Calibration of a general optical equation for remote sensing of suspended sediments in a moderately turbid estuary

Abstract A general algorithm for determining suspended sediment concentrations in the surface waters of estuaries has been developed for use with satellite data. The algorithm uses a three-parameter general optical equation to relate suspended sediment concentrations to water reflectances that have been corrected for sun angle effects, atmospheric path radiance, and tidal excursion. Using data collected by the advanced very high resolution radiometer on five different dates, reflectances were determined using two different methods, one providing maximum correction for haze and the other providing minimum sensitivity to pigments. For both methods, in situ and remotely sensed samples from Delaware Bay acquired within 3.5 hours of each other agreed to within 60% at the 95% confidence level. Pixel and subpixel scale spatial variations and variability associated with in situ measurements produced about 50% of the differences. Chlorophyll concentrations of \u3e50 μg/L produced a discrepancy in the reflectance method that provided the best haze correction. The parameter values may be adjusted to allow for variations in sediment size and pigment variations, allowing application of the calibration to estuaries having optically different suspended sediments

Remote estimation of the diffuse attenuation coefficient in a moderately turbid estuary

Abstract Solutions of the radiative transfer equation are used to derive relationships of water reflectance to the diffuse attenuation coefficient (K) in moderately turbid water (K \u3e 0.5 m−1). Data sets collected from the NOAA AVHRR and in situ observations from five different dates confirm the appropriateness of these relationships, in particular the logistic equation. Values of K calculated from the reflectance data agree to within 60% of the observed values, although the reflectance derived using a more comprehensive aerosol correction is sensitive to chlorophyll concentrations greater than 50 μg L−1. Agreement between in situ and remote observations improves as the time interval between samples is narrowed

Dynamics of an intense Alexandrium catenella red tide in the Gulf of Maine: satellite observations and numerical modeling

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Li, Y., Stumpf, R. P., McGillicuddy, D. J.,Jr, & He, R. Dynamics of an intense Alexandrium catenella red tide in the Gulf of Maine: satellite observations and numerical modeling. Harmful Algae, 99, (2020): 101927, doi:10.1016/j.hal.2020.101927.In July 2009, an unusually intense bloom of the toxic dinoflagellate Alexandrium catenella occurred in the Gulf of Maine. The bloom reached high concentrations (from hundreds of thousands to one million cells L−1) that discolored the water and exceeded normal bloom concentrations by a factor of 1000. Using Medium Resolution Imaging Spectrometer (MERIS) imagery processed to target chlorophyll concentrations (>2 µg L−1), patches of intense A. catenella concentration were identified that were consistent with the highly localized cell concentrations observed from ship surveys. The bloom patches were generally aligned with the edge of coastal waters with high-absorption. Dense bloom patches moved onshore in response to a downwelling event, persisted for approximately one week, then dispersed rapidly over a few days and did not reappear. Coupled physical-biological model simulations showed that wind forcing was an important factor in transporting cells onshore. Upward swimming behavior facilitated the horizontal cell aggregation, increasing the simulated maximum depth-integrated cell concentration by up to a factor of 40. Vertical convergence of cells, due to active swimming of A. catenella from the subsurface to the top layer, could explain the additional 25-fold intensification (25 × 40=1000-fold) needed to reach the bloom concentrations that discolored the water. A model simulation that considered upward swimming overestimated cell concentrations downstream of the intense aggregation. This discrepancy between model and observed concentrations suggested a loss of cells from the water column at a time that corresponded to the start of encystment. These results indicated that the joint effect of upward swimming, horizontal convergence, and wind-driven flow contributed to the red water event, which might have promoted the sexual reproduction event that preceded the encystment process.DJM gratefully acknowledges support of the Woods Hole Center for Oceans and Human Health, funded jointly by the National Science Foundation (OCE-1314642 and OCE-1840381) the National Institute of Environmental Health Sciences (P01ES021923–01 and P01 ES028938–01). RH acknowledges support made possible by NOAA grant NA15NOS4780196 and NA16NOS0120028

Satellite Remote Sensing of Cyanobacteria: Success Stories of Management Taking Action and the CyAN Data Sharing App

Support the environmental management and public use of U.S. lakes by detecting and quantifying algal blooms and related water quality indicators using satellite data records

Integration of data for nowcasting of harmful algal blooms

Harmful algal blooms (HABs) are a significant and potentially expanding problem around the world. Resource management and public health protection require sufficient information to reduce the impacts of HABs by response strategies and through warnings and advisories. To be effective, these programs can best be served by an integration of improved detection methods with both evolving monitoring systems and new communications capabilities. Data sets are typically collected from a variety of sources, these can be considered as several types: point data, such as water samples; transects, such as from shipboard continuous sampling; and synoptic, such as from satellite imagery. Generation of a field of the HAB distribution requires all of these sampling approaches. This means that the data sets need to be interpreted and analyzed with each other to create the field or distribution of the HAB. The HAB field is also a necessary input into models that forecast blooms. Several systems have developed strategies that demonstrate these approaches. These range from data sets collected at key sites, such as swimming beaches, to automated collection systems, to integration of interpreted satellite data. Improved data collection, particularly in speed and cost, will be one of the advances of the next few years. Methods to improve creation of the HAB field from the variety of data types will be necessary for routine nowcasting and forecasting of HABs

Performance Metrics for the Assessment of Satellite Data Products: An Ocean Color Case Study

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities

Characterizing a cyanobacterial bloom in western Lake Erie using satellite imagery and meteorological data

