Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 6, 1047-1053 (2020)www.astesj.com

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ISSN: 2415-6698

Estimating LAI of Rice Using NDVI Derived from MODIS Surface ReflectanceRushikesh Kulkarni1,* Kiyoshi Honda2
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, 412115 India

1

Department of Astronautics and Aeronautics, Chubu University, 1200 Matsumoto-cho, Kasugai, Aichi 487-8501, Japan

2

ARTICLE INFO
Article history:
Received: 29 October, 2020
Accepted: 20 November, 2020
Online: 14 December, 2020
Keywords:
Rice LAI
In-situ LAI
MODIS Surface reflectance1.

ABSTRACTThe measurement of Leaf Area Index is of fundamental importance in agriculture andecological research because it reflects plant growth. This study aims to estimate LAI from,NDVI derived from standard eight days composite MODIS surface reflectance, acquired
during the rice growing period.
A two-stage model was developed to relate the LAI of rice to NDVI derived from MODIS
surface reflectance. In the first stage, a correlation has been developed between ‘MODISNDVI’ and ‘in situ NDVI’ derived from spectral reflectance measured with
spectrophotometer, in rice field of Suphanburi Province, Thailand. In the second stage,correlation has been developed between in situ NDVI and, in situ LAI measured withhandheld LAI-2000 instrument. Eventually, a model has been derived using these two
relationships, which estimate LAI from MODIS Surface Reflectance, in an irrigated rice
field, central Thailand.

Introduction

In agriculture and ecology, historically, the Leaf Area Index(LAI) concept is used for the estimation and evaluation of crop
production and crop yield and the scheduling of irrigation andamendments during crop cultivation [1]. In order to assess growthand vegetation intensity across the globe, monitoring the
distribution and change of LAI is significant. It is directly related
to the interception and absorption of light by the canopy. It also
influences heat balance and evaporation from the cropping field
and thus, accurate assessment of LAI is necessary [2]. However,many factors are important if the reflectivity of the crop canopy is
to be assessed. These include the complexity of canopy design,
improvements to the internal arrangement of leaves andbackground effects of the soil [1].
Direct measurement of canopy LAI is accurate but extremelylabor intensive and destructive [3]. Remote sensing techniques
have been used to measure LAI on a landscape scale or even global
scale. The benefit of the remote sensing technique is that it cancollect plant canopy information on a global scale without
interfering with normal plant growth [4, 5]. The introduction of
simple NDVI-LAI relationships has yielded reliable results over a
12-year period in the estimate of green vegetation abundance [6].
There are two common approaches to estimate LAI using remote
Corresponding Author: Rushikesh Kulkarni, rushikeshk@sitpune.edu.in*

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https://dx.doi.org/10.25046/aj0506127

sensing [7]. The first one is associated with spectral vegetationindex (SVI) and the second one, is by modelling approach [7]. In
the first approach, an empirical relationship between a Vegetation
Index (VI) and LAI has to be established. One major drawback ofthis method is that equation have to be established for eachvegetation type. Thus, it requires substantial LAI measurements
and corresponding remote sensing data [8]. This method is
unsuitable for pixels of low-resolution remote sensing dataenclosing a heterogeneous field. Another approach to estimate LAI
is by using Bi-Directional Distribution Function (BRDF) models
such as Spectral Absorption of Inclined Leaves (SAIL). Thesemodels usually consist of a set of equations that relate surface
physical properties to observed signals as a function of
wavelength. Physical properties may include soil reflectance,
canopy architecture, optical properties and geometricconfiguration of sensing systems as well as illumination sources. .
The input parameters needed for BRDF models are singlescattering albedo of individual leaves, leaf inclination distribution
and anisotropic properties of both canopy and soil substrates.
BRDF models can then be theoretically inverted to obtain themodel parameters, and some vegetation parameters can be
estimated [7].
The relationship between growth traits and Normalized
Differential Vegetation Index (NDVI) to assess their potential use
in estimating the growth of rice crops has been examined [9].
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Experiments were conducted at the Taiwan Agriculture ResearchInstitute (TARI) experimental farm on loam soil during the firstand second cropping seasons (1996-1997). Plants were sampled at
two weeks intervals after transplanting till harvest and the LAI was
computed at each new sampling date. A LI-1800 high spectral
resolution portable Spectroradiometer (LI-COR Inc., Nebraska,
USA) was used to measure the reflectance to calculate in-situ
NDVI. Yang and Su then developed a correlation between thecomputed LAI and in-situ NDVI. A linear regression gave resultswith R2=0.982 for SPOT HRV and R2= 0.985 for Landsat MSS.
As these spectral transformations are highly correlated, estimationof growth traits based on NDVI can be accurate for both in-situ
NDVI and satellite-derived NDVI. Thus reflectance band of HRV
and MSS sensors could be used as predictors of canopy
characteristics [9].
In-Situ LAI and spectral reflection data were assessed for the
2004-2005 period in part of Suphan Buri, Thailand [10]. This wasdone in order to develop a relationship between LAI and NDVI atground level. In 2004, the researchers developed a model toestimate ground based LAI from ground-based NDVI. This is a
specific case, since the remote sensing data corresponding to thefield data collection period was not available. It was assumed thatNDVI from Landsat MSS and from SPOT VGT to be equal andthus, used the model developed by Yang and Su (1998) for MSS
to estimate ground based NDVI from SPOT VGT [10]. Yang andSu’s LAI - NDVI correlation estimated LAI in the range of 0.082
to 1.848 for SPOT VGT data which seemed too low compared to
the LAI field data collected by Srinuandee in 2004-05, which were
measured in the range of 0.0 to 7.15. Srinuandee’s field based
correlation estimated LAI in the range of 2.03 to 5.3002 for a pixel
in SPOT VGT.
Srinuandee’s model showed little improvement compared to
Yang and Su’s model however, impressive variation in result havenot been observed. Improvements made by Srinuandee on Yang
and Su’s model are not proving conclusive. Thus, the main goalof this research was to estimate LAI from Moderate Resolution
Imaging Spectroradiometer (MODIS) based NDVI with majorchanges on Srinuandee’s efforts. Indeed, in-situ LAI data
recorded by Srinuandee seems to be overestimated and BRDF
effect has not been considered during data collection. BRDF
effect is an important factor to be considered in case of lowresolution remote sensing data where satellite have wide swath
since the measurements vary according to different viewing
angles.
2.

