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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #126599
Title: USING HYPERSPECTRAL REMOTE SENSING DATA TO QUANTIFY WITHIN-FIELD SPATIAL VARIABILITY

Author
item HONG, SUK - UNIV OF MO
item Sudduth, Kenneth - Ken
item Kitchen, Newell
item PALM, HARLAN - UNIV OF MO
item WIEBOLD, WILLIAM - UNIV OF MO

Submitted to: Intnl Conference On Geospatial Information In Agriculture And Forestry
Publication Type: Proceedings
Publication Acceptance Date: 11/7/2001
Publication Date: 11/7/2001
Citation: Hong, S.Y., Sudduth, K.A., Kitchen, N.R., Palm, H.L., Wiebold, W.J. 2001. Using hyperspectral remote sensing data to quantify within-field spatial variability. In: Proceedings Third International Conference on Geospatial Information in Agriculture And Forestry. Ann Arbor, Michigan. CDROM.

Interpretive Summary: Precision farming relies on the ability to efficiently and economically collect and interpret data describing the variability within cropped fields. Remote sensing from satellites or airplanes is one approach to obtaining the data needed for precision farming. Depending on the wavelength bands of data collected and the ground conditions, remote sensing data may be used to examine variation in soil properties, crop condition, or crop yield. Recently hyperspectral remote sensing data have become available to the agricultural research community. In contrast to conventional, or multispectral, remote sensing data where only a few data bands are obtained, hyperspectral data includes tens to hundreds of channels. While this large amount of data may allow us to develop better relationships between the remote sensing and actual field conditions, it also greatly increases the complexity of the data processing and interpretation procedure. In this study, we examined 120-band aircraft hyperspectral data in the visible and near-infrared which were collected over two Missouri corn/soybean fields on multiple dates in 1999 and 2000. A number of statistical procedures were employed to relate the hyperspectral data to soil properties and crop yield. We found that bare soil images obtained in the spring were significantly correlated with soil properties. We also found that image data acquired at the proper growth stage (usually in August for these fields and crops) were highly correlated with grain yield. This research will benefit other researchers, producers, and consultants who may be interested in using hyperspectral data to understand crop and soil differences for precision agriculture.

Technical Abstract: The relationship between hyperspectral 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 images were obtained during the 1999 and 2000 growing seasons. Images included 120 bands from 457 to 823 nm with a spatial resolution of 1 m. Geometric distortion of the pushbroom-type 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 hyperspectral 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 image data were derived.