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TitleIntegration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area
AuthorHong, G; Zhang, A; Zhou, F; Brisco, B
SourceInternational Journal of Applied Earth Observation and Geoinformation vol. 28, no. 1, 2014 p. 12-19,
Alt SeriesNatural Resources Canada, Contribution Series 20181032
PublisherElsevier BV
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing
ProgramClimate Change Geoscience
Released2014 05 01
AbstractAlfalfa presents a huge potential biofuel source in the Prairie Provinces of Canada. However, it remains a challenge to find an ideal single satellite sensor to monitor the regional spatial distribution of alfalfa on an annual basis. The primary interest of this study is to identify alfalfa spatial distribution through effectively differentiating alfalfa from grasslands, given their spectral similarity and same growth calendars. MODIS and RADARSAT-2 ScanSAR narrow mode were selected for regional-level grassland and alfalfa differentiation in the Prairie Provinces, due to the high frequency revisit of MODIS, the weather independence of ScanSAR as well as the large area coverage and the complementary characteristics SAR and optical images. Combining MODIS and ScanSAR in differentiating alfalfa and grassland is very challenging, since there is a large spatial resolution difference between MODIS (250 m) and ScanSAR narrow (50 m). This study investigated an innovative image fusion technique for combining MODIS and ScanSAR and obtaining a synthetic image which has the high spatial details derived from ScanSAR and the colour information from MODIS. The field trip was arranged to collect ground truth to label and validate the classification results. The fusion classification result shows significant accuracy improvement when compared with either ScanSAR or MODIS alone or with other commonly-used data combination methods, such as multiple files composites. This study has shown that the image fusion technique used in this study can combine the structural information from high resolution ScanSAR and colour information from MODIS to significantly improve the classification accuracy between alfalfa and grassland.

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