Potential of mapping dissolved oxygen in the Little Miami River using Sentinel-2 images and machine learning algorithms

Salas, Eric Ariel L.; Subburayalu, Sakthi Kumaran; Partee, Eric B.; Willis, Leeoria P.; Mitchell, Kayla R.

doi:10.1016/j.rsase.2022.100759

Cited by 7 publications

(8 citation statements)

References 40 publications

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“…The fold‐averaged R 2 of DO for each satellite fall close to the skill of the respective Pc:Chla models, which may also indicate the importance of Pc:Chla model skill in the accuracy of subsequent DO model results. Recent efforts to monitor DO using Sentinel‐2 imagery have often used spectral information to model DO directly (Batur & Maktav, 2018; Salas et al., 2022; Tian et al., 2023); research investigating modeling DO with Sentinel‐3 imagery appears limited. While these existing DO models show skill locally, they are often difficult to generalize (Sagan et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake

Beal,

Özdoğan,

Block

2024

Water Resources Research

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Excessive algae growth can lead to negative consequences for ecosystem function, economic opportunity, and human and animal health. Due to the cost‐effectiveness and temporal availability of satellite imagery, remote sensing has become a powerful tool for water quality monitoring. The use of remotely sensed products to monitor water quality related to algae and cyanobacteria productivity during a bloom event may help inform management strategies for inland waters. To evaluate the ability of satellite imagery to monitor algae pigments and dissolved oxygen conditions in a small inland lake, chlorophyll‐a, phycocyanin, and dissolved oxygen concentrations are measured using a YSI EXO2 sonde during Sentinel‐2 and Sentinel‐3 overpasses from 2019 to 2022 on Lake Mendota, WI. Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. A novel machine learning‐based dissolved oxygen modeling approach is developed using algae pigment concentrations as predictors. Best model results based on Sentinel‐2 (Sentinel‐3) imagery achieved R2 scores of 0.47 (0.42) for chlorophyll‐a, 0.69 (0.22) for phycocyanin, and 0.70 (0.41) for Pc:Chla. Dissolved oxygen models achieved an R2 of 0.68 (0.36) when applied to Sentinel‐2 (Sentinel‐3) imagery, and Pc:Chla is found to be the most important predictive feature. Random forest models are better suited to water quality estimations in this system given built in methods for feature selection and a relatively small data set. Use of these approaches for estimation of Pc:Chla and dissolved oxygen can increase the water quality information extracted from satellite imagery and improve characterization of algae conditions among inland waters.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake

Beal,

Özdoğan,

Block

2024

Water Resources Research

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“…Temperature can control the life of aquatic organisms and affect the solubility of compounds in water, including oxygen [16]. Dissolved oxygen (DO) is the most important indicator of other water parameters for assessing water quality [17]. Apart from temperature, other factors also affect the solubility of oxygen in water.…”

Section: Methodsmentioning

confidence: 99%

Assessment of lead heavy metal pollution in Ciliwung River

Juwana,

Nurjayati,

Hidawati

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Heavy metal contributed in bio-magnification process. So, its presence needs to be monitored and evaluated to prevent and control its hazards to human health and sustainability river ecosystem. One of that heavy metal is lead. This study aims to assessment the lead metal pollution in Ciliwung River because one of its uses is as water resources for Regional Drinking Water Company (PDAM). This study used a survey and analytical method. There were 12 samples of water column and 10 samples of sediments that distributed from upstream to the beginning of downstream. The parameters determined in site used water checker included temperature, pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Dissolved Oxygen (DO), and Oxidation Reduction Potential (ORP). While the lead contained in sample used Automatic Absorption Spectrophotometric (AAS). The result showed that lead content in all samples exceeded the quality standards and the lead on sediment were more than in water column. Lead in the Ciliwung River is mostly due to anthropogenic sources because it occurred naturally in small amounts. Downstream, the EC, TDS, and ORP values increase, while the pH and DO value decreases. The ORP values at all sampling points were positive. It means that deoxygenation occurred along the Ciliwung River. The implication is needed the additional raw water processing that accompanied further economic analysis to make sure the water availability for the community sustained.

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“…Thus, satellite remote sensing is a useful complementary method to traditional monitoring approaches [15] and has great potential for use in water quality parameter (WQP) monitoring. Recent research has indicated that satellite remote sensing technology can be effectively used for indirect monitoring of DO concentrations across various types of water bodies [16][17][18][19][20][21][22][23]. Specifically, researchers have developed numerous models based on remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and Landsat), including regional statistical models [16] and machine learning algorithms (e.g., random forest (RF) and support vector regression (SVR)) [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has indicated that satellite remote sensing technology can be effectively used for indirect monitoring of DO concentrations across various types of water bodies [16][17][18][19][20][21][22][23]. Specifically, researchers have developed numerous models based on remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and Landsat), including regional statistical models [16] and machine learning algorithms (e.g., random forest (RF) and support vector regression (SVR)) [17][18][19][20]. These models indirectly infer the DO concentrations in different water bodies globally, including the Yangtze River estuary [16], the California Current System [21], the coastal waters of Korea [22], and smaller water bodies, such as lakes [18].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Dissolved Oxygen Concentrations in the Coastal Waters of Zhejiang Using Landsat-8/9 Imagery

Dong,

Wang,

Song

et al. 2024

Remote Sensing

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The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.

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Potential of mapping dissolved oxygen in the Little Miami River using Sentinel-2 images and machine learning algorithms

Cited by 7 publications

References 40 publications

A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake

A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake

Assessment of lead heavy metal pollution in Ciliwung River

Monitoring Dissolved Oxygen Concentrations in the Coastal Waters of Zhejiang Using Landsat-8/9 Imagery

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