Advances in Remote Sensing, 2013, 2, 269-275 Published Online September 2013 (
Determining the Best Optimum Time for Predicting
Sugarcane Yield Using Hyper-Temporal Satellite Imagery
Shingirirai Mutanga1,2, Chris van Schoor2, Phindile Lukhele Olorunju2,
Tichatonga Gonah3, Abel Ramoelo4
1Africa Institute of South Africa, Science and Technology Programme, Pretoria, South Africa
2University of Pretoria, Industrial Engineering Department, Pretoria, South Africa
3Council for Scientific and Industrial Research (CSIR), Earth Observation Research Group,
Natural Resource and Environment Unit, Pretoria, South Africa
4Department of Water Affairs, Hydrogeology Section, Pretoria, South Africa
Received April 11, 2013; revised May 11, 2013; accepted June 11, 2013
Copyright © 2013 Shingirirai Mutanga et al. This is an open access article distributed under the Creative Commons Attribution Li-
cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Hyper-temporal satellite imagery provides timely up to date and relatively accurate information for the management of
crops. Nonetheless models which use high time series satellite data for sugarcane yield estimation remain scant. This
study det e rmined the be st optimum time for predicting suga rc ane yield using the normalized d i f ference veg etation i ndex
(NDVI) derived from SPOT-VEGETATION images. The study used actual yield data obtained from the mill and re-
lated it to NDVI of several two-month periods of integration spread along the sugarcane growing cycle. Findings were
in agreement with results of previous stud ies which indicated that the best acquisition period of satellite images for the
assessment of sugarcane yield is about 2 months preceding the beginning of harvest. Overall, of the five years tested to
determine the relationship between actual yield and integrated NDVI, three years showed a significant positive rela-
tionship with a highest r2 value of 85%. The study however warrants further investigation to improve and develop ac-
curate operational sugarcane yield estimation models at the local level given that other years had weak results. Such
hybrid models may combine different vegetation indexes with agro-meteorological models which take into account
broader crop’s physiologi cal, growth dem a nds, and soil managem ent whi ch are equally importa nt when predi cti ng yiel d.
Keywords: Sugarcane; NDVI; Yields; Spot Vegetation
1. Introduction
The 21st Century demands the promotion of fast track
modernization and diversification of the sugar sector to
convert it into an efficient cane industry aimed at pro-
ducing sufficient stocks for manufacturing sugar, for
energy [1], and other by products [2,3]. Remote sensing
in the form of hyper-temporal satellite imagery is one of
the tools that can be used to provide timely up to date
and relatively accurate information for the management
of sugar cane crop. Several studies have applied remote
sensing techniques in sugarcane monitoring. Particular
focus has been on the crop’s classification [4] areal ex-
tent mapping [5] thermal age group identification [6],
varietal discrimination [7], crop health and nutritional
status monitoring [8,15]. There is limited applied remote
sensing for sugarcane yield prediction yet it has been
used successfully on graneous crops, such as maize and
wheat [9-12], linked the paucity of publications of ex-
perimental results to the difficulty in collating data and
the lengthy of the growing period of sugarcane. Given
the crop’s relevance in today’s world economy, and the
scant models developed this far, the study uses SPOT
VEGETATION multi-temporal images to estimate the
optimum time for sugarcane yield prediction.
1.1. Sugarcane Growth
Sugarcane is a perennial crop of the Saccharum genus
that is grown in the warm tropics and sub tropics [13]. In
the sub tropics the crop is often grown in irrigated areas
due to its high water requirements. Its growth is charac-
terized by three development stages namely: sprouting,
tillering, stalk grow th and maturation [8]. Th e maturation
is triggered by a decrease in soil water content, in tem-
perature and nitrogen availability. This stage is charac-
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terised by the end of stalk growth and, declining leaf wa-
ter content and turgor [8,14].
1.2. Estimation of Sugarcane Yield
Sugarcane yield estimation is critical for a number of
stakeholders, among which includes, planters growers,
industrial producers and policy makers. Industrial pro-
ducers and growers might be interested in avoiding costs
through the optimization of harvest campaigns. Policy
makers might want to quantify outputs for national statis-
tics and also address food security issues [15].
