Abstract
The chlorophyll meter (CM) has been commonly used for in-season nitrogen (N) management of corn (Zea mays L.). Nevertheless, it has limited potential for site-specific N management in large fields due to difficulties in using it to generate N status maps. The objective of this study was to determine how well CM readings can be estimated using aerial hyper-spectral and simulated multi-spectral remote sensing images at different corn growth stages. Two field experiments were conducted in Minnesota, USA during 2005 involving different N application rates and timings on a corn-soybean [Glycine max (L.) Merr.] rotation field and a corn-corn rotation field. Four flights were made during the growing season using the AISA Eagle Hyper-spectral Imager and CM readings were collected at four or five different growth stages. The results indicated that single multi-spectral and hyper-spectral band or vegetation index could explain 64-86% and 73-88% of the variability in CM readings, respectively, except at growth stage V9 in the corn-soybean rotation field where no band or vegetation index could explain more than 37% of the variability in CM readings. Multiple regression analysis demonstrated that the combination of 2-4 broad-bands or 3-8 narrow-bands could explain 41-92% or 61-94% of the variability in CM readings across the two fields and different corn growth stages investigated. It was concluded that the combination of CM readings with high spatial resolution hyper-spectral or multi-spectral remote sensing images can overcome the limitations of using them individually, thus offering a practical solution to N deficiency detection and possibly in-season site-specific N management in large continuous corn fields or at later stages in corn-soybean rotation fields.
Original language | English (US) |
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Pages (from-to) | 45-62 |
Number of pages | 18 |
Journal | Precision Agriculture |
Volume | 10 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2009 |
Keywords
- Chlorophyll meter
- Continuous corn
- Corn-soybean rotation
- Hyper-spectral
- Multi-spectral
- Precision nitrogen management
- Remote sensing images