Content uploaded by Budong Qian
Author content
All content in this area was uploaded by Budong Qian on Nov 19, 2014
Content may be subject to copyright.
Estimating plant area index for
monitoring crop growth dynamics
using Landsat-8 and RapidEye
images
Jiali Shang
Jiangui Liu
Ted Huffman
Budong Qian
Elizabeth Pattey
Jinfei Wang
Ting Zhao
Xiaoyuan Geng
David Kroetsch
Taifeng Dong
Nicholas Lantz
Estimating plant area index for monitoring crop growth
dynamics using Landsat-8 and RapidEye images
Jiali Shang,a,*Jiangui Liu,aTed Huffman,aBudong Qian,aElizabeth
Pattey,aJinfei Wang,bTing Zhao,aXiaoyuan Geng,aDavid Kroetsch,a
Taifeng Dong,aand Nicholas Lantza
aEastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, Ottawa,
Ontario K1A 0C6, Canada
bWestern University, Department of Geography, London, Ontario N6A 5C2, Canada
Abstract. This study investigates the use of two different optical sensors, the multispectral imager
(MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8
for mapping within-field variability of crop growth conditions and tracking the seasonal growth
dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season
for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at
different growth stages using digital hemispherical photography at two corn fields, two winter wheat
fields, and two soybean fields. Comparison between several conventional vegetation indices derived
from concurrently acquired image data by the two sensors showed a good agreement. The two-band
enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were
derived from the surface reflectance of the two sensors. The study showed that EVI2 was more
resistant to saturation at high biomass range than NDVI. A linear relationship could be used for
crop green effective PAI estimation from EVI2, with a coefficient of determination (R2)of0.85and
root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be
able to capture the seasonal dynamics of the three crops. ©The Authors. Published by SPIE under a
Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in
part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JRS.8.085196]
Keywords: RapidEye; Landsat-8; plant area index; crop seasonal development.
Paper 14482SS received Aug. 6, 2014; revised manuscript received Oct. 9, 2014; accepted for
publication Oct. 13, 2014; published online Nov. 12, 2014.
1 Introduction
High-spatial resolution optical remote sensing observations can provide crop information at a
spatial scale suitable for field to subfield level studies. The capability for simultaneous acquis-
ition over a large area allows for capturing spatial variability due to underlying soil properties
and management practices. It can greatly alleviate the workload for conducting crop surveys or
field measurements. The time series observation is especially useful for tracking the seasonal
trend of crop growth and improving our understanding of canopy functioning. Multiple optical
remote sensing products over a growing season have been used for crop biomass and yield esti-
mation with a radiation use efficiency model (RUE)1and have proven to be useful in reducing the
uncertainty of several input descriptors of crop models using the data assimilation approach.2,3
Unlike the moderate-resolution satellite sensors such as the MODIS and AVHRR, the relatively
longer revisiting cycle of a high-resolution satellite sensor is largely affected by cloud contami-
nation and hence leads to missed acquisitions during part of the key growth stages. For con-
tinuous monitoring of crop seasonal development trends, it is advantageous to be able to
use data available from different sensors to shorten the revisit cycle.
The Landsat series sensors have provided high-resolution Earth observation (EO) data since
1972. This long-term record is now continuously carried on by the Landsat data continuity mis-
sion (LDCM)4with the launch of the operational land imager (OLI) onboard the Landsat-8 in
*Address all correspondence to: Jiali Shang, E-mail: jiali.shang@agr.gc.ca
Journal of Applied Remote Sensing 085196-1 Vol. 8, 2014
February 2013. The revisit cycle of a Landsat series sensor is 16 days. Due to the free-access
policy, data acquired by the Landsat series satellites provide an essential resource for retrospective
as well as prospective studies for a wide range of research and application users. Compared with its
predecessors, the newly launched OLI sensor has a similar band-set configuration in the solar
reflective range and two additional bands, one in the deep blue range designed for water resources
and coastal zone studies, and another in the shortwave infrared range for cirrus cloud detection.
