Abstract
Envisat’s MERIS and its successor Sentinel OLCI have proven invaluable for documenting algal bloom conditions in coastal and inland waters. Observations over turbid eutrophic waters, in particular, have benefited from the band at 708 nm, which captures the reflectance peak associated with intense algal blooms and is key to line-height algorithms such as the Maximum Chlorophyll Index (MCI). With the MERIS mission ending in early 2012 and OLCI launched in 2016, however, time-series studies relying on these two sensors have to contend with an observation gap spanning four years. Alternate sensors, such as MODIS Aqua, offering neither the same spectral band configuration nor consistent spatial resolution, present challenges in ensuring continuity in derived bloom products. This study explores a neural network (NN) solution to fill the observation gap between MERIS and OLCI with MODIS Aqua data, delivering consistent algal bloom spatial extent products from 2002 to 2020 using these three sensors. With 14 bands of MODIS level 2 partially atmospherically corrected spectral reflectance as the NN input, the missing MERIS/OLCI band at 708 nm required for the MCI is simulated. The resulting NN-derived MODIS MCI (NNMCI) is shown to be in good agreement with MERIS and OLCI MCI in 2011 and 2017 respectively over the western basin of Lake Erie (R2 = 0.84, RMSE = 0.0032). To overcome the impact of MODIS sensor saturation over bright water targets, which otherwise renders pixels unusable for bloom detection using R-NIR wavebands, a variant NN model is employed which uses the 9 MODIS bands with the lowest probability of saturation to simulate the MCI. This variant NN predicts MCI with only a small increase in uncertainty (R2 = 0.73, RMSE = 0.005) allowing reliable estimates of bloom conditions in those previously unreported pixels. The NNMCI is shown to be robust when applied beyond the initial training dataset on Lake Erie, and when re-trained on different geographic areas (Lake Winnipeg and Lake of the Woods). Despite differences in spatial, temporal, and spectral resolution, MODIS algal bloom presence/absence was correctly classified in >92% of cases and bloom spatial extent derived within 25% uncertainty, allowing the application to the 2012–2015 time period to form a continuous and consistent multi-mission monitoring dataset from 2002 to 2020.
Highlights
Several satellite ocean color missions have been in operation over the last few decades (e.g., SeaWiFS, Moderate Resolution Imaging Spectroradiometer (MODIS), MEdium Resolution Imaging Spectrometer (MERIS), VIIRS, OLCI) forming an invaluable contiguous record of aquatic color radiometry since 1997
While MODIS Aqua provides reliable ocean color observations during the 2012–2015 gap between MERIS and its successor OLCI, MODIS does not provide the same spectral configuration to ensure consistency in chlorophyll retrievals using algorithm approaches requiring a waveband at ~708 nm (e.g., Maximum Chlorophyll Index (MCI), Cyanobacteria Index (CI), MPH)
Solution is proposed, which uses 14 MODIS bands to simulate the required bands utilized in the MCI to derive a MODIS MCI-derived chlorophyll and subsequent algal bloom spatial extent metric consistent with MERIS and OLCI
Summary
Several satellite ocean color missions have been in operation over the last few decades (e.g., SeaWiFS, MODIS, MERIS, VIIRS, OLCI) forming an invaluable contiguous record of aquatic color radiometry since 1997. Many freshwater systems have seen an increase in the severity of HABs, attributed in large part to cultural eutrophication, and climate change impacts on key in-lake and watershed drivers [5,6,7,8]. Lake Erie, for example, is the most biologically productive of North America’s Laurentian Great Lakes and has suffered a resurgence of HABs in the last two decades [9] resulting in a well-documented ecosystem, public health, Remote Sens. Satellite remote sensing is routinely used to complement in-lake monitoring programs to report on algal bloom conditions on Lake. The science maturity is such that several operational remotely sensed algal bloom products are available for Lake Erie and distributed to the research and water-resource management stakeholder community [2,13,14].
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