Nanosatellite Hyperspectral Imaging Performance Modeling for Ocean Color Detection.
Earth’s oceans are an integral sub-system of our planet, an invaluable resource, and an informative proxy for understanding human-related climate impact. Ocean color observations are particularly useful for monitoring and modeling phytoplankton, valuable fauna that form the basis of the marine food...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153796 |
| _version_ | 1811071153371873280 |
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| author | Payne, Cadence |
| author2 | Cahoy, Kerri |
| author_facet | Cahoy, Kerri Payne, Cadence |
| author_sort | Payne, Cadence |
| collection | MIT |
| description | Earth’s oceans are an integral sub-system of our planet, an invaluable resource, and an informative proxy for understanding human-related climate impact. Ocean color observations are particularly useful for monitoring and modeling phytoplankton, valuable fauna that form the basis of the marine food web, produce an estimated 50-85% of breathable oxygen, and provide the largest and most efficient mechanism for oceanic carbon capture. Monitoring the behavioral response of phytoplankton to the impact of increased anthropogenic input at a scale observable by spacecraft provides information on ocean health at large. More effective space-based monitoring requires increased spectral, temporal, and spatial resolution compared with currently available performance from legacy instruments such as MODIS, MERIS, and SeaWiFS. Data coverage without temporal gaps is necessary for monitoring short- and long-term trends, and high spectral resolution is required for taxonomic species discrimination and identification of in-water optical constituents in turbid, coastal regions. Nanosatellites hosting ocean-sensing hyperspectral imagers may offer gap-filling solutions by providing complementary measurements with high spectral, spatial, and temporal resolution that align spectrally with legacy data.
This work investigates the utility of nanosatellite solutions for targeting the ocean color observational needs of increased spatial coverage and spectral resolution. Two reference nanosatellite architectures, AEROS and HYPSO-1, are evaluated to derive sensor performance with respect to measurement requirements and sensitivities. Each mission hosts an ocean sensing hyperspectral imaging payload with unique architectures, and their performance represents a benchmark for nanosatellite solutions. In this thesis, the capabilities of nanosatellite hyperspectral imagers are analyzed by using environment models and developing detailed instrument simulations. Synthetic atmospheric scenes are produced for three regions using the Py6S, open-source radiative transfer model. Model outputs provide top-of-atmosphere spectral radiance across a tradespace of environmental factors and viewing geometries. Regions are selected for their global climate relevance and proximity to the coast, as coastal observations require higher spectral resolution. The three target regions are geographically distributed to represent a diverse set of potential nanosatellite imaging scenes to assess performance for both ideal and non-optimal imaging conditions.
A radiometric performance model is developed to determine the nanosatellite hyperspectral imagers’ signal-to-noise ratio for all generated scenes, enabling the identification of imaging and operational constraints. The imagers’ noise equivalent spectral radiance is derived to determine the imaging sensitivity to input signals and minimal detection limits. Performance is contrasted between the two reference missions, and each mission is evaluated for their compliance with identified measurement needs. These needs are captured by a set of mission, system, and payload requirements derived from community reports, constituent retrieval algorithms, and lessons learned from legacy missions. These requirements are scaled for compatibility with the nanosatellite platform to enable assessments of design limitations and potential opportunities for improvement. Model derivation and results are discussed and design limitations of the nanosatellite platforms are identified.