Materials and methods

2.1. Description of Study Area
The case study was conducted in rice field of Song PhiNong district, Suphan Buri Province, central part of Thailand
during January’ 2006 to April’ 2006 which is the growing periodof first crop in Suphan Buri province. The geographic location of
study site as shown in Figure 1. Experiment was carried out in thehomogeneous rice field which was detected using Landsat 7 ETM+, land use map and aerial photographs.
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The topography of study area was mostly flat lowland terrain.
Thus the rice fields in this area remains completely flooded during
rainy season. The climate of the Province characterized mainly by
three seasons: (i) rainy season from May to October (ii) cool anddry season from November to February (iii) hot and dry seasonfrom March to May. The average annual rainfall in the central plain
is between 900-1300 mm, average solar radiation about 17 MJ m2 -1
d and temperature ranges from 33-340C to 23-24 0C.

Figure 1: Location of study area in Suphan Buri Province, Central Thailand

3.

Field Data Collection

The spectral reflectance and LAI were measured using a
Spectroradiometer and a LAI-2000 Plant Canopy Analyser (PCA)
respectively from the first stage of rice growth till the harvesting
period. LAI was measured at 9:00 am to 11:00 am because LAI
canopy analyser doesn’t need direct solar radiation to measure LAI
and LAI measurements should be done with low sun elevation
angle which is in between morning time or in the evening time.
Five different locations were sampled at each time of ground
trothing.
In-situ spectral reflectance readings used to calculate NDVI
have been recorded at five different angles in East and West
directions separately in order to create similar circumstances assatellite data; since, satellite moves in North-South direction withacquiring reflectance in East-West direction. Each measurement
was recorded with 150 intervals, starting from 00 to 600, marked ona simple handmade clinometer in order to examine BRDF effect in
the field refer Figure 2. Five readings of spectral reflectance at eachfive different angles in East and West directions and five LAI
readings, from five sampled locations were averaged for each day
of ground truthing. Total eight readings of spectral reflectance ateach five different angle and eight readings of LAI, were measured
in field during the period of 12th February to 2nd April. The fibre
optics wire of the spectrometer was clamped at each angle andspectral reflectance readings were recorded in both directions, so
total ten readings of spectral reflectance has been measured eachday of ground truthing. One end of fibre optic wire was at a heightof 1.5 m from ground level and another end was connected tospectrometer which was further connected to computer throughUSB port. The spectrometer measures spectral reflectance in therange of 304 nm to 1135 nm with an increment of 1 nm. In order
to compare in situ NDVI with MODIS data, average reflectancefor red band was calculated from wavelength of 620 nm to 670 nm
and that for near infrared band was calculated from wavelength of
841 nm to 876 nm.
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19th Mar

26th Mar
02nd Apr

14th Mar - 21st Mar

22nd Mar - 29th Mar
30th Mar - 06th Mar

Figure 2: Schematic diagram of clinometer setup used to measure surface
reflectance in the field with various angles.

In-situ NDVI estimated from field spectral reflectancemeasured in each week of data collection has been compared withNDVI estimated from 8 days composite MODIS surface
reflectance acquired on corresponding week of ground thruthing,refer figure 5. Two field data collection dates, 26th Feb and 3rd
March, are members of 8 days composite of 26th February to 5th
March (Table 1).
The general flow chart of the methodology is shown in figure
3. The atmospheric corrected MODIS surface reflectance isobtained and compared to the field NDVI derived using a fiberoptic spectroradiometer.
The relationship between MODIS derived NDVI and LAI isestablished as shown in flow chart of methodology figure 3.
4.

Results and discussion

4.1. Correlation analysis of in situ NDVI and NDVI derived
from MODIS
Thus, eight values of in situ NDVI were compared with sevenvalues of MODIS derived NDVI. NDVI derived from MODIS
surface reflectance and in situ NDVI did not show the same patternin early crop development.

Figure 3: Overall methodology of model development

However, they agree in the full vegetative stage. In the field,
early growing stage paddy has a dispersed pattern of newly
germinated rice plants along with water, refer figure 4. In this case,muddy soil was easily visible at this stage, which induces canopy
background contamination error. While in case of observationfrom MODIS, angular effect make the field looks different fromthe way it actually looks with naked eyes. Thus, angular effect was
responsible for higher NDVI in case of MODIS observations.
Difference between “true” NDVI, as would be measured at thesurface, and that actually determined from the space are sensitive
to attenuation by atmosphere and by aerosols [11]. In this case
MODIS BRDF nadir adjusted 16 days composite data wascompared with in situ NDVI figure 6. Similar growing pattern was
obtained with in situ ground truth measurement measured at nadir.
However, it was rather difficult to compare 16 days composite datawith a data acquired on single day.

Table 1: Comparison of field data collection dates and MODIS 8-days composite
dates

Field Data collection
dates

MODIS 8-days composite
dates

12th Feb

10th Feb - 17th Feb

26th Feb

26th Feb - 05th Mar

19th Feb
03rd Mar
12th Marwww.astesj.com

18th Feb - 25th Feb

26th Feb - 05th Mar

06th Mar - 13th Mar

Figure 4: Photograph of paddy field taken at nadir and at off-nadir.

MODIS NDVI was compared with average in situ NDVI
estimated from NDVI from all five locations in the field, in both
directions i.e. East and West, for all five observation angles.
Ground truth surface reflectance was collected in morning timefrom 9:00 am to 11:00 am.
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Linear correlation coefficient (R2) was calculated between insitu NDVI estimated in five angles and NDVI derived from
MODIS surface reflectance,(Table 2). The results obtained weremore or less seems to be equal in all cases figure 7[a-e]. The in situ
NDVI was linearly interpolated to corresponding view zenith
angle of MODIS.

22 Mar

81

22.45

24.01

0.874830 Mar

89

19.39

25.14

0.76431.0
0.9

in situ NDVI at 00

0.8
0.7
0.6
0.5
y= 2.447x - 1.1838
R2 = 0.65

0.4
0.3
0.65

0.70

0.75

0.80

0.85

0.90

MODIS NDVI

(a)

00

1.0
0.9

in situ NDVI at 150

Figure 5: Plot between estimated NDVI in field at 00 viewing angle and NDVI
from MODIS surface reflectance.

0.8
0.7
0.6
0.5

y = 2.437x - 1.1793
R2 = 0.66

0.4
0.3
0.65

0.70

0.75

0.80

0.85

0.90

MODIS NDVI

(b)

150

1.0

Figure 6: Plot of NDVI computed from MODIS Nadir adjusted data, NDVI
estimated from field-collected data
Table 2: Linear relationships between in situ NDVI at different angle and
MODIS NDVI

Observation angle

Relationship

R2

00
150

y= 2.447x – 1.1838
y= 2.437x – 1.1793

0.65
0.66

300
450

y= 2.447x – 1.1838
y= 2.532x – 1.2469

0.65
0.67

600

y= 2.488x – 1.2068

0.68

in situ NDVI at 300

0.9

02 Feb0.6
0.5
y = 2.447x - 1.1838

0.3
0.65

R2 = 0.65

0.70

0.75

0.85

0.90

(c)

300

1.0
0.9

33

View
zenith
angle
6.77

solar
zenithangle
39.20

0.5987

10 Feb

41

7.93

36.59

0.664918 Feb

49

6.43

30.69

0.775726 Feb

57

8.39

32.14

0.796206 Mar

65

22.26

28.93

0.819914 Mar

73

20.07

29.22

0.7835

NDVI

0.8
0.7
0.6
0.5

y = 2.5319x - 1.2469
R2 = 0.67

0.4
0.3
0.65

0.70

0.75

0.80

0.85

0.90

MODIS NDVI

(d)www.astesj.com

0.80

MODIS NDVI

in situ NDVI at 450

DOY

0.7

0.4

Table 3: Characteristics of MODIS on respective dates.

MODIS data
available day

0.8

450

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The interpolated in situ NDVI was adjusted to standardized form
i.e. at nadir observation. Thus, the in situ NDVI was derived from
MODIS NDVI in multistage approach.

1.1
1.0

0.8

in situ NDVI at 0 degree

in situ NDVI at 600

0.9

0.7
0.6
0.5

y = 2.4883x - 1.2068
R2 = 0.68

0.4
0.3
0.65

0.70

0.75

0.80

0.85

0.90

MODIS NDVI

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

y = 0.9586x + 0.0274
R2 = 0.9982
0.0(e) 600
Figure 7: Relationship between in situ NDVI estimated at all five different
angles and MODIS NDVI

In-situ NDVIinterpolated = 2.3959* NDVIMODIS – 1.1448(1)

Model derived in-situ NDVIat 00 = 0.9586 * NDVIinterpolated + 0.024(2)Model derived in-situ NDVI at 00 = 2.2967*NDVIMODIS – 1.0727

(3)

0.2

0.4

0.6

0.8

1.0

Figure 9: Plot of in situ NDVI estimated at 00 and interpolated in situ NDVI.
1.0
0.9

NDVI

0.8

MODIS metadata file represents the specifications of the
MODIS surface reflectance product. (Table 3). The in-situ
NDVIS measured at various angles is interpolated to thecorresponding zenith angle of the MODIS. The results are shownin Table 4.
Interpolated in situ NDVI
0.379419 Feb

0.56750.736203 Mar

0.825212 Mar

0.864219 Mar

0.861626 Mar

0.849202 Apr

0.7941

0.3
12-Feb 19-Feb 26-Feb

interpolated in situ NDVI

12-Mar

19-Mar 26-Mar

2-Apr

(a)
1.0

0.9

0.8

0.7

0.6
y = 0.6503 x + 0.2532
0.5

R2 = 0.64

0.4

0.9

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0

in situ NDVI at 0 from field

0.8

(b)
Figure 10: Comparison of in situ NDVI estimated at 00 in field and in situ NDVI
obtained by derived model

0.7

The presence of clouds has been shown to have a major
impact on the field data set. The remote sensing data collected on
the day 19th March and 22nd March have lots of errors which
induced errors in calculation of NDVI and LAI.

0.6
0.5
y = 2.3959x - 1.1448
R2 = 0.66

0.4

0.70

0.75

0.80

0.85

MODIS NDVI

Figure 8: Relationship between MODIS NDVI and interpolated.

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3-Mar

Date

1.0

0.3
0.65

in situ NDVI at 00
model NDVI

0.4

in situ NDVI from derived model

26 Feb0.6
0.5

Table 4: Interpolated in situ NDVI corresponding to view zenith of MODIS

Field data collection Day
12 Feb

0.7

0.90

The field data collection in this study started with datacollection from seven locations but due to proper care andmaintenance of rice at two locations in the field, data collection
from only five sites could be considered till the end of study.
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Validation of Model

The derived NDVI from model showed nearly same pattern withan in situ NDVI estimated at 00 in the field, figure 10 [a-b]. In the
beginning of rice growth, modelled in situ NDVI seems to beapproaching to get better fitness with in situ NDVI. However, inthe last stage of rice growth, few contaminations induced in remotesensing data were intending to the modelled in situ NDVI tooscillate from expected output.
6.

because of the contamination error in the remote sensing data. The
remote sensing data acquired on 22nd of March estimated higher
value of NDVI which caused an error in LAI calculation.
In situ NDVI at 00 = 2.2967 NDVIMODIS – 1.0727(5)

LAI = (NDVIin situ / 0.5074)3.1318(6)8

Correlation analysis of in situ NDVI and in-situ LAI

7

In-situ NDVI and in-situ LAI were not linear together. The
sensitivity of NDVI to LAI becomes increasingly weak withincreasing LAI beyond a threshold value, which was typicallybetween 2 and 3 [11]. NDVI increases almost linearly withincreasing LAI and then asymptotic region was observed in which
NDVI increases very slowly with increasing LAI. Ground surface
was almost completely covered by leaves in asymptotic region.

