Stroppiana et al. - 2006 - Evaluation of LAI-2000 for leaf area index monitor

Field Crops Research 99 (2006) 167–170
Short communication
Evaluation of LAI-2000 for leaf area index monitoring in paddy rice
Daniela Stroppiana a, *, Mirco Boschetti a,b , Roberto Confalonieri c ,
Stefano Bocchi b , Pietro Alessandro Brivio a
a IREA CNR, Institute for Electromagnetic Sensing of the Environment, Via Bassini 15, 20133 Milano, Italy
b Department of Crop Science, Section of Agronomy, University of Milano, Via Celoria 2, 20133 Milano, Italy
c Institute for the Protection and Security of the Citizen, Joint Research Centre of the European Commission,
AGRIFISH Unit, MARS-STAT Sector, TP 268–21020 Ispra (VA), Italy
Received 19 January 2006; received in revised form 27 March 2006; accepted 5 April 2006
www.elsevier.com/locate/fcr
Abstract
Leaf area index (LAI) is a variable of primary importance for crop monitoring and it is usually measured by labor intensive destructive field
sampling. Indirect non-destructive estimates by optical instruments are a competitive alternative to direct methods for frequent measurements
and over large areas. The work presented here evaluated the adequacy and the range of reliability of the LAI-2000 estimates for rice (Oryza
sativa L.) by comparison with LAI measurements derived by destructive sampling. Field data were collected in 2004 for an Indica type variety
grown under different levels of nitrogen fertilization. The comparison showed that LAI-2000 estimates and destructive LAI measurements are
well correlated (R 2 > 0.8) whereas the correlation decreases when LAI values are lower than one (R 2 < 0.6). Moreover, when LAI > 1, the
estimates derived by discarding the wide angle reading (fifth ring) of the optical instrument are closer to the actual LAI values compared to the
standard LAI-2000 outputs. The regression line is, in fact, not statistically different (P = 0.25) from the 1:1 line. In these application
conditions, LAI-2000 estimates are correct within 32%. Therefore, if the range of applicability is restricted to LAI values greater than one
and the wide angle readings are discarded, LAI-2000 is an appropriate instrument for in situ LAI estimation in paddy rice fields.
# 2006 Elsevier B.V. All rights reserved.
Keywords: LAI; Indirect estimation; Direct LAI measurement; Oryza sativa L
1. Introduction
Leaf area index (LAI) [m 2 m 2 ], defined as the total oneside
area of photosynthetic tissue per unit ground surface
area (Watson, 1947), is one of the most important variables
in climatic (Lockwood, 1999; Ewert, 2004), ecological
(Chen et al., 2002) and agronomical research studies
(Soltani and Galeshi, 2002). Leaves are the active interface
for energy, carbon and water exchange between plants and
the atmosphere (Cutini et al., 1998) and therefore LAI is a
key variable in most of the models developed for the
simulation of carbon and water dynamics.
Methods for in situ LAI measurement can be grouped in
two main categories (Jonckheere et al., 2004; Weiss et al.,
2004): direct and indirect. Direct methods measure the area
* Corresponding author. Tel.: +39 0223699454; fax: +39 0223699300.
E-mail address: stroppiana.d@irea.cnr.it (D. Stroppiana).
of representative leaf samples and they are the only way to
retrieve the actual leaf area. Besides the effort involved in
collecting and measuring the samples, direct methods
require an additional effort to estimate field plant density and
optimal sample size (Confalonieri et al., 2006). For these
reasons, direct methods can be applied over small areas and
for a limited number of measurements during the crop cycle.
Alternatively, indirect methods estimate LAI by observing
other, easy to measure variables; such methods include
optical instruments, which are widely used to measure the
radiation transmitted through the canopy (i.e. canopy gap
fraction) (Welles and Cohen, 1996). Based on the Beer–
Lambert law, these methods assume that the total amount of
radiation intercepted by a canopy depends on the incident
irradiance, the structure of the canopy (i.e. LAI) and its
optical properties (Monsi and Saeki, 1953). LI-COR LAI-
2000 (LI-COR, Inc., Nebraska, USA) is one of the most
widely used optical instruments for in situ LAI estimation.
