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Remote Sens. 2014, 6(7), 6549-6565; doi:10.3390/rs6076549

Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery

1
Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Science (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
2
European Commission, DG-JRC, Institute for Environment and Sustainability, Monitoring Agricultural Resources Unit-H04, 21027 Ispra, VA, Italy
3
Remote Sensing Department-IREA-National Research Council (CNR), Via Bassini 15, 20133 Milano, Italy
4
Istituto di Agronomia e Coltivazioni Erbacee, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29100 Piacenza, Italy
5
Consiglio Per la Ricerca e la Sperimentazione in Agricoltura (CRA), Research Unit of Food Technology, Via Venezian 26, 20133 Milano, Italy
*
Author to whom correspondence should be addressed.
Received: 19 March 2014 / Revised: 9 July 2014 / Accepted: 10 July 2014 / Published: 18 July 2014
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Abstract

This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha−1) in four replicates. Leaf and canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI), defined as the ratio between the leaf actual N concentration (%Na) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%Nc)) calculated through the dry mass at the time of the flight (Wflight). The inputs required to calculate the NNI (i.e., %Na and Wflight) have been estimated through regression analyses between field data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index/Modified Triangular Vegetation Index 2) showed the best performances in estimating the %Na (R2 = 0.59) and MTVI2 in estimating the Wflight (R2 = 0.80). The %Na and the Wflight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive a variable rate N fertilization map based on the actual crop N status from an aerial hyperspectral image. View Full-Text
Keywords: Nitrogen Nutrition Index; nitrogen concentration; airborne; hyperspectral; precision farming; vegetation indices; variable rate fertilization; Zea mays L. Nitrogen Nutrition Index; nitrogen concentration; airborne; hyperspectral; precision farming; vegetation indices; variable rate fertilization; Zea mays L.
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Cilia, C.; Panigada, C.; Rossini, M.; Meroni, M.; Busetto, L.; Amaducci, S.; Boschetti, M.; Picchi, V.; Colombo, R. Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery. Remote Sens. 2014, 6, 6549-6565.

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