Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

Remote Sensing, Год журнала: 2023, Номер 15(14), С. 3534 - 3534

Опубликована: Июль 13, 2023

This study provides a comprehensive review of the efforts utilized in measurement water quality parameters (WQPs) with focus on total dissolved solids (TDS) and suspended (TSS). The current method used TDS TSS includes conventional field gravimetric approaches. These methods are limited due to associated cost labor, spatial coverages. Remote Sensing (RS) applications have, however, been over past few decades as an alternative overcome these limitations. Although they also present underlying atmospheric interferences images, radiometric spectral resolution issues. Studies WQPs RS, therefore, require knowledge utilization best mechanisms. use RS for retrieval TDS, TSS, their forms has explored many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) satellite such those Landsat, Sentinel-2, Aqua, Terra platforms. properties serve inputs deep learning analysis statistical, machine models. Methods retrieve WQP measurements dependent optical inland bodies. While is optically active parameter, inactive low signal–noise ratio. detection visible, near-infrared, infrared bands some process that (usually) co-occurs changes affecting active. revealed significant improvements incorporating approaches estimating WQPs. findings reveal improved spatiotemporal potential effectively detect For effective monitoring we recommend employing correction mechanisms reduce image interference, exploration fusion microwave bands, high-resolution hyperspectral ML models, calibration validation observed data measured methods. Further could development new technology UAVs produce real-time situ TSS. presented this aid consolidating understanding advancement single repository thereby offering stakeholders, researchers, decision-makers, regulatory bodies go-to information resource enhance mitigation impairments.

Язык: Английский

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

Remote Sensing, Год журнала: 2023, Номер 15(7), С. 1938 - 1938

Опубликована: Апрель 4, 2023

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

Язык: Английский

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

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(3)

Опубликована: Фев. 17, 2024

Язык: Английский

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Assessing intra and interannual variability of water quality in the Sundarban mangrove dominated estuarine ecosystem using remote sensing and hybrid machine learning models

Journal of Cleaner Production, Год журнала: 2024, Номер 442, С. 140889 - 140889

Опубликована: Янв. 26, 2024

Язык: Английский

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Forest restoration improves habitat and water quality in tropical streams: A multiscale landscape assessment

The Science of The Total Environment, Год журнала: 2025, Номер 963, С. 178256 - 178256

Опубликована: Янв. 17, 2025

Язык: Английский

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Monitoring inland water via Sentinel satellite constellation: A review and perspective

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2023, Номер 204, С. 340 - 361

Опубликована: Сен. 28, 2023

Язык: Английский

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An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai

The Science of The Total Environment, Год журнала: 2023, Номер 880, С. 163389 - 163389

Опубликована: Апрель 7, 2023

The optical insensitivity of non-optically active water quality parameters (NAWQPs) presents a significant challenge for remote sensing-based quantitative monitoring, which is an important tool assessment and management. Based on the analysis samples from Shanghai, China, it was found that spectral morphological characteristics body were obviously different under combined effect multiple NAWQPs. In view this, in this paper, machine learning method proposed retrieval urban NAWQPs by using multi-spectral scale feature (MSMCF). integrates both local global features, employs multi-scale approach to enhance its applicability stability, providing more accurate robust solution. To explore MSMCF retrieving NAWQPs, methods tested terms accuracy stability measured data three hyperspectral data. As can be seen results, has good performance, applied with resolutions certain ability suppress noise. Further indicates sensitivity each NAWQP features varies. research findings paper promote development sensing technology prevention treatment deterioration, provide reference related research.

Язык: Английский

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Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms

Water, Год журнала: 2024, Номер 16(4), С. 553 - 553

Опубликована: Фев. 11, 2024

Remote sensing technology applications for water quality inversion in large rivers are common. However, their application to medium/small-sized bodies within rural areas is limited due the low spatial resolution of remote images. In this work, a typical small river was selected, and high-resolution unmanned aerial vehicle (UAV) multispectral images ground monitoring data were obtained. Then, comparative analysis three univariate regression models nine machine learning (Ridge Regression (RR), Support Vector (SVR), Grid Search (GS-SVR), Random Forest (RF), (GS-RF), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Convolutional (CNN), Catboost (CBR)) accuracy prediction turbidity (TUB), total nitrogen (TN), phosphorus (TP) performed. TUB can be achieved by simple statistical models. The CBR model exhibited best performance index inversions on test set evaluation metrics: R2 (0.90~0.92), RMSE (7.57 × 10−3~1.59 mg/L), MAE (0.01~1.30 RPD (3.21~3.56), NSE (0.84~0.92). pollution study area closely related its land-use pattern, excessive irrational fertilizer application, distribution pollutant outlets.

Язык: Английский

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Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

Sustainability, Год журнала: 2024, Номер 16(2), С. 559 - 559

Опубликована: Янв. 9, 2024

Accurate prediction of spatial variation in water quality small microwaters remains a challenging task due to the complexity and inherent limitations optical properties microwaters. In this paper, based on unmanned aerial vehicles (UAV) multispectral images amount measured data, performance seven intelligent algorithm-optimized SVR models predicting concentration chlorophyll (Chla), total phosphorus (TP), ammonia nitrogen (NH3-N), turbidity (TUB) micro bodies were compared analyzed. The results show that Gray Wolf optimized model (GWO-SVR) has highest comprehensive performance, with R2 0.915, 0.827, 0.838, 0.800, respectively. addition, even when dealing limited training samples different data periods, GWO-SVR also shows remarkable stability portability. Finally, according forecast results, influencing factors pollution discussed. This method practical significance improving intelligence level body monitoring.

Язык: Английский

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Remote Sensing Application in Ecological Restoration Monitoring: A Systematic Review

Remote Sensing, Год журнала: 2024, Номер 16(12), С. 2204 - 2204

Опубликована: Июнь 17, 2024

In the context of continuous degradation global environment, ecological restoration has become a primary task in environmental governance. this process, remote sensing technology, as an advanced monitoring and analysis tool, plays key role restoration. This article reviews application technology monitoring. Based on comprehensive literature field sensing, it systematically summarizes major in-orbit spaceborne airborne sensors their related products. further proposes series evaluation indicators for from four aspects: forests, soil, water, atmosphere, elaborates calculation methods these indicators. addition, paper also evaluating effectiveness restoration, including subjective evaluation, objective methods. Finally, we analyze challenges faced by effectiveness, such issues with precision extraction, limitations spatial resolution, diversity review looks forward to future technologies, potential applications integrated aerospace terrestrial multi-data fusion, machine learning technologies. study reveals monitoring, aiming provide efficient tools innovative strategies assessment

Язык: Английский

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