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Ground-based hyperspectral imaging has a unique advantage in analyzing the component information of field crop due to
its characteristics of combining image with spectrum. However, how to fully utilize its data advantages need to be
studied specifically. This paper collected the spectral reflectance of corn leaves using the Pushbroom Imaging
Spectrometer (PIS) in different growth stages. Then, the red edge position (REP) were identified through six algorithms:
first derivative reflectance (FDR), polynomial function fitting (POLY), four points inserting (FPI), line extrapolate
method(LEM), inverted gauss (IG), Lagrange interpolation (LAGR); and the correlation between REP and chlorophyll
content was explored on the basis of studying the red edge amplitude changes. The results showed that: 1) The REP
obtained by different algorithms changed between 690 nm and 740 nm in which the amplitude changes of red edge for
the FDR, POLY and LAGR were maximum and varied from 692 nm to 730nm; the amplitude changes of the FPI and
LEM varied from 713 nm to 740nm; while the IG algorithm was the narrowest and varied only between 702 nm and 710
nm. 2) Considering the relationship between REP and chlorophyll concentration under different conditions (i.e. growth
stages, species, fertilization and leaf positions), the FDR and LAGR performed well in maize under different conditions;
the IG was suitable for different growth stages; the FPI had a good effect in distinguishing different varieties; the POLY
was suitable for different fertilization; the LEM had wider changes for red edge amplitude and a significant correlation
with chlorophyll content, but the correlation coefficient was smaller than other algorithms and this phenomenon needed
to be further studied. The above research results provided some references for quantitatively retrieving crop nutrients
using ground-based hyperspectral imaging data.
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