To use remotely sensed spectral data for determining rates and timing of variable rate nitrogen (N) applications at a commercial scale, the most reliable indicators of crop N status must be determined. This study evaluated the ability of hyperspectral remote sensing to predict N stress in potatoes (Solanum tuberosum) during two growing seasons (2010 and 2011). Spectral data were evaluated using ground based measurements of leaf N concentration. Two canopy-scale hyperspectral images were acquired with an AISA-Eagle hyperspectral camera in both years. The experiment included five N treatments with varying rates and timing of N fertilizer and two potato cultivars, Russet Burbank (RB) and Alpine Russet (AR). Partial Least Squares regression (PLS) models resulted in the best prediction of leaf N concentration (r2=0.79, Root Mean Square Error of Cross Validation (RMSECV)=14% across dates for RB; r2=0.77, RMSECV=13% across dates for AR). Applying the Nitrogen Sufficiency Index (NSI) formula to spectral indices/models made them mostly insensitive to the effects of cultivar. The most promising technique for determining N stress in potato based on spectral indices was found to be the MERIS Terrestrial Chlorophyll Index (MTCI) due to a combination of relatively high r2 values, lower RMSECVs, and high accuracy assessment. Pairwise comparison tests from the means separation showed that spectral indices/models from the imagery resulted in more statistically significant groupings of crop stress levels for the spectra than leaf N concentration because canopy-scale spectral data are affected by both tissue N concentration and biomass. The results of this study suggest that upon proper sensor calibration, canopy-scale spectral data may be the most sensitive tool available to detect N status of a potato crop.
Bibliographical noteFunding Information:
The authors wish to thank the Binational Agricultural Research and Development (BARD) Fund (Grant Award No. IL-4255-09 ) for funding this project. Financial support was also provided through the Hueg-Harrison fellowship and the Minnesota Area II Potato Growers Council .
© 2014 Elsevier B.V.
Copyright 2017 Elsevier B.V., All rights reserved.
- Accuracy assessment
- Hyperspectral imagery
- Nitrogen sufficiency index
- Partial least squares regression
- Spectral index