Since 2011, unprecedented massive stranding of the brown floating macro-algae Sargassum
has been observed along the coastline of French Guyana, the Antilles Islands and the Caribbean Sea. Sargassum
algal patterns look like large aggregations transported by currents over long distances across the Atlantic Ocean. Satellite data are thus highly suitable to monitor Sargassum
spatial distribution. A first spectral index was defined in 2006 using ESA/Copernicus’s Envisat/MERIS (Medium Resolution Imaging Spectrometer) satellite sensor (300 m resolution), the so-called Maximum Chlorophyll Index (MCI), which is based on the water leaving radiance peak induced by Sargassum
optical signature at 709 nm [1
]. It has been shown that this peak reveals the presence of a high concentration of chlorophyll a
at the surface, hence allowing the detection of extensive areas of pelagic vegetation (Sargassum
]). The MCI index was used to determine the spatial distribution of Sargassum
aggregations in the Gulf of Mexico and in the western Atlantic waters [3
]. Following the MERIS era, ESA/Copernicus’s Sentinel-3/OLCI (Ocean and Land Colour Instrument) was launched on board Sentinel-3 in 2016. OLCI was designed to provide similar spatial resolution and spectral bands to MERIS, thus ensuring a continuity in the satellite data sets for Sargassum
detection purposes [5
] proposed a Floating Algae Index (FAI) using data acquired by NASA’s Terra/MODIS (Moderate-Resolution Imaging Spectroradiometer) and Aqua/MODIS sensors to detect and trace blooms of Ulva prolifera
macroalgae species in the Yellow Sea near Qingdao, China ([7
]). Because FAI was defined using the vegetation red-edge reflectance, observable between 675 and 750 nm, it could be used to detect any floating vegetation including Sargassum
]. However, because there was no effective cloud-masking method for FAI, both Sargassum
and clouds showed high FAI values. To overcome this difficulty, Wang and Hu [10
] defined the Alternative Floating Algae Index (AFAI) using data measured at relevant spectral bands such as 667 nm, 748 nm and 869 nm, which are less sensitive to the cloud contamination than the FAI.
Highly spatially resolved sensors such as ESA/Copernicus’s Sentinel-2/MSI (Multi Spectral Instrument), offering a typical spatial resolution between 10 m and 60 m, can also be relevant to detect Sargassum
species. Ody et al. [11
] recently defined the Modified Floating Algae Index (MFAI) by adjusting the AFAI index to the MSI spectral features using the bands at 665 nm, 833 nm and 940 nm.
In addition to Sargassum
species detection, algae indices are required to quantify their fractional coverage, noted FC, within a pixel and ultimately the resulting biomass per unit area. The fractional coverage was defined in Wang and Hu [10
] as the proportion of a pixel area occupied by “pure” Sargassum
mats representative of their study area. Wang and Hu [10
] proposed a linear relationship between the fraction of pixels covered by Sargassum
species and the AFAI. To estimate the AFAI value for a pixel covered by 100% of Sargassum
species, they used the average spectrum of in situ measurements of Sargassum
mats combined with radiative transfer simulations. Wang et al. [12
] extended that approach to estimate biomass amount using field measurements.
This study focuses on the methodological aspects for improving the detection of Sargassum presence and coverage over oceanic waters. The first objective of the current study is to verify that Sargassum could be accurately detected with MODIS and confirm the relevance of the AFAI for various spatial scales, namely between 1 km resolution (MODIS) and 20 m resolution (MSI), using data from both satellite sensors acquired at the same location and at the same time. Note that high resolution data are required to further investigate the influence of Sargassum on coastal ecosystems (e.g., stranding, invasion). A new relationship between the AFAI deviation and the Sargassum fractional coverage FC is then proposed based on satellite observations of large Sargassum aggregations using MSI sensors and using radiative transfer simulations. Finally, this study not only investigates floating algae coverage but provides insights on the influence of the Sargassum reflectance spectrum and the Sargassum submersion using an original adaptation of a radiative transfer model.
The paper is organized as follows. The study areas, the data and the methodology are outlined in Section 2
. The feasibility of downsampling the detection of Sargassum
from higher to lower satellite sensor spatial resolution is examined in Section 3
based on the comparison between AFAI derived from MODIS and MSI sensors. The consistency between AFAI and the fractional coverage is also studied in Section 3
. The influence of various parameters (such as Sargassum
immersion depth, Sargassum
physiological state and water turbidity) on the relationship between AFAI and FC is discussed in Section 4
This study shows two important results and findings: (i) the Sargassum detection based on AFAI can be downsampled from 20 m to 1 km, allowing Sargassum monitoring with moderate resolution sensors such as MODIS; (ii) the variability of the coefficient of proportionality K to derive Sargassum coverage from AFAI could be significant, thus showing that a unique/invariant value should not be used.
