Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping

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dc.contributor.author
Robertson, Laura Dingle
Davidson, Andrew
McNairn, Heather
Hosseini, Mehdi
Mitchell, Scott
De Abelleyra, Diego
Verón, Santiago
Cosh, Michael H.
dc.date.accessioned
2023-04-14T15:09:52Z
dc.date.available
2023-04-14T15:09:52Z
dc.date.issued
2020-06-30
dc.description.abstract - en
Few countries are using space-based Synthetic Aperture Radar (SAR) to operationally produce national-scale maps of their agricultural landscapes. For the past ten years, Canada has integrated C-band SAR with optical satellite data to map what crops are grown in every field, for the entire country. While the advantages of SAR are well understood, the barriers to its operational use include the lack of familiarity with SAR data by agricultural end-user agencies and the lack of a ‘blueprint’ on how to implement an operational SAR-based mapping system. This research reviewed order of operations for SAR data processing and how order choice affects processing time and classification outcomes. Additionally this research assessed the impact of speckle filtering by testing three filter types (adaptive, multi-temporal and multi-resolution) at varying window sizes for three study sites with different average field sizes. The Touzi multi-resolution filter achieved the highest overall classification accuracies for all three sites with varying window sizes, and with only a small (< 2%) difference in accuracy relative to the Gamma Maximum A Posteriori (MAP) adaptive filter which had similar window sizes across sites. As such, the assessment of order of operations for noise reduction and terrain correction was completed using the Gamma MAP adaptive filter. This research found there was no difference in classification accuracies regardless of whether noise reduction was applied before or after terrain correction. However, implementing the terrain correction as the first operation resulted in a 10 to 50% increase in processing time. This is an important consideration when designing and delivering operational systems, especially for large geographies like Canada where hundreds of SAR images are required. These findings will encourage country-wide, regional and global food monitoring initiatives to consider SAR sensors as an important source of data to operationally map agricultural production.
dc.identifier.citation
Dingle Robertson, L., Davidson, A., McNairn, H., Hosseini, M., Mitchell, S., De Abelleyra, D., Verón, S., &; Cosh, M. H. (2020). Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping. International Journal of Remote Sensing, 41(18), 7112–7144. https://doi.org/10.1080/01431161.2020.1754494
dc.identifier.doi
https://doi.org/10.1080/01431161.2020.1754494
dc.identifier.issn
1366-5901
dc.identifier.uri
https://open-science.canada.ca/handle/123456789/167
dc.language.iso
en
dc.publisher
Taylor & Francis
dc.rights.openaccesslevel - en
Green
dc.rights.openaccesslevel - fr
Vert
dc.subject - en
Agriculture
dc.subject - fr
Agriculture
dc.subject.en - en
Agriculture
dc.subject.fr - fr
Agriculture
dc.title - en
Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping
dc.title.fosrctranslation - fr
Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping
dc.type - en
Article
dc.type - fr
Article
local.article.journalissue
18
local.article.journaltitle
International Journal of Remote Sensing
local.article.journalvolume
41
local.article.pagination
7112-7144
local.peerreview - en
Yes
local.peerreview - fr
Oui
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