HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters
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 spe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/15/3006 |
| _version_ | 1797525208129601536 |
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| author | Tadzio Holtrop Hendrik Jan Van Der Woerd |
| author_facet | Tadzio Holtrop Hendrik Jan Van Der Woerd |
| author_sort | Tadzio Holtrop |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T09:09:28Z |
| format | Article |
| id | doaj.art-c173b49fddc0408a8cce48b742abfb7f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:09:28Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c173b49fddc0408a8cce48b742abfb7f2023-11-22T06:07:26ZengMDPI AGRemote Sensing2072-42922021-07-011315300610.3390/rs13153006HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland WatersTadzio Holtrop0Hendrik Jan Van Der Woerd1Department of Water & Climate Risk, Institute for Environmental Studies (IVM), Vrije Universiteit, De Boelelaan 1111, 1081 HV Amsterdam, The NetherlandsDepartment of Water & Climate Risk, Institute for Environmental Studies (IVM), Vrije Universiteit, De Boelelaan 1111, 1081 HV Amsterdam, The NetherlandsBiomass 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></semantics></math></inline-formula> 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.https://www.mdpi.com/2072-4292/13/15/3006HYDROPTocean colorradiative transferhyperspectralinversionphytoplankton size class |
| spellingShingle | Tadzio Holtrop Hendrik Jan Van Der Woerd HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters Remote Sensing HYDROPT ocean color radiative transfer hyperspectral inversion phytoplankton size class |
| title | HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters |
| title_full | HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters |
| title_fullStr | HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters |
| title_full_unstemmed | HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters |
| title_short | HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters |
| title_sort | hydropt an open source framework for fast inverse modelling of multi and hyperspectral observations from oceans coastal and inland waters |
| topic | HYDROPT ocean color radiative transfer hyperspectral inversion phytoplankton size class |
| url | https://www.mdpi.com/2072-4292/13/15/3006 |
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