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The NERC and CEH trade marks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the 2 ecological status of inland waterbodies and for human and animal health. In this study we 3 examine the performance of four semi-analytical algorithms for the retrieval of chlorophyll a 4 (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic 5Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle 6Sensor. The retrieval accuracies are compared to those returned by semi-empirical band-ratio 7 algorithms optimised for these datasets. The best-performing algorithm for the retrieval of 8Chl a was a semi-empirical band-ratio model (R 2 = 0.832; RMSE = 29.8%). The best 9 performing model for the retrieval of C-PC was a semi-analytical nested band ratio model (R 2 10 = 0.984; RMSE = 3.98 mg m -3 ). The concentrations of C-PC retrieved using the semi-11 analytical model were also correlated with those of cyanobacterial cells (R 2 = 0.380) and of 12 the particulate and total (particulate plus dissolved) pools of microcystins (R 2 = 0.858 and 13 0.896 respectively). Importantly, both the semi-empirical and semi-analytical algorithms 14 were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below 15 current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential 16 of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial 17 blooms for human health protection, at regional and global scales. 18 19

Inland and coastal waterbodies are critical components of the global biosphere. Timely monitoring is necessary to enhance our understanding of their functions, the drivers impacting on these functions and to deliver more effective management. The ability to observe waterbodies from space has led to Earth observation (EO) becoming established as an important source of information on water quality and ecosystem condition. However, progress toward a globally valid EO approach is still largely hampered by inconsistences over temporally and spatially variable in-water optical conditions. In this study, a comprehensive dataset from more than 250 aquatic systems, representing a wide range of conditions, was analyzed in order to develop a typology of optical water types (OWTs) for inland and coastal waters. We introduce a novel approach for clustering in situ hyperspectral water reflectance measurements (n 5 4045) from multiple sources based on a functional data analysis. The resulting classification algorithm identified 13 spectrally distinct clusters of measurements in inland waters, and a further nine clusters from the marine environment. The distinction and characterization of OWTs was supported by the availability of a wide range of coincident data on biogeochemical and inherent optical properties from inland waters. Phylogenetic trees based on the shapes of cluster means were constructed to identify similarities among the derived clusters with respect to spectral diversity. This typification provides a valuable framework for a globally applicable EO scheme and the design of future EO missions.

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Time-series airborne remote sensing was used to monitor diurnal changes in the spatial distribution of a bloom of the potentially toxic cyanobacterium Microcystis aeruginosa in the shallow eutrophic waters of Barton Broad, United Kingdom. High spatial resolution images from the Compact Airborne Spectrographic Imager (CASI-2) were acquired over Barton Broad on 29 August 2005 at 09:30 h, 12:00 h, and 16:00 h Greenwich mean time. Semiempirical water-leaving radiance algorithms were derived for the quantification of chlorophyll a (R 2 5 0.96) and C-phycocyanin (R 2 5 0.95) and applied to the CASI-2 imagery to generate dynamic, spatially resolving maps of the M. aeruginosa bloom. The development of the bloom may have been fostered by a combination of the recent improvements in the ambient light environment of Barton Broad, allied to the acute depletion of bioavailable nutrient pools, and stable hydrodynamic conditions. The vertical distribution of M. aeruginosa was highly transient; buoyant colonies formed early morning and late afternoon near-surface aggregations across the lake during periods of nonturbulent mixing (wind speed ,4 m s 21 ). However, the extent of these near-surface aggregations was highly spatially variable, and nearshore morphometry appeared to be crucial in creating localized regions of nonturbulent water in which pronounced and persistent near-surface aggregations were observed. The formation of these near-surface scums would have been vital in alleviating light starvation in the turbid waters of Barton Broad. The calm water refuges in which persistent near-surface accumulations occurred may have been an important factor in determining the persistence of the bloom.

Image correction for atmospheric effects (iCOR) is an atmospheric correction tool that can process satellite data collected over coastal, inland or transitional waters and land. The tool is adaptable with minimal effort to hyper-or multi-spectral radiometric sensors. By using a single atmospheric correction implementation for land and water, discontinuities in reflectance within one scene are reduced. iCOR derives aerosol optical thickness from the image and allows for adjacency correction, which is SIMilarity Environmental Correction (SIMEC) over water. This paper illustrates the performance of iCOR for Landsat-8 OLI and Sentinel-2 MSI data acquired over water. An intercomparison of water leaving reflectance between iCOR and Aerosol Robotic Network-Ocean Color provided a quantitative assessment of performance and produced coefficient of determination (R 2) higher than 0.88 in all wavebands except the 865 nm band. For inland waters, the SIMEC adjacency correction improved results in the rededge and near-infrared region in relation to optical in situ measurements collected during field campaigns.

Cyanobacterial blooms are an increasing threat to water quality and global water security caused by the nutrient enrichment of freshwaters. There is also a broad consensus that blooms are increasing with global warming, but the impacts of other concomitant environmental changes, such as an increase in extreme rainfall events, may affect this response. One of the potential effects of high rainfall events on phytoplankton communities is greater loss of biomass through hydraulic flushing. Here we used a shallow lake mesocosm experiment to test the combined effects of: warming (ambient vs. +4°C increase), high rainfall (flushing) events (no events vs. seasonal events) and nutrient loading (eutrophic vs. hypertrophic) on total phytoplankton chlorophyll‐a and cyanobacterial abundance and composition. Our hypotheses were that: (a) total phytoplankton and cyanobacterial abundance would be higher in heated mesocosms; (b) the stimulatory effects of warming on cyanobacterial abundance would be enhanced in higher nutrient mesocosms, resulting in a synergistic interaction; (c) the recovery of biomass from flushing induced losses would be quicker in heated and nutrient‐enriched treatments, and during the growing season. The results supported the first and, in part, the third hypotheses: total phytoplankton and cyanobacterial abundance increased in heated mesocosms with an increase in common bloom‐forming taxa—Microcystis spp. and Dolichospermum spp. Recovery from flushing was slowest in the winter, but unaffected by warming or higher nutrient loading. Contrary to the second hypothesis, an antagonistic interaction between warming and nutrient enrichment was detected for both cyanobacteria and chlorophyll‐a demonstrating that ecological surprises can occur, dependent on the environmental context. While this study highlights the clear need to mitigate against global warming, oversimplification of global change effects on cyanobacteria should be avoided; stressor gradients and seasonal effects should be considered as important factors shaping the response.

Numerous algorithms have been developed to retrieve chlorophyll-a (Chla) concentrations (mg m-3) from Earth observation (EO) data collected over optically complex waters. Retrieval accuracy is highly variable and often unsatisfactory where Chla co-occurs with other optically active constituents. Furthermore, the applicability and limitations of retrieval algorithms across different optical complex systems in space and time are often not considered. In the first instance, this paper provides an extensive performance assessment for 48 Chla retrieval algorithms of varying architectural design. The algorithms are tested in their original parametrisations and are then retuned using in-situ remote sensing reflectance (Rrs(λ), sr-1) data (n = 2807) collected from 185 global inland and coastal aquatic systems encompassing 13 different optical water types (OWTs). The paper then demonstrates retrieval performance across the full dataset of observations and within Accepted refereed manuscript of: Neil C, Spyrakos E, Hunter PD & Tyler AN (2019) A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types.