One of the utilities in which re mote sensing can be ex-
ploited in sugarcane production is in yield prediction
expressed in terms of the tones of the sugarcane stalks
per hectare [15]. This is enabled by the incorporation of a
sugarcane growth model based on the Normalized Dif-
ference Vegetation Index (NDVI), one of the vegetation
indices that are derived from visible and near-infrared
channels [13]. It is a measurement of the “greenness” of
a given area thus in the long term NDVI provides an in-
dication of the trend of intensity of any agricultural ac-
tivity [16].
The NDVI is calculated as illustrated on Equation (1);
where IR and R stand for the near-infrared and visible
(red) respectively [13].
Literature points that the utility of remote sensing data
to forecast sugar cane yield is li mited by:
a) Proprietary nature of past wo r k [1 7]
b) Length of harvest season, the lack of direct link be-
tween sugar cane yield and crop radiometry [8]
c) Uncertainty of growth models [18-20] applied the
conversion of above ground dry biomass into crop
yield for a number of crops; however this study esti-
mated fixed scale sugarcane yield based on NDVI.
This study attempted to respond to the following ques-
What is the optimum time for predicting sugar cane
yield using NDVI derived from hyper-temporal satel-
lite imagery?
Is there any significant difference between sugarcane
yield predictions over several trimesters throughout
the year?
Is there any significant relationship between Actual
Yield of sugarcane and Normalized Density Vegeta-
tion Inde x?
2. Methodology
2.1. Study Area
This study was conducted at Mkwasine Estate located in
the South East lowveld of Zimbabwe. This region is
characterized by low and erratic summer precipitation of
less than 450 mm per annum and high temperatures of
around 35˚C. Sugarcane is thus grown under irrigation as
the conditions are conducive for the growth of the crop
within a 12 month cycle. The growing cycle starts be-
tween September and October and harvesting occurring
between June and August. Out of the 45,000 hectares of
sugarcane plantations, 9000 ha are managed by 840 small
to medium scale out growers under the Commercial
Sugarcane Farmers Association (CSFA) and the Zim-
babwe Sugarcane Farmers Association (ZSFA) [17].
Figure 1 shows the geographic location of the study area,
which has a historical trend of contested land resource,
following the fast track resettlement program of the year
2.2. Sampling, Data Collection and Analysis
Figure 2 provides an overview of the methods applied
and described in the next sections. A ground truthing
exercise was carried out to collect sugarcane production
data in the field using stratified random sampling based
on whether the farmers produced sugarcane or other
crops. A sample of twenty sugarcane plots with an aver-
age area of 20 hectares was selected randomly with in the
forty identified plots. Coordinates for each plot were
taken. All the plots comprised of ratoon crops which are
normally harvested at about 12 month interval for 4 years
or more, before the crop is renewed. Actual yield levels
were obtained from Mkwasine milling plant, where the
farmers sell their sugarcane output.
2.3. Hyper-Temporal Satellite Imagery
Hyper temporal satellite imagery in form of composite
10 days decadal NDVI images (S10 products) at 1 km ×
1 km resolution from April 1998 to December 2009 for
the study were extracted from SPOT-4 VEGETATION.
The SPOT VEGETATION system has a spatial resolu-
tion of 1.15 km at nadir and a swath width of 2250 kilo-
meters that can cover almost all the globe’s landmasses
while orbiting 14 times a day [21]. It comprises of bands
2 (red; 0.61 - 0.68 µm) and 3 (near IR; 0.78 - 0.89 µm)
which are the main wavelength bands for deriving the
NDVI. The NDVI indicates chlorophyll activity and was
calculated from (band 3- band 2)/(band 3 + band 2); the
index was then converted to a digital number (DN value)
in the 0 - 255 data range using the fomula: DN = (NDVI
+ 0.1)/0.004. This was to make the data handy with data
analysis [22] .The NDVI composite consist of 393 im-
ages (April 1998 to December 2009) taken for a period
of 11 years which were obtained from [23]. The down-
loded NDVI images were geo-referenced and declouded.