Among the new generation high-resolution optical sensors, the multispectral imager (MSI) is oper-
ated onboard the RapidEye, a satellite constellation consisting of five identical and cross-calibrated
satellites. The constellation has daily global visibility with an off-nadir pointing angle below
20 deg, and a nadir revisit period of about 6.7 days.5Data from this commercial satellite sensor
have been used in a variety of studies including land use/cover classification6,7and quantitative
estimation of vegetation descriptors.7–9A novel feature for the RapidEye sensor is a red-edge
channel that is typically not found in a conventional multispectral satellite sensor, but which poten-
tially provides a tool for better estimation of leaf or canopy nitrogen content from space.10
Plant area index (PAI) is an important vegetation descriptor used in many land surface models
(e.g., Refs. 11 and 12), as leaf is the interface for energy exchange in the biosphere.13 The assimi-
lation of PAI derived from remote sensing data into crop models has shown to improve biomass
and yield estimation,2,3,14,15 thus PAI is one of the most desired crop descriptors to be derived
from EO technologies. Although different approaches have been developed for retrieving PAI
from optical remote sensing data,16–18 a simple regression approach is found to be effective for
PAI estimation from a vegetation index at a farm or regional scale across a growing season.19
This study investigates the use of Landsat-8 OLI and RapidEye MSI sensors for crop PAI esti-
mation. The objective was to evaluate the compatibility of information derived from these two
sensors for monitoring the seasonal development and mapping the spatial variability of crops.
The study was carried out in southern Ontario during the 2013 growing season. Three major
annual field crops, corn, soybeans, and winter wheat, were studied.
2 Material and Methods
2.1 Study Site
The study site is a 15 ×15 km agricultural area in the North Easthope Township in southern
Ontario, Canada (43.3° N, 80.8°W; Fig. 1). It is within the Mixedwood Plains Ecozone, one
Fig. 1 The North Easthope (Ontario) study site; image is by RapidEye acquired on April 17, 2013;
W, C, and S represent winter wheat, corn, and soybeans, respectively.
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-2 Vol. 8, 2014
of the major agricultural areas in Canada. This ecozone is characterized by cool winters, warm to
hot summers, fertile soils, and abundant water supply that provide ideal conditions for ample
livestock and agricultural production.20 The study site has an average elevation of about 350 m
above sea level. On the cropland, soybeans, winter wheat, and corn are the three major annual
crops in this region, with about one-third of the area being rotated with perennial crops (hay and
pasture). The area was selected as an experimental site for land productivity studies using EO
technologies in 2013. Two winter wheat and two corn fields were selected for a nitrogen treat-
ment experiment. A recommended level of nitrogen was applied in the four fields, except for a
rectangular area about 90 ×90 m in each field where no nitrogen was applied. The intent of this
experiment was to evaluate the impact of nitrogen application on the productivity of these two
crops. A total of 45 sample sites were deployed in the four fields, where field data were collected
throughout the growing season to capture the variability of crop growth conditions associated
with different nitrogen treatments and soil types. In addition, data were also collected from two
soybean fields with relatively uniform soil properties. No nitrogen was added in the soybean
fields as the plant is able to fix most of the nitrogen it needs through its symbiotic relationship
with rhizobia bacteria.21
2.2 Remote Sensing Data
RapidEye was scheduled to acquire images over the study site every 10 days between April and
September in 2013. However, only five images were cloud free; no successful acquisition was
made during the midseason (June and July). Landsat-8 was launched in February 2013 and
started to provide free-access data. A total of seven landsat-8 images across the growing season
were obtained from the USGS archive, including four images within the three midseason months
(June, July, and August). Detailed information on the images is provided in Table 1. The over-
pass time of the RapidEye satellite is about 1 h later than that of Landsat-8 and is closer to solar
noon. Thus, for acquisitions made on the same day, the Sun elevation angle is larger and the Sun
position is closer to the south for RapidEye. The view zenith angle of the MSI sensor was the
smallest on April 26 (1.8 deg) and largest on April 17 (17.5 deg). OLI is fixed for nadir view and
the maximum view zenith angle is smaller than 7.5 deg.
Table 1 Remote sensing images acquired over the study site in 2013. OLI is the sensor onboard
Landsat-8; the MSI sensor is identified by “RE”followed by the satellite identification number of the
RapidEye constellation; θs, ϕs and θv are solar zenith, solar azimuth, and view zenith angle,
respectively; the view zenith of OLI is smaller than 7.5 deg; visibility is obtained from the hourly
meteorological data of the nearest meteorological station (Climate ID 6144239).
