Landsat observations of chlorophyll-a variations in Lake Taihu from 1984 to 2019

Cao, Zhigang; Ma, Ronghua; Mélack, John M.; Duan, Hongtao; Liu, Miao; Kutser, Tiit; Xue, Kun; Shen, Ming; Qi, Tianci; Yuan, Huili

doi:10.1016/j.jag.2021.102642

Cited by 24 publications

(23 citation statements)

References 59 publications

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“…Random forests currently dominate as the most popular tree-based algorithms, followed by decision trees (DTs) and XGBoost. However, XGBoost is gaining popularity due to its remarkable predictive accuracy, which rivals that of NNs [23,36,180,193,209,211,217,219]. Recent studies demonstrate XGBoost's impressive capabilities in a variety of satellite-based water quality monitoring tasks [23,36,180,193,209,211,217,219].…”

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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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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“…The extracted bands are BLUE, GREEN, RED, Near-Infrared(NIR), Short-Wave Infrared 1(SWIR1), and Short-Wave Infrared 2(SWIR2), whose name is based on Landsat-8 (Cao Z et al, 2022). A total of 259 remote satellite images matching the in-site data date were finally obtained after deleting the large cloud cover images, as shown in Table 1.…”

Section: Data Sources and Processingmentioning

confidence: 99%

“…The two shortwave infrared bands of the Landsat sensor can improve the model's performance and eliminate some errors caused by aerosols (Cao Z et al, 2022). In this study, three spectral indices were selected as independent spectral variables.…”

Section: Extraction Of Spectral Datamentioning

confidence: 99%

Improving lake chlorophyll-a interpreting accuracy by combing spectral and texture features of remote sensing

Yang

Zhang

Gao

et al. 2023

Preprint

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Cyanobacterial blooms in lakes fueled by increasing eutrophication have garnered global attention, and high-precision remote sensing retrieval of chlorophyll-a (Chla) is essential for monitoring the blooms. Previous studies have focused on the spectral features extracted from remote sensing images and their relationship with chlorophyll-a concentrations in water bodies, ignoring the texture features in remote sensing images which is beneficial to improve interpreting accuracy. This study explores the texture features in remote-sensing images. It proposes a retrieval method for estimating lake Chla concentration by combining spectral and texture features of remote sensing images. Remote sensing images from Landsat 5, and 8 were used to extract NIR-Red, GREEN-BLUE, MNDWI, and KIVU bands ratio. The gray-level co-occurrence matrix (GLCM) of remote sensing images was used to obtain a total of 8 texture features; then three texture indices were calculated using texture features. Finally, a random forest regression was used to establish a retrieval model of in-site Chla concentration from texture and spectral index. Results showed that texture features are significantly correlated with lake Chla concentration, and they can reflect the temporal and spatial distribution change of Chla. The retrieval model combining spectral and texture indices has better performance (R2 = 0.801, RMSE = 16.0 µg·L− 1) than the model without texture features(R2 = 0.746, RMSE = 16.2 µg·L− 1). The proposed model performance varies in different Chla concentration ranges and is excellent in predicting higher concentrations. This study evaluates the potential of incorporating texture features of remote sensing images in lake water quality estimation and provides a novel remote sensing method to better estimate lake Chla concentration.

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“…By leveraging large datasets, Remote Sens. 2023, 15, 4487 2 of 18 these algorithms can establish complex relationships between water quality parameters and multiple variables, enabling the estimation of crucial parameters, such as the Secchi disk depth (SDD) [5], chlorophyll-a (Chl-a) [6], total suspended matter (TSM) [7], and chromophoric dissolved organic matter (CDOM) [8], and they have been successfully applied to the long-term monitoring of multiple lakes on a large spatial scale [9][10][11]. Nonetheless, it is crucial to note that, in addition to these optical characteristic parameters, other non-optical parameters are also closely related to lake eutrophication and play a crucial role in assessing the safety of the water environment, such as total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH 3 -N), and the permanganate index (COD Mn ) [12,13]; however, there are some difficulties in estimating these indices in turbid inland water bodies.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional direct inversion method is simple and has good results, but it is not suitable for lakes with complex relationships, while the indirect inversion is detrimental to the model accuracy, so currently these two inversion methods are only applied to a single lake. In contrast, machine learning has higher application ability on a large regional scale, which is not only well proven in parameters with optical characteristics, such as chlorophyll-a [6] and Secchi disk depth [26], but also shows good robustness in the inversion of non-optical parameters, such as dissolved CO 2 [27,28]. Although some scholars believe that machine learning has the potential for application in the remote sensing estimation of TP for lakes at a large regional scale [22], a complete proof and application have not been given.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Total Phosphorus Concentration in Lakes in the Yangtze-Huaihe Region Based on Sentinel-3/OLCI Images

Wang,

Jiang,

Jiang

et al. 2023

Remote Sensing

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Total phosphorus (TP) concentration is a crucial parameter to assess eutrophication in lakes. As one of the most concentrated regions for freshwater lakes, the Yangtze-Huaihe region plays a significant role in monitoring TP concentrations for the sustainable utilisation of China’s water resources. In this study, a TP concentration estimation model suitable for large-sized lake groups was developed using a combination of measured and remote sensing data powered by advanced machine learning algorithms. Compared to traditional empirical models, the model developed in this study demonstrates significant accuracy in fitting (R2 = 0.53, RMSE = 0.08 mg/L, MAPE = 34.20%). Moreover, the application of this model to lakes in the Yangtze-Huaihe region from 2017 to 2022 has been conducted. The multi-year average TP concentration was 0.18 mg/L. Spatial distribution analyses showed that total phosphorus concentrations were higher in small lakes. In terms of temporal changes, the interannual decreases in total phosphorus concentrations were 0.02 mg/L, 0.01 mg/L, and 0.01 mg/L for small, medium, and large lakes, respectively. We also found that large lakes typically exhibited a “high in spring and summer, low in autumn and winter” pattern until 2020, but transitioned to a “high in summer and autumn, low in spring and winter” pattern after 2020 due to the removal of closed fish nets, which were having a significant impact on the lake ecosystem. Other lakes in the area consistently showed a pattern of “high in spring and summer, low in autumn and winter” during the six-year period. These findings may provide useful references and suggestions for the environmental protection and management of lakes in China.

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Landsat observations of chlorophyll-a variations in Lake Taihu from 1984 to 2019

Cited by 24 publications

References 59 publications

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

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

Improving lake chlorophyll-a interpreting accuracy by combing spectral and texture features of remote sensing

Estimation of Total Phosphorus Concentration in Lakes in the Yangtze-Huaihe Region Based on Sentinel-3/OLCI Images

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