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Wide-area analysis-ready radar backscatter composites

Small, David; Rohner, Christoph; Miranda, Nuno; Rüetschi, Marius; Schaepman, Michael E (2022). Wide-area analysis-ready radar backscatter composites. IEEE Transactions on Geoscience and Remote Sensing, 60:1-14.

Abstract

The benefits of composite products are well known to users of data from optical sensors: cloud-cleared composite reflectance or index products are commonly used as an analysis-ready data (ARD) layer. No analogous composite
products are currently in widespread use that is based on spaceborne radar satellite backscatter signals. Here, we present a methodology to produce wide-area ARD composite backscatter images. They build on the existing heritage of geometrically and radiometrically terrain corrected level 1 products. By combining backscatter measurements of a single region seen from multiple satellite tracks (incl. ascending and descending), they are able to provide wide-area coverage with low latency. The analysis-ready composite backscatter maps provide flattened backscatter estimates that are geometrically and radiometrically corrected for slope effects. A mask layer annotating the local quality of the
composite resolution is introduced. Multiple tracks are combined by weighting each observation by its local resolution, generating seamless wide-area backscatter maps suitable for applications ranging from wet snow monitoring to land cover classification or short-term change detection.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Physical Sciences > Electrical and Electronic Engineering
Physical Sciences > General Earth and Planetary Sciences
Uncontrolled Keywords:General Earth and Planetary Sciences, Electrical and Electronic Engineering
Language:English
Date:1 January 2022
Deposited On:18 Nov 2021 11:48
Last Modified:26 Dec 2024 02:37
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1558-0644
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1109/tgrs.2021.3055562
Project Information:
  • Funder: ESA-funded Telespazio Vega
  • Grant ID:
  • Project Title:
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  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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