Date Sensor θs (°) ϕs (deg) θv (deg) Visibility (km)
April 17 RE3 32.6 181.5 17.5 16.1
April 17 OLI 36.1 147.9 16.1
April 26 OLI 33.2 146.5 16.1
April 27 RE4 29.3 177.2 1.8 16.1
May 25 RE3 22.2 181.9 13.2 16.1
June 04 OLI 25.5 137.7 16.1
June 20 OLI 25.1 135.0 16.1
July 15 OLI 27.4 135.7 16.1
August 23 OLI 35.9 146.4 16.1
September 17 RE4 41.3 180.0 5.9 16.1
September 17 OLI 43.8 154.6 16.1
September 28 RE1 45.6 179.8 14.1 16.1
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-3 Vol. 8, 2014
Raw data of the RapidEye and Landsat-8 images were first converted into radiance using the
calibration coefficients in the associated metadata files provided by the vendor, then atmospheric
correction was applied to transform the data into surface reflectance using the 6S code.22 The
midlatitude summer atmospheric model and the continental aerosol model were used for the
reflectance conversion. Visibility was obtained from the hourly data record of the nearest
meteorological station in the Kitchener-Waterloo region (Climate ID 6144239) in southern
Ontario. Images from both satellites were provided with initial geometric correction and geore-
ference. The geometric accuracy of Landsat-8 images was found to be adequate, and the
RapidEye images were recorrected against a 10-m road network vector map when spatial dis-
tortion was apparent.
2.3 Field Data
Field data collection included crop type, height, phenology, soil moisture, and PAI every 12
days. PAI was measured using the digital hemispherical photography (DHP) method23 with
a Nikon D300S camera and a 10.5-mm fisheye lens. At each sample site, 14 digital photos
were taken in two transects with a 5-m distance across the row direction, and 15 m along
the row direction in each transect. When the vegetation was short, photos were taken downward
looking from above the canopy at a distance >1mto the canopy top; when the canopy was tall,
photos were taken upward looking from the soil surface. Effective and total plant area index were
derived from the photos using the Caneye software in the lab.24 We intend to link the measured
effective green plant area index (PAIe) with vegetation indices.
2.4 Cross Calibration of Vegetation Indices
In order to fully benefit from the data acquired by both Landsat-8 and RapidEye sensors for
quantitative monitoring of crop growth conditions throughout the growing season, an evaluation
of data consistency is required and a cross calibration between the two sensors should be per-
formed. Cross calibration of different sensors could be based on prelaunch measurements using
standard sources in the laboratory. In practice, cross calibration is often performed postlaunch
using one of the two approaches: 1) through statistical analysis of images concurrently acquired
by the tested sensors over the same area;25 and 2) using a vicarious calibration method to com-
pare the predicted top-of-atmospheric radiance using a radiative transfer model and ground refer-
ence spectral data measured during satellite overpass.26 In this study, the first approach was
selected to evaluate information consistency. Instead of cross calibrating absolute radiance/
reflectance of the correspondent bands of the two sensors, we compared vegetation indices
derived from surface reflectance, because they have been reported to have been successfully
used to quantitatively estimate crop descriptors.19 A few conventional vegetation indices
(Table 2) based on the visible and near infrared (NIR) reflectance and supported by the
Table 2 The compared vegetation indices; Ris surface reflectance, and the subscripts G,Rand
NIR represent the green, red, and near infrared bands, respectively.
Vegetation index Formula References
NDVI: Normalized difference vegetation index ðRNIR −RRÞ∕ðRNIR þRRÞ28
GNDVI: Green NDVI ðRNIR −RGÞ∕ðRNIR þRGÞ29
RNDVI: renormalized difference vegetation Index ðRNIR −RGÞ∕ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðRNIR þRGÞ
p30
SAVI: soil-adjusted vegetation index 1.5ðRNIR −RRÞ∕ðRNIR þRRþ0.5Þ31
OSAVI: optimized soil-adjusted vegetation Index 1.16ðRNIR −RRÞ∕ðRNIR þRRþ0.16Þ32
MTVI2: modified triangular vegetation index 1.5ð1.2ðRNIR −RGÞ−2.5ðRR−RGÞÞ∕
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ð2RNIR þ1Þ2−6RNIR þ5ffiffiffiffiffiffiffi
RR
p−0.5
p33
EVI2: two band enhanced vegetation index 2.5ðRNIR −RRÞ∕ðRNIR þ2.4RRþ1Þ34
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-4 Vol. 8, 2014
band configuration of both sensors were selected, which involve reflectance in the NIR, red, and
green bands. As the relative response functions of a sensor band are the driving factor of differ-
ence in measurements between sensors,27 they were shown in Fig. 2together with the reflectance
spectrum of a typical crop and soil. The three OLI bands are narrower than those of the MSI and
cover different spectral ranges. Hence, it is worth noting that the difference between the cor-
respondent spectral bands of the two sensors convenes the difference of target reflectance spec-
trum in the spanned spectral range as well as the difference in signal transmission.
