Abstract
ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.
Highlights
Chl-a is an important indicator of the trophic state in lakes and reservoirs, showing patterns associated with internal processes and natural stressors (HONEYWILL; PATERSON; HAGERTHEY, 2002; DUKA; CULLAJ, 2009; SCHALLES et al, 1998)
The multiple linear regression (MLR) and artificial neural network (ANN) models showed a smaller loss of performance (R2 = 0.50, Bias = –1.36, RMSE% = 29.9% and R2 = 0.46, Bias = –1.78, RMSE% = 24.6%) and maintained satisfactory accuracy compared to the nonparametric multiplicative regression (NPMR) model (e.g., Central point)
This study evaluated the use of several models to retrieve Chl-a concentrations from MODIS Terra and Aqua reflectance data in a large shallow subtropical lake
Summary
Chl-a is an important indicator of the trophic state in lakes and reservoirs, showing patterns associated with internal processes and natural stressors (HONEYWILL; PATERSON; HAGERTHEY, 2002; DUKA; CULLAJ, 2009; SCHALLES et al, 1998). Limitations of Chl-a detection by remote sensing include atmospheric correction methods and sensor limitations, as well as the influences of detritus, the presence of colored dissolved organic matter (CDOM), and scattering by Total Suspended Matter (TSM), which are difficult to detect because they affect the optical properties of water (DARECKI; STRAMSKI, 2004; HU, 2009; WU et al, 2009) Another limitation is the light reflected off the bottom, which may affect the accuracy of the empirical algorithm because the signal received by the sensor varies as a function of the wavelength and with the clarity of the water (CARDOSO et al, 2012; SHUBHA, 2000; LEE et al, 2001)
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