Using Airborne Hyperspectral and Satellite Multispectral data to Quantify Within-Field Spatial Variability
2002
Hong, S.Y. | Sudduth, K.A. | Kitchen, N.R. | Palm, H.L. | Wiebold, W.J.
The relationship between hyperspectral and multispectral remotely sensed images and ground based soil and crop information was investigated for two central Missouri experimental fields in a corn (Zea mays L.)soybean (Glycine max L.) rotation. Multiple airborne hyperspectral and IKONOS satellite images were obtained during the 1999 and 2000 growing seasons. Hyperspectral images covered 120 bands from 457 to 823 nm with a spatial resolution of 1 m. Multispectral IKONOS images included four bands (blue, green, red, and near-infrared) with a 4 m spatial resolution. Geometric distortion of the pushbroom-type hyperspectral sensor caused by aircraft attitude change during image acquisition was corrected with a rubber sheeting transformation. Within-field data collection included crop yield, soil electrical conductivity (ECa), and soil chemical properties. Simple correlation, multiple regression, and principal component analysis were used to identify those remotely sensed data most highly related with field measured soil and crop properties. Blue wavelengths were most highly correlated with ECa measurements. For corn, the early reproductive stage provided the best relationships between final yield data and spectral signatures in both years. For soybeans, yield data were highly correlated with wavelengths in the near infrared region from August images in both 1999 and 2000. Maps estimating soil ECa and corn yield from hyperspectral and multispectral images were derived.
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