Monitoring Of Water Quality Parameters Research Articles

Recent Issues and Challenges in the Study of Inland Waters

This paper addresses several important problems and methods related to studies of inland waters based on the existing scientific literature. The use of UAVs in freshwater monitoring is described, including recent contact and non-contact solutions. Due to a decline in biological diversity in many parts of the globe, the main threats are described together with a modern method for algae and cyanobacteria monitoring utilizing chlorophyll a fluorescence. Observed disturbances in the functioning of river biocenoses related to mine waters’ discharge, causing changes in the physico-chemical parameters of waters and sediments, give rise to the need to develop more accurate methods for the assessment of this phenomenon. Important problems occurring in the context of microplastic detection, including the lack of unification, standardization and repeatability of the methods used, were described. In conclusion, accurate results in the monitoring of water quality parameters of inland waters can be achieved by combining modern methods and using non-contact solutions.

Statistical evaluation of trends of water quality monitoring parameters relevant to cyanobacterial blooms in the urban tropical reservoir.

The accelerated growth of cyanobacteria in water bodies is a global critical environmental issue caused by continuous discharges of effluents into the environment that are rich in phosphorus and nitrogen. So, cyanobacteria have found propitious conditions for proliferation, provoking significant ecological imbalances. Cyanobacteria produce cyanotoxins, which are harmful to life, and compounds like 2-methylisoborneol and geosmin that affect water's taste and odor. This study analyzed a long-term database of important environmental parameters from a tropical reservoir in São Paulo State, Brazil. The statistical methods of correlation matrices and principal component analysis were used. Data analysis revealed a significant relationship between cyanobacteria growth and high levels of phosphate and nitrogen. Furthermore, positive correlations were found among concentrations of biocidal elements like antimony, arsenic, and selenium related to cyanobacterial bloomings. These correlations can be attributed to agricultural wastewaters and/or possible algicide used to control these microorganisms.

Water quality assessment of the Educandos basin in Manaus, Brazil: use of the water quality index and multivariate statistical techniques.

The city of Manaus has undergone an industrialization process that has negatively influenced the wastewater that makes up the urban part of the city. Monitoring water quality parameters and seasonal changes are essential for decision-making for the recovery of the basin's waters. The study evaluated, through the Water Quality Index (WQI) and multivariate statistical techniques, the seasonal and spatial variation in water quality in the Educandos basin, Manaus. The 14 sampling points were selected in the years 2020, 2021 and 2022. The analyzes followed the recommendations of the Stantard Methods for the Examination of Water and Wastewater. The IQA showed that seasonality has not been enough to cause an improvement in water quality. Cluster analysis ranked similar sample points in relation to water quality. The main component showed that turbidity, total solids and biological oxygen demand are the most relevant parameters for PC1, while for PC2 they were Total Coliforms, Total Nitrogen and pH. The multivariate analysis proved to be efficient for monitoring the basin, considering the process of environmental deterioration, resulting from the receipt of waste of mixed domestic and industrial origin.

Water Quality of the Meia Ponte River in the Catchment Region for Public Supply

Purpose: This research aimed to evaluate the physical-chemical and microbiological quality of water in a certain region of the Meia Ponte river basin, through monitoring water quality parameters, and also to verify the interference of the rainy season in water treatment.   Methods: Physicochemical and microbiological analyzes were carried out, evaluating the following parameters: pH, turbidity, color, temperature, total coliforms and Escherichia coli. The location chosen to carry out the collections was the water collection point for public supply from the Meia Ponte River.   Results and conclusion: The physicochemical results obtained in this work were in accordance with CONAMA Resolution 357/2005 of the Ministry of the Environment; as for the microbiological results, which are based on CONAMA 374/2000, only the verified values for Escherichia coli are in accordance with the resolution.   Research implications: The conservation of water sources is extremely important to ensure that the quality and quantity of treated water are in satisfactory condition to meet the population's demand. The quality of the water will depend on the activities that are carried out around the river basin.   Originality/value: Verification of the interference of seasonality typical of the region, in the quality of water for public supply.

The Impact of Biofloc Technology on Water Quality in Aquaculture: A Systematic Meta-Analysis

A technique called biofloc technology (BFT) is an environmentally friendly method for aquaculture in which a successful growing cycle depends on the maintenance and monitoring of water quality parameters. Studies have revealed that improving water quality in BFT and maintaining the safety range of the parameters can help to increase the growth performance of cultured species. Following a systematic review of the literature, a meta-analysis was performed to explore how some important water parameters such as pH, dissolved oxygen (DO), nitrite (NO2–N), nitrate (NO3–N), ammonia (NH3–N), total ammonia nitrogen (TAN), total suspended solids (TSS), and alkalinity were influenced by different BFT systems. The PRISMA screening process was followed, and 33 studies were eligible for the meta-analysis. The meta-analyses showed that NO2–N and TSS were significantly affected by BFT, while pH, DO, NO3–N, NH3–N, TAN, and alkalinity were not significantly influenced by this system. The analyses revealed that NO2–N had a significant negative effect size due to BFT, whereas TSS showed a significant positive effect size. The study also revealed some publication bias in which few experiments of some studies showed extremely positive and negative effect sizes due to BFT application in the system. Overall, the findings suggest clear evidence of the profound influence of BFT on the water quality parameters in different aquaculture systems, suggesting the future development of BFT for sustainable and environmentally friendly aquaculture production.

Seasonal Monitoring of Water Quality Parameters and Pesticides at the Altınapa Reservoir Watershed

Altınapa Reservoir is a reservoir located on the Meram Stream in the Konya Province in Türkiye that supplies drinking water. The Altınapa Reservoir feeds the Konya Drinking Water Treatment Plant with 37.8 million m3 of water annually and the treated water is delivered by the drinking water network. The aim of this study was to determine the effect of agricultural activities and settlements on water quality in the Altınapa Reservoir Watershed. In the study, major water quality parameters (pH, electrical conductivity, total organic carbon, total nitrogen, nitrite, nitrate, phosphorus, phosphate, and total suspended solids) and pesticides were monitored at four stations (K1, K2, K3, K4) on the sub-streams feeding reservoir. Major water quality parameters were monitored at monthly intervals for 12 months between June 2020 and May 2021, and pesticides were monitored seasonally at 4 seasons (Fall 2019, Winter 2020, Summer 2020, and Spring 2021). A correlation matrix was used to assess the connections between nine indicators of water quality. The annual average conductivity and pH values of the samples taken from four different stations of the Meram Stream were 432 µS/cm and 8.04, respectively. The highest value for total nitrogen was 10.73 mg/L and it was 0.87 mg/L for nitrate. Annual average total organic carbon values were determined as 1.35, 1.69, 1.51, and 1.46 mg/L at K1, K2, K3, and K4 stations, respectively. Specific UV absorbance indicated that organic matters of water are mostly hydrophilic and have low aromatic content. In seasonal pesticide monitoring, 71 different compounds were detected in water samples. The compounds whose concentrations exceeded Turkish Environmental Quality Standards were identified as DDD-op, DDE-p.p', diflufenican, and imidacloprid. Pesticides constitute most of the micropollutants detected in water samples. The highest positive correlation among water quality parameters was obtained between conductivity and nitrate (0.81) and total nitrogen (0.88).

Development of The Navigator: A Lagrangian sensing system to characterize surface freshwater ecosystems

Most freshwater aquatic studies rely on Eulerian monitoring, i.e., water quality and quantity are monitored using grab samples or semi-continuous sensors deployed at fixed cross-sections. While Eulerian monitoring is practical, it provides a limited understanding of spatial and temporal heterogeneity. We designed and built The Navigator, a Lagrangian (i.e., along a flow path) monitoring system that offers cost-effective solutions for in-situ, real-time data collection in surface freshwater ecosystems. The Navigator features a suite of technologies, including an autonomous surface vehicle with GPS and LTE connectivity, water quality sensors, a depth sonar, a camera, and a webpage dashboard to visualize real-time data. With these technologies, The Navigator provides insight into where, how, and why water quality and quantity change over time and space as it moves with the current or follows user-specified pathways. We tested The Navigator monitoring water quality parameters at high spatial-temporal resolution in multiple surface water bodies in New Mexico (USA) to: (1) identify water quality changes associated with land use changes along a 7th-order reach in the Rio Grande, (2) identify the fate of wildfire disturbances ∼175 km downstream of a burned watershed affected by the largest wildfire ever recorded in the state, (3) monitor the water quality of a recreational fishing pond in the City of Albuquerque. Our three successful tests confirm that The Navigator is an affordable (USD 5,101 in 2023) monitoring system that can be used to address questions involving mass and energy balances in surface waters.

