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

Remote Sensing

Hendrik Jan Van Der Woerd¹  Tadzio Holtrop¹ 

[1] Department of Water & Climate Risk, Institute for Environmental Studies (IVM), Vrije Universiteit, De Boelelaan 1111, 1081 HV Amsterdam, The Netherlands;

关键词: HYDROPT;    ocean color;    radiative transfer;    hyperspectral;    inversion;    phytoplankton size class;   

DOI  :  10.3390/rs13153006

来源: DOAJ

【 摘 要 】

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 $R_{rs}$ 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.

【 授权许可】

Unknown