Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn

• Published in: Precision agriculture 2009-02, Vol.10 (1), p.45-62
• Main Authors: Miao, Yuxin, Mulla, David J, Randall, Gyles W, Vetsch, Jeffrey A, Vintila, Roxana
• Format: Article
• Language: English
• Subjects: Agricultural practices; Agricultural production; Agriculture; Agronomy; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Chlorophyll; Computer Science; Corn; Crop rotation; Experiments; Field tests; Growing season; Life Sciences; Nitrogen; Physics; Precision farming; Remote sensing; Remote sensing systems; Remote Sensing/Photogrammetry; Soil Science & Conservation; Soil sciences; Soybeans; Statistics for Engineering; Studies; Vegetables; Vegetation
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-008-9091-z

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.