Optimizing Surface Type Definitions in Radiance-to-Irradiance Conversions for Future Earth Radiation Budget Satellite Measurements.
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| Title: | Optimizing Surface Type Definitions in Radiance-to-Irradiance Conversions for Future Earth Radiation Budget Satellite Measurements. |
|---|---|
| Authors: | van den Heever, Mathew1,2 (AUTHOR) mava5537@colorado.edu, Gristey, Jake J.1,2,3,4 (AUTHOR), Pilewskie, Peter1,3,5 (AUTHOR) |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 4, p648. 25p. |
| Subjects: | K-means clustering, Radiance, Cluster analysis (Statistics), Radiative transfer, United States. National Aeronautics & Space Administration, Terrestrial radiation, Remote sensing devices |
| Abstract: | Highlights: What are the main findings? Consensus clustering applied to an ensemble of K-means solutions shows that seven surface groups optimize the clustering space for both radiance- and anisotropic-factor-based analyses. Clustering results show that water bodies, snowy surfaces, and bright deserts form distinct surface groups, while vegetated surface classes can be consolidated. What are the implications of the main findings? The derived surface groups provide a statistically based framework to support Libera ADM development and evaluation. Water bodies, snowy surfaces, and a bright desert surface remain as distinct surface groups. A few surface classes, such as tundra and closed shrublands, may be reclassified and grouped with other surfaces, refining the CERES–TRMM surface classification. Angular Distribution Models (ADMs) are essential for converting observed radiances from satellite sensors to the energy-budget–relevant quantity of irradiance. In preparation for the NASA Libera mission, this study presents a data-driven framework to identify optimal groupings of International Geosphere–Biosphere Programme (IGBP) surface types for Libera's split-shortwave ADMs, in an effort to minimize the uncertainty associated with radiance-to-irradiance conversions while maintaining operational feasibility. Using data from the Clouds and the Earth's Radiant Energy System (CERES) Flight Model 5 (FM-5), K-means clustering is applied within angular bins to capture viewing-geometry-dependent radiometric behavior. These angular clustering solutions are then assessed via hierarchical consensus clustering to derive consistent surface groups. The analysis suggests seven surface groups (K = 7) optimize the surface clustering space. The resulting classifications are broadly consistent with historical CERES–TRMM ADM surface definitions, preserving radiometrically distinct surfaces such as water bodies and snowy surfaces while highlighting opportunities to consolidate vegetative IGBP surface classes. This study provides an objective and physically grounded basis for defining Libera ADM surface groups, ensuring a robust balance between model accuracy and operational simplicity. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 191972733 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimizing Surface Type Definitions in Radiance-to-Irradiance Conversions for Future Earth Radiation Budget Satellite Measurements. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22van+den+Heever%2C+Mathew%22">van den Heever, Mathew</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> mava5537@colorado.edu</i><br /><searchLink fieldCode="AR" term="%22Gristey%2C+Jake+J%2E%22">Gristey, Jake J.</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pilewskie%2C+Peter%22">Pilewskie, Peter</searchLink><relatesTo>1,3,5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 4, p648. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Radiance%22">Radiance</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Radiative+transfer%22">Radiative transfer</searchLink><br /><searchLink fieldCode="DE" term="%22United+States%2E+National+Aeronautics+%26+Space+Administration%22">United States. National Aeronautics & Space Administration</searchLink><br /><searchLink fieldCode="DE" term="%22Terrestrial+radiation%22">Terrestrial radiation</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing+devices%22">Remote sensing devices</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Consensus clustering applied to an ensemble of K-means solutions shows that seven surface groups optimize the clustering space for both radiance- and anisotropic-factor-based analyses. Clustering results show that water bodies, snowy surfaces, and bright deserts form distinct surface groups, while vegetated surface classes can be consolidated. What are the implications of the main findings? The derived surface groups provide a statistically based framework to support Libera ADM development and evaluation. Water bodies, snowy surfaces, and a bright desert surface remain as distinct surface groups. A few surface classes, such as tundra and closed shrublands, may be reclassified and grouped with other surfaces, refining the CERES–TRMM surface classification. Angular Distribution Models (ADMs) are essential for converting observed radiances from satellite sensors to the energy-budget–relevant quantity of irradiance. In preparation for the NASA Libera mission, this study presents a data-driven framework to identify optimal groupings of International Geosphere–Biosphere Programme (IGBP) surface types for Libera's split-shortwave ADMs, in an effort to minimize the uncertainty associated with radiance-to-irradiance conversions while maintaining operational feasibility. Using data from the Clouds and the Earth's Radiant Energy System (CERES) Flight Model 5 (FM-5), K-means clustering is applied within angular bins to capture viewing-geometry-dependent radiometric behavior. These angular clustering solutions are then assessed via hierarchical consensus clustering to derive consistent surface groups. The analysis suggests seven surface groups (K = 7) optimize the surface clustering space. The resulting classifications are broadly consistent with historical CERES–TRMM ADM surface definitions, preserving radiometrically distinct surfaces such as water bodies and snowy surfaces while highlighting opportunities to consolidate vegetative IGBP surface classes. This study provides an objective and physically grounded basis for defining Libera ADM surface groups, ensuring a robust balance between model accuracy and operational simplicity. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18040648 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 648 Subjects: – SubjectFull: K-means clustering Type: general – SubjectFull: Radiance Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Radiative transfer Type: general – SubjectFull: United States. National Aeronautics & Space Administration Type: general – SubjectFull: Terrestrial radiation Type: general – SubjectFull: Remote sensing devices Type: general Titles: – TitleFull: Optimizing Surface Type Definitions in Radiance-to-Irradiance Conversions for Future Earth Radiation Budget Satellite Measurements. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: van den Heever, Mathew – PersonEntity: Name: NameFull: Gristey, Jake J. – PersonEntity: Name: NameFull: Pilewskie, Peter IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 4 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
