Estimation of absorption and backscatter values from in-situ radiometric water measurements

Water color remote sensing algorithms for case II waters like the Great Lakes require accurate characterization of the inherent optical properties (IOPs) within the water column to successfully estimate concentrations of chlorophyll (chl), dissolved organic carbon (doc), and suspended minerals(sm). IOP coefficients (i.e. absorption and backscatter) necessary to create hydro-optical(HO) models for each of the Great Lakes can be measured directly using expensive in situ instrumentation (such as ac-s and bb9) or derived from in situ radiometric measurements of light entering and exiting the water surface (ie. Ed and Lu) . We have utilized data collected from a Satlantic Profiling Radiometer and the MODIS satellite to generate through a model, absorption and backscatter values for CHL, and SM and absorption for the visible portion of DOC (CDOM) for data collected in 2010 on Lakes Michigan and Huron. These calculated values were then compared to coincident in situ measurements of the absorption and backscatter for the three parameters

Synergistic approach to measuring lake properties using satellite and in-situ remote sensing

Radar and electro-optical remote sensing data have been combined with in situ measurements of lakes on the North Slope of Alaska to obtain a baseline characterization of these lakes, determine change detection, document salt water intrusion, and investigate yellow billed loon habitat preference. This multi-faceted program, which also has applicability to the Great Lakes basin, has been aimed at using cost-effect technologies to investigate the remote lakes. In-situ data collected includes measurements using our autonomous water quality and bathymetry mapping robot instruments. ALWAS and BathyBoat robotic data have been used to provide baseline data as well as control and algorithm validation points for satellite remote sensing applications. Specifically, water depths from ALWAS and BathyBoat have been used in electro-optical and radar based water depth algorithms to produce bathymetry and volume of lakes on the North Slope. Additionally, in-situ data from the ALWAS buoys have been used to tune and validate satellite methods to then extend estimates of turbidity, chlorophyll, and salinity (expressed in alterations of aquatic vegetation and shoreline communities) to lakes that have not been directly sampled. These observations can then be linked to trophic index, saltwater intrusion, vegetation, and habitat

Water quality observations in the Great Lakes using an optimized satellite bio-optical algorithm

The Color Producing Agent Algorithm (CPA-A) is a semi-analytical inverse radiative transfer bio-optical model to retrieve water quality parameters from satellite observed reflectance. The CPA-A requires knowledge of the inherent optical properties of a given water body to produce accurate retrievals of the primary color producing agents (CPA) namely chlorophyll (CHL), suspended matter (SM), and CDOM. An optimized set of inherent optical properties, known as a hydro-optical (HO) model, has been generated for Lakes Michigan, Superior, and Huron that produce robust retrievals annually and intraannually for the MODIS mission (2002-2013). The optimized HO model was used to generate long term time series estimates of several water quality parameters including CHL, SM, CDOM, DOC, attenuation, absorption, backscatter, and photic depth. The diffuse attenuation coefficient (Kd) and photic depth are functions of CPA concentration and are therefore inherently retrievable with the CPA-A. Retrieved concentrations of CPA-A derived water quality parameters compare favorably with in situ measurements in the upper three Lakes. This complete set of water quality parameters provides unique observations of the lower food web including primary production to help better understand ecological changes due to anthropogenic forcing and climate change

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases