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Whitcomb, J, Chen, R, Clewley, D, Kimball, JS, Pastick, NJ, Yi, Y and Moghaddam, M 2023 Maps of active layer thickness in northern Alaska by upscaling P-band polarimetric synthetic aperture radar retrievals. Environmental Research Letters, 19 (1). 014046. https://doi.org/10.1088/1748-9326/ad127f
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Whitcomb_2024_Environ._Res._Lett._19_014046.pdf - Published Version Available under License Creative Commons Attribution. Download (5MB) | Preview |
Abstract/Summary
Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5–9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11–12 cm and bias of 2.7–6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.
| Item Type: | Publication - Article |
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| Additional Keywords: | ABoVE, AirMOSS, ALT, SAR, upscaling, random forests, machine learning |
| Divisions: | Plymouth Marine Laboratory > National Capability categories > NERC EO Data Analysis and AI Service (NEODAAS) |
| Depositing User: | S Hawkins |
| Date made live: | 02 Jan 2024 10:07 |
| Last Modified: | 10 Jan 2024 11:00 |
| URI: | https://plymsea.ac.uk/id/eprint/10090 |
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