Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam

Bình, Nguyễn An; Hoa, Pham Viet; Thao, Giang Thi Phuong; Duan, Ho Dinh; Thu, Phan Minh

doi:10.1016/j.jag.2022.102951

Cited by 5 publications

(7 citation statements)

References 38 publications

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“…This observation is consistent with findings by other researchers [106][107][108][109] who observed good correlations between Sentinel-2-based Chl-a concentration measurements and in-situ data. This finding was further validated through ANOVA, which revealed statistically insignificant differences at seven of the eight stations that were used as sources of validation reference data (Table 5).…”

Section: Discussionsupporting

confidence: 93%

“…This finding is consistent with observations by other researchers in different coastal areas worldwide. In Vietnam's coastal waters, for example, Bihn et al [109] successfully retrieved Chl-a concentrations from Sentinel 3A images (R 2 = 0.58, RMSE = 1.018 mg/m 3 ). Moses et al [110] report similar results from a study of the Sea of Azov (a shallow north of the Black Sea) in which they obtained accurate Chl-a concentrations with accuracies in the order of 90%.…”

Section: Discussionmentioning

confidence: 99%

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Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa

Mathe,

Hamandawana

2023

Sustainability

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Eleven level-2 Sentinel 3A OLCI images that were acquired between 29 March 2017 and 11 December 2017 were used to assess their ability to retrieve oceanic Chl-a concentrations in South Africa’s Algoa and St Francis Bays. This was done by applying a 7-colour gradient pallet in the SNAP software to produce oceanic Chl-a concentration maps on a scale ranging from 0.1–30 mg/m3. The validation of Sentinel’s Chl-a’s retrieval potentials was based on temporally corresponding in-situ data from eight stations. Comparative analysis of the image-based and in-situ Chl-a concentrations revealed statistically significant correlations (r 0.609–r 0.899, ᾶ 0.05) at five stations out of the eight that were used as sources of reference data. This finding is helpful as an objectively premised source of insights on how to sustainably utilise the oceanic resources at our disposal. It is also useful because it verifiably demonstrates that Sentinel images can be reliably used to retrieve usable information on Chl-a concentrations in lieu of the costly sea-surface-based in-situ measurements at appropriate temporal and spatial scales.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa

Mathe,

Hamandawana

2023

Sustainability

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“…Unsupervised methods, such as those used in data preprocessing or within complex data pipelines, have been successfully used in this research area. These include dimensionality reduction techniques such as PCA [52,65], variational autoencoders (VAEs) [50,255,258,259], and DINEOF [48,53,258,259,278,279]. They have been applied to deal with both high dimensionality and high spatial resolution of satellite data.…”

Section: Machine or Deep Learning Model Choicementioning

confidence: 99%

“…Factors such as natural variations in water chemistry and geological features may also contribute to the presence of type II water in some inland areas. Coastal water study areas investigated by most of the researchers included the transitional zones between land and sea, including estuaries, deltas, and nearshore regions [32,33,46,158,159,167,187,199,212,217,[225][226][227]258,278,279] (Figure 6d). These areas often exhibit a complex interplay of influences from both land-based sources and oceanic processes.…”

Section: The Water Quality Classesmentioning

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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“…Traditional approaches of chl-a estimation involve field observation through on-site sensors and laboratory analysis of collected water samples, which are limited by their high-cost, time-consuming, and labor-intensive nature; therefore, they are unsuitable for large spatiotemporal scales [11,12]. Instead, satellite remote sensing technology, with its enhanced spatiotemporal coverage, has greatly advanced our comprehension of nearsurface ocean phenomena and played an important role in supporting the monitoring of aquatic-related processes, especially HABs [13,14]. Therefore, a large amount of work based on remote sensing products has been conducted to explore chl-a, the important water quality variable, and carry out research on concentration inversion algorithms [15][16][17][18][19], environmental factors influencing mechanisms [20,21], trend prediction [4,22,23], and other aspects to assist HABs monitoring and prevention.…”

Section: Introductionmentioning

confidence: 99%

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Hong,

Qin,

2024

Applied Sciences

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Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

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Evaluation of Chlorophyll-a estimation using Sentinel 3 based on various algorithms in southern coastal Vietnam

Cited by 5 publications

References 38 publications

Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa

Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa

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

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

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