Content uploaded by Ashmitha Nihar
Author content
All content in this area was uploaded by Ashmitha Nihar on Feb 24, 2022
Content may be subject to copyright.
RESEARCH ARTICLE
Sugarcane Crop Type Discrimination and Area Mapping at Field Scale
Using Sentinel Images and Machine Learning Methods
Ashmitha Nihar
1
•N. R. Patel
1
•Shweta Pokhariyal
1
•Abhishek Danodia
1
Received: 30 September 2020 / Accepted: 12 October 2021
ÓIndian Society of Remote Sensing 2021
Abstract
Crop mapping and acreage estimation are the simplest yet the most critical issues in agriculture. Remote sensing tech-
nology has been extensively used in the past few decades for executing these tasks. The objective of this study is to map
sugarcane fields at a catchment level and segregate the plant and ratoon fields using the freely available Sentinel-1 and
Sentinel-2 data. The study is carried out at the Kisan Sahkar Chini Mill catchment in the Saharanpur district of Uttar
Pradesh. The objective was achieved by a two-step process where firstly the sugarcane fields are identified using Random
Forest and SVM classifiers over temporal optical and microwave images. The most accurate result is used as a crop mask to
separate the plant and ratoon fields. This was achieved by attempting a phenology based classification and spectral based
classification. The results revealed that temporal Sentinel-2 data are highly competent in classifying sugarcane at farm level
and segregating the plant and ratoon fields. The sugarcane crop mask was created with a kappa coefficient of 0.95 using the
SVM classifier, and the plant and ratoon fields were discriminated using the Random Forest classifier with a kappa
coefficient of 0.81. The sugarcane crop area was estimated to be approximately 535 acres of plant crop and 560 acres of the
ratoon crop while the mill estimate was 520 acres and 540 acres, respectively. The results showed that Sentinel-2 has
promising capabilities and is a convenient asset in delineating small-sized farms and classifying sugarcane and its crop
types.
Keywords Sugarcane mapping Random forest SVM Ratoon Sentinel Machine learning Farm scale
Remote sensing Object and Pixel based
Introduction
Agriculture is the chief source of social well-being in most
parts of the World. In India, agriculture’s importance is
hardly overstated, as it plays an enormous role in the
economy. The necessity and significance of timely and
reliable estimation of crop acreage and production for the
use of decision-makers, including but not limited to the
producers, processors, managers and the government is
widely acknowledged (Nihar et al., 2019). Misleading
production estimates give way to flawed policies regarding
imports, exports and stockholding of agriculture-based
commodities. It leads to problems for farmers, traders and
consumers through market distortion. Hence the colossal
risks and drawbacks of unreliable crop area and production
estimates are self-explanatory for India, a country that
caters more than 1.3 billion people.
Sugarcane (Saccharum officinarum), a perennial cash
crop of the grass family Poaceae, is one of the widely
grown crops that contribute significantly to the national
exchequer and employs over a million people in India. The
crop is one of India’s top priority commercial crops, and
yet the official estimations frequently vary widely from
those of the trade and industry. While the sugarcane crop
&Ashmitha Nihar
ashmitha.sab@gmail.com
N. R. Patel
nrpatel@iirs.gov.in
Shweta Pokhariyal
shwetapokhariyal9@gmail.com
Abhishek Danodia
abhidanodia@iirs.gov.in
1
Agriculture and Soils Department, Indian Institute of Remote
Sensing, ISRO, 4, Kalidas Road,
Dehradun 248001, Uttarakhand, India
123
Journal of the Indian Society of Remote Sensing
https://doi.org/10.1007/s12524-021-01444-0(0123456789().,-volV)(0123456789().,-volV)
acreage information at a regional scale is vital in terms of
trade decisions, insurance and policy-making, they are also
crucial at a field level to the crushing mills in terms of
budget, crop plan, commercial strategy, harvest scheduling,
field expansion or renewal.
Crop acreage estimation is cumbersome when carried
out manually because of the vast land size, which is easily
overcome by satellite imagery. Remote Sensing (RS) is a
powerful and compelling technology for crop inventory
mapping, acreage estimation, monitoring, phenology
extraction and production estimation of crops with rea-
sonable accuracy depending on sensor characteristics
(Patel et al., 2006). Applications of RS technology for the
agricultural industry has attracted research from several
scholars worldwide. It is now being normalized to use RS
to understand and answer many of the spatial queries
regarding agriculture. Classification of crops to estimate
their area and extent is among the most common applica-
tions of satellite-based RS. Crop inventory mapping rests
as the basis for other studies like yield estimation,
parameter retrieval and the crop response to the biotic and
abiotic factors in its environment. By appropriate selection
of spatial and spectral resolution and processing tech-
niques, satisfying results can be achieved by RS technology
for the use of sugarcane crop (Abdel-Rahman & Ahmed,
2008).
