חיפוש מתקדם
Nigon, T.J., Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, United States
Mulla, D.J., Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, United States
Rosen, C.J., Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, United States
Cohen, Y., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Bet-Dagan, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Bet-Dagan, Israel
Knight, J., Department of Forest Resources, University of Minnesota, St. Paul, MN, United States
Rud, R., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Bet-Dagan, Israel
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. © 2014 Elsevier B.V.
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תנאי שימוש
Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars
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Nigon, T.J., Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, United States
Mulla, D.J., Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, United States
Rosen, C.J., Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, United States
Cohen, Y., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Bet-Dagan, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Bet-Dagan, Israel
Knight, J., Department of Forest Resources, University of Minnesota, St. Paul, MN, United States
Rud, R., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Bet-Dagan, Israel
Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars
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. © 2014 Elsevier B.V.
Scientific Publication
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