Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic
Since the late 1990s, NASA’s Earth Observing System constellation of satellites has provided continuous, long-term observations of atmospheric and surface processes on Earth including surface albedo and Bidirectional Reflectance Distribution Function (BRDF) that are generated as an operational produ...
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ftgfzpotsdam:oai:gfzpublic.gfz-potsdam.de:item_5018824 2023-10-09T21:44:15+02:00 Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic Hanasoge Nataraja, V. 2023 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018824 eng eng info:eu-repo/semantics/altIdentifier/doi/10.57757/IUGG23-2059 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018824 XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) info:eu-repo/semantics/conferenceObject 2023 ftgfzpotsdam https://doi.org/10.57757/IUGG23-2059 2023-09-17T23:43:20Z Since the late 1990s, NASA’s Earth Observing System constellation of satellites has provided continuous, long-term observations of atmospheric and surface processes on Earth including surface albedo and Bidirectional Reflectance Distribution Function (BRDF) that are generated as an operational product using cloud-cleared, multi-angle surface reflectances over the course of several days. The BRDF is central to imagery-based cloud and aerosol retrievals, while the surface albedo is a fundamental Earth energy budget parameter. Yet, this product is currently unavailable at higher latitudes where (1) the low contrast between clouds and sea ice/snow poses a challenge for cloud clearing, and (2) drifting ice floes are not accounted for, resulting in a significant gap in our understanding of the Arctic radiation budget. To address this gap, we propose the development of a BRDF/albedo product for moving sea ice floes and snow called the Sea Ice Floe and Snow Albedo Tracker (SIF-SAT). By leveraging multi-overpass, multi-angular satellite data, SIF-SAT will retrieve BRDF and albedo under low contrast and moving surface conditions. We combine existing cloud masks with machine learning (ML) models to produce cloud-cleared scenes in the Arctic. These scenes are then fed to a segmentation algorithm to identify individual sea ice floes and their reflectances are tracked over time to obtain BRDF and albedo. This presentation will primarily focus on the cloud-clearing model which has implications for radiation science in polar regions. SIF-SAT will enhance our capabilities in the Arctic and enable more accurate estimates of the cloud-radiative effect and ice-albedo feedback. Conference Object albedo Arctic Sea ice GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) Arctic |
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Open Polar |
collection |
GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) |
op_collection_id |
ftgfzpotsdam |
language |
English |
description |
Since the late 1990s, NASA’s Earth Observing System constellation of satellites has provided continuous, long-term observations of atmospheric and surface processes on Earth including surface albedo and Bidirectional Reflectance Distribution Function (BRDF) that are generated as an operational product using cloud-cleared, multi-angle surface reflectances over the course of several days. The BRDF is central to imagery-based cloud and aerosol retrievals, while the surface albedo is a fundamental Earth energy budget parameter. Yet, this product is currently unavailable at higher latitudes where (1) the low contrast between clouds and sea ice/snow poses a challenge for cloud clearing, and (2) drifting ice floes are not accounted for, resulting in a significant gap in our understanding of the Arctic radiation budget. To address this gap, we propose the development of a BRDF/albedo product for moving sea ice floes and snow called the Sea Ice Floe and Snow Albedo Tracker (SIF-SAT). By leveraging multi-overpass, multi-angular satellite data, SIF-SAT will retrieve BRDF and albedo under low contrast and moving surface conditions. We combine existing cloud masks with machine learning (ML) models to produce cloud-cleared scenes in the Arctic. These scenes are then fed to a segmentation algorithm to identify individual sea ice floes and their reflectances are tracked over time to obtain BRDF and albedo. This presentation will primarily focus on the cloud-clearing model which has implications for radiation science in polar regions. SIF-SAT will enhance our capabilities in the Arctic and enable more accurate estimates of the cloud-radiative effect and ice-albedo feedback. |
format |
Conference Object |
author |
Hanasoge Nataraja, V. |
spellingShingle |
Hanasoge Nataraja, V. Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic |
author_facet |
Hanasoge Nataraja, V. |
author_sort |
Hanasoge Nataraja, V. |
title |
Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic |
title_short |
Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic |
title_full |
Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic |
title_fullStr |
Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic |
title_full_unstemmed |
Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic |
title_sort |
cloud removal using machine learning for brdf/albedo retrievals in the arctic |
publishDate |
2023 |
url |
https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018824 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
albedo Arctic Sea ice |
genre_facet |
albedo Arctic Sea ice |
op_source |
XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.57757/IUGG23-2059 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018824 |
op_doi |
https://doi.org/10.57757/IUGG23-2059 |
_version_ |
1779322040507957248 |