0378-4290/$ – see front matter # 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.fcr.2006.04.002

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D. Stroppiana et al. / Field Crops Research 99 (2006) 167–170
However, the accuracy of the estimates greatly depends on
the extent to which a set of assumptions necessary for the
inversion of the Beer–Lambert law (e.g. random foliage
distribution within the canopy) are satisfied. The departure
from these assumptions and some limiting factors of the
optical instruments, such as the influence of non-photosynthetically
active tissues and the saturation behaviour
shown for high LAI values (Jonckheere et al., 2004; Leblanc
and Chen, 2001), can badly affect the accuracy of the
estimates. However, very few works have focused on
assessing the accuracy of indirect LAI estimates for crops
(e.g. Wilhelm et al., 2000; Sean Malone et al., 2002) and, to
the Authors’ knowledge, only Dingkuhn et al. (1999)
provided results for paddy rice although limited to the 0.5–
2.0 range of LAI values.
This work aimed to evaluate the suitability of LAI-2000
for LAI estimation in paddy rice fields and to determine its
range of reliability through comparison with direct
measurements. Since Wilhelm et al. (2000) showed that
the discard of the wide viewing angle readings of the LAI-
2000 instrument can improve LAI estimates in corn crops,
we also analysed and discussed the advantages gained by
reprocessing the LAI-2000 data in the case of paddy rice.
2. Materials and methods
Direct LAI measurements and indirect LAI-2000 estimates
were performed during a field experiment carried out in
Northern Italy in 2004 (Confalonieri et al., 2006). This work
focuses on the data collected for the cultivar Gladio that was
scatter seeded (May 24) and grown under two fertilization
treatments: 0 and 160 kg N ha 1 . Nitrogen fertilization was
supplied in two doses of 80 kg N ha 1 at the beginning of
tillering (June 22) and at panicle initiation (July 20).
Destructive LAI measurements were performed in eight
plots (four replicates per treatment) with a quasi-weekly time
step during the crop cycle (June 16–24–30, July 7–14–26,
August 12) by randomly collecting six plants within each plot
(Gomez, 1972; Vaesen et al., 2001; Dingkuhn et al., 1999).
Plant density was estimated after germination, by
acquiring two digital photos (Canon, PowerShot 70) within
each plot and by counting the number of plants inside a
quarter of a square meter. The estimated mean value was
assumed as the plot’s plant density.
Since only the leaf blade area contributes to LAI
(Yoshida, 1981), the sampled leaf blades were separated
from stems (culms and sheaths) and laid on a flat surface,
digital photographs were acquired (Canon, PowerShot 70),
with a pixel resolution of 0.03 cm, and the leaf area was
derived by unsupervised ISO image classification (ENVI,
Version 4.0, Research Systems, Inc., Boulder, CO, USA).
LAI was computed as the product of the average plant’s leaf
area by the plot’s plant density.
LAI-2000 measurements, performed at the same time as
the destructive samplings, were acquired at sunset or on
overcast days with a single-sensor mode and a sequence of
one above, four below and one above readings regularly
distributed within each plot. In order to reduce the influence
of the adjacent plots and of the operator, a 458 view-cap was
applied on the optics. The standard LAI-2000 outputs (five
rings, 5R) were reprocessed, using the LI-COR C2000
software, in order to discard the wide viewing angle reading
and to estimate the four ring (4R) LAI.
The comparison between direct and indirect LAI
measurements was performed by regression analysis. The
significance of the difference between the 5R and 4R
regression lines was assessed by applying a parallelism test
(Zar, 1996). The same test was exploited in order to verify
whether the regression lines were statistically different from
the 1:1 line, which represents the perfect fit between
measurements and estimates.
The agreement between direct observations and LAI-
2000 estimates was evaluated using the relative root mean
square error (R.R.M.S.E.) for estimating the average error of
estimates. The modelling efficiency (EF; Greenwood et al.,
1985; 1 1; optimum = 1; if positive, indicates that the
estimate is better than the average of measured values) was
also computed to evaluate the correspondence between
measured and estimated trends.
3. Results and discussion
Fig. 1 shows the temporal trend of the average LAI for the
two fertilization treatments as derived by direct measurements.
LAI values range between 0.11 and 7.23 with a
statisticallysignificantdifferencebetweenthetwofertilization
treatments only on the two sampling dates after the second
fertilization (**P < 0.01 and ***P < 0.001, respectively).
LAI-2000 estimates are shown in Fig. 2, where the
standard 5R outputs and the reprocessed 4R ones are
compared to direct measurements. Also LAI-2000 estimates
were statistically different among treatments only on the last
Fig. 1. The LAI temporal trend, as derived from direct measurements, for
the two N fertilization treatments: T0 and T1. Star markers highlights the
statistically different LAI values (**P < 0.01 and ***P < 0.001).