Several different approaches have come in to place to
map crops accurately. A plethora of studies have been
performed to evaluate different types of data such as
optical, microwave and hyperspectral data to map sugar-
cane. While the optical data provide information about the
pigments, cellular content and structure, microwave data
are more responsive to the crop’s geometry and dielectric
properties. Pandey et al. (2019) estimated the sugarcane
crop acreage using Landsat and IRS ResourceSat datasets
with the help of extensive field sampling and illustrated the
importance of the sensor’s spatial resolution map sugar-
cane at mill catchment level. Singla et al. (2018), proposed
that the temporal profile of Normalized Difference Vege-
tation Index (NDVI) can be efficiently used to discriminate
the sugarcane crop types at any given scale. High-resolu-
tion LISS – IV data is often explored to study sugarcane
mapping in India (Singh et al., 2020; Verma et al., 2017).
The synergistic use of optical and microwave datasets to
map sugarcane is also being explored recently (Jiang et al.,
2019; Wang et al., 2020).
Several studies have reported satisfying results for the
application of satellite imagery in mapping sugarcane.
However, minimal research has been extended into map-
ping the types of sugarcane, namely the plant and the
ratoon. Ratooning is the practice of regrowing sugarcane
from the existing roots of the crop after harvesting. While
the planted crop grows over twelve months, the ratoon crop
generally takes only nine to ten months. The ratoon crops
grow faster as they already have a well-established root
system. The plant crops provide higher sugar recovery rates
and have a higher number of tillers. The classification of
sugarcane and these crop types is performed in this study
using the two major algorithms Support Vector Machine
(SVM) and Random Forest (RF).
Recently, the advanced classifiers SVM and RF have
drawn much attention from the scientific community due to
their high accuracy (Mather and Tso, 2009). SVM stands
robust and consistent regardless of high data dimension-
ality, data imbalance and limited training data (Sheykh-
mousa et al., 2019; Ustuner et al., 2016), while RF is more
common due to its easy implementation, speed and
insensitivity to overfitting (Belgiu & Dra
˘gu, 2016; Moun-
trakis et al., 2011). It is also reported that in the case of
lower resolution images, RF performs consistently better
than SVM. However, SVM outperforms RF while classi-
fying data containing a higher number of features
(Sheykhmousa et al., 2020).
With the help of the two competent classifiers, mapping
of sugarcane crop types at a farm level is performed over
the freely available high-resolution Sentinel-1 and Sen-
tinel-2 data. This study is an attempt to comparatively
assess the viability and efficiency of the two characteris-
tically different datasets to map sugarcane at field level and
discriminate the crop type in a study area with an average
field size of around 0.4 acres.
Materials and Methods
Study Area
The Kisan Sahkari Chini Mill (KSCM) Catchment located
in the town of Nanauta in the district of Saharanpur, Uttar
Pradesh was chosen as the area of interest for this study.
The districts of Uttarakhand, namely Dehradun and
Haridwar neighbour Saharanpur by the North and the West,
respectively; the Karnal and Ambala districts of Haryana
border Saharanpur by the East and the District of Muzaf-
farnagar of Uttar Pradesh on the South. The catchment area
extends between 29.62 °N and 29.83 °N latitude, and
between 77.20 °E and 77.54 °E longitude. The mill
catchment contains sugarcane fields from around 165 vil-
lages covering a total area of over 460 square kilometres
with an average sugarcane field size being 0.4 acres. The
KSC Mill currently has a cane crushing capacity of 5000
tons per day, with an average sugarcane yield of 833
quintals/hectare and average sugar return of 8.45%.
The heart of the economy of this district is agriculture
with sugarcane as the principal commercial crop. Rice,
wheat, mustard, potato and tomato are among the other
Journal of the Indian Society of Remote Sensing
123
crops grown in the area. Mango orchards and Poplar
plantations are also commonly found here. The Saharanpur
district experiences a humid subtropical climate with
annual precipitation ranging from 750 to 1200 mm and
temperature ranging from 4 °C in winters up to 40 °Cin
summers. Figure 1shows a false colour composite image
of the KSCM catchment.
Data Used
The European Union’s Radar imaging Sentinel-1A (C-
Band) datasets and the optical Earth observation datasets of
the twin satellites Sentinel-2A and Sentinel-2B were used
in this study for mapping sugarcane in the catchment area.
The Sentinel-2 mission provides optical images at a tem-
poral resolution of 5 days with high resolution up to 10 m
offering an extensive range of crop monitoring applica-
tions. The bottom of atmosphere reflectance (Level 2A)
products (available globally since 2018) were collected for
the period January 2019 to January 2020 (77 images) to
monitor and extract the field parcel’s phenology in the
study area. The study area lies on the junction of two image
tiles, and hence, all the images had to be mosaicked. Out of
this dataset, one cloud-free image each from April, May,
October and November was chosen for the spectral
classification. Table 1shows a list of all the satellite ima-
gery used in this study.
