Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.

There is a growing use of remote sensing observations for detecting and quantifying freshwater cyanobacteria populations, yet the inherent optical properties of these communities in natural settings, fundamental to bio-optical algorithms, are not well known. Towards bridging this knowledge gap, we measured a full complement of optical properties in western Lake Erie during cyanobacteria blooms in the summers of 2013 and 2014. Our measurements focus attention on the optical uniqueness of cyanobacteria blooms, which have consequences for remote sensing and bio-optical modeling. We found the cyanobacteria blooms in the western basin during our field work were dominated by Microcystis, while the waters in the adjacent central basin were dominated by Planktothrix. Chlorophyll concentrations ranged from 1 to over 135 ${\mu}g/L$ across the study area with the highest concentrations associated with Microcystis in the western basin. We observed large, amorphous colonial Microcystis structures in the bloom area characterized by high phytoplankton absorption and high scattering coefficients with a mean particle backscatter ratio at 443nm greater than 0.03, which is higher than other plankton types and more comparable to suspended inorganic sediments. While our samples contained mixtures of both, our analysis suggests high contributions to the measured scatter and backscatter coefficients from cyanobacteria. Our measurements provide new insights into the optical properties of cyanobacteria blooms, and indicate that current semi-analytic models are likely to have problems resolving a closed solution in these types of waters as many of our observations are beyond the range of existing model components. We believe that different algorithm or model approaches are needed for these conditions, specifically for phytoplankton absorption and particle backscatter components. From a remote sensing perspective, this presents a challenge not only in terms of a need for new algorithms, but also for determining when to apply the best algorithm for a given situation. These results are new in the sense that they represent a complete description of the optical properties of freshwater cyanobacteria blooms, and are likely to be representative of bloom conditions for other systems containing Microcystis cells and colonies.

This study focuses on predicting harmful algal bloom (HAB) events in Lake Okeechobee, a shallow lake in Florida. A spatiotemporal deep learning model is employed to predict the levels of cyanobacteria Microcystis aeruginosa present in the lake for a single-day and a 14-day prediction horizon. Datasets collected from remote sensing (i.e., satellite images from January 2018 to December 2020) and from a physics-based simulation model (i.e., daily simulation from January 2018 to December 2020) are available. Owing to the low quality of remote sensing data caused by various environmental and technical issues, the two available datasets are fused together to create a multisource hybrid dataset for deep learning model training. A convolutional long-short term memory (ConvLSTM) deep neural model is trained on the datasets, and the results of the predictions are compared to the true cyanobacterial index for that time period. Findings include the following: 1) the deep learning model, ConvLSTM, shows promising performance for short- and mid-term HAB forecasting; and 2) the hybrid dataset that fuses remote sensing with physics-based modeling (a.k.a. modeling based on fundamental physical and biogeochemical principles) speeds up the model learning and improves its performance significantly. The proposed methodologies are reliable and cost-effective and could be used to forecast algal bloom occurrences in shallow lakes with limited sparse observations.

An inversion algorithm based on a forward optical model based on a radiative transfer equation is developed in terms of IOPs with explicit inclusion of the volume scattering function (VSF). Key results from the analysis are: 1) the inversion retrievals perform to comparable levels as the operational configuration of the NASA’s Generalized Inherent Optical Property (GIOP) framework; 2) closure error between RT r _rs and measured r _rs have no significant effect on predicting the accuracy of the derived IOPs; and 3) is important to ZTT, but not explicitly included in other types of comparable semi-analytic algorithms.