The distribution and intensity of a bloom of the toxic cyanobacterium, Microcystis aeruginosa, in western Lake Erie was characterized using a combination of satellite ocean-color imagery, field data, and meteorological observations. The bloom was first identified by satellite on 14 August 2008 and persisted for more than 2 months. The distribution and intensity of the bloom was estimated using a satellite algorithm that is sensitive to near-surface concentrations of M. aeruginosa. Increases in both area and intensity were most pronounced for wind stress less than 0.05 Pa. Area increased while intensity did not change for wind stresses of 0.05–0.1 Pa, and both decreased for wind stress greater than 0.1 Pa. The recovery in intensity at the surface after strong wind events indicated that high wind stress mixed the bloom through the water column and that it returned to the surface once mixing stopped. This interaction is consistent with the understanding of the buoyancy of these blooms. Cloud cover (reduced light) may have a weak influence on intensity during calm conditions. While water temperature remained greater than 15°C, the bloom intensified if there were calm conditions. For water temperature less than 15°C, the bloom subsided under similar conditions. As a result, wind stress needs to be considered when interpreting satellite imagery of these blooms

Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques

We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previous approaches for the detection of KB HABs in the WFS primarily used observations from the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite. They depended on the remote sensing reflectance signal at the 678 nm chlorophyll fluorescence band (Rrs678) needed for both the normalized fluorescence height (nFLH) and Red Band Difference algorithms (RBD) currently used. VIIRS which has replaced MODIS-A, unfortunately does not have a 678 nm fluorescence channel so we customized the NN approach to retrieve phytoplankton absorption at 443 nm (aph443) using only Rrs measurements from existing VIIRS channels at 486, 551 and 671 nm. The aph443 values in these retrieved VIIRS images, can in turn be correlated to chlorophyll-a concentrations [Chla] and KB cell counts. To retrieve KB values, the VIIRS NN retrieved aph443 images are filtered by applying limiting constraints, defined by (i) low backscatter at Rrs 551 nm and (ii) a minimum aph443 value known to be associated with KB HABs in the WFS. The resulting filtered residual images, are then used to delineate and quantify the existing KB HABs. Comparisons with KB HABs satellite retrievals obtained using other techniques, including nFLH, as well as with in situ measurements reported over a four year period, confirm the viability of the NN technique, when combined with the filtering constraints devised, for effective detection of KB HABs

Categorizing the severity of paralytic shellfish poisoning outbreaks in the Gulf of Maine for forecasting and management

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 103 (2014): 277-287, doi:10.1016/j.dsr2.2013.03.027.Development of forecasting systems for harmful algal blooms (HABs) has been a long-standing research and management goal. Significant progress has been made in the Gulf of Maine, where seasonal bloom forecasts are now being issued annually using Alexandrium fundyense cyst abundance maps and a population dynamics model developed for that organism. Thus far these forecasts have used terms such as “significant”, “moderately large” or “moderate” to convey the extent of forecasted paralytic shellfish poisoning (PSP) outbreaks. In this study, historical shellfish harvesting closure data along the coast of the Gulf of Maine were used to derive a series of bloom severity levels that are analogous to those used to define major storms like hurricanes or tornados. Thirty-four years of PSP-related shellfish closure data for Maine, Massachusetts and New Hampshire were collected and mapped to depict the extent of coastline closure in each year. Due to fractal considerations, different methods were explored for measuring length of coastline closed. Ultimately, a simple procedure was developed using arbitrary straight-line segments to represent specific sections of the coastline. This method was consistently applied to each year’s PSP toxicity closure map to calculate the total length of coastline closed. Maps were then clustered together statistically to yield distinct groups of years with similar characteristics. A series of categories or levels was defined (“Level 1: Limited”, “Level 2: Moderate”, and “Level 3: Extensive”) each with an associated range of expected coastline closed, which can now be used instead of vague descriptors in future forecasts. This will provide scientifically consistent and simply defined information to the public as well as resource managers who make decisions on the basis of the forecasts.Research support provided through the Woods Hole Center for Oceans and Human Health, National Science Foundation (NSF) Grants OCE-0430724, and OCE-0911031; and National Institute of Environmental Health Sciences (NIEHS) Grant 1-P50-ES012742-01, the ECOHAB Grant program through NOAA Grant NA06NOS4780245, and the PCM HAB Grant program through NOAA Grant NA11NOS4780023

Physical drivers facilitating a toxigenic cyanobacterial bloom in a major Great Lakes tributary

The Maumee River is the primary source for nutrients fueling seasonal Microcystis-dominated blooms in western Lake Erie\u27s open waters though such blooms in the river are infrequent. The river also serves as source water for multiple public water systems and a large food services facility in northwest Ohio. On 20 September 2017, an unprecedented bloom was reported in the Maumee River estuary within the Toledo metropolitan area, which triggered a recreational water advisory. Here we (1) explore physical drivers likely contributing to the bloom\u27s occurrence, and (2) describe the toxin concentration and bacterioplankton taxonomic composition. A historical analysis using 10-years of seasonal river discharge, water level, and local wind data identified two instances when high-retention conditions occurred over ≥ 10 d in the Maumee River estuary: in 2016 and during the 2017 bloom. Observation by remote sensing imagery supported the advection of cyanobacterial cells into the estuary from the lake during 2017 and the lack of an estuary bloom in 2016 due to a weak cyanobacterial bloom in the lake. A rapid-response survey during the 2017 bloom determined levels of the cyanotoxins, specifically microcystins, in excess of recreational contact limits at sites within the lower 20 km of the river while amplicon sequencing found these sites were dominated by Microcystis. These results highlight the need to broaden our understanding of physical drivers of cyanobacterial blooms within the interface between riverine and lacustrine systems, particularly as such blooms are expected to become more prominent in response to a changing climate