Remote sensing of soybean canopy to identify calcareous soils
Natalia Rogovska and Alfred Blackmer,
Dept. of Agronomy, Iowa State University
Plot 1
Introduction
Soybean plants often show symptoms of iron deficiency chlorosis
on calcareous soils. Such soils often occur in complex spatial
patterns, which are difficult to characterize.
Plot 1
Plot 2
Plot 2
Observations of spatial patterns in plant size (and canopy cover)
within fields as revealed by aerial photographs suggested that
plant size may be correlated with carbonate concentrations and,
therefore, that patterns in plant size may give some indication of
spatial patterns in soil carbonate content. Especially in fields
where soybeans were planted in rows 76 cm apart, spatial
patterns in plant growth could be easily characterized by analysis
of color, which is primarily determined by the amounts of soil
showing between rows. As shown by example in Fig. 1a, the
amount of soybean growth often ranged from complete canopy
cover to no canopy cover due to death of soybean seedlings.
Plot 3
Plot 3
Plot 4
Although spatial patterns in plant growth often resemble
boundaries on soil map units, the spatial patterns in soybean
growth occurred in much finer scales than soils are mapped. The
patterns in soybean growth often showed continuous gradation
rather than the distinct boundaries imposed by the normal
categorical classification of soils into map units. In order to study
calcareous soils it is important to map them correctly.
The objective of this study was to understand how remote
sensing of soybean canopy can be used to identify and map
calcareous soils.
Materials and methods
Soybean fields that offer great variability in plant height and
canopy closure as revealed by aerial images were selected in
central Iowa. Aerial images of 3 fields (RGB and NIR) were taken
in the mid July and georeferenced using ArcView GIS software.
Green Normalized Difference Vegetation Index (GNDVI), which is
related to the proportion of photosynthetically absorbed radiation,
was calculated from NIR images using ArcView (Fig. 1b).
160
y=249.5-18.5X
r2 = 0.86, p<0.0001
GNDVI
140
100
80
60
6
7
8
Stress Index
9
Plot 5
Flooded areas
a
Plot 5
b
c
Figure 1. Different levels of stress imposed on plants as revealed by a: aerial
image, b: GNDVI, c: reclassification of GNDVI into different classes.
GNDVI was then reclassified into different classes (usually six) or levels of plant growth
(Fig.1c). Three to four soil samples were taken from different GNDVI classes and analyzed
for pH and calcium carbonate equivalent (CCE). A newly developed Stress Index (SI), which
combines effects of pH and CCE (Rogovska N. 2004. Soil pH and carbonate effects on
soybean yield. M.S. thesis, Iowa State University), was calculated using the equation
SI=pH+0.14CCE
Results and discussions
Regression analysis showed statistically significant (p<0.001) correlations between Stress
Index and GNDVI in all three fields. The r2 ranged from 0.54 to 0.86 with a mean of 0.75. The
graph and image show examples of analyses done at one field.
120
5
Plot 4
10
Remote sensing of plant canopies offers a promising way to select fields and sampling points
within fields. This approach minimizes sampling problems caused by variation in soil
properties that occur in complex spatial patterns within a field because remote sensing
essentially classifies soils before samples are collected. Regression analyses between
measured soil properties and amount of biomass, which closely relates to soybean yields,
are used to define relationships between soybean yield and stresses imposed by calcareous
and high pH soils.