Cloud Removal using Machine Learning for BRDF/Albedo Retrievals in the Arctic ...
<!--!introduction!--> 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 gener...
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| Format: | Conference Object |
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GFZ German Research Centre for Geosciences
2023
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| Online Access: | https://dx.doi.org/10.57757/iugg23-2059 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5018824 |
| Summary: | <!--!introduction!--> 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 ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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