6
5

LAI

5.

4
3
2

A linear correlation coefficient (R2) was calculated between in situ
NDVI estimated at 00 and in situ LAI. However, most of the LAI
measurements were in the range of 4 to 5. A non-linear correlation
analysis between in situ NDVI and in situ LAI was used in thisstudy.

in situ LAI
estimated LAI

1
0

12-Feb 19-Feb 26-Feb 3-March12-March19-March26-March 2-April

Date

(a)

0.50
0.40
0.30
0.20
0.10
0.00
0.00

6
5

estimated LAI

in situ NDVI at 0 degree

7

1.00
0.90
0.80
0.70
0.60

y = 0.5074x 0.3193
R2 = 0.95
1.00

2.00

3.00

4.00

5.00

0
0

Eventually the result obtained by non-liner correlation
analysis between in situ NDVI estimated at 00 and in situ LAI is:(4)

Final Model development

The final model involves two parts, the first one is a NDVI
transformed from satellite to in situ and the second one from insitu NDVI to LAI. In the first part, similar variables (NDVI) werecompared. In the second part, the spectral reflectance and an insitu LAI were measured at the same location and at the same timein the rice field, which makes this multi stage modeling approach
more realistic than single stage model. However direct
relationship between MODIS NDVI and in situ LAI was not usedin this study.
In situ NDVI was calculated with RMSE of 0.603 and that offor LAI was estimated at 2.120. The high RMSE could be induced
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y = 0.7649x+0.9886
R2 = 0.61

1Figure 11: In situ NDVI computed at Nadir and in-situ LAI collected from filed.

7.

3
2

6.00

LAI

In situ NDVI at 00 = 0.5074 LAI0.3193

4

1

2

3

4

5

6

in situ LAi(b)Figure 12: Validation of model LAI with in situ LAI

The LAI estimated with developed model grow similar as thein situ LAI measured. Coefficient of correlation (R2) was found tobe 0.61 which could be increased with increasing accuracy of
remote sensing data.
8.

Conclusions

In this study, field-based rice LAI is estimated from standard
MODIS surface reflectance product. We proposed multi stage
approach to derive LAI from MODIS surface reflectance. In thefirst stage, in situ NDVI is derived from MODIS NDVI and in thesecond stage, field-based LAI is derived from in situ NDVI.
BRDF corrected relationship is considered in the first stage.
The angular effect in MODIS NDVI causes little higher
values of modeled in situ NDVI than in situ NDVI estimated at
00. In the case of in situ measurements of surface reflectance small
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area is sampled by fiber optics wire as compared to remotesensing data. If the number of locations in the site is increased
more realistic result can be obtained. The data information of rice
growth in a year definitely improves the result. The accuracy ofthe model could be increased with increasing in the field datacollection for entire period of rice cropping cycle in a year.
The model could be used for regional scale with MODIS
250m and accuracy can be checked with MODIS 500m. The model
can also be further developed for other crops like sugarcane, maize
etc. The model could be improved for mixed pixel condition. The
LAI could be estimated by using some other vegetation indicessuch as Soil Adjusted Vegetation Index (SAVI) and accuracy
check can be done with the derived model in this study.

Rice Vegetation,” in Asian Conference on Remote Sensing (ACRS),
Proceeding of the 19th Asian Conference on Remote Sensing ACRS: 16-20
November 1998, Manila., 1998.
[10] I.A. Srinuandee Panithan, Honda Kiyoshi, Chemin Yann, “SWAP Crop
Model Parameter Identification using SPOT Vegetation in Suphanburi,
Thailand.,” in Proceeding of the 25th Asian Conference on Remote Sensing,
ACRS 2004, November 22-16, 2004, Chiang Mai, Thailand, 2004.
[11] T.N. Carlson, D.A. Riziley, J. Ji, “On the Relation between NDVI ,Fractional Vegetation Cover , and Leaf Area Index,” Remote Sensing ofEnvironment, 62, 241–252, 1997.

In order to enhance the applicability of LAI-NDVI
relationship, more crops and more satellite remote sensing data
sets should be assessed and checked. The use of multi-angularremote sensing for LAI estimation may be further tested andinvestigated using radiative transfer models.
Acknowledgements
We thank Ms. Kamal Thapa for her assistance and support duringin-situ data measurement and also thankful to Thai research
students from the RS-GIS Field of Study for helping as interpreter,
Thai- English-Thai during field data measurement.
Conflict of Interest
The authors declare no conflict of interest.
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