Estimation of chlorophyll-a Concentration of lakes based on SVM algorithm and Landsat 8 OLI images

Zhang, Teng; Huang, Meifa; Wang, Zhongjing

doi:10.1007/s11356-020-07706-7

Cited by 31 publications

(11 citation statements)

References 37 publications

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“…Recent research by Zhang et al [71] applied an SVM model with an RBF kernel on Landsat 8 OLI images to estimate the chlorophyll-a concentrations of multiple lakes in China. Their 90 match-up samples were formed using Landsat OLI images acquired on two cloud-free days, respectively, in December 2017 and in March 2018, over five lakes.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Wang

Liu

et al. 2020

Remote Sensing

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Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.

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Section: Comparison With Other Studiesmentioning

confidence: 99%

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Wang

Liu

et al. 2020

Remote Sensing

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“…Neural networks (NNs) or artificial neural networks (ANNs) as they are commonly known [40], which include feed-forward neural networks (FFNN) [30], recurrent neural networks (RNN) [22,41,42], and convolutional neural networks (CNN) [43], have emerged as the go-to algorithms in this field. In this domain, SVMs have also proven to be highly adaptable, with researchers experimenting through selecting different kernel functions that best suit their data, such as sigmoid, linear, radial basis function (RBF), or polynomial [44].…”

Section: An Overview Of Machine and Deep Learning Techniques Used In ...mentioning

confidence: 99%

“…This sets them apart from opaque algorithms like NNs and support vector machines (SVMs). SVM algorithms hold a prominent position in the field of satellite-based water quality monitoring, and their inclusion in numerous studies showcases their remarkable potential for this application [22,25,26,30,33,34,40,44,46,75,178,181,182,185,192,194,[200][201][202]. SVMs, in particular, prove to be highly suitable for satellite-based water quality monitoring due to their exceptional capability to handle large datasets characterized by a high number of features.…”

Section: Machine or Deep Learning Model Choicementioning

confidence: 99%

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author’s presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution.

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“…Some commonly used methods are correlation coefficient analysis (CC), successive projections algorithm (SPA), competitive adaptive reweighted sampling method (CARS), etc. For example, Zhang et al [12] used CC to analyze band data in Landsat 8 and measured Chl-a concentrations in Donghu Lake, China, where the feature band with a high correlation coefficient with Chl-a was selected to construct a prediction model. Zhang et al [13] used SPA to analyze the sensitive band of the remote sensing feature data, and selected the most sensitive band to construct the Chl-a concentration prediction model of Qinghai Lake.…”

Section: Introductionmentioning

confidence: 99%

A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake

Wu,

Fu,

et al. 2023

IEEE Access

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Chlorophyll-a (Chl-a) is an important parameter of water bodies, but due to the complexity of optics in water bodies, it is currently difficult to accurately predict Chl-a concentration in water bodies by traditional methods. In this paper, Sentinel-2 remote sensing images is used as the data source combined with measured data, and Ulansuhai Lake is taken as the study area. An adaptive ant colony exhaustive optimization (A-ACEO) algorithm is proposed for feature selection and combined with a novel intelligent algorithm of optimizing support vector regression (SVR) by genetic algorithm (GA) for prediction of Chl-a concentration. The ant colony optimization (ACO) algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GA-SVR model is built by optimizing SVR using GA with the selected feature bands as input, and comparing with the traditional SVR model. The simulation results show that under the same conditions, using A-ACEO algorithm to select feature bands as inputs can effectively reduce the model complexity, and improve the model prediction performance, which provides a valuable reference for monitoring Chl-a concentration in lakes.

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Estimation of chlorophyll-a Concentration of lakes based on SVM algorithm and Landsat 8 OLI images

Cited by 31 publications

References 37 publications

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake

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