Effective detection and tracking of Karenia brevis Harmful Algal Blooms (KB HAB) that frequently plague the coasts and beaches of the West Florida Shelf (WFS) is important because of their negative impacts on ecology. They pose threats to fisheries, human health, and directly affect tourism and local economies. Detection and tracking capabilities are needed for use with the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite, so that HABs monitoring capabilities, which previously relied on imagery from the Moderate Resolution Imaging Spectroradiometer Aqua, can be extended to VIIRS. Unfortunately, VIIRS, unlike its predecessor MODIS-A, does not have a 678 nm channel to detect chlorophyll fluorescence, which is used in the normalized fluorescence height (nFLH) algorithm, or in the Red Band Difference (RBD) algorithm. Both these techniques have demonstrated that the remote sensing reflectance signal from the MODIS-A fluorescence band (Rrs 678 nm) helps in effectively detecting and tracking KB HABs in the WFS. To overcome the lack of a fluorescence channel on VIIRS, the approach described here, bypasses the need for measurements at 678nm, and permits extension of KB HABs satellite monitoring to VIIRS. The essence of the approach is the application of a standard multiband neural network (NN) inversion algorithm, previously developed and reported by us, that takes VIIRS Rrs measurements at the 486, 551 and 671nm bands as inputs, and produces as output the related Inherent Optical Properties (IOPs), namely: absorption coefficients of phytoplankton (aph443) dissolved organic matter (ag) and non-algal particulates (adm) as well as the particulate backscatter coefficient, (bbp) all at 443nm. We next need to relate aph443 in the VIIRS NN retrieved image to equivalent KB HABs concentrations. To do this, we apply additional constraints, defined by (i) low backscatter manifested as a maximum Rrs551 value and (ii) a minimum [Chla] threshold (and hence an equivalent minimum aph443min value) that are both known to be associated with KB HABs in the WFS. These two constraining filter processes are applied sequentially to the VIIRS NN retrieved aph443 image. First an image is made of retrieved VIIRS Rrs551. A mask is then made of all pixels with Rrs551≥ Rrs551max, the maximum value known to be compatible with the existence KB HABs. This is applied, as a filter to the VIIRS NN retrieved aph443 image to exclude pixels with Rrs551≥ Rrs551max. The residual image will then only show aph443 values that comply with Rrs551≤ Rrs551max. Then, in a second filter process, all values of aph443 ≤ aph443min are eliminated. The residual image will now only show aph443 values that are compatible with both criteria for KB HABs, and are therefore representative of KB HABs. It will be shown that when both these filter condition are applied to VIIRS NN aph443 retrievals, they can be used to effectively delineate and quantify KB HABs in the WFS. The KB HABs retrieved in this manner also show good correlations with in-situ KB HABs measurements as well as with nFLH retrievals and other techniques to which the same filtering criteria have been applied, confirming the viability of the approach.
Samir Ahmed and Ahmed El-Habashi, "Neural network retrievals of Karenia brevis harmful algal blooms in the West Florida Shelf (Conference Presentation)," Proc. SPIE 9999, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2016, 99990H (Presented at SPIE Remote Sensing: September 27, 2016; Published: 14 December 2016); https://doi.org/10.1117/12.2242079.5229530629001.
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