D. Stroppiana et al. / Field Crops Research 99 (2006) 167–170 169
Table 1
The number of data points (n), the mean measured and estimated LAI, the
results of the regression analysis, the estimated R.R.M.S.E. and the fitting
index EF for different ranges of LAI values and for both standard (5R) and
reprocessed (4R) LAI-2000 estimates
Full range Range (0–1) Range (>1)
5R 4R 5R 4R 5R 4R
n 54 24 30
Mean LAI (plan.) 1.83 0.49 2.87
Mean LAI-2000 1.59 1.73 0.56 0.54 2.41 2.68
R 2 0.85 0.83 0.56 0.57 0.77 0.74
Slope 0.78 0.92 1.29 1.35 0.78 0.95
Intercept 0.17 0.04 0.07 0.11 0.15 0.10
R.R.M.S.E. (%) 39.34 39.89 69.39 71.43 31.71 32.40
EF 0.82 0.82 0.50 0.59 0.67 0.66
Fig. 2. The LAI-2000 estimates and the direct LAI measurements. The 5R
(4R) regression is shown as the grey (black) continuous line.
two sampling dates (*P < 0.05). In both cases of 5R and 4R
estimates, the coefficient of determination (R 2 0.83,
***P < 0.001) is in agreement with values published by
Dingkuhn et al. (1999); note, however, that the LAI range
covered by this study is greatly enlarged with respect to the
results previously published (0.5–2 m 2 m 2 ). The parallelism
test showed that the 5R and 4R regression lines are
statistically different (***P < 0.001) and the 4R line,
despite a slope closer to 1 (i.e. estimates closer to the
actual value), was found to be statistically different from the
1:1 line (* P < 0.05).
The graphs show that LAI-2000 tends to overestimate
when LAI < 1 and to increasingly underestimate as LAI
increases above one. The analysis of the LAI-2000 Difn
(Diffuse non-interceptance) output parameter, which can
be used to compute the coefficient of diffused light
extinction of the Beer–Lambert law (k =LAI-2000
estimates/ln(Difn)) (Dingkuhn et al., 1999), showed that
when LAI is lower than one, the estimated k (1.30–4.5) lies
outside the range of acceptable values (0.29–0.81; Kiniry
et al., 2001). It is probable that the low LAI at the early
growing stages badly affects the accuracy of the below
canopy instrument readings of the transmitted radiation
thus compromising LAI estimation. These results were
further confirmed by the regression analysis performed
separately for LAI < 1 (Table 1). It is indeed when
LAI > 1 that the 4R regression line was found not to be
statistically different from the 1:1 line (P =0.25) thus
highlighting a satisfying fit between measurements and
estimates. Therefore, the reprocessing of the standard LAI-
2000 estimates should be always preferred and the use of
the instrument should be restricted to LAI values greater
than 1. When these two conditions occur, LAI-2000
provides estimates with R.R.M.S.E. < 33%. Finally, the
value of the fitting index (EF = 0.66), significantly greater
than zero, further confirms the good performance of the
optical instrument.
4. Conclusions
This work evaluated the suitability and the range of
reliability of LAI-2000 estimates for paddy rice (Oryza
sativa L.) by comparison with direct field LAI measurements.
The LAI-2000 standard outputs were also reprocessed
in order to derive LAI estimates without the wide
angle readings (fifth optical ring). Results show that LAI
measurements and LAI-2000 estimates are in agreement
(R 2 > 0.7, R.R.M.S.E. < 33%, EF = 0.66) when LAI > 1
and the data is processed to derive LAI from only four
optical detectors’ readings. When these conditions occur the
regression line between the direct measurements and the 4R
LAI-2000 estimates was found not to be statistically
different (P = 0.25) from the 1:1 line (i.e. perfect fit). It is
probable that, when LAI < 1, the low density of the rice
canopy badly affects LAI-2000 measurements; therefore for
low LAI values direct methods should be rather used. These
results extend to paddy rice results provided by published
work on other crops. Other authors (Gower et al., 1999) also
highlighted saturation behaviour of the optical instrument
due to canopy closure in correspondence of high LAI values;
the experimental data available in this work, which covered
the rice growing cycle until maximum LAI, did not show this
effect.
References
Chen, J.M., Pavlic, G., Brown, L., Cihlar, J., Leblanc, S.G., White, H.P.,
Hall, R.J., Peddle, D.R., King, D.J., Trofymow, J.A., Swift, E., Van der
Sanden, J., Pellikka, P.K.E., 2002. Derivation and validation of Canadawide
coarse-resolution leaf area index maps using high-resolution
satellite imagery and ground measurements. Remote Sens. Environ.
