Location: Hydrology and Remote Sensing LaboratoryTitle: Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping
|DINGLE ROBERTSON, L. - Agriculture And Agri-Food Canada|
|DAVIDSON, A. - Agriculture And Agri-Food Canada|
|MCNAIRN, H. - Agriculture And Agri-Food Canada|
|HOSSEINI, M. - Universite De Sherbrooke|
|MITCHELL, S. - Carleton University - Canada|
|DE ABELLEYRA, D. - Instituto De Clima Y Agua (INTA)|
|VERON, S. - Instituto De Clima Y Agua (INTA)|
Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/28/2020
Publication Date: 6/30/2020
Citation: Dingle Robertson, L., Davidson, A., McNairn, H., Hosseini, M., Mitchell, S., De Abelleyra, D., Veron, 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.
Interpretive Summary: Synthetic aperture radar is a useful remote sensing technology, but it is only used by a few countries to monitor the landscape. New missions are being developed, however, which will use this method; therefore, it is an opportune time to establish workflows and methodologies regarding how to process this new data stream, specifically with regards to land cover classification. This study was initiated to establish orders of operation with the processing workflow for satellite radar data to optimize operational capability. Terrain correction provided a processing time advantage while other filtering processes did not have a significant impact. This processing time will be important to consider for large scale agricultural mapping workflows, that will benefit from synthetic aperture radar missions.
Technical Abstract: 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, multitemporal 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.