The Potential of Different Reflectance-Based Algorithms to Retrieve Phycocyanin Concentration through Remote Sensing: Application in a Hypereutrophic Mediterranean Lake ^†

Abstract

Cyanobacterial blooms impact aquatic environments and human health. Cyanobacterial biomass is usually estimated using traditional time-consuming and costly field sampling techniques. A remote sensing approach is time and cost efficient and feasible for repetitive monitoring. In this work, we test the potential of various algorithms to retrieve phycocyanin concentration in a Mediterranean lake. Field Spectro radiometric measurements and sampling were performed during 2016 and 2017. The results obtained prove that various ratio models can be used for the estimation of phycocyanin, with the model “R(700)/R(600)” being the best (R² = 0.716). This research highlights the potential of cyanobacteria mapping using various available satellites.

1. Introduction

Phytoplankton is the base of aquatic food webs and can be found in both fresh and marine waters [1,2,3]. The monitoring of their dynamics is essential as they are considered biological indicators for evaluating the ecological status of both marine and freshwater. Eutrophication, a process resulting from land degradation [4,5] and anthropogenic activities, promotes increased phytoplankton concentrations. Blooms of cyanobacteria, a group of phytoplankton, can result in the production of toxins in aquatic ecosystems, threatening human health and aquatic inhabitants [6].

The hazards of cyanobacterial blooms, a worldwide problem, have raised awareness among policy makers of the need to continuously monitor the cyanobacterial biomass in inland water. Cyanobacterial biomass can be estimated using traditional field sampling techniques, laboratory analysis, and the cell counting method. Despite being accurate, this method is time-consuming, labor-intensive, and cost-ineffective. Remote sensing is considered an alternative monitoring method that is cost- and time-efficient and feasible for the repetitive and continuous monitoring of phytoplankton and cyanobacteria [7]. The chlorophyll-a (chl-a) pigment was previously used in order to estimate cyanobacterial biomass. It was later proved that chl-a is an inaccurate estimator of cyanobacteria since it is common in all photosynthetic phytoplankton groups. However, phyocycanin (PC) is a unique pigment that is only present in cyanobacteria, rather than in other phytoplankton groups.

The progress of remote sensing has helped researchers to exploit phycocyanin to develop reflectance-based empirical, semi-empirical, and band ratio algorithms for estimating cyanobacterial biomass. In this research, we aim to test the potential of various published algorithms to derive [PC] at Lake Qaraoun using remote sensing techniques to recommend the potential satellite imageries suitable for deriving phycocyanin from space.

2. Materials and Methods

2.1. Study Area

Lake Qaraoun is the largest freshwater body in Lebanon. It has a surface area of 12 km², a maximum depth of 60 m, and a maximum volume of about 220 × 10⁶ m³ [8,9]. The reservoir water is used for various purposes, including irrigation, drinking, domestic, recreational activities, and fisheries [2]. Global warming resulted in cyanobacterial blooms of Microcystis aeruginosa and Chrysosporum ovalisporum in the reservoir [10,11]. In 2012, a cyanobacterial toxin produced by Chrysosporum ovalisporum, known as cylindrospermopsin, was detected in the lake [12].

2.2. Field Sampling and Remote Sensing Reflectance (Rrs)

Cyanobacterial species from each subsample were determined based on taxonomic keys such as colony morphology, cell structure and dimensions, and mucilage characteristics [13]. Microscopic identifications and enumerations were performed using a phase contrast microscope (Nikon TE200, Nikon, Melville, New York, NY, USA). Cyanobacteria counting was carried out under a ×40 objective using a 40-band Nageotte chamber.

Water samples for phycocyanin PC laboratory estimation were filtered under a low vacuum through 0.2 μm nucleopore membrane filters (Millipore, Burlington, MA, USA). PC pigments were then extracted using 50 mM phosphate buffer for further estimation through spectrophotometric method.

Eight field campaigns were conducted during 2016 and 2017 (Figure 1). Field spectroradiometric measurements were carried out using a spectroradiometer (Field Spec 4 ASD, Malvern Panalytical, Malvern, UK) and were acquired in the visible and near-infrared range (350–2500 nm) with a 1 nm spectral resolution.

3. Results

The phycocyanin concentration varied between 18 and 170 µg/L during the different field campaigns in 2016 and 2017. Phycocyanin was heterogenous throughout the lake and showed considerable variation on 2 November 2016 ranging between 34 and 170 µg/L (Figure 1a). Two main cyanobacterial genera (Microcystis sp. and Chrysoporum sp.) were identified during the campaigns in which field spectroradiometer measurements were performed in 2016 and 2017. The proportions of both genera are presented in Figure 1b. Chrysoporum Sp. dominated the cyanobacteria population until August, when Microcystis sp. outcompeted it.

