2024
DOI: 10.1109/jstars.2024.3359648
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Mapping Water Clarity in Small Oligotrophic Lakes Using Sentinel-2 Imagery and Machine Learning Methods: A Case Study of Canandaigua Lake in Finger Lakes, New York

Rabia Munsaf Khan,
Bahram Salehi,
Milad Niroumand-Jadidi
et al.

Abstract: Optical remote sensing of water quality poses challenges in small oligotrophic lakes due to the diminishing contribution of constituents to the water-leaving radiance as water clarity increases. For monitoring water clarity over such lakes, this study utilizes machine learning models and data from citizen science to develop effective models for retrieving Secchi disk depth (SDD) in Canandaigua Lake, USA. Using Sentinel-2 band ratios as input, we trained Random Forest (RF), Adaptive Boosting (AB), Extreme Gradi… Show more

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“…However, ensemble machine learning algorithms can help alleviate these limitations by integrating multiple models to improve the accuracy and robustness of predictions [44]. Among these, the extreme gradient boosting (XGBoost) algorithm, as an efficient integrated learning technique, has demonstrated excellent performance in several fields [45][46][47]. Meanwhile, the partial least squares regression (PLSR) algorithm, as a well-established linear regression analysis method, is particularly suitable for datasets in which the number of predictor variables is larger than the number of observed samples, and it has been widely used in soil spectral analysis [48][49][50].…”
Section: Introductionmentioning
confidence: 99%
“…However, ensemble machine learning algorithms can help alleviate these limitations by integrating multiple models to improve the accuracy and robustness of predictions [44]. Among these, the extreme gradient boosting (XGBoost) algorithm, as an efficient integrated learning technique, has demonstrated excellent performance in several fields [45][46][47]. Meanwhile, the partial least squares regression (PLSR) algorithm, as a well-established linear regression analysis method, is particularly suitable for datasets in which the number of predictor variables is larger than the number of observed samples, and it has been widely used in soil spectral analysis [48][49][50].…”
Section: Introductionmentioning
confidence: 99%