To perform cross calibration of the vegetation indices, three pairs of OLI and MSI images
were analyzed, the ones acquired on April 17, April 26/27, and September 17 (Table 1). Random
samples were generated inside the 15 ×15 km2area with a constraint of a 150-m minimum
distance, and a circular buffer with a 45-m radius was used in ArcGIS to generate polygons
for data extraction from all three pairs of images. The buffer helps to reduce random noise
due to imperfect geometric correction.
The OLI image acquired on April 26 was contaminated by clumps of clouds, and thus a mask
representing cloud and cloud shadow was created to eliminate the contaminated samples from
the pairs of images acquired on April 26 and 27. NIR reflectance smaller than 0.06 was treated as
shadow and red reflectance larger than 0.2 was treated as cloud. The extracted vegetation indices
of the two sensors were then compared to obtain a transfer function to convert MSI indices to
equivalent OLI indices.
2.5 Plant Area Index Estimation
Regression analysis was used to establish empirical relationships between the PAIeobtained
using the DHP method and the cross calibrated vegetation indices in order to map the crop
PAI over the study area from the images to extract crop biophysical descriptors and to track
seasonal growth dynamics on a field or plot basis.
3 Results
3.1 Cross Calibration of the Vegetation Indices
Comparison of the vegetation indices derived from the three paired Landsat-8 OLI and RapidEye
MSI images is shown in Fig. 3. Data from the two sensors are mostly correlated with strong
linear relationships, with a few scattered samples due to residual effects of cloud/shadow con-
tamination in the OLI image from the second pair (April 26/27) and a thin haze in the MSI image
from the first pair (April 17). Samples of normalized difference vegetation index (NDVI), green
normalized difference vegetation index (GNDVI), and optimized soil adjusted vegetation index
(OSAVI) [Figs. 3(a),3(b), and 3(e)] were distributed more parallel along the 1:1 line than the
other indices, with a negative intercept showing an overestimate of the three indices by OLI data.
Regression of the samples of soil adjusted vegetation index (SAVI) and renormalized difference
Fig. 2 Relative spectral response functions of the green (G), red (R) and near infrared (NIR) bands
of Landsat-8 OLI and RapidEye MSI sensors, with typical reflectance spectra of a crop and soil.
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-5 Vol. 8, 2014
vegetation index (RDVI) [Figs. 3(c) and 3(d)] had the smallest slopes (<0.72) and a positive
intercept (>0.06), showing an underestimate at low vegetation cover and overestimate at
high vegetation cover of the indices by OLI data. The intercepts of the linear regression of
the two-band enhanced vegetation index (EVI2) [Fig. 3(g)] and the modified triangular vegeta-
tion index (MTVI2) [Fig. 3(f)] samples were the smallest (<0.01), with a slope of 0.874 and
0.790, respectively. This indicates that a cross calibration could be made by simply multiplying
the MSI indices by a single factor. Compared to a full linear regression, the simple multiplication
method led to a maximum error of 2.5% for EVI2 and 0.7% for MTVI2, which occurs at the
largest values of these two indices.
EVI2 samples from the three pairs of images were labeled differently in Fig. 3(h).Itis
observed that the same linear regression equation between EVI2 of the two sensors would
be sufficient for the three dates, which span from the start of the growing season in April to
Fig. 3 Comparison among the three paired Landsat-8 OLI and RapidEye MSI vegetation indices
NDVI (a), GNDVI (b), RDVI (c), SAVI (d), OSAVI (e), MTVI2 (f), and EVI2 (g); linear regression and
the 1:1 lines are also shown; EVI2 from the three pairs of images were labeled differently in (h) to
show that there was no apparent difference among the three dates.
