Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping. Issue 18 (16th September 2020)

• Record Type: Journal Article
• Authors: Dingle Robertson, Laura; Davidson, Andrew; McNairn, Heather; Hosseini, Mehdi; Mitchell, Scott; De Abelleyra, Diego; Verón, Santiago; Cosh, Michael H.
• Is Part Of: International journal of remote sensing. Volume 41:Issue 18(2020)
• Page Start: 7112
• Page End: 7144
• Publication Date: 2020-09-16
• Subjects: SAR -- RADARSAT-2 -- Sentinel-1 -- speckle filter -- operational -- agriculture -- order of operations; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1754494
• Languages: English
• ISSNs: 0143-1161

Abstract

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, 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.