eclouded means: using by image and pixel the supplied D
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Figure 1. Location of the study area.
quality record, only pixels with a “good” radiometric
quality for bands 2 (red; 0.61 - 0.68 µm) and 3 (near
IR;0.78 - 0.89 µm), and not having “shadow” “cloud” or
“uncertain”, but “clear” as general quality, were kept (re-
moved pixels were labeled as ‘missing’) [24]. In-order to
extract the hyper temporal Mean, Maximum and Mini-
mum NDVI the study applied Zonal Statistics for all the
20 plots.
cane production and NDVI integrated over all combina-
tions of continuous time intervals of two to three months
(based on starting date which is around September and
October, duration and the burning season which is the
harvest period often around June to August). Integrating
the value of NDVI over a period of time implies amal-
gamating the condition or “greenness” of the crop over
its growth cycle and assuming this accumulated condi-
tion will be correlated with the overall production or
yield of the crop.
2.4. Statistical Analysis
The study tested if there is any significant difference
The study examined regressions between annual sugar-
Figure 2. Simplified schema of the methodological ap-
between the different R2 of the various time intervals
using analysis of variance, in-order to obtain the best
optimum time for predicting sugarcane yield as illus-
trated on Figure 3. This was repeated over 4 harvesting
seasons. The later results were illustrated using box plots
representation of NDVI for the different 5 years. While
the study was able to obtain NDVI data for the period
1998 to 2009, the actual yield data ob tained fro m the mill
was not consistent for the whole 10 year period; hence
the analysis was undertaken for 5 years for each of the
3. Results and Discussion
3.1. What Is the Best Optimum Time for
Predicting Sugar Cane Yield?
To estimate the best time of the year when the NDVI
related to the sugarcane yield, the bi-monthly NDVI was
plotted against the correlation coefficient. The bi-
monthly periods used were April to May, June to July,
August to September, October to November, December
to January and February to March. This comes from the
notion that the bi-monthly time periods can give a good
differentiation of the shooting, growing, burning or har-
vesting stages, in which also the NDVI varies. From this
regression analysis, the bi-monthly time period with the
highest correlation coefficient shows the time in the sug-
arcane growth cycle in which there is the strongest rela-
tionship between NDVI and sugarcane yield. As such
this depicts the best time of predicting sugarcane yield as
shown on Figure 3.
The optimum time for estimating sugarcane yields is
during the period December to March where the correla-
tion coefficient was between 0.58 and 0.61. The box
plots shown on Figure 4 however show some outliers in
the different months. A possible explanation to this might
be that, NDVI sometimes peaks during the early stages
of the development cycle of the crop. Results are thus in
line with previous studies which depicted that the best
Figure 3. Regression coeficient of the relationship between
Integrated NDVI and actual yield for 20 samples ploted
against different months of the sugarcane plant growth cyle.
Figure 4. Regression coeficient of the relationship between
Integrated NDVI and actual yield ploted against different
months of the sugarcane plant growth cyle repeatede over 5
time for predicting yield using NDVI is the pre harvest
period [5,25,26]. Normally sugarcane fields have com-
pletely closed canopies at least two months before har-
vest period. It is known that the main driving factors of
the variations in NDVI are the amount of vegetation ex-
pressed by LAI. This is in line with [8]’s finding which
showed a highest correlation of 0.98 between Leaf Area
Index (LAI) and NDVI. Presumably that’s the same pe-
riod the study anticipates to get a good relationship with
actual yield. During the optimum period, sugarcane crop
would have reached its vegetative growth peak charac-
terized by a high intensity of greenness thus we antici-
pate a better correlation with yield. For the other bi-
monthly periods, the correlation coefficient was low pos-
sibly because of the different crop development stages
such as shoot development, vegetative growth and har-
Copyright © 2013 SciRes. ARS
vesting making these periods no t optimum for estimating
sugarcane yield. Likewise during the harvest period the
sugarcane crop loses its greenness as leaves are burnt,
hence the NDVI decreases, a similar trend observed over
the different years in the analysis.
