A Regional L-Band High Biomass Estimation Framework Leveraging Spaceborne Lidar and Interferometric Data to Overcome Backscatter Saturation
Abstract
We propose a framework to estimate high above ground biomass (AGB) from L-band SAR imagery leveraging spaceborne lidars such as GEDI or ICESat-2 and repeat-pass coherence. Our results indicate we are able to overcome model saturation typically associated with purely backscatter methodologies. We validate our approach using lidar-derived AGB maps from the AfriSAR datasets at Mondah, Ogooue, and Lope. We apply our framework to UAVSAR and ALOS-2 imagery to obtain 50 meter resolution biomass maps. We obtain < 60% nRMSE (in some cases much better) with negligible relative bias using a multiscale random forest model. We illustrate that the inclusion of coherence can significantly improve high AGB estimation particularly at the coastal site Mondah.
Additional Information
© 2020 IEEE. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration © 2019 California Institute of Technology. Government sponsorship acknowledged.Attached Files
Published - 09323318.pdf
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Additional details
- Eprint ID
- 108187
- Resolver ID
- CaltechAUTHORS:20210224-153455870
- NASA/JPL/Caltech
- Created
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2021-02-24Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field