Jeremy Kravitz
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View article: Determining the Spectral Requirements for Cyanobacteria Detection for the CyanoSat Hyperspectral Imager with Machine Learning
Determining the Spectral Requirements for Cyanobacteria Detection for the CyanoSat Hyperspectral Imager with Machine Learning Open
This study determines an optimal spectral configuration for the CyanoSat imager for the discrimination and retrieval of cyanobacterial pigments using a simulated dataset with machine learning (ML). A minimum viable spectral configuration w…
View article: Simulated Inherent Optical Properties of Aquatic Particles using The Equivalent Algal Populations (EAP) model
Simulated Inherent Optical Properties of Aquatic Particles using The Equivalent Algal Populations (EAP) model Open
Paired measurements of phytoplankton absorption and backscatter, the inherent optical properties central to the interpretation of ocean colour remote sensing data, are notoriously rare. We present a dataset of Chlorophyll a (Chl a ) -speci…
View article: Synechococcus
Synechococcus Open
Synechococcus-like Chl a-specific IOPs for Deffs 0.4, 0.8, 1.2, 1.8 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Raphidophytes
Raphidophytes Open
Raphidophyte-like Chl a-specific IOPs for Deffs 12, 24, 48, 60 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Eustigmatophytes
Eustigmatophytes Open
Eustigmatophyte-like Chl a-specific IOPs for Deffs 2, 6, 12, 24 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Mean measured in vivo absorption (imaginary RI)
Mean measured in vivo absorption (imaginary RI) Open
These means represent the inputted imaginary RI for the shell sphere of each phytoplankton group.
View article: Cyanobacteria (red mode)
Cyanobacteria (red mode) Open
"Red mode" Cyanobacteria-like Chl a-specific IOPs for Deffs 2, 6, 12, 24 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Diatoms (Centric)
Diatoms (Centric) Open
Diatom-like (Centric group) Chl a-specific IOPs for Deffs 6, 12, 24, 48 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Dinoflagellates
Dinoflagellates Open
Dinoflagellate-like Chl a-specific IOPs for Deffs 2, 6, 12, 24 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Cyanobacteria (blue mode)
Cyanobacteria (blue mode) Open
"Blue mode" Cyanobacteria-like Chl a-specific IOPs for Deffs 2, 6, 12, 24 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Pelagophytes
Pelagophytes Open
Pelagophyte-like Chl a-specific IOPs for Deffs 1, 2, 3, 4 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Cryptophytes
Cryptophytes Open
Cryptophyte-like Chl a-specific IOPs for Deffs 2, 6, 12, 24 and 48 micron, and intracellular chl a densities of 2, 5 and 8 kg per m3. IOPs derived from measured in vivo absorption and modelled using a two-layered spherical geometry.
View article: Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach
Potential for High Fidelity Global Mapping of Common Inland Water Quality Products at High Spatial and Temporal Resolutions Based on a Synthetic Data and Machine Learning Approach Open
There is currently a scarcity of paired in-situ aquatic optical and biogeophysical data for productive inland waters, which critically hinders our capacity to develop and validate robust retrieval models for Earth Observation applications.…