Relationships of Water Quality Parameters for Hydroponic Production of Kale (Brassica oleracea) with In-Ground Passive Cooling System

Greenhouses in Philippines are encountering high ambient air temperature problem. Along with this, the water quality directly affects the growth, development and yield of hydroponically grown plants.  This study was conceptualized through the principle of cooling the nutrient solution that will effectively regulate plant’s growth and yield with relatively low investment and easier management.  One of the essential elements of a functional hydroponics system is water quality monitoring, hence this study utilized a monitoring system for water quality parameters.  Nutrient solution was closely monitored to maintain and ensure that nutrient levels are not too low to inhibit growth and not too high to be potentially toxic.  The study was conducted to develop a monitoring system for environmental and water quality parameters of in-ground passive cooling system for hydroponic production of kale.  Specifically, the study aimed to determine the relationships of water quality parameters such as pH, EC, TDS, and temperature for kale production, compare the performance of an in-ground passive cooling system (IGPCS) and without cooling system (Control) on hydroponically grown kale, and to evaluate the water productivity of the in-ground passive cooling system.  This study utilized both in-ground passive cooling system (IGPCS) and control (without cooling system) to compare its performance based on the growth and yield of the kale, and to determine the water productivity of utilizing IGPCS.  In addition, water quality parameter such as pH, EC, TDS, and temperature were closely monitored using the installed monitoring system, and relationships of these water quality parameters were established.  The gathered data were analyzed using trend analysis in Microsoft Excel, Analysis of variance (ANOVA) was computed in Completely Randomized Design (CRD) and comparison among means were compared using Least Significant Difference (LSD) Test at 5% level.  Results revealed that EC and TDS of nutrient solution are inversely proportional to pH and temperature of nutrient solution, moreover EC and TDS are directly proportional, also with pH and temperature of nutrient solution. The performance of the IGPCS and without cooling system is comparable with growth and yield of kale in terms of leaf length, leaf width, number of leaves, weight and yield at harvest, but are significantly affected by the location of growing pipes where crops received less and ample amount of sunlight.  Since the temperature of IGPCS was reduced up to 3°C, kale plants with cooling system uptake less water compared to without cooling system and results to higher productivity.  Therefore, regulating one of the water quality parameters directly affects other water quality parameters, so relationships of these water quality parameters were established through close monitoring of water quality parameters using the monitoring system.  Utilization of IGPCS reduced the temperature of the nutrient solution up to 3°C which gave higher yield (44.01 g/plant) than without cooling system (41.18 g/plant).  Kale production in tropical areas can have higher yield using the in-ground passive cooling system that reduced water consumption, resulting to higher productivity.

Spatial-temporal variability analysis of water quality using remote sensing data: A case study of Lake Manyame

Worldwide, the quality of freshwater in inland water bodies has been a major issue of concern due to the negative impact of human activities. With the increase in global population, it is projected that the quality of the water resources will deteriorate. Quantitative information on the state of water quality is quite crucial in water resources planning and conservation. Conventional or ground-based measuring tools are more time demanding, expensive for monitoring water quality parameters of inland water bodies, resulting in incomprehensive coverage in time and space. Due to the paucity of images with fine spatial and temporal resolution like Sentinel 2, provides invaluable information at a fine spatial scale for water quality monitoring to supporting progress towards achieving Sustainable Developments Goals (SDGs). This study quantified the spatial and temporal variations of water quality parameters of Total Nitrogen (TN), Turbidity, Chlorophyll-a (Chl-a) and Total Suspended Matter (TSS) derived from cloud free and remotely sensed Sentinel 2 satellite data for a period from 2017 to 2022 for Lake Manyame in Zimbabwe. Furthermore, the research developed empirical models based on the linear regression between in-situ water sample data and water quality indicators of Sentinel 2. The results showed that between 2017 and 2022, the water quality in Lake Manyame significantly fluctuated. The regression coefficients (R²) between remote sensed water quality parameters and field or sample water data ranged from R² = 0.63 to R² = 0.95, providing a promising possibility for operational use of freely available remote sensing data in water quality monitoring in data constrained countries.The study demonstrated the importance and capability of using freely available Sentinel 2 data, with fine spatial and temporal resolution in providing invaluable information and evaluating on the state and indicators of water quality in inland water bodies in space and time. Such information is crucial in informing resource managers and decision makers in conserving water resources.

