Abstract
The advancements in space technology have facilitated water quality (WQ) monitoring of lake conditions at a spatial resolution of 10 m by freely accessible Sentinel-2 images. The main aim of this article was to elucidate the necessity of spatiotemporal WQ monitoring of the shrinking Lake Burdur in Türkiye by examining the relation between field and satellite data with a state-of-the-art machine learning-based regression algorithm. This study focuses on detection of algal blooms and WQ parameters, which are chlorophyll-a (Chl-a) and suspended solids (SS). Furthermore, this study leverages the advan- tage of geographic position of Lake Burdur, located at the overlap of two Sentinel-2 frames, which enables the acquisition of satellite images at a temporal resolution of 2-3 days. The findings enrich the understanding of the lake's dynamic structure by rapidly monitor- ing the occurrence of algal blooms. High accuracies were achieved for Chl-a (R-squared: 0.93) and SS (R-squared: 0.94) detection.
Original language | English |
---|---|
Pages (from-to) | 85-87 |
Number of pages | 3 |
Journal | Photogrammetric Engineering and Remote Sensing |
Volume | 90 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2024 |
Bibliographical note
Publisher Copyright:© 2024 American Society for Photogrammetry and Remote Sensing.