Spectral indices for estimating total dissolved solids in freshwater wetlands using semi-empirical models. A case study of Guartinaja and Momil wetlands

Avila, Doris Mejia; Torres-Bejarano, Franklin; Lara, Zoraya Martínez

doi:10.1080/01431161.2022.2057205

Cited by 10 publications

(10 citation statements)

References 54 publications

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“…Examples of optically active parameters include suspended particulate matter (SPM) or sediment concentrations, CDOM, turbidity, and chlorophyll-a concentrations. Non-optically active parameters on the other hand include TP, TN, COD, DO, and TDS [138,139]. Studies have also proposed two classifications of water bodies based on reflectance ratios at 443 and 550 nm namely Case I and II waters [47].…”

Section: Characterization Of Water Quality Parametersmentioning

confidence: 99%

“…Studies have considered ways of improving the accuracy of their results obtained from high-resolution images through techniques such as pan-sharpening. Other studies resampled their pixel resolution such as the case of [139] where the 20 m resolution Sentinel 2 image was resampled to 10 m resolution. Pan-sharpening or downscaling, unlike conventional resampling, is a fusion technique that allows for the artificial enhancement of the spatial resolution of the image by fusing it with a higher-resolution image.…”

Section: Limitationsmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Characterization Of Water Quality Parametersmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

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

et al. 2023

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“…To determine the optimal lag time to use, we based our analysis on how frequently the TDS concentration changes in the lake using autocorrelation of TDS values at different lag times, as presented by Equation ( 9). The lag time at which the highest autocorrelation occurs was taken as the period in which the change in the TDS is most apparent or pronounced [17,[117][118][119], as presented in Figure 7.…”

Section: Model Performance Assessmentmentioning

confidence: 99%

“…where Cov (X t , X t−z ,) are the covariances between the time series at time t and the lagged time (t − z), and σ t and σ t−z are the standard deviations for the time series at time t and lagged time, t − z. Values range from −1 to 1, with 1 meaning perfect positive correlations [17,118]. The time series of the observed and estimated TDS were produced to visually inspect the performance of the model.…”

Section: Model Performance Assessmentmentioning

confidence: 99%

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A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Adjovu

Stephen

Ahmad

2023

Water

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Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and temperature. Compared to monitoring TDS concentrations, monitoring EC and temperature is simpler, inexpensive, and takes less time. This study, therefore, applied several machine learning (ML) approaches to estimate TDS concentration in Lake Mead using EC and temperature data. Standalone models including the support vector machine (SVM), linear regressors (LR), K-nearest neighbor model (KNN), the artificial neural network (ANN), and ensemble models such as bagging, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) models were used in this study. The models’ performance were evaluated using several performance metrics aimed at providing a holistic assessment of each model. Metrics used include the coefficient of determination (R2), mean absolute error (MAE), percent mean absolute relative error (PMARE), root mean square error (RMSE), the scatter index (SI), Nash–Sutcliffe model efficiency (NSE) coefficient, and percent bias (PBIAS). Results obtained showed varying model performance at the training, testing, and external validation stage of the models, with obtained R2 of 0.77–1.00, RMSE of 2.28–37.68 mg/L, an MAE of 0.14–22.67 mg/L, a PMARE of 0.02–3.42%, SI of 0.00–0.06, NSE of 0.77–1.00, and a PBIAS of 0.30–0.97 across all models for the three datasets. We utilized performance rankings to assess the model performance and found the LR to be the best-performing model on the external validation datasets among all the models (R2 of 0.82 and RMSE of 33.09 mg/L), possibly due to the established existence of a relationship between TDS and EC, although this may not always be linear. Similarly, we found the XGBoost to be the best-performing ensemble model based on the external validation with R2 of 0.81 and RMSE of 34.19 mg/L. Assessing the overall performance of the models across all the datasets, however, revealed GBM to produce a superior performance based on the ranks, possibly due to its ability to reduce overfitting and improve generalizations. The findings from this study could be employed in assisting water resources managers and stakeholders in effective monitoring and management of water resources to ensure their sustainability.

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Method for assessing spectral indices efficiency for mapping tropical wetlands—SIA_MW

Mejia Ávila,

Lobo Cabeza,

Soto Barrera

2023

Appl Geomat

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Spectral indices for estimating total dissolved solids in freshwater wetlands using semi-empirical models. A case study of Guartinaja and Momil wetlands

Cited by 10 publications

References 54 publications

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

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

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Method for assessing spectral indices efficiency for mapping tropical wetlands—SIA_MW

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