Monitoring of sea surface temperature, chlorophyll, and turbidity in Tunisian waters from 2005 to 2020 using MODIS imagery and the Google Earth Engine

Time series ocean color data product facilitates the study of spatio-temporal variability of various physical parameters of water, which allows the monitoring of water quality, interannual variability in phytoplankton biomass, and seasonal representation at different latitudes.Its role in marine biogeochemistry, water quality distribution, and climate fluctuation is very crucial., However, the lack of in situ measurement induces poor management and knowledge of the dynamics of Tunisian coastal waters. Therefore, this study aims to develop a workflow to monitor Tunisian waters based on long-term spatial observations of sea surface temperature, chlorophyll, and turbidity observations.Long-term sea surface observations were automatically obtained by processing the daily Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua data via the Google Earth Engine (GEE) platform from 2005 to 2020. The recorded average monthly and yearly trends are validated by point-based measurements from the Gulf of Gabes and a qualitative analysis based on the bibliographic synthesis of offshore measurement campaigns in Tunisian waters. The hottest years were 2006, 2007, and 2017 while the coldest ones (12–28 °C) were 2011, 2012, and 2016. The highest chlorophyll content (10 and 5 μg L-1) was observed in 2006, 2007, 2011, 2012, 2015, 2016, and 2019. In addition, Turbidity peaks ranging between 11 nephelometric turbidity units (NTU) and 5 NTU were identified during December and January of 2005 2008, and 2011. Moreover, seasonal cyclicity and high correlations between estimated parameters were observed. Overall, combining the Google Earth Engine tool with daily MODIS data was effective for the routine monitoring of water quality parameters that is fast, accurate, and important for Tunisian coast management.

Investigation on Water Quality for Farmed Aquatic Species by IoT Monitoring System

The objective of this research is to optimize fish growth in super-intensive aquaculture systems by addressing water quality issues. Changes in water parameters can significantly impact fish growth and even lead to mortality. To overcome this problem, real-time monitoring of water quality parameters is crucial, and the data should be automatically transmitted to the entrepreneur through the Internet of Things (IoT). This allows the owner to access real-time data remotely, eliminating the need to be physically present at the fish cage. By monitoring the data on a smartphone, the owner can efficiently manage the water quality. In this study, a water quality monitoring system utilizing temperature, pH, and ESP32, with the parameter values displayed on the Blynk platform was developed. The system successfully measures real-time water conditions and transmits the data to the Blynk application. The test results demonstrate the system's capability to receive and send data, but further analysis is needed to evaluate the accuracy and reliability of the measurements and identify any noteworthy findings.

Predicting the formation of disinfection by-products using multiple linear and machine learning regression

Controlling the formation of disinfection byproducts (DBPs) requires prior knowledge of DBP formation potential. Mathematical models can accurately predict the formation of DBPs and have the advantage of reducing laboratory tests and related costs. Researchers continue to develop new models for specific regions but rarely used external data sets to evaluate the predictive ability of previous models. Most of the models focus on total trihalomethanes (THMs), and the predictive models for emerging DBPs (e.g., chloral hydrate (CH)) are lacking. Moreover, little discussion is available on comparing linear and machine learning (ML) algorithms in predicting the formation of DBPs. This study investigated the predictive models of CH, chloroform, THMs, dichloroacetic acid, trichloroacetic acid, and haloacetic acids based on stepwise multiple linear regression and ML regression using easily monitored water quality parameters (i.e., pH, UV254, and total organic carbon (TOC)). Among these parameters, UV254 is the dominant parameter in predicting the formation of target DBPs and deserves more attention in future studies. Among the models for the target DBPs, the model for CH using stepwise multiple linear regression was shown as follows: LnCH = 8.945 + 0.558 × Ln(UV254) – 2.37 × Ln(pH) + 0.152 × Ln(TOC). The support vector regression (MAPE = 2.578–5.798%, R2 = 0.665–0.802) and random forest regression (MAPE = 2.867–5.346%, R2 = 0.671–0.965) performed better than traditional stepwise linear regression (MAPE = 2.857–6.671%, R2 = 0.602–0.770) in the training and testing set. This emphasized that ML algorithms were viable alternatives to conventional linear regression in the management of DBPs.

