Abstract: The purpose of this study was to examine the capability of hyperspectral narrow wavebands within the 400–900 nm range for distinguishing five cash crops commonly grown in Northeastern Ontario, Canada. Data were collected from ten different fields in the West Nipissing agricultural zone (46°24'N lat., 80°07'W long.) and included two of each of the following crop types; soybean (Glycine max), canola (Brassica napus L.), wheat (Triticum spp.), oat (Avena sativa), and barley (Hordeum vulgare). Stepwise discriminant analysis was used to assess the spectral separability of the various crop types under two scenarios; Scenario 1 involved testing separability of crops based on number of days after planting and Scenario 2 involved testing crop separability at specific dates across the growing season. The results indicate that select hyperspectral bands in the visual and near infrared (NIR) regions (400–900 nm) can be used to effectively distinguish the five crop species under investigation. These bands, which were used in a variety of combinations include B465, B485, B495, B515, B525, B535, B545, B625, B645, B665, B675, B695, B705, B715, B725, B735, B745, B755, B765, B815, B825, B885, and B895. In addition, although species classification could be achieved at any point during the growing season, the optimal time for satellite image acquisition was determined to be in late July or approximately 75–79 days after planting with the optimal wavebands located in the red-edge, green, and NIR regions of the spectrum.
Keywords: hyperspectral remote sensing; precision agriculture; crop separability; optimal timing; wheat; canola; soybean; oat; barley
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Wilson, J.H.; Zhang, C.; Kovacs, J.M. Separating Crop Species in Northeastern Ontario Using Hyperspectral Data. Remote Sens. 2014, 6, 925-945.
Wilson JH, Zhang C, Kovacs JM. Separating Crop Species in Northeastern Ontario Using Hyperspectral Data. Remote Sensing. 2014; 6(2):925-945.
Wilson, Jeffrey H.; Zhang, Chunhua; Kovacs, John M. 2014. "Separating Crop Species in Northeastern Ontario Using Hyperspectral Data." Remote Sens. 6, no. 2: 925-945.