Finding (i) could be consolidated using more data. However, since the theoretical analysis established that both MODIS/δAFAI and MSI/δAFAI are proportional to the Sargassum fractional coverage, finding (i) corroborates theory. Furthermore, the lack of MODIS-MSI concomitant observations makes it highly difficult to conduct the full statistical analysis that would be required to rigorously examine the time variability of Sargassum over seasonal cycles. Since their orbits are very different, the concomitance of MODIS and MSI observations of the same areas at the same time are rare. Thus, very few concomitant Sargassum detections are available for a direct comparison, bearing in mind that MSI time series is much shorter than MODIS, and MSI geographic coverage much smaller.
Regarding finding (ii), the intent of this study is to highlight a methodological issue when deriving Sargassum coverage from δAFAI using a “universal” (i.e., unique) value of coefficient K, as is commonly the case. This study proposes an empirical method to determine K.
Deeper statistical analysis dealing with the K values derived empirically will need to be carried out based on massive data processing. However, the methodology proposed in this study is not suited for performing such an extensive statistical analysis because it requires selecting specific Sargassum aggregations that are large enough to yield a distribution of δAFAI sufficiently sampled and relies on the hypothesis that those aggregations contain at least a few MSI pixels with fractional coverage close to 100% to derive K with the kernel density estimation technique. In addition to the poor geographic coverage of MSI, especially over open ocean waters, those requirements considerably limit the amount of exploitable data, hence the feasibility of deriving statistical trends for K.
The consistency between AFAI derived from MODIS and MSI sensors using satellite data acquired above scenes that are influenced by Sargassum
was demonstrated in Section 3
although the spatial resolution of these sensors differs by almost two orders of magnitude. The MSI spatial resolution (20 m) provides pixels that can be entirely covered with “pure” Sargassum
(large aggregations of type 5 in Ody et al. [11
]) while the MODIS spatial resolution (1 km) can only provide pixels with a lesser coverage of Sargassum
. Consequently, MODIS can hardly provide satisfactory conditions to make sure the cover of Sargassum
The variability in K values with physiological state is significant as shown in Section 3
. Other variables such as the water turbidity or the Sargassum
depth could potentially have an influence on δAFAI and thus, on the K values (Table 6
). Typically, K increases with turbidity (because the water reflectance increases in Equation (7)). K decreases with the immersion depth because of the strong absorption of the radiation by pure seawater molecules in the near infrared. Such variations of K with depth corroborate the observations made by Ody et al. [11
] who highlighted that the sea state could have a direct influence on Sargassum
submersion and subsequently on the fractional coverage.
In this study, it was also verified through simulations that the sunglint has no influence on K. This is because the sunglint reflectance is spectrally flat, thus it does not alter the calculation of AFAI; hence, K remains pretty insensitive to the sunglint.
The current study calculated and empirically derived a slope K twice higher than the theoretical value proposed by Wang and Hu [10
]. The value estimated by Wang and Hu (0.0441) is derived from the Sargassum
reflectance spectrum based on the average of more than 50 spectra measured in the Gulf of Mexico and off Bermuda using a hand-held spectrometer. Consequently, they derived a value of K that relates the measured δAFAI to the fractional coverage of Sargassum
mats that are typical of their study area, i.e., with average Sargassum
density, not maximum
density; hence the value of K they derived cannot be used to derive a fractional coverage of “pure” Sargassum
On the contrary, the value of K retrieved in the current study, using the Sargassum
spectrum measured in mesocosm (Figure 4
) and MSI satellite data, relates the measured δAFAI to the fractional coverage of “pure” Sargassum
. A decrease by a factor of 2 of the K value can lead to an overestimation of the fractional cover of Sargassum
by a factor of 2 as well because of the linear relationship between δAFAI and K. Therefore, the selection and the use of a “pure” Sargassum
reflectance spectrum are crucial to accurately estimate the fractional coverage and further the biomass. However, the retrieved value of K is only representative of the study area.
The feasibility of downsampling the detection of Sargassum
from higher to lower satellite sensor spatial resolutions, namely, 20 m (Sentinel-2/MSI) and 1 km (MODIS) was demonstrated. The slope value of the linear relationship between δAFAI and the Sargassum
fractional coverage, noted as K in this paper, was calculated from theory and empirically checked using remotely sensed data from MSI. One original feature of the study was to adapt a radiative transfer model, namely the Lee’s model, to take into account Sargassum
aggregations located at a given depth while previous studies only consider aggregations floating at the sea surface. The adaption of Lee’s model is also relevant for analyzing the influence of various parameters such as the water turbidity, the submersion and the Sargassum
physiological state (e.g., healthy or senescent) on the K value. It was shown that the use of a unique K value to calculate the fractional coverage, as previously proposed by Wang and Hu [10
], is not relevant since it could lead to a wrong estimate of the Sargassum
fractional coverage (typically by a factor of 2 as shown here) and the associated Sargassum
biomass. This study does have a large-scale impact on the estimate of Sargassum
coverage and thus biomass in the Atlantic Ocean. Further work could consist in investigating the influence of the sea state and of the Sargassum
physiological state on the Sargassum
depth to improve estimates of Sargassum
fractional coverage and biomass. The relation between δAFAI and FC could also be applied in other regions (Gulf of Mexico, Yellow Sea, and East China Sea) and could be extended to other types of similar floating algae such as Enteromorpha prolifera
or Porphyra yezoensis