Jaringan Fog untuk Pemantauan Real Time Kualitas Air Tambak Udang

Effective and efficient monitoring of water quality conditions in shrimp ponds is crucial to enhance shrimp production. Therefore, this research discusses a water quality monitoring system that provides real-time data and information on pond water quality parameters using a Fog network. The monitored water quality parameters include dissolved oxygen (DO), temperature, and pH. The values of these parameters are obtained in real-time through sensors within the Fog network. The data is then transmitted and stored in a database on a web server. The parameter values for temperature, pH, and DO in the database are illustrated in the form of graphs, representing the current condition of water quality. The system can also automatically provide notifications and alerts regarding the current monitored water quality condition. This enables prompt action to be taken if the parameter values deviate from the normal threshold limits

Application of artificial intelligence for forecasting surface quality index of irrigation systems in the Red River Delta, Vietnam

Water sources for irrigation systems in the Red River Delta are crucial to the socioeconomic growth of the region's communities. Human activities (discharge) have polluted the water source in recent years, and the water source from upstream is limited. Currently, the surface water quality index (WQI), which is calculated from numerous surface water quality parameters (physical, chemical, microbiological, heavy metals, etc.) is frequently used to evaluate the surface water quality of irrigation systems. However, the calculation of the WQI from water quality monitoring parameters remains constrained due to the need for a large number of monitoring parameters and the relative complexity of the calculation. To better serve the assessment of surface water quality in the study area, it is crucial and essential to conduct research to identify an efficient and accurate method of calculating the WQI. This study used machine learning and deep learning algorithms to calculate the WQI with minimal input data (water quality parameters) to reduce the cost of monitoring surface water quality. The study used the Bayes method (BMA) to select important parameters (BOD5, NH4+, PO43−, turbidity, TSS, coliform, and DO). The results indicate that the machine learning model is more effective than the deep learning model, with the gradient boosting model having the most accurate prediction results because it has the highest coefficient of determination R2 (0.96). This is a solid scientific basis and an important result for the application of machine learning and deep learning algorithms to calculate WQI for the research area. The study also demonstrated the potential of artificial intelligence algorithms to improve water quality forecasting compared to traditional methods with minimal cost and time.

Morphological changes analysis using 3D bathymetric surveys in Chilia Branch - Bystroe Channel bifurcation area

The Bystroe Channel project with transboundary environmental impact in the Danube Delta, area of great ecological significance that has already a strong anthropic footprint, requires close monitoring of water quality parameters in order to determine their tendencies and their impact on the ecosystem components. Riverbed bathymetry surveys using multibeam echo-sounders are of high interest due to the data resolution and coverage capabilities that surpass the single-beam methods. Two riverbed elevation datasets, recorded in consecutive years, have been used to carry out morphological comparative analysis for the area where Chilia branch bifurcates in Bystroe Channel and Old Stambul. The analysis has been performed both on the bathymetry grids as a whole and on 3 longitudinal and 9 transversal river sections, the morphological changes values being in majority included in [-0.5 m;+0.5 m], with a minimum of -2.4 m and a maximum of 2.2 m, showing the bifurcation influence on the erosion/deposition processes results.

Sanitizer Type and Contact Time Influence Salmonella Reductions in Preharvest Agricultural Water Used on Virginia Farms

No Environmental Protection Agency (EPA) chemical treatments for preharvest agricultural water are currently labeled to reduce human health pathogens. The goal of this study was to examine the efficacy of peracetic acid- (PAA) and chlorine (Cl)-based sanitizers against Salmonella in Virginia irrigation water. Water samples (100 mL) were collected at three time points during the growing season (May, July, September) and inoculated with either the 7-strain EPA/FDA-prescribed cocktail or a 5-strain Salmonella produce-borne outbreak cocktail. Experiments were conducted in triplicate for 288 unique combinations of time point, residual sanitizer concentration (low: PAA, 6 ppm; Cl, 2-4 ppm or high: PAA, 10 ppm; Cl, 10-12 ppm), water type (pond, river), water temperature (12°C, 32°C), and contact time (1, 5, 10 min). Salmonella were enumerated after each treatment combination and reductions were calculated. A log-linear model was used to characterize how treatment combinations influenced Salmonella reductions. Salmonella reductions by PAA and Cl ranged from 0.0 ± 0.1 to 5.6 ± 1.3 log10 CFU/100 mL and 2.1 ± 0.2 to 7.1 ± 0.2 log10 CFU/100 mL, respectively. Physicochemical parameters significantly varied by untreated water type; however, Salmonella reductions did not (p = 0.14), likely due to adjusting the sanitizer amounts needed to achieve the target residual concentrations regardless of source water quality. Significant differences (p < 0.05) in Salmonella reductions were observed for treatment combinations, with sanitizer (Cl > PAA) and contact time (10 > 5 > 1 min) having the greatest effects. The log-linear model also revealed that outbreak strains were more treatment-resistant. Results demonstrate that certain treatment combinations with PAA- and Cl-based sanitizers were effective at reducing Salmonella populations in preharvest agricultural water. Awareness and monitoring of water quality parameters are essential for ensuring adequate dosing for the effective treatment of preharvest agricultural water.

