Description: Monthly global 4km satellite products spanning September 1997 to December 2020. The data contains Particle Size Distribution (PSD) parameters of an assumed power-law PSD, absolute and fractional size-partitioned phytoplankton carbon and associated variables such as particulate organic carbon (POC) and Chlorophyll-a as derived from the PSD algorithm. The retrieval is based on a backscattering bio-optical model using two particle populations and coated spheres for phytoplankton inherent optical properties (IOP) modeling, and a retrieval using spectral angle mapping (SAM - where satellite spectra are classified using a comparison to a collection of modeled end-member spectra, by treating spectra as vectors and using their dot product). Partial uncertainties are given as standard deviation and are estimated using a combination of Monte Carlo simulations and analytical error propagation. An empirical tuning factor is given for attaining more realistic estimated model concentrations of POC and Chlorophyll-a. The tuning factor is multiplicative, to be applied in linear space. This tuning factor has not been applied to the monthly data, users can choose whether or not to apply it to absolute carbon and Chlorophyll-a concentrations. The factor does not affect retrievals of fractional contributions of phytoplankton size classes to total phytoplankton carbon. Monthly climatologies files and an overall climatology file are also provided, and in those files, both untuned (tuning factor not applied) and tuned (tuning factor applied) variables are provided, for user convenience. Input remote-sensing reflectance data are v5.0 of the Ocean Colour -Climate Change Initiative (OC-CCI) of the European Space Agency. The OC-CCI general reference is Sathyendranath et al. (2019; doi:10.3390/s19194285), and for v5.0 of the dataset, the reference is Sathyendranath et al. (2021; doi:10.5285/1dbe7a109c0244aaad713e078fd3059a). More detailed metadata, including geospatial metadata, are given in the netCDF files. Variable names should be self-explanatory. Quick browse images are provided as well. Coastlines in these quick browse images are from v2.3.7 of the GSHHS data set - see Wessel and Smith (1996) (doi:10.1029/96JB00104). Modeling and data processing was done in MATLAB ®.

Description: Harmful algal blooms (HABs) occur in virtually all coastal regions of the world as well as many lakes, and are typically associated with a rapid proliferation of phytoplankton cells, but even low cell numbers of highly toxic species may cause harmful e ects in the ecosystem and/or the surrounding environment. Dense algal blooms produce a significant phytoplankton contribution to the water body’s optical signal, making HAB applications an instinctively attractive one for ocean colour radiometry. Indeed, there exists some spectacular satellite imagery of algal blooms the world over (e.g., Figure 1.1). But beyond the attractiveness of the imagery, this monograph addresses the extent to which ocean colour radiometry can inform scientifically in HAB regions, both towards answering research questions as well as for use in the operational detection and management systems necessary for the mitigation of harmful health, economic and recreational impacts of HABs. The potential for harm caused by these blooms is two-fold: in the first instance, the algal assemblage itself may contain toxins poisonous to organisms. Aquatic and non-aquatic animals alike can be a ected by these toxins, which tend to increase through successive trophic levels, accumulating up the food chain. These organisms (primarily dinoflagellates and diatoms) and the nature of their impacts, including paralytic shellfish poisoning, amnesic shellfish poisoning and neurotoxic shellfish poisoning, are described in Chapters 4, 5 and 6. Another set of toxin-containing HABs are the high-biomass cyanobacterial blooms which frequently occur in lakes, rivers, estuaries and coastal seas, and are considered harmful for diverse reasons including contamination of drinking water, concentration of toxins in higher trophic level organisms (e.g., health of cattle and wildlife), and the associated reduction of the recreational, economic and ecological value of a ected water bodies. Cyanobacterial blooms are increasing in frequency and intensity, perhaps in response to climate change. Several case studies of remote sensing of cyanobacteria blooms in lakes as well as in the Baltic Sea are discussed in Chapter 7. The other mechanism by which harm may be caused is by the algal biomass growing so large, and the phytoplankton bloom so dense, that it impacts the health of the ecosystem by other biophysical means while not actually comprising toxic species. Dense blooms can clog the gills of fish and invertebrates as described in Chapter 8. One of the most serious environmental consequences of a dense bloom is that of anoxia — where oxygen is depleted by respiration and decay to such an extent that all oxygen-dependent organisms in the ecosystem are a ected (Pitcher and Jacinto 2019). Those that are mobile move away from the oxygen-depleted water, whether into an una ected area of the ocean or out of the water altogether e.g., lobster walkouts. These impacts are described in Chapter 9. Also discussed in this chapter is a sub-category of non-toxic harmful blooms called ecologically disruptive algal blooms (EDABs), comprising certain small-celled algal species which disrupt trophic dynamics by non-chemical means. This chapter presents case studies where the aquaculture industry is impacted by blooms of this type, as well as blooms that threaten the ecological health of subtropical estuaries. This IOCCG monograph addresses both groups of HABs in the context of the use of satellite ocean colour data to detect, identify, monitor, manage and project/predict HAB events. HABs, while anomalous by definition, are in some regions a normal occasional occurrence in perfectly healthy ecosystems. Many areas are subject to physical and biophysical forcing which primes these systems for regular seasonal HABs. Other HAB events may occur suddenly and unexpectedly, for example as a result of unusual nutrient inputs. Yet other HABs are fairly persistent in their presence and intensity, for example cyanobacterial populations in inland water bodies in China, Europe and Southern Africa (see Chapter 7). Each HAB system has its own unique forcings and resultant character, making a one-size-fits-all approach to satellite data use highly challenging. With increasingly large proportions of global populations living in proximity to HAB-vulnerable water bodies, the societal impact of HABs is increasing as well. Drinking and agricultural water supplies are under increasing pressure across the globe, and eutrophication of these water sources is one of the most pressing freshwater problems we face today. This has resulted in demand for operational HAB monitoring and management systems to predict, observe and mitigate the e ects of HAB events. Chapter 10 presents some examples of the development and implementation of such systems. In the context of climate change, an increase in the frequency and intensity of HABs is anticipated in many regions of the world, and is specifically of great concern in areas used for aquaculture to support food security and economic sustainability. proximity to HAB-vulnerable water bodies, the societal impact of HABs is increasing as well. Drinking and agricultural water supplies are under increasing pressure across the globe, and eutrophication of these water sources is one of the most pressing freshwater problems we face today. This has resulted in demand for operational HAB monitoring and management systems to predict, observe and mitigate the e ects of HAB events. Chapter 10 presents some examples of the development and implementation of such systems. In the context of climate change, an increase in the frequency and intensity of HABs is anticipated in many regions of the world, and is specifically of great concern in areas used for aquaculture to support food security and economic sustainability.

Description: State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, China supported the printing of this document

Description: OPENASFA INPUT Correct citation for this publication: IOCCG (2021). Observation of Harmful Algal Blooms with Ocean Colour Radiometry. Bernard, S., Kudela, R., Robertson Lain, L. and Pitcher, G.C. (eds.), IOCCG Report Series, No. 20, International Ocean Colour Coordinating Group, Dartmouth, Canada. http://dx.doi.org/10.25607/OBP-1042

Description: Published

Description: Refereed