Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes.

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll , a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies.

Spectral correlation in MODIS water-leaving reflectance retrieval uncertainty.

Spectral remote sensing reflectance, R(λ) (sr), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved R. Furthermore, since the associated algorithms require R at multiple spectral bands, the spectral (i.

Informing ocean color inversion products by seeding with ancillary observations.

Ocean reflectance inversion algorithms provide many products used in ecological and biogeochemical models. While a number of different inversion approaches exist, they all use only spectral remote-sensing reflectances (R(λ)) as input to derive inherent optical properties (IOPs) in optically deep oceanic waters. However, information content in R(λ) is limited, so spectral inversion algorithms may benefit from additional inputs.

Assessing the suitability of lakes and reservoirs for recreation using Landsat 8.

Water clarity has long been used as a visual indicator of the condition of water quality. The clarity of waters is generally valued for esthetic and recreational purposes. Water clarity is often assessed using a Secchi disk attached to a measured line and lowered to a depth where it can be no longer seen.

Bayesian approach to a generalized inherent optical property model.

Relationships between the absorption and backscattering coefficients of marine optical constituents and ocean color, or remote sensing reflectances R(λ), can be used to predict the concentrations of these constituents in the upper water column. Standard inverse modeling techniques that minimize error between the modeled and observed R(λ) break down when the number of products retrieved becomes similar to, or greater than, the number of different ocean color wavelengths measured. Furthermore, most conventional ocean reflectance inversion approaches, such as the default configuration of NASA's Generalized Inherent Optical Properties algorithm framework (GIOP-DC), require a priori definitions of absorption and backscattering spectral shapes.

Recent changes in cyanobacteria algal bloom magnitude in large lakes across the contiguous United States.

Cyanobacterial blooms in inland lakes produce large quantities of biomass that impact drinking water systems, recreation, and tourism and may produce toxins that can adversely affect public health. This study analyzed nine years of satellite-derived bloom records and compared how the bloom magnitude has changed from 2008-2011 to 2016-2020 in 1881 of the largest lakes across the contiguous United States (CONUS). We determined bloom magnitude each year as the spatio-temporal mean cyanobacteria biomass from May to October and in concentrations of chlorophyll-a.

Optimizing retrieval spaces of bio-optical models for remote sensing of ocean color.

We investigated the optimal number of independent parameters required to accurately represent spectral remote sensing reflectances ( ) by performing principal component analysis on quality controlled in situ and synthetic data. We found that retrieval algorithms should be able to retrieve no more than four free parameters from spectra for most ocean waters. In addition, we evaluated the performance of five different bio-optical models with different numbers of free parameters for the direct inversion of in-water inherent optical properties (IOPs) from in situ and synthetic data.

Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales.

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms-the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3.

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a.

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state.

Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification.

Ocean color (OC) remote sensing requires compensation for atmospheric scattering and absorption (aerosol, Rayleigh, and trace gases), referred to as atmospheric correction (AC). AC allows inference of parameters such as spectrally resolved remote sensing reflectance (();) at the ocean surface from the top-of-atmosphere reflectance. Often the uncertainty of this process is not fully explored.

A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes.

Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation.

Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales.

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria.

Seasonal bias in global ocean color observations.

In this study, we identify a seasonal bias in the ocean color satellite-derived remote sensing reflectances (();) at the ocean color validation site, Marine Optical BuoY. The seasonal bias in () is present to varying degrees in all ocean color satellites examined, including the Visible Infrared Imaging Radiometer Suite, Sea-Viewing Wide Field-of-View Sensor, and Moderate Resolution Imaging Spectrometer. The relative bias in has spectral dependence.

Particulate Backscattering in the Global Ocean: A Comparison of Independent Assessments.

How well do we know the particulate backscattering coefficient (b) in the global ocean? Satellite lidar b has never been validated globally and few studies have compared lidar b to b derived from reflectances (via ocean color) or in situ observations. Here, we validate lidar b with autonomous biogeochemical Argo floats using a decorrelation analysis to identify relevant spatiotemporal matchup scales inspired by geographical variability in the Rossby radius of deformation. We compare lidar, float, and ocean color b at the same locations and times to assess performance.

Assessing cyanobacterial frequency and abundance at surface waters near drinking water intakes across the United States.

This study presents the first large-scale assessment of cyanobacterial frequency and abundance of surface water near drinking water intakes across the United States. Public water systems serve drinking water to nearly 90% of the United States population. Cyanobacteria and their toxins may degrade the quality of finished drinking water and can lead to negative health consequences.

