Retrieval of Chlorophyll-a and vegetation indices Using Sentinel-2 MSI Imagery in El-Burullus Lake, Egypt | ||||
| SVU-International Journal of Engineering Sciences and Applications | ||||
| Volume 5, Issue 1, June 2024, Page 79-88 PDF (995.35 K) | ||||
| Document Type: Original research articles | ||||
| DOI: 10.21608/svusrc.2023.218492.1134 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Walaa Assar  
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| Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University, Egypt | ||||
| Abstract | ||||
| Chlorophyll-a (Chl-a) is considered an important indicator for assessing many environmental issues. Monitoring Chl-a accurately through remote sensing technology is an efficient approach to observing water quality and providing early warnings of water eutrophication which overcomes the limitations in field campaigns as well as cost constraints. The Sentinel-2 Multi-Spectral Imager (S2-MSI) was used to analyse El-Burullus Lake, which is considered the second-largest natural lake in Egypt. The Case-2 Regional/Coast Colour (C2RCC) atmospheric correction processor was applied to retrieve different optically active water constituents such as Chl-a, the total suspended matter and coloured dissolved organic matter, as well as vegetation indicators. The spatial distribution marked the eutrophicated areas along El-Burullus Lake which result from the inflow from a large number of polluted agricultural drains. The results of the validation between the captured S2-MSI Chl-a and in situ measurements confirmed a clear underestimation in the level of the retrieved C2RCC parameters. The low R2 values mean the bio-optical model better fits the variations in the in situ Chl-a content, as the F-test showed a significant level for all the parameters. All the results, which included low values for the mean bias error (MBE), root mean square error and normalized root mean square error, demonstrated that the empirical model exhibits a good performance with the bio-optical model, and the positive MBE results indicated the underestimation in the in situ Chl-a data. Accordingly, the S2-MSI imageries proved their applicability for the efficient mapping, water quality assessment and sustainable protection of the lake ecosystem | ||||
| Keywords | ||||
| Chlorophyll-a; El-Burullus Lake; Sentinel-2; Statistical Analysis; Vegetation Indicators | ||||
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