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Article

HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters

1
Department of Water & Climate Risk, Institute for Environmental Studies (IVM), Vrije Universiteit, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands
2
Department of Freshwater and Marine Ecology (FAME), Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, P.O. Box 94240, 1090 GE Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editor: Seunghyun Son
Remote Sens. 2021, 13(15), 3006; https://doi.org/10.3390/rs13153006
Received: 25 June 2021 / Accepted: 26 July 2021 / Published: 30 July 2021
Biomass estimation of multiple phytoplankton groups from remote sensing reflectance spectra requires inversion models that go beyond the traditional band-ratio techniques. To achieve this objective retrieval models are needed that are rooted in radiative transfer (RT) theory and exploit the full spectral information for the inversion. HydroLight numerical solutions of the radiative transfer equation are well suited to support this inversion. We present a fast and flexible Python framework for forward and inverse modelling of multi- and hyperspectral observations, by further extending the formerly developed HydroLight Optimization (HYDROPT) algorithm. Computation time of the inversion is greatly reduced using polynomial interpolation of the radiative transfer solutions, while at the same time maintaining high accuracy. Additional features of HYDROPT are specification of sensor viewing geometries, solar zenith angle and multiple optical components with distinct inherent optical properties (IOP). Uncertainty estimates and goodness-of-fit metrics are simultaneously derived for the inversion routines. The pursuit to retrieve multiple phytoplankton groups from remotely sensed observations illustrates the need for such flexible retrieval algorithms that allow for the configuration of IOP models characteristic for the region of interest. The updated HYDROPT framework allows for more than three components to be fitted, such as multiple phytoplankton types with distinct absorption and backscatter characteristics. We showcase our model by evaluating the performance of retrievals from simulated Rrs spectra to obtain estimates of 3 phytoplankton size classes in addition to CDOM and detrital matter. Moreover, we demonstrate HYDROPTs capability for the inter-comparison of retrievals using different sensor band settings including coupling to full spectral coverage, as would be needed for NASA’s PACE mission. The HYDROPT framework is now made available as an open-source Python package. View Full-Text
Keywords: HYDROPT; ocean color; radiative transfer; hyperspectral; inversion; phytoplankton size class; NASA PACE HYDROPT; ocean color; radiative transfer; hyperspectral; inversion; phytoplankton size class; NASA PACE
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MDPI and ACS Style

Holtrop, T.; Van Der Woerd, H.J. HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters. Remote Sens. 2021, 13, 3006. https://doi.org/10.3390/rs13153006

AMA Style

Holtrop T, Van Der Woerd HJ. HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters. Remote Sensing. 2021; 13(15):3006. https://doi.org/10.3390/rs13153006

Chicago/Turabian Style

Holtrop, Tadzio, and Hendrik Jan Van Der Woerd. 2021. "HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters" Remote Sensing 13, no. 15: 3006. https://doi.org/10.3390/rs13153006

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