Effect of bio-optical parameter variability and uncertainties in reflectance measurements on the remote estimation of chlorophyll-a concentration in turbid productive waters: modeling results

Abstract

Most algorithms for retrieving chlorophyll-a concentration (Chla) from reflectance spectra assume that bio-optical parameters such as the phytoplankton specific absorption coefficient (a *) or the chlorophyll-a fluorescence quantum yield ͑͒ are constant. Yet there exist experimental data showing large ranges of variability for these quantities. The main objective of this study was to analyze the sensitivity of two Chla algorithms to variations in bio-optical parameters and to uncertainties in reflectance measurements. These algorithms are specifically designed for turbid productive waters and are based on red and near-infrared reflectances. By means of simulated data, it is shown that the spectral regions where the algorithms are maximally sensitive to Chla overlap those of maximal sensitivity to variations in the above bio-optical parameters. Thus, to increase the accuracy of Chla retrieval, we suggest using spectral regions where the algorithms are less sensitive to Chla, but also less sensitive to these interferences. a * appeared to be one of the most important sources of error for retrieving Chla. However, when the phytoplankton backscattering coefficient ͑b b, ͒ dominates the total backscattering, as is likely during algal blooms, variations in the specific b b, may introduce large systematic uncertainties in Chla estimation. Also, uncertainties in reflectance measurements, which are due to incomplete atmospheric correction or reflected skylight removal, seem to affect considerably the accuracy of Chla estimation. Instead, variations in other bio-optical parameters, such as or the specific backscattering coefficient of total suspended particles, appear to have minor importance. Suggestions regarding the optimal band locations to be used in the above algorithms are finally provided.

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

“…Curran & Novo, 1988;Dekker et al, 2001;Doraxan et al, 2002) and Miller, 2003;;Del Castillo & Miller 2008;Mannino et al, 2008;Ficek et al, 2011). Combinations of more spectral bands have been also introduced in band ratio algorithms with variable results in order to account for the confounding effects of other optically active constituents (Dall'Olmo et al, 2003;Dall'Olmo & Gitelson, 2006;Gitelson et al, 2007;Le et al, 2009). In parallel, research in optically complex waters has focused on the pursuit of particular signal attributes (e.g., peak position and height) of the water-leaving radiative signal that appear to be unique to a specific parameter such as Chl-a fluorescence (Esaias et al, 1998;Gower et al, 1999;Matthews et al, 2012).…”

Optical remote sensing of lakes: an overview on Lake Maggiore

“…The absorption of CDOM decreases exponentially with increasing wavelengths while it has negligible backscattering (Kirk, N o n -c o m m e r c i a l u s e o n l y 1994). The particulate constituents that attenuate incoming light include suspended sediments (both organic and inorganic) and phytoplankton (Morel and Prieur, 1977;Dall'Olmo and Gitelson, 2006), quantified by the main photosynthetic pigment, chl-a but also by accessory pigments as PC (Simis et al, 2007). Light scattering by suspended sediments strongly depends on the particles size, shape, and composition while absorption by mineral particulates is usually low (Wozńiak and Stramski, 2004).…”

Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS

“…The NDCI uses 665 and 708 nm and operates similarly to the Normalized Difference Vegetative Index (NDVI) binding higher and lower index values between −1 and 1 and reducing seasonal variability and sun angle influences. A comparison of the NDCI effectiveness in Case 2 waters of the Chesapeake and Delaware Bays found NDCI was the most effective model, followed by Moses et al [25] and then by Dall'Olmo and Gitelson [23] (Table 1). In a further effort to test the effectiveness of these algorithms for estuarine algal bloom mapping, we chose Moses et al [25] and NDCI [21] to calibrate and validate Chl a in the Indian River Lagoon (IRL), FL a shallow estuary in which severe algal blooms have occurred in recent years Table 1.…”

“…Dall'Olmo and Gitelson [23] developed a three-band approach using bands 665, 708 and 753 nm. They chose 665 nm because 681 nm was believed to be influenced by the fluorescence of Chl a.…”

“…Comparison of MERIS Chl a estimation algorithms (Adapted from Mishra and Mishra [21]). Dall'Olmo and Gitelson [23] studied the Chesapeake Bay, USA; Moses et al [24] studied the Sea of Azov; Mishra [21] studied Chesapeake, Delaware, and Mobile Bays and the Mississippi Delta, USA.…”