Retrieval and validation of the Secchi disk depth values (Zsd) from the Sentinel-3/OLCI satellite data in the Persian Gulf and the Gulf of Oman

Kabiri, Keivan

doi:10.1007/s11356-023-27625-7

Environ Sci Pollut Res

2023

DOI: 10.1007/s11356-023-27625-7

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Retrieval and validation of the Secchi disk depth values (Zsd) from the Sentinel-3/OLCI satellite data in the Persian Gulf and the Gulf of Oman

Keivan Kabiri

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2024

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Cited by 7 publications

References 43 publications

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Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF1-WFV satellite data

Cao,

Li,

Jia

et al. 2024

J. Ocean. Limnol.

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Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF1-WFV satellite data

Cao,

Li,

Jia

et al. 2024

J. Ocean. Limnol.

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Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery

Colkesen,

Ozturk,

Altuntas

2024

Stoch Environ Res Risk Assess

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One of the main threats to freshwater resources is pollution from anthropogenic activities such as rapid urbanization and excessive agricultural nutrient runoff. Remote sensing technologies have been effectively used in monitoring and mapping rapid changes in the marine environment and assessing the overall health of freshwater ecosystems. The main goal of this study is to comparatively evaluate the performance of index-based and classification-based approaches in mapping dense floating algal blooms observed in Lake Burdur using Sentinel-2 imagery. For index-based mapping, algae-specific indices, namely the Floating Algae Index (FAI), Adjusted Floating Algae Index, Surface Algal Blooms Index (SABI), and Algal Blooms Detection Index (ABDI), were used. At the same time, pixel- and object-based Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory Network (LSTM) were utilized for classification-based algal mapping. For this purpose, seven Sentinel-2 images, selected through time series analysis performed on the Google Earth Engine platform, were used as the primary dataset in the application. The results show that high-density floating algae formations can be detected over 99% by both indices and classification-based approaches, whereas pixel-based classification is more successful in mapping low-density algal blooms. When two-class thematic maps representing water and floating algae classes were considered, the maps produced by index-based FAI using an appropriate threshold value and the classification-based RF algorithm reached an overall accuracy of over 99%. The highest algae density in the lake was observed on July 13, 2021, and was determined to be effective in ~ 45 km2 of the lake’s surface.

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UAV and satellite remote sensing for inland water quality assessments: a literature review

Wasehun,

Hashemi Beni,

Di Vittorio

2024

Environ Monit Assess

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Retrieval and validation of the Secchi disk depth values (Zsd) from the Sentinel-3/OLCI satellite data in the Persian Gulf and the Gulf of Oman

Cited by 7 publications

References 43 publications

Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF1-WFV satellite data

Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF1-WFV satellite data

Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery

UAV and satellite remote sensing for inland water quality assessments: a literature review

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