Evaluating Statistical Machine Learning Algorithms for Classifying Dominant Algae in Juam Lake and Tamjin Lake, Republic of Korea

South Korea’s National Institute of Environmental Research (NIER) operates an algae alert system to monitor water quality at public water supply source sites. Accurate prediction of dominant harmful cyanobacterial genera, such as Aphanizomenon, Anabaena, Oscillatoria, and Microcystis, is crucial for managing water source contamination risks. This study utilized data collected between January 2017 and December 2022 from Juam Lake and Tamjin Lake, which are representative water supply source sites at the Yeongsan River and Seomjin River basins. We performed an exploratory data analysis on the monitored water quality parameters to understand overall fluctuations. Using data from 2017 to 2021 as training data and 2022 data as test data, we compared the dominant algal classification accuracy of 11 statistical machine learning algorithms. The results indicated that the optimal algorithm varied depending on the survey site and evaluation criteria, highlighting the unique environmental characteristics of each site. By predicting dominant algae in advance, stakeholders can better prepare for water source contamination accidents. Our findings demonstrate the applicability of machine learning algorithms as efficient tools for managing water quality in water supply source systems using monitoring data.

ANALYSIS OF THE REMOTELY SENSED WATER QUALITY PARAMETERS OF THE INSUBRIC LAKES: METHODS AND RESULTS OF THE INTERREG SIMILE PROJECT

Abstract. Lakes are a fundamental component of the environment and the territory and represent a precious source of fresh water for various uses. The area of the Prealps north of the Po valley in Italy is characterized by the presence of lakes which represent almost 80% of the total volume of fresh water in Italy (Rogora et al., 2018). The Insubric lakes (Lugano, Maggiore and Como) have their shared basins between Italy and Switzerland, and they are the objective of the SIMILE project, a cross-border Italian-Swiss project that aims to improve their coordinated management and strengthen stakeholder participation in the processes of knowledge and monitoring of water resources (Brovelli et al., 2019) by analyzing data acquired from in-situ to satellite sensors. The present work refers to data collected by remote sensing methods which offer the possibility to obtain synoptic views of water bodies to monitor water quality parameters (WQPs) such as the chlorophyll-a (Chl-a), the total suspended matter (TSM) and the lake surface water temperature (LSWT) (Giardino et al., 2013). This work presents an extensive evaluation of the space-time trends of the parameters based on the SIMILE remote sensing database.

Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager.

LASSO (L1) Regularization for Development of Sparse Remote-Sensing Models with Applications in Optically Complex Waters Using GEE Tools

Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then creating empirical water quality models using approaches such as multi-linear regression or step-wise linear regression. These approaches, which require modelers to select model parameters, may not be well suited for optically complex waters, where interference from suspended solids, dissolved organic matter, or other constituents may act as “confusers”. For these waters, it may be useful to include non-standard terms, which might not be considered when using traditional methods. Recent machine-learning work has demonstrated an ability to explore large feature spaces and generate accurate empirical models that do not require parameter selection. However, these methods, because of the large number of included terms involved, result in models that are not explainable and cannot be analyzed. We explore the use of Least Absolute Shrinkage and Select Operator (LASSO), or L1, regularization to fit linear regression models and produce parsimonious models with limited terms to enable interpretation and explainability. We demonstrate this approach with a case study in which chl-a models are developed for Utah Lake, Utah, USA., an optically complex freshwater body, and compare the resulting model terms to model terms from the literature. We discuss trade-offs between interpretability and model performance while using L1 regularization as a tool. The resulting model terms are both similar to and distinct from those in the literature, thereby suggesting that this approach is useful for the development of models for optically complex water bodies where standard model terms may not be optimal. We investigate the effect of non-coincident data, that is, the length of time between satellite image collection and in situ sampling, on model performance. We find that, for Utah Lake (for which there are extensive data available), three days is the limit, but 12 h provides the best trade-off. This value is site-dependent, and researchers should use site-specific numbers. To document and explain our approach, we provide Colab notebooks for compiling near-coincident data pairs of remote-sensing and in situ data using Google Earth Engine (GEE) and a second notebook implementing L1 model creation using scikitlearn. The second notebook includes data-engineering routines with which to generate band ratios, logs, and other combinations. The notebooks can be easily modified to adapt them to other locations, sensors, or parameters.