Figure 2 shows the spectral features observed in the reflectance data from the cyanobacteria-dominated turbid water in Lake Qaraoun. It shows absorption peaks highlighting the phytoplankton and PC absorption in the key spectral regions.

The potential of 10 developed algorithms to retrieve the phycocynanin concentration in Lake Qaraoun was tested (

Table 1. Coefficient of correlation of the 10 tested phycocyanin-retrieving algorithms.

Algorithm	Name	Reference	R2
$\frac{R\left(650\right)}{R\left(625\right)}$	SC00	[14]	0.6662
$\frac{R\left(700\right)}{R\left(600\right)}$	MI09	[15]	0.716
$\frac{R\left(709\right)}{R\left(600\right)}$	SM12	[16]	0.6607
$\frac{R\left(724\right)}{R\left(600\right)}$	MM09	[15]	0.5408
$\frac{R\left(709\right)}{R\left(620\right)}$	SI05	[17]	0.6593
$\frac{R\left(678\right)}{R\left(667\right)}$	Am09	[18]	0.4176
$\frac{R\left(681\right)}{R\left(665\right)}$	Be16	[19]	0.4035
$\frac{R\left(700\right)}{R\left(622\right)}$	Be16	[19]	0.7048
R(600) − R(648) − R(625)	DE93*	[20]	0.1217
R(600) + R(648) − R(624)	DE93	[20]	0.1895

). The results showed that both linear models, [R(600) + R(648)] − R(624)] as well as [R(600) − R(648) − R(625)], weakly correlated with the measured [PC], with R² = 0.1895 and 0.1217, respectively. On the other hand, $\frac{R\left(700\right)}{R\left(600\right)}$ strongly correlated with the measured [PC], with R² = 0.716 (Table 1, Figure 3).

4. Discussion

The results obtained in this work showed that band ratio algorithms were found to be better than linear models for estimating [PC]. In 2009, Mishra et al. found that some band ratio algorithms are sensitive to chl-a and are thus unsuitable for estimating [PC]. They proved that the MI09 algorithm “R(700)/R(600)” is not sensitive to chl-a, but sensitive to phycocyanin [15]. This agrees with our results that showed the potential of “R(700)/R(600)” to estimate [PC]. It was shown that the SC00 model “R(650)/R(625)” has low sensitivity to both PC and chl-a, while SI05 “R(709)/R(620)” is a good estimator of PC, but is also highly sensitive to chl-a [21]. Meanwhile, MM09 “R(724)/R(600)” is highly sensitive to both PC and chl-a, indicating that these algorithms are unsuitable for [PC] retrieval, or suitable only in specific environments. This study also proved that MI09 “R(700)/R(600)” is highly sensitive to PC and has low sensitivity to chl-a. This agrees with our results, indicating that MI09 is efficient and has high potential to retrieve [PC] in nature.

In addition, Lake Qaraoun is subject to various sources of pollution, including industrial wastes, sewage, and untreated wastewater, as well as agricultural run-off [9]. These anthropogenic pressures affect the clarity of the water and cause an elevation in turbidity. This, in turn, may affect the reflectance of the various bands used, and hence cause errors in the estimated [PC]. This poses another problem when using remote sensing tools to derive [PC]. Thus, an additional evaluation of the algorithms (particularly “R(700)/R(600)”) in other lakes—eutrophic and mesotrophic, as well as oligotrophic—is needed in order to validate the suitability of this algorithm in all regions.

The importance of these algorithms is that they can be used as indicators when choosing between different satellite imagery to map CHABs, based on their visible and NIR bands. The phycocyanin in Lake Champlain’s Missisquoi Bay was successfully mapped by applying the SI05 “R(709)/R(620)” model to Quickbird and MERIS images [22]. This indicates that [PC] can be effectively estimated from Quickbird and MERIS images.

In fact, band ratio algorithms can effectively retrieve [PC] in small aquatic inland waters from Worldview-2, and Sentinel-2 images and, to a lesser extent, from Landsat-8 images. Worldview-2 images are the most favorable images for mapping CHABs, followed by Sentinel-2, then Landsat-8 images. Future satellite imaging systems should include a narrow band focused on phycocyanin absorption (620 nm) [19].

5. Conclusions

In this work, the model “R(700)/R(600)” was the most suitable algorithm for estimating PC in Lake Qaraoun. Most PC retrieval band ratio algorithms can be applied to satellite images to directly derive PC and, hence, map CHABs. Further analysis shall be carried out in order to test the sensitivity of these algorithms to phytoplankton groups other than cyanobacteria. Further studies are needed, as well new algorithms, to retrieve phycocyanin concentrations from remote sensing techniques.