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-6 Vol. 8, 2014
the end of the season in September. A cross calibration of EVI2 could be performed using the
following equation derived from the regression analysis:
EVI2OLI ¼1.1663EVI2MSI:(1)
Equations for cross calibration of other indices can be similarly derived. The strong corre-
lation between the indices of the two sensors suggests that vegetation growth information
derived from the two sensors is consistent upon cross calibration. This compatibility increases
the rate of cloud-free acquisition using these two optical sensors, contributing to improve tem-
poral coverage over a growing season.
3.2 Estimation of Effective Green Plant Area Index from Vegetation Indices
A previous study showed that a semi-empirical relationship can be used for green PAI estimation
from vegetation indices derived from the Landsat series data.19 Results in the study show that
NDVI quickly becomes saturated with crop growth, which leads to a faster increase of uncer-
tainty in PAI estimation. EVI2 and MTVI2 have comparable performances in terms of their
responsiveness to increase in green PAI. To further evaluate the performance of EVI2 to estimate
PAI, the relationship between EVI2 and NDVI and the measured green effective PAI is shown
in Fig. 4.
The tendency of NDVI easily becoming saturated with PAI increase is apparent from
Fig. 4(a). When PAIeis low (<3), NDVI varies over a wide range (between 0.16 and 0.64),
indicating a high sensitivity to PAI during the vegetative stages. The NDVI of a large number
of samples was stable close to 0.9, while PAIeranged between 2.0 and 4.5. The saturation ten-
dency was much less for EVI2 [Fig. 4(b)], and a linear regression can be established for PAIe
estimation:
PAIe¼5.5666EVI2 −0.7218:(2)
Comparison between the estimated and the measured PAIefor the three crops is shown in
Fig. 5, with a coefficient of determination (R2) of 0.85 and a root-mean-square error (RMSE) of
0.53 (n¼169).
3.3 Seasonal Variation of Plant Area Index
Using Eq. (2), maps of PAIein the 2013 growing season could be generated from EVI2 for each
date when there was image acquisition. The seasonal development trends of the three crops, corn,
winter wheat, and soybean, are illustrated in Fig. 6using the measured and estimated PAIe. The
estimated PAIeof the three crops was in good agreement with the measured values, and they
align with the development trends of the growth calendar of the crops.
Fig. 4 Relationship between the measured green effective plant area index (PAIe) and NDVI (a)
and EVI2 (b); the RapidEye data were from May 25 only.
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-7 Vol. 8, 2014
Fig. 6 Seasonal development trends of corn, winter wheat, and soybean, as illustrated by the
measured and estimated green effective PAI (PAIe); CD: calendar day; nitrogen treatments of
corn and winter wheat are labeled.
Fig. 5 Comparison between measured and estimated green effective plant area index (PAIe)
using EVI2; linear regression and the 1∶1lines are also shown; the RapidEye data were from
May 25 only.
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-8 Vol. 8, 2014
Corn and soybean were planted in early to mid-May and developed to a stage during which
PAIecould be confidently measured using the DHP method in the field and estimated from the
Earth observation data acquired from late May to early June. The green PAI of corn reached the
maximum toward the end of July, remained at this level until early September, and then started to
decline. As estimated from the last RapidEye image acquired on September 28, the corn green
PAIestill remained at a detectable level [Fig. 6(a)]. For soybean, the green PAIestarted to
increase from zero at approximately the same time as corn, but with a slower rate to reach
the maxima roughly in mid-August. It then dropped quickly and declined to a very low
level after mid-September. For winter wheat, the green PAI started to grow right after the spring
snow melt, reached peak stage in mid-June, and declined to half of its peak value in mid-July.
Winter wheat in the study area is usually harvested between the end of July and early August.
After the harvest season of winter wheat, green vegetation was observed to develop in the fields
from the images acquired postharvest as a result of weeds and wheat regrowth. As anticipated,
the seasonal development trends revealed by the estimated green PAI captured the effects of
nitrogen application on the winter wheat and corn crops, with a lower level of green PAI
observed through the whole season for the areas without nitrogen compared with the areas
with the recommended nitrogen application [Figs. 6(a) and 6(b)].
