Skip to main content
ARS Home » Research » Publications at this Location » Publication #130907


item Fitzgerald, Glenn
item Barnes, Edward
item Pinter Jr, Paul
item Clarke, Thomas

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 3/2/2002
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Remote sensing has been used in precision agriculture to relate variation in the imagery to measured ground conditions. Generally, the image data is transformed to derived values that relate the spectral domain to biophysical parameters, such as correlating the Normalized Difference Vegetation Index (NDVI) to percent cover or categorizing image variation using classification procedures. A technique new to precision agriculture, spectral mixture analysis (SMA), may improve the relationships between imagery and measured ground data by providing fraction maps of the parameters of interest. This has the potential to quantify the relative amount of a component present and locate it within a field. Remotely-sensed imagery was acquired on six dates in 2001 from a 3.4 ha cotton field at the Maricopa Agricultural Center, Maricopa, Arizona. Sensors included a thermal scanner (8-14 microns), a color digital camera, and a Dycam agricultural camera that provided red and near-infrared images. All images were georegistered. Various image maps were produced based on different image analysis techniques including NDVI,principal components, unsupervised classification, and SMA fraction maps. Locations were selected in the imagery where various crop and soil parameters were collected. Ground data collected within 48 hours after flights included percent crop cover, plant height, leaf area index, plant fresh and dry weight, and petiole nitrate content. Mean pixel values were calculated from areas corresponding to ground data locations and used in regression analysis. A comparison of correlations between image data derived from the various image analysis techniques and ground data is presented.