Monitoring spatial and temporal variation of water quality parameters using time series of open multispectral data
27 Jun 2019-Vol. 11174
TL;DR: In this paper, a 4-year (2013-2016) result of in situ monitoring of surface water bodies in Serbia are used for calibration and validation of algorithm for water quality monitoring based on Landsat 8 satellite image.
Abstract: Water bodies are among most sensitive ecological environments. In order to ensure good water quality and establish framework for their protection European Parliament was adopted the Water Framework Directive (WFD) (Directive 2000/60/EC). The biological, hydro morphological and physic chemical quality parameters which are relevant for assessment of ecological status of water body are defined in Annex V of WFD. Traditionally, quality of surface water bodies are monitored by in situ measurements resulting in low spatial and temporal resolution of historical data. Remote sensing has great potential for monitoring and identification of water bodies over large scale regions in a more effective and efficient manner. In order to provide reliable monitoring of water quality, surface reflection derived by multispectral sensors need to be integrated with in situ measurements. Relationship between remote sensing and in situ data is usually modeled by using empirical, machine learning or deep learning algorithms. In this study, a 4-year (2013-2016) result of in situ monitoring of surface water bodies in Serbia are used for calibration and validation of algorithm for water quality monitoring based on Landsat 8 satellite image. The Turbidity, Suspending Sediments, Total Phosphorus and Total Nitrogen (physic chemical parameters) in region of Vojvodina, Republic of Serbia are monitored. The Neuron Networks and Supported Vector Machine are used to analyzing correlation between in situ measurements and Landsat 8 atmospherically corrected satellite images. Feature more, capabilities of Landsat 8 are compared with Sentinel 2 images (2-years, 2015-2016). In situ data are provided by Agency for environment protection of Serbia.
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TL;DR: In this paper, the spectral reflectance bands from both sensors were used as predictors and stepwise variable selection combined with a priori knowledge based on other studies were used to optimize the choice of predictors.
Abstract: . The low operational cost of using freely available remote sensing data is a strong incentive for water agencies to complement their field campaigns and produce spatially distributed maps of some water quality parameters. The objective of this study is to compare the performance of Sentinel-2 MSI and Landsat-8 OLI sensors to produce multiple regression models of water quality parameters in a hydroelectric reservoir in Brazil. Physical-chemistry water quality parameters were measured in loco using sensors and also analysed in laboratory from water samples collected simultaneously. The date of sampling corresponded to the almost simultaneous overflight of Sentinel-2B and Landsat-8 satellites which provided a means to perform a fair comparison of the two sensors. Four optically active parameters were considered: chlorophyll-a, Secchi disk depth, turbidity and temperature (the latter using Landsat-8 TIR sensor). Other six optically non-active parameters were also considered. The multiple regression models used the spectral reflectance bands from both sensors (separately) as predictors. The reflectance values were based on averaging kernels of 30 m and 90 m. Stepwise variable selection combined with a priori knowledge based on other studies were used to optimize the choice of predictors. With the exception of temperature, the other optically active parameters yielded strong regression models from both the Sentinel and Landsat sensors, all with r2 > 0.75. The models for the optically non-active parameters produced less striking results with r2 as low as 0.03 (temperature) and as high or better than > 0.8 (pH and Dissolved oxygen).
18 citations
01 Aug 2021
12 citations
29 Jan 2021
TL;DR: In this paper, the authors review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas.
Abstract: Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas We also describe a vision that integrates these machine-learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources
8 citations
01 Nov 2020
TL;DR: In this article, the authors proposed a framework for future enhancement for water quality monitoring using remote sensing and evaluated and analyzed most of the recent methodologies that used remote sensing for monitoring the water quality parameters in terms of benefits and limitations.
Abstract: Water quality monitoring is an important issue of worldwide concern to investigate if the water quality measurements are suitable for national standards or not. Remote sensing data can be used to effectively assess water sources. Different sensors are recently used for water quality investigation; these sensors are differed according to their spectral, spatial, and temporal properties. This paper proposes a framework for future enhancement for water quality monitoring using remote sensing. The authors evaluate and analyze most of the recent methodologies that used remote sensing for monitoring the water quality parameters in terms of benefits and limitations. Five quality parameters are examined including 1) optical parameters (Chlorophyll-a (Chi-a), Total Suspended Sediments (TSS), and turbidity), and 2) non-optical parameters (Total Phosphorus (TP) and Total Nitrogen (TN)).
4 citations
TL;DR: In this paper, a hybrid model of a Binary Whale Optimization Algorithm (BWOA) and Artificial Neural Network (ANN) is applied to determine the relationship between extracted reflectance values from Sentinel-2A images and analyzed samples.
Abstract: Monitoring water quality is an important challenge in both developed and developing countries. Remote sensing data can form a highly frequent dataset with acceptable spatial coverage that can be used to remotely monitor water quality. This paper presents a novel automated model for remotely monitoring water quality to address the problem of insufficient samples and save the time and cost of sample collection. The proposed model estimates both optical and non-optical water quality parameters via Sentinel-2A data. A bio-inspired hybrid model of a Binary Whale Optimization Algorithm (BWOA) and Artificial Neural Network (ANN) (BWOA-ANN) is applied to determine the relationship between extracted reflectance values from Sentinel-2A images and analyzed samples. The novelty of this model is to solve two main problems of remote water quality monitoring: poor applicability and low non-optical parameter estimation accuracy. For the first problem, a proposed fully automated model with band selection using the BWOA to automatically select the optimal features (Sentinel-2A bands) that are suitable for each water quality parameter. The second problem is addressed by automatically detecting the relationship between non-optical parameters, such as the total phosphorus, and optical parameters, such as chlorophyll-a. Three datasets with different locations, seasons, and parameters were selected to test the proposed BWOA-ANN. The experimental results demonstrated good regression with a mean ${R}^{2}$ value of 0.916 for optical parameters and 0.890 for non-optical parameters. The proposed model was found to outperform the ANN with an ${R}^{2}$ value higher by 40% and 52% for the optical and non-optical parameters, respectively.
3 citations
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"Monitoring spatial and temporal var..." refers methods in this paper
...ANNs are pattern-recognition algorithms which consists of an interconnected groups of artificial neurons, and it processes information using a connection approach to computation [20]....
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TL;DR: This paper reviews remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology that is particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples.
Abstract: A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.
2,546 citations
TL;DR: In this article, a special issue on Remote Sensing of Inland Waters comprises 16 articles on freshwater ecosystems around the world ranging from lakes and reservoirs to river systems using optical data from a range of in situ instruments as well as airborne and satellite platforms.
459 citations
TL;DR: In this paper, a semi-empirical single-band turbidity retrieval algorithm using the near infrared (NIR) band at 859 nm in highly turbid waters is assessed.
290 citations