3.4 Mapping of Crop Plant Area Index
Equation (2) was applied to EVI2 to generate PAIemaps for the three crops. As an example, the
seasonal change of a corn, winter wheat, and soybean field is shown in Fig. 7. For the soybean,
PAI was at the early emergence stage on May 25, so PAIewas at a very low level. The crop
slowly developed until June 20 with an average PAIeof 0.5, then quickly jumped to a value of
3.0 on July 15, and reached the peak stage on August 23 with a PAIeof about 4.0. The PAIe
rapidly declined in September as shown on the map for September 17. Except for the area with-
out N application, the growth conditions were largely uniform across the whole fields and
through the growing season, showing limited variability related to the soil properties and
topography.
For winter wheat, the average PAIewas about 1.8 on May 25. The winter crop had grown
quite well after snow melt, with the absence of N application clearly shown on the map
(PAIe∼1.1). The average PAIeincreased to 3.2 on June 20, and decreased to 1.3 on July 15.
Since the winter wheat was harvested before the acquisition of the last two images on August 23
and September 17, PAIedid not represent the condition of the winter wheat studied. The plots
without N showed the strongest contrast to the rest of the field on June 20, with an average PAIe
Fig. 7 Spatial variability and seasonal variation of effective PAI (PAIe) estimated from remote
sensing data; RGB image is color composite of RapidEye image bands 5-3-2; the black delineated
squares mark the field areas without N application in the corn and wheat fields. The field is 34.5 ha
for corn, 10.5 ha for winter wheat, and 21.4 ha for soybean.
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-9 Vol. 8, 2014
about half that of the normal value. The plot without N application also appeared to have a higher
PAIeon August 23 and September 17. This probably happened because weeds had less com-
petition from the winter wheat crop earlier in the season which enhanced their development.
The corn had similar PAIedynamics to soybeans up to August 23 as observed from the PAIe
maps; however, photosynthesis was still quite active till September 17, and the PAIemaps
showed that the corn crop had a slower senescent rate than that of the soybean crop, as the
PAIeestimated from the OLI data was about 3.0 until September 17, indicating a significant
proportion of green PAI was still present. The absence of N application was more apparent
in the later growth stage, when lower PAIewere estimated on August 23 and September 17.
If a single liner equation of EVI2 was generated for estimating the green PAIeof corn, soybean,
and winter wheat all together, the coefficient of determination was 0.85 and the RMSE was 0.53.
4 Conclusions
The OLI sensor onboard the newly launched Landsat-8 satellite starts to provide high quality EO
data. Together with its predecessors, it will be a very important data source for local to regional
studies. With an alternative design, the MSI onboard the RapidEye satellites provides high qual-
ity scientific data of high-spatial resolution and with a short revisiting cycle. The results from this
study showed that, following proper radiometric calibration and atmospheric correction, vegeta-
tion indices derived from the data acquired by the two sensors were in very good agreement. This
indicates that the two sensors have good and stable absolute radiometric calibration. Cross cal-
ibration of vegetation indices derived from data acquired by the two sensors using a linear trans-
formation allowed for the combined use of the two sensors for a quantitative study as high spatial
and temporal resolution remote sensing data are required for continuous monitoring of crop
growth conditions throughout the whole growth cycle. The EVI2 and the MTVI2 derived
from the two sensors could be cross calibrated using a simple multiplier. Comparison between
the ground measured effective green PAI and the vegetation indices clearly confirmed that the
EVI2 had a better sensitivity than the NDVI at high PAI, and is preferred for estimating crop PAI
over the season. Good results were obtained by using only one EVI2-based linear equation for
the three crops to monitor the green effective PAI. Using the EVI2 for PAI estimation of corn,
soybean, and winter wheat combined with a linear equation, a coefficient of determination of
0.85 and an RMSE of 0.53 were achieved.
Acknowledgments
This study was funded by Agriculture and Agri-Food Canada and the Canadian Space Agency
through a research project on land productivity using Earth observation and crop modeling. The
authors would like to acknowledge the contribution of many students from the Western
University who helped with the field data collection. The authors also like to thank the
local farmers who have granted access to their fields.
References
1. J. Liu et al., “Estimating crop stresses, aboveground dry biomass and yield of corn using
multi-temporal optical data combined with a radiation use efficiency model,”Remote Sens.
Environ. 114(6), 1167–1177 (2010).
2. H. Fang et al., “Corn‐yield estimation through assimilation of remotely sensed data into the
CSM‐CERES‐Maize model,”Int. J. Remote Sens. 29(10), 3011–3032 (2008).