The results of this thesis demonstrate the challenges of satisfying measurement needs designed for state-of-the-art ocean color imagers with the nanosatellite platform. However, it is found that both the AEROS and HYPSO-1 nanosatellite missions achieve partial compliance with the SNR requirement of 200 for VIS/NIR bands with the implementation of spectral binning. Both missions also achieve partial compliance with the noise-equivalent spectral radiance levels desired for VIS/NIR bands, and the HYPSO-1 mission is fully compliant with the maximum required value. Recommendations for future improvements, including imaging system design modifications that support high SNR in high-priority VIS/NIR and SWIR bands, as well as the necessity of a combined ocean surface-atmospheric radiative transfer model for environmental modeling are provided. |
| first_indexed | 2024-09-23T08:46:51Z |
| format | Thesis |
| id | mit-1721.1/153796 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T08:46:51Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1537962024-03-16T04:00:53Z Nanosatellite Hyperspectral Imaging Performance Modeling for Ocean Color Detection. Payne, Cadence Cahoy, Kerri Minchew, Brent Menezes, Viviane Kerekes, John McCarthy, Sean Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Earth’s oceans are an integral sub-system of our planet, an invaluable resource, and an informative proxy for understanding human-related climate impact. Ocean color observations are particularly useful for monitoring and modeling phytoplankton, valuable fauna that form the basis of the marine food web, produce an estimated 50-85% of breathable oxygen, and provide the largest and most efficient mechanism for oceanic carbon capture. Monitoring the behavioral response of phytoplankton to the impact of increased anthropogenic input at a scale observable by spacecraft provides information on ocean health at large. More effective space-based monitoring requires increased spectral, temporal, and spatial resolution compared with currently available performance from legacy instruments such as MODIS, MERIS, and SeaWiFS. Data coverage without temporal gaps is necessary for monitoring short- and long-term trends, and high spectral resolution is required for taxonomic species discrimination and identification of in-water optical constituents in turbid, coastal regions. Nanosatellites hosting ocean-sensing hyperspectral imagers may offer gap-filling solutions by providing complementary measurements with high spectral, spatial, and temporal resolution that align spectrally with legacy data. This work investigates the utility of nanosatellite solutions for targeting the ocean color observational needs of increased spatial coverage and spectral resolution. Two reference nanosatellite architectures, AEROS and HYPSO-1, are evaluated to derive sensor performance with respect to measurement requirements and sensitivities. Each mission hosts an ocean sensing hyperspectral imaging payload with unique architectures, and their performance represents a benchmark for nanosatellite solutions. In this thesis, the capabilities of nanosatellite hyperspectral imagers are analyzed by using environment models and developing detailed instrument simulations. Synthetic atmospheric scenes are produced for three regions using the Py6S, open-source radiative transfer model. Model outputs provide top-of-atmosphere spectral radiance across a tradespace of environmental factors and viewing geometries. Regions are selected for their global climate relevance and proximity to the coast, as coastal observations require higher spectral resolution. The three target regions are geographically distributed to represent a diverse set of potential nanosatellite imaging scenes to assess performance for both ideal and non-optimal imaging conditions. A radiometric performance model is developed to determine the nanosatellite hyperspectral imagers’ signal-to-noise ratio for all generated scenes, enabling the identification of imaging and operational constraints. The imagers’ noise equivalent spectral radiance is derived to determine the imaging sensitivity to input signals and minimal detection limits. Performance is contrasted between the two reference missions, and each mission is evaluated for their compliance with identified measurement needs. These needs are captured by a set of mission, system, and payload requirements derived from community reports, constituent retrieval algorithms, and lessons learned from legacy missions. These requirements are scaled for compatibility with the nanosatellite platform to enable assessments of design limitations and potential opportunities for improvement. Model derivation and results are discussed and design limitations of the nanosatellite platforms are identified. The results of this thesis demonstrate the challenges of satisfying measurement needs designed for state-of-the-art ocean color imagers with the nanosatellite platform. However, it is found that both the AEROS and HYPSO-1 nanosatellite missions achieve partial compliance with the SNR requirement of 200 for VIS/NIR bands with the implementation of spectral binning. Both missions also achieve partial compliance with the noise-equivalent spectral radiance levels desired for VIS/NIR bands, and the HYPSO-1 mission is fully compliant with the maximum required value. Recommendations for future improvements, including imaging system design modifications that support high SNR in high-priority VIS/NIR and SWIR bands, as well as the necessity of a combined ocean surface-atmospheric radiative transfer model for environmental modeling are provided. Ph.D. 2024-03-15T19:24:37Z 2024-03-15T19:24:37Z 2024-02 2024-02-16T20:56:23.079Z Thesis https://hdl.handle.net/1721.1/153796 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Payne, Cadence Nanosatellite Hyperspectral Imaging Performance Modeling for Ocean Color Detection. |
| title | Nanosatellite Hyperspectral Imaging Performance Modeling for Ocean Color Detection. |
| title_full | Nanosatellite Hyperspectral Imaging Performance Modeling for Ocean Color Detection. |
| title_fullStr | Nanosatellite Hyperspectral Imaging Performance Modeling for Ocean Color Detection. |
| title_full_unstemmed | Nanosatellite Hyperspectral Imaging Performance Modeling for Ocean Color Detection. |
| title_short | Nanosatellite Hyperspectral Imaging Performance Modeling for Ocean Color Detection. |
| title_sort | nanosatellite hyperspectral imaging performance modeling for ocean color detection |
| url | https://hdl.handle.net/1721.1/153796 |
| work_keys_str_mv | AT paynecadence nanosatellitehyperspectralimagingperformancemodelingforoceancolordetection |