Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars

Tyler J. Nigon, David J. Mulla, Carl J. Rosen, Yafit Cohen, Victor Alchanatis, Joseph Knight, Ronit Rud

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)36-46
Number of pages11
JournalComputers and Electronics in Agriculture
Volume112
DOIs
StatePublished - Mar 1 2015

Bibliographical note

Funding 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 .

Publisher Copyright:
© 2014 Elsevier B.V.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • Accuracy assessment
  • Hyperspectral imagery
  • Nitrogen sufficiency index
  • Partial least squares regression
  • Spectral index
  • Variability

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