3. G. Jégo, E. Pattey, and J. Liu, “Using leaf area index, retrieved from optical imagery, in the
STICS crop model for predicting yield and biomass of field crops,”Field Crops Res. 131(0),
63–74 (2012).
4. M. A. Wulder et al., “Landsat continuity: issues and opportunities for land cover monitor-
ing,”Remote Sens. Environ. 112(3), 955–969 (2008).
5. G. Chander et al., “Radiometric and geometric assessment of data from the RapidEye con-
stellation of satellites,”Int. J. Remote Sens. 34(16), 5905–5925 (2013).
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-10 Vol. 8, 2014
6. C. Schuster, M. Förster, and B. Kleinschmit, “Testing the red edge channel for improving
land-use classifications based on high-resolution multi-spectral satellite data,”Int. J. Remote
Sens. 33(17), 5583–5599 (2012).
7. E. Adam et al., “Land-use/cover classification in a heterogeneous coastal landscape using
RapidEye imagery: evaluating the performance of random forest and support vector
machines classifiers,”Int. J. Remote Sens. 35(10), 3440–3458 (2014).
8. S. Fritsch et al., “Validation of the collection 5 MODIS FPAR product in a heterogeneous
agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery,”Int. J.
Remote Sens. 33(21), 6818–6837 (2012).
9. S. Jiali et al., “Estimation of crop ground cover and leaf area index (LAI) of wheat using
RapidEye satellite data: prelimary study,”in Proc. 2012 First Int. Conf. on Agro-
Geoinformatics (Agro-Geoinformatics),pp.1–5, Institute of Electrical and Electronics
Engineering, Shanghai, China (2012).
10. A. Ramoelo et al., “Regional estimation of savanna grass nitrogen using the red-edge band of
thespaceborneRapidEyesensor,”Int. J. Appl. Earth Obs. Geoinf. 19(0), 151–162 (2012).
11. B. J. van den Hurk, P. Viterbo, and S. O. Los, “Impact of leaf area index seasonality on the
annual land surface evaporation in a global circulation model,”J. Geophys. Res. Atmos.
108(D6), 4191 (2003).
12. L. Jarlan et al., “Analysis of leaf area index in the ECMWF land surface model and impact
on latent heat and carbon fluxes: application to West Africa,”J. Geophys. Res. Atmos.
113(D24), D24117 (2008).
13. J. Monteith and M. Unsworth, Principles of Environmental Physics: Plants, Animals, and
the Atmosphere, Academic Press, Boston (2013).
14. L. Prévot et al., “Assimilating optical and radar data into the STICS crop model for wheat,”
Agronomie 23(4), 297–303 (2003).
15. L. Dente et al., “Assimilation of leaf area index derived from ASAR and MERIS data into
CERES-Wheat model to map wheat yield,”Remote Sens. Environ. 112(4), 1395–1407
(2008).
16. C. Bacour et al., “Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from
top of canopy MERIS reflectance data: principles and validation,”Remote Sens. Environ.
105(4), 313–325 (2006).
17. F. Baret et al., “LAI, fAPAR and fCover CYCLOPES global products derived from
VEGETATION: Part 1: principles of the algorithm,”Remote Sens. Environ. 110(3),
275–286 (2007).
18. Y. Knyazikhin et al., “Synergistic algorithm for estimating vegetation canopy leaf area index
and fraction of absorbed photosynthetically active radiation from MODIS and MISR data,”
J. Geophys. Res. Atmos. 103(D24), 32257–32275 (1998).
19. J. Liu, E. Pattey, and G. Jégo, “Assessment of vegetation indices for regional crop green LAI
estimation from Landsat images over multiple growing seasons,”Remote Sens. Environ.
123(0), 347–358 (2012).
20. E. S. W. Group, A National Ecological Framework for Canada, Centre for Land and
Biological Resources Research (1996).
21. F. Salvagiotti et al., “Growth and nitrogen fixation in high-yielding soybean: impact of
nitrogen fertilization,”Agron. J. 101(4), 958–970 (2009).
22. E. F. Vermote et al., “Second simulation of the satellite signal in the solar spectrum, 6S: an
overview,”IEEE Trans. Geosci. Remote Sens. 35(3), 675–686 (1997).
23. V. Demarez et al., “Estimation of leaf area and clumping indexes of crops with hemispheri-
cal photographs,”Agric. For. Meteorol. 148(4), 644–655 (2008).
