sample and 900 spectral signatures were analysed during campaigns from 2010 to 2014 study period. The mean values of Chlorophyll-a varied between 6.983mgL&lt;sup&gt;-1&lt;/sup&gt; and 24.858mgL&lt;sup&gt;-1&lt;/sup&gt;, Turbidity varied between 16.583mgL&lt;sup&gt;-1&lt;/sup&gt; and 48.867mgL&lt;sup&gt;-1&lt;/sup&gt; and Secchi depth varied between 0.104mgL&lt;sup&gt;-1&lt;/sup&gt; and 0.375mgL&lt;sup&gt;-1&lt;/sup&gt; over the study period considering the two lakes during pre and post monsoon seasons. The band ratios of the reflected spectra at R670/R710, R710/R740 and R710/R550 are used for the development of the mathematical model of chlorophyll-a, Turbidity and Secchi depth respectively. The trained sets of the pixels extracted from the hyperspectral data for pure spectra are processed for preparing the water quality distribution maps. When subjected to multi-variant statistical tests of significance, the models have yielded satisfactory R&lt;sup&gt;2&lt;/sup&gt; values. The model versus in situ analysis results demonstrated R&lt;sup&gt;2&lt;/sup&gt;= 0.81% for Chlorophyll-a, R&lt;sup&gt;2&lt;/sup&gt;= 0.81% for Turbidity and R&lt;sup&gt;2&lt;/sup&gt;= 0.78% for Secchi depth correlation and that of model versus satellite data exhibited R&lt;sup&gt;2&lt;/sup&gt;= 0.60% for Chlorophyll-a, R&lt;sup&gt;2&lt;/sup&gt;= 0.66% for Turbidity and R&lt;sup&gt;2&lt;/sup&gt;= 0.65 % for Secchi depth mean efficiency.