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http://cibnor.repositorioinstitucional.mx/jspui/handle/1001/1694| Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery | |
| MIGUEL ANGEL MATUS HERNANDEZ NORMA YOLANDA HERNANDEZ SAAVEDRA Raúl Octavio Martínez Rincón | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| DOI: 10.1371/journal.pone.0205682 URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0205682 ISSN: 1932-6203 | |
| Chlorophyll-a concentration | |
| "Chlorophyll-a (Chl-a) concentration is a key parameter to describe water quality in marine and freshwater environments. Nowadays, several products with Chl-a have derived from satellite imagery, but they are not available or reliable sometimes for coastal and/or small water bodies. Thus, in the last decade several methods have been described to estimate Chl-a with high-resolution (30 m) satellite imagery, such as Landsat, but a standardized method to estimate Chl-a from Landsat imagery has not been accepted yet. Therefore, this study evaluated the predictive performance of regression models (Simple Linear Regression [SLR], Multiple Linear Regression [MLR] and Generalized Additive Models [GAMs]) to estimate Chl-a based on Landsat imagery, using in situ Chl-a data collected (synchronized with the overpass of Landsat 8 satellite) and spectral reflectance in the visible light portion (bands 1–4) and near infrared (band 5). These bands were selected because of Chl-a absorbance/ reflectance properties in these wavelengths. According to goodness of fit, GAM outperformed SLR and MLR. However, the model validation showed that MLR performed better in predicting log-transformed Chl-a. Thus, MLR, constructed by using four spectral bands (1, 2, 3, and 5), was considered the best method to predict Chl-a. The coefficients of this model suggested that log-transformed Chl-a concentration had a positive linear relationship with bands 1 (coastal/aerosol), 3 (green), and 5 (NIR). On the other hand, band 2 (blue) suggested a negative relationship, which implied high coherence with Chl-a absorbance/ reflectance properties measured in the laboratory, indicating that Landsat 8 images could be applied effectively to estimate Chl-a concentrations in coastal environments." | |
| Public Library of Science | |
| 2018 | |
| Artículo | |
| PLOS ONE | |
| Inglés | |
| Matus-Hernández MA , Hernández- Saavedra NY, Martínez-Rincón RO (2018) Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery. PLoS ONE 13(10): e0205682. | |
| ALGOLOGÍA (FICOLOGÍA) | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos |
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