24. M. Weiss and F. Baret, Can-Eye V6. 1 User Manual, Emmah, p. 47, INRA, Avignon, France
(2010).
25. P. M. Teillet et al., “Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5
TM sensors based on tandem data sets,”Remote Sens. Environ. 78(1–2), 39–54
(2001).
26. P. M. Teillet et al., “A generalized approach to the vicarious calibration of multiple Earth
observation sensors using hyperspectral data,”Remote Sens. Environ. 77(3), 304–327
(2001).
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-11 Vol. 8, 2014
27. P. M. Teillet et al., “Impacts of spectral band difference effects on radiometric cross-cal-
ibration between satellite sensors in the solar-reflective spectral domain,”Remote Sens.
Environ. 110(3), 393–409 (2007).
28. J. Rouse, Jr., “Monitoring the vernal advancement and retrogradation (green wave effect)
of natural vegetation,”in NASA/GSFC, Final Report, Greenbelt, Maryland, pp. 1–137
(1974).
29. A. A. Gitelson, Y. J. Kaufman, and M. N. Merzlyak, “Use of a green channel in remote
sensing of global vegetation from EOS-MODIS,”Remote Sens. Environ. 58(3), 289–298
(1996).
30. J.-L. Roujean and F.-M. Breon, “Estimating PAR absorbed by vegetation from bidirectional
reflectance measurements,”Remote Sens. Environ. 51(3), 375–384 (1995).
31. A. R. Huete, “A soil-adjusted vegetation index (SAVI),”Remote Sens. Environ. 25(3), 295–
309 (1988).
32. G. Rondeaux, M. Steven, and F. Baret, “Optimization of soil-adjusted vegetation indices,”
Remote Sens. Environ. 55(2), 95–107 (1996).
33. D. Haboudane et al., “Hyperspectral vegetation indices and novel algorithms for predicting
green LAI of crop canopies: modeling and validation in the context of precision agricul-
ture,”Remote Sens. Environ. 90(3), 337–352 (2004).
34. Z. Jiang et al., “Development of a two-band enhanced vegetation index without a blue
band,”Remote Sens. Environ. 112(10), 3833–3845 (2008).
Jiali Shang received her BSc in physical geography from Beijing Normal University, Beijing,
China, in 1984, the MA degree in geography from the University of Windsor, Windsor, Canada,
in 1996, and a PhD degree in environmental studies from the University of Waterloo, Waterloo,
Canada, in 2005. She is a research scientist with the Agriculture and Agri-Food Canada (AAFC),
Ottawa, ON, Canada. She specializes in the application of optical and radar integration for
agriculture.
Ted Huffman specializes in spatial analysis of agricultural land use, land management and
farming systems. He has undergraduate degrees in agriculture and geography and a PhD in
remote sensing. His work is applied in national multidisciplinary projects related to the health
of soil, air, and water. He has served on the Environmental Indicators program of the OECD, the
Good Practice Guidance of the IPCC and Canada’s National Inventory Reports to the UNFCCC.
Jinfei Wang received BS and MSc from Peking University, Beijing, China, and a PhD from
University of Waterloo, Waterloo, Canada. She is currently a professor with the Department of
Geography, the University of Western Ontario, Canada. Her research interests include methods
for information extraction from remotely sensed imagery and its applications in urban, wetland
and agricultural cropland environments using high resolution multispectral, hyperspectral, lidar,
and radar data.
David Kroetsch is a senior soil resource specialist with Agriculture and Agri-Food Canada,
specializing in soil survey upgrades and research on soil re-survey and the spatial and temporal
change in soil and landscape attribute information using geospatial digital information and mod-
eling. He is an adjunct professor in the School of Environmental Sciences, University of Guelph,
involved with the development and instruction of a Graduate Diploma field course in soil and
landscape inventory.
Nicholas Lantz received his BSc in biology and GIS, and MSc degree in remote sensing, both
from the University of Western Ontario (UWO), Canada. He has worked at the Canada Centre
for Remote Sensing and Agriculture and Agri-Food Canada. His main research interests include
utilizing high-resolution satellite imagery for invasive plant monitoring, land cover change
detection, and seasonal snow mapping.
Biographies of the other authors are not available.
Shang et al.: Estimating plant area index for monitoring crop growth dynamics. . .
Journal of Applied Remote Sensing 085196-12 Vol. 8, 2014