Evaluation of a satellite-based cyanobacteria bloom detection algorithm using field-measured microcystin data.

Widespread occurrence of cyanobacterial harmful algal blooms (CyanoHABs) and the associated health effects from potential cyanotoxin exposure has led to a need for systematic and frequent screening and monitoring of lakes that are used as recreational and drinking water sources. Remote sensing-based methods are often used for synoptic and frequent monitoring of CyanoHABs. In this study, one such algorithm - a sub-component of the Cyanobacteria Index called the CI, was validated for effectiveness in identifying lakes with toxin-producing blooms in 11 states across the contiguous United States over 11 bloom seasons (2005-2011, 2016-2019).

Bayesian retrieval of optically relevant properties from hyperspectral water-leaving reflectances.

Current methods to retrieve optically relevant properties from ocean color observations do not explicitly make use of prior knowledge about property distributions. Here we implement a simplified Bayesian approach that takes into account prior probability distributions on two sets of five optically relevant parameters, and conduct a retrieval of these parameters using hyperspectral simulated water-leaving reflectances. We focus specifically on the ability of the model to distinguish between two optically similar phytoplankton taxa, diatoms and Noctiluca scintillans.

Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms.

Spectroradiometric satellite observations of the ocean are commonly referred to as "ocean color" remote sensing. NASA has continuously collected, processed, and distributed ocean color datasets since the launch of the Sea-viewing Wide-field-of-view Sensor (SeaWiFS) in 1997. While numerous ocean color algorithms have been developed in the past two decades that derive geophysical data products from sensor-observed radiometry, few papers have clearly demonstrated how to estimate measurement uncertainty in derived data products.

Measurement of Cyanobacterial Bloom Magnitude using Satellite Remote Sensing.

Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs.

Comparing level-2 and level-3 satellite ocean color retrieval validation methodologies.

Many ocean color data applications leverage global spatially composited level-3 (L3) satellite data because of their regular Earth-grid frame of reference. However, ocean color satellite retrieval performance is routinely evaluated on level-2 (L2) data at the native satellite swath resolution and geometries. This study assesses how accurately binned and gridded L3 data represent L2 satellite data products via satellite-to-in situ match-up activities.

Sensitivity of inherent optical properties from ocean reflectance inversion models to satellite instrument wavelength suites.

The Earth science community seeks to develop climate data records (CDRs) from satellite measurements of ocean color, a continuous data record which now exceeds twenty years. Space agencies will launch additional instruments in the coming decade that will continue this data record, including the NASA PACE spectrometer. Inherent optical properties (IOPs) quantitatively describe the absorbing and scattering constituents of seawater and can be estimated from satellite-observed spectroradiometric data using semi-analytical algorithms (SAAs).

CHLOROPHYLL ALGORITHMS FOR OCEAN COLOR SENSORS - OC4, OC5 & OC6.

A high degree of consistency and comparability among chlorophyll algorithms is necessary to meet the goals of merging data from concurrent overlapping ocean color missions for increased coverage of the global ocean and to extend existing time series to encompass data from recently launched missions and those planned for the near future, such as PACE, OLCI, HawkEye, EnMAP and SABIA-MAR. To accomplish these goals, we developed 65 empirical ocean color (OC) maximum band ratio (MBR) algorithms for 25 satellite instruments using the largest available and most globally representative database of coincident chlorophyll and remote sensing reflectances. Excellent internal consistency was achieved across these OC 'Version -7' algorithms, as demonstrated by a median regression slope and coefficient of determination (R) of 0.

Radiative Transfer Modeling of Phytoplankton Fluorescence Quenching Processes.

We report the first radiative transfer model that is able to simulate phytoplankton fluorescence with both photochemical and non-photochemical quenching included. The fluorescence source term in the inelastic radiative transfer equation is proportional to both the quantum yield and scalar irradiance at excitation wavelengths. The photochemical and nonphotochemical quenching processes change the quantum yield based on the photosynthetic active radiation.

An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing.

Ocean color measured from satellites provides daily global, synoptic views of spectral waterleaving reflectances that can be used to generate estimates of marine inherent optical properties (IOPs). These reflectances, namely the ratio of spectral upwelled radiances to spectral downwelled irradiances, describe the light exiting a water mass that defines its color. IOPs are the spectral absorption and scattering characteristics of ocean water and its dissolved and particulate constituents.