Comparative Analysis of Empirical and Machine Learning Models for Chl<i>a</i> Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges
نویسندگان
چکیده
Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences various water constituents and uncertainties the atmospheric correction (AC) process. Although algorithms have been developed estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), production both accurate distribution maps time series has proven challenging, limiting use remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra (Rrsδ) Sentinel-2 Landsat-8 images as input representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against situ seven ice-free seasons (N ∼ 200; 2014–2020), SVR model outperformed locally tuned, Rrsδ-fed empirical models (Normalized Difference Chlorophyll Index, 2- 3-band, OC3) Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance trained MDN, with an error ∼35%. Comparison models, AC processors (iCOR, ACOLITE), radiometric products (Rayleigh-corrected, surface, top-of-atmosphere reflectance) showed that best optimal (up 100 mg m−3) were produced using coupled SVR-iCOR system.
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ژورنال
عنوان ژورنال: Canadian Journal of Remote Sensing
سال: 2023
ISSN: ['0703-8992', '1712-7971', '1712-798X']
DOI: https://doi.org/10.1080/07038992.2023.2215333