Monitoring multi-water quality of internationally important karst wetland through deep learning, multi-sensor and multi-platform remote sensing images: A case study of Guilin, China

Yang, Wenlan; Fu, Bolin; Li, Sunzhe; Lao, Zhinan; Deng, Tengfang; He, Wen; He, Hongchang; Chen, Ze heng

doi:10.1016/j.ecolind.2023.110755

Cited by 17 publications

(3 citation statements)

References 38 publications

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“…In a dynamic environment with significant seasonal variations in concentration (Chl-a: 9.0-127.1 mg/m 3 , TSS: 3.2-135.5 mg/L), the developed MLR model demonstrated higher performance compared to previous studies, exhibiting robustness to complex water quality changes through its incorporation of the 3-4 band algorithms and band ratios [22,53]. While the MLR model performed well, the growing availability of more sophisticated machine learning algorithms, like random forests, support vector machines, neural networks, and deep learning, suggests that even higher accuracy is possible [54,55]. These models outperform traditional linear regression capabilities in handling complex data patterns and nonlinear relationships.…”

Section: Performance Of the Chlorophyll-a Estimation Modelmentioning

confidence: 81%

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Jang,

Kim,

Kim

et al. 2024

Remote Sensing

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Small-scale reservoirs located in river estuaries are a significant water resource supporting agricultural and industrial activities; however, they face annual challenges of eutrophication and algal bloom occurrences due to excessive nutrient accumulation and watershed characteristics. Efficient management of algal blooms necessitates a comprehensive analysis of their spatiotemporal distribution characteristics. Therefore, this study aims to develop a chlorophyll-a (Chl-a) estimation model based on high-resolution satellite remote sensing data from Sentinel-2 multispectral sensors and multiple linear regression. The multiple linear regression (MLR) models were constructed using multiple reflectance-based variables that were collected over 2 years (2021–2022) in an estuarine reservoir. A total of 21 significant input variables were selected by backward elimination from the 2–4 band algorithms as employed in previous Chl-a estimation studies, along with the Sentinel-2 B1-B8A wavelength ratio. The developed algorithm exhibited a coefficient of determination of 0.65. Spatiotemporal variations in Chl-a concentration generated by the algorithm reflected the movement of high Chl-a concentration zones within the body of water. Through this analysis, it turned out that Sentinel-2-based spectral images were applicable to a small-scale reservoir which is relatively long and narrow, and the algorithm estimated changes in concentration levels over the seasons, revealing the dynamic nature of Chl-a distributions. The model developed in this study is expected to support effective algal bloom management and water quality improvement in a small-scale reservoir or similar complex water quality water bodies.

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Section: Performance Of the Chlorophyll-a Estimation Modelmentioning

confidence: 81%

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Jang,

Kim,

Kim

et al. 2024

Remote Sensing

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“…There have also been studies using Spectral Angular Distance (SAD) and Euclidean distance (ED) to measure the difference between two spectra in different years, which has proven to be the best similarity metric for detecting dual time variation (Huang et al, 2020;Ji et al, 2015). With the development of machine learning and deep learning in remote sensing, some artificial intelligence technologies have also been applied in wetland remote sensing classification (DeLancey et al, 2020;Rezaee et al, 2018;Yang et al, 2023). However, some machine learning models, in particular deep neural networks (DNNs), cannot be very well understood (Räz and Beisbart, 2022;Sullivan, 2022); the performance will vary depending on the architectures and hyperparameter selection of the chosen algorithm (Shrestha and Mahmood, 2019;Yang and Shami, 2020); applying them globally is extremely hard (Shanthamallu and Spanias, 2021).…”

Section: B Methodsmentioning

confidence: 99%

Classification Feature Optimization for Global Wetlands Mapping

Yan,

Niu

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Wetlands play an important role in supporting biodiversity conservation and helping sustainable development. Time series wetland classification requires a series of sample sets acquired at different times. To meet this need, we proposed a method for automatically producing global wetland samples based on 13 global and regional wetland-related datasets and millions of images from Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-1 Synthetic Aperture Radar (SAR) Ground Range Detected (GRD), and Sentinel-2 Multispectral Instrument (MSI) sensors. On this basis, we analyzed the classification capabilities of wetland features in the above four sensors and obtained a multi-sensor, full spectral band, multi-index wetland classification feature dataset at a global scale. The analysis results show that the shortwave infrared band (SWIR) and Land Surface Water Index (LSWI) can distinguish non-wetlands from other land cover types well, while the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and thermal infrared bands (TIR) could help distinguish water bodies and wetlands. C bands in Sentinel 1 perform well in identifying water bodies, but the ability to distinguish wetlands is limited. The mapping coefficients of the corresponding spectral bands and indexes between Sentinel-2, MODIS data, and Landsat were obtained to help realize the collaborative use of multi-sensor image data. This paper could provide guidance for large-scale wetland classification and mapping research.

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“…Compared to the traditional on-site monitoring of total phosphorus and ammonia nitrogen, remote sensing technology provides a fast and wide-ranging means of obtaining the spatial and temporal distribution characteristics of water quality parameters. Numerous researchers have utilized different sensor data, such as MODIS and GOCI, to conduct remote sensing inversion studies on water quality parameters, including total phosphorus, total nitrogen, and ammonia nitrogen [6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

Shi,

Qiu,

et al. 2024

Water

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Remote sensing technology plays a crucial role in the rapid and wide-scale monitoring of water quality, which is of great significance for water pollution prevention and control. In this study, the downstream and nearshore areas of the Huaihe River Basin were selected as the study area. By utilizing spectral information from standard solution measurements in the laboratory and in situ water quality data matched with satellite spatiotemporal data, inversion models for total phosphorus (TP) and ammonia nitrogen (NH3-N) water quality parameters were developed. The validation results using field measurements demonstrated that the inversion models performed well, with coefficients of determination (R2) of 0.7302 and 0.8024 and root mean square errors of 0.02614 mg/L and 0.0368 mg/L for total phosphorus and ammonia nitrogen, respectively. By applying the models to Sentinel-2 satellite images from 2022, the temporal and spatial distribution characteristics of total phosphorus and ammonia nitrogen concentrations in the study area were obtained. The ammonia nitrogen concentration ranged from 0.05 to 0.30 mg/L, while the total phosphorus concentration ranged from 0.10 to 0.40 mg/L. Overall, the distribution appeared to be stable. The southern region of the Guan River estuary showed slightly higher water quality parameter concentrations compared to the northern region, while the North Jiangsu Irrigation Main Canal estuary was affected by the dilution of river water, resulting in lower concentrations in the estuarine area.

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Monitoring multi-water quality of internationally important karst wetland through deep learning, multi-sensor and multi-platform remote sensing images: A case study of Guilin, China

Cited by 17 publications

References 38 publications

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability

Classification Feature Optimization for Global Wetlands Mapping

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

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