80, 165–184.
Confalonieri, R., Stroppiana, D., Boschetti, M., Gusberti, D., Bocchi, S.,
Acutis, M., 2006. Analysis of rice sample size variability due to
development stage, nitrogen fertilization, sowing technique and variety
using the visual jackknife. Field Crops Res. 97, 135–141.
Cutini, A., Matteucci, G., Scarascia Mugnozza, G., 1998. Estimation of leaf
area index with the Li-Cor LAI 2000 in deciduous forests. Forest Ecol.
Manag. 105, 55–65.

170
D. Stroppiana et al. / Field Crops Research 99 (2006) 167–170
Dingkuhn, M., Johnson, D.E., Sow, A., Audebert, A.Y., 1999. Relationships
between upland rice canopy characteristics and weed competitiveness.
Field Crop. Res. 61, 79–95.
Ewert, F., 2004. Modelling plant responses to elevated CO2: how important
is leaf area index? Ann. Bot. 93, 619–627.
Gomez, K.A., 1972. Techniques for field experiments with rice. International
Rice Research Institute. Los Banos, Philippines.
Gower, S.T., Kucharik, C.J., Norman, J.M., 1999. Direct and indirect
estimation of leaf area index, fAPAR, and net primary production of
terrestrial ecosystems. Remote Sens. Environ. 70, 29–51.
Greenwood, D.J., Neeteson, J.J., Draycott, A., 1985. Response of potatoes
to N fertilizer: dynamic model. Plant Soil 85, 185–203.
Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M.,
Baret, F., 2004. Review of methods for in-situ leaf area index determination.
Part I. Theories, sensors and hemispherical photography. Agric.
Forest Meteorol. 121, 19–35.
Kiniry, J.R., McCauley, G., Xie, Y., Arnold, J.G., 2001. Rice parameters
describing crop performance of Four U.S. Cultivars. Agron. J. 93, 1354–
1361.
Leblanc, S.G., Chen, J.M., 2001. A practical scheme for correcting multiple
scattering effects on optical LAI measurements. Agric. Forest Meteorol.
110, 125–139.
Lockwood, J.G., 1999. Is potential evapotranspiration and its relationship
with actual evapotranspiration sensitive to elevated atmospheric CO2
levels? Clim. Change 41, 193–212.
Malone, S., Herbert Jr., D.A., Holshouser, D.L., 2002. Evaluation of the
LAI-2000 plant canopy analyzer to estimate leaf area in manually
defoliated soybean. Agron. J. 94, 1012–1019.
Monsi, M., Saeki, T., 1953. Über den Lichtfaktor in den Pfanzengesellschaften
und seine Bedeutung für die Stoffproduktion. Jpn. J. Bot. 14, 22–52.
Soltani, A., Galeshi, S., 2002. Importance of rapid canopy closure for wheat
production in a temperate sub-humid environment: experimentation and
simulation. Field Crop. Res. 77, 17–30.
Vaesen, K., Gilliams, S., Nackaerts, K., Coppin, P., 2001. Ground-measured
spectral signatures as indicators of ground cover and leaf area index: the
case of paddy rice. Field Crop. Res. 69, 13–25.
Watson, D.J., 1947. Comparative physiological studies in the growth of field
crops. I. Variation in net assimilation rate and leaf area between species
and varieties, and within and between years. Ann. Bot. 11, 41–76.
Weiss, M., Baret, F., Smith, G.J., Jonckheere, I., Coppin, P., 2004. Review of
methods for in-situ leaf area index (LAI) determination: Part II. Estimation
of LAI, errors and sampling. Agric. Forest Meteorol. 121, 37–53.
Welles, J.M., Cohen, S., 1996. Canopy structure measurement by gap fraction
analysis using commercial instrumentation. J. Exp. Bot. 47, 1335–1342.
Wilhelm, W.W., Ruwe, K., Schlemmer, M.R., 2000. Comparison of three
leaf area index meters in a corn canopy. Crop Sci. 40, 1179–1183.
Yoshida, S., 1981. Fundamentals of Rice Crop Science. International rice
Research Institute. Los Banos, Philippines.
Zar, J.H., 1996. Biostatistical analysis., 3rd ed. Pretice Hall, Hupper Sandler
River, New Jersey, USA.