Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status

Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and... Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may outweigh the benefits of the information. Hobbyists have been acquiring aerial photography from radio-controlled model aircraft; we evaluated these very-low-cost, very high-resolution digital photography for use in estimating nutrient status of corn and crop biomass of corn, alfalfa, and soybeans. Based on conclusions from previous work, we optimized an aerobatic model aircraft for acquiring pictures using a consumer-oriented digital camera. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for the same reflectance because of differences in the exposure settings selected by the digital camera. To account for differences in exposure a Normalized Green–Red Difference Index [(NGRDI  = (Green DN  − Red DN)/(Green DN  + Red DN)] was used; this index was linearly related to the normalized difference of the green and red reflectances, respectively. For soybeans, alfalfa and corn, dry biomass from zero to 120 g m−2 was linearly correlated to NGRDI, but for biomass greater than 150 g m−2 in corn and soybean, NGRDI did not increase further. In a fertilization experiment with corn, NGRDI did not show differences in nitrogen status, even though areas of low nitrogen status were clearly visible on late-season digital photographs. Simulations from the SAIL (Scattering of Arbitrarily Inclined Leaves) canopy radiative transfer model verified that NGRDI would be sensitive to biomass before canopy closure and that variations in leaf chlorophyll concentration would not be detectable. There are many advantages of model aircraft platforms for precision agriculture; currently, the imagery is best visually interpreted. Automated analysis of within-field variability requires more work on sensors that can be used with model aircraft platforms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status

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Kluwer Academic Publishers
Copyright © 2005 by Springer Science+Business Media, Inc.
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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  • Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance
    Daughtry, C. S. T.; Walthall, C. L.; Kim, M. S.; Brown Colstoun, E.; McMurtrey, J. E.

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