Abstract
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.
Highlights
Coastal environments are highly productive and complex marine ecosystems because they show the interaction of various natural and anthropogenic phenomena that provide an important source of nutrients for phytoplankton and aquatic organisms, as well as for various human activities
Several algorithms have been developed to measure Chl-a based on the relationship that exists between the reflectance of different wavelengths from sensors designed for monitoring Chl-a in marine environments, such as Coastal Zone Color Scanner (CZCS) with a spatial resolution of 825 m [7]; Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) of 1130 m [8,9]; Medium Resolution Imaging Spectrometer (MERIS) of 300 m [10]; and Moderate Resolution Imaging Spectroradiometer (MODIS) of 250 m, 500 m and 1000 m [11,12]
This study evaluated the use of Landsat 8 for estimating Chl-a concentration in the coastal water body located in northwestern Mexico by field data collection, simple linear regression, multiple linear regression and generalized additive models, using as response variable logtransformed Chl-a and reflectance values as spectral predictive variables of the visible part of light and near infrared (NIR)
Summary
Coastal environments are highly productive and complex marine ecosystems because they show the interaction of various natural and anthropogenic phenomena that provide an important source of nutrients for phytoplankton and aquatic organisms, as well as for various human activities. Chlorophyll-a based on Landsat imagery water bodies have been under significant stress due to anthropogenic alterations and climate variations that are increasingly frequent events, such as algal blooms [1,2]. In these environments, Chl-a has been considered as one of the most important parameters for measuring water quality, so it can be used as an indicator of ecosystem health [3,4]. Several difficulties have been reported when performing adequate monitoring of these environments, among which those of low resolution can only be applied effectively in homogeneous open sea areas but not for spatially complex coastal environments, such as bays or estuaries that require a higher spatial resolution for their study [1,5]
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