Arguably the illustrated results show weak correlation
coefficient for the best optimu m pred iction trimester ov er
the different tested years. Results of ANOVA indicated
that the Fcritical is 2.4, which is less than th e P-value of 2.6
showing a significant difference between the means of
the different trimesters. This therefore implies that there
is no other better trimester which can be used to predict
yield given the cropping calendar. The low correlations
coefficients are explained on the next set of results.
3.2. Relations between Sugarcane Yield and
Integrated NDVI: Is There Any Significant
Relationship between Integrated NDVI and
Annual Yield?
The relationship between integrated NDVI of the pre
harvest season determined in this study and actual sug-
arcane yield from the mill was estimated using linear
regression with r esults shown on Table 1 . Overall, of the
five years tested three years showed a sign ificant po sitive
relationship between NDVI and the actual yield with a
highest r-square (r2) value of 85%. This analysis shows
that areas with high NDVI coincide with years of high or
better yields. Findings of this study concur with a study
by [27] which found a positive relationship between
NDVI derived from NOAA AVHHR and sugarcane yield
at a regional scale.
Notwithstanding in some instances the standard error
of the yield estimate shows a relatively low precision,
while some areas showed a weak relationship between
NDVI and actual yield. Various possible explanations
can help explain this scenario. While SPOT VEGETA-
TION has a strength in high temporal resolution, the spa-
tial resolution is course i.e. (1 * 1 KM), implying that the
measured reflectance will be affected by other factors
other than the condition of the sugarcane. Possibly the
reliability of the imagery as a means of estimating the
Table 1. Simple regression analysis between yield and
integrated NDVI for different periods of study.
Years R2 RMSE F-stats P
2005 0.41 10.27 12.51 0.002000
2006 0.59 9.84 25.41 0.000085
2007 0.64 7.36 32.52 0.000021
2008 0.85 4.80 100.3 0.000000
2009 0.80 9.11 72.03 0.000000
yield might be compromised by the time interval be-
tween the date the image was taken and the harvest date.
It can be argued that the relationship between inte-
grated NDVI and yield depends on a variety of environ-
mental conditions [28], and production related factors.
As an example canopy condition of sugarcane is mainly
determined by moisture and chlorophyll status as meas-
ured by NDVI however it’s not the only facto r determin-
ing yield. Other extern al factors such as droughts, nutria-
ent deficiency might influence the yield, which might be
a reason for the weak relationship shown on Table 1 and
Figures 3 and 4. Other factors which might need to be
taken into account could include the harvesting process
and transportation to the mill. Often substantial amount
of cane can be lost before selling to the mills, and such
cane is unaccounted for as the actual yield used in this
study was based on records from the milling company.
The fact that our results were based on field measure-
ments of output at the mill negates this notion since re-
sults indicated some satisfactory relationship. This shows
the potential of high time series data to predict yields
though with room for improvement.
4. Conclusions
Hyper-temporal satellite imagery (Spot Vegetation) can
play a significant role in sugarcane management. The
main finding of this study is that the preceding two
months before harvest is the optimum period for predict-
ing yield using NDVI . Despite the weak correlation coef-
ficient for the optimum prediction trimester over the dif-
ferent tested years there is no better trimester which can
be used to predict yield given the cropping calendar. Had
it been that this study analysed data for the anticipated
10-year period better informed results might have been
Based on the 20 samples for each of the five years
tested to determine the relationship between actual yield
and integrated NDVI, three years showed a significant
positive relationship showing the potential of high time
series data in yield prediction.
In a nutshell the study warrants further investigatio n to
improve and develop accurate operational sugarcane yield
estimation models at the local lev el given the evidence of
weak results for other years. Such hybrid models may
combine different vegetation indices with agro-meteoro-
logical models which take into account broader crop’s
physiological, growth demands, soil management which
are equally important when predicting yield. Post yield
prediction factors which may affect the sugarcane need
to be taken into account for example diseases, poor har-
vesting processes, and cane transportation losses. It might
also be advantageous to consider a model which inte-
grates high spatial resolu tion imagery with high tempor al
Copyright © 2013 SciRes. ARS
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