Four Sentinel-1A SAR images from April, June, August
and October were collected for this study to map sugarcane
field parcels. These images were downloaded as Level -1
Ground Range Detected (GRD) products obtained by
Interferometric Wide (IW) swath mode. The four images
were pre-processed to obtain the VV and VH polarized
backscatter images and stacked.
Around 250 Ground Truth (GT) Points were collected in
the last week of September 2019. The sugarcane crop type
and approximate dates of sowing and harvest were also
collected along with other land-use information. These GT
points were used to train and validate the classifiers in an
80:20 split, respectively.
Methodology
This study aims to map the sugarcane field parcels in the
KSCM catchment and classify the sugarcane plant and
ratoon crops using machine learning methods and estimate
their area. This is achieved by using a two-step process
where the sugarcane crop field parcels are separated from
the other land-use classes first, and the fields are then
segregated as plant or ratoon. Temporal microwave and
optical data were used for this purpose. Both object-based
Fig. 1 False Colour Composite of Study Area
Journal of the Indian Society of Remote Sensing
123
and pixel-based approaches were performed to map the
field parcels. The overall flow-chart and steps involved in
discrimination of sugarcane crop types in the mill catch-
ment area and for one specific village (Gudamb) are pre-
sented in Fig. 2.
Sugarcane Field Mapping
Mapping of the sugarcane crop fields was performed by
object-based and pixel-based classifications using the SVM
and RF classifiers. Image segmentation was done on a
cloud-free optical image of October 2019, resulting in
several objects or segments. The image objects layer was
used to support the object-based classification for both
optical and SAR images. Temporal Image Composites
were prepared from the four SAR images and four optical
images using the data listed in Table 1. The classification
was carried out to separate the major classes such as set-
tlement, water-body, sugarcane, paddy, vegetables, mango
and poplar plantations. The different types of classification
carried out were: (1) object-based classification on single
date optical image (2) object-based classification on the
temporal optical image, (3) object-based classification on
temporal SAR image and (4) pixel-based classification
Table 1 Optical and SAR
imagery used in this study Classification methods Sensor Dates
SAR temporal Sentinel-1 28-04-2019; 03-06-2019; 14-08-2019; 17-10-2019
Phenology retrieval Sentinel-2 January 2019 – January 2020 (77 images)
Optical temporal Sentinel-2 25-04-2019; 30-05-2019; 17-10-2019; 16-11-2019
Optical single date Sentinel-2 17-10-2019
Image Segmentation Sentinel-2 17-10-2019
Fig. 2 Flow-Chart Methodology for field-scale sugarcane crop type discrimination and area mapping
Journal of the Indian Society of Remote Sensing
123
over the temporal optical image. The points from the GT
campaign were used as training points for the classifiers.
The GT points were split into 80% and 20% for training
and evaluation, respectively. The sugarcane crop mask is
obtained from the best performing classifier and dataset
combination after eliminating the other land-use classes.
Sugarcane Plant Versus Ratoon Discrimination
After separation of the sugarcane field parcels from the
other pixels, they are categorized into plant and ratoon
crops using the SVM and RF classifiers. The plant versus
ratoon segregation was performed using (1) the temporal
optical image and (2) an image stack of vegetation phe-
nology parameters. Vegetation phenology refers to sea-
sonal changes or life events in a crop’s growing period.
Phenology metrics for the sugarcane crop were derived
from the TIMESAT software using temporal NDVI data
computed from Sentinel-2 images for the period January
2019 to January 2020. The TIMESAT program generates
smooth functions to extract phenological parameters. The
curve fitting processes are based on least-square fit where
the local functions are fused to form Global functions that
describe NDVI over the full seasonal cycle. This software
provides an interface to fit a time series of Vegetation
Indices data into nonlinear mathematical functions (Ek-
lundh & Jo
¨nsson, 2017). For this study, the Asymmetric
Gaussian model was fit into a time series of NDVI images.
The temporal resolution was reduced from 5 to 15 days by
taking a maximum NDVI pixel composite to avoid cloud
interference in the optical data. The phenological metrics
namely Start of Season (SOS), End of Season (EOS),
Length of the growing period or season (LOS), Maximum
NDVI value during the growing period, Time of occur-
rence of Maximum value, Value of NDVI at SOS and EOS
and Amplitude were extracted from the NDVI data. The
eight parameters extracted from TIMESAT were written
into images, and a composite image was made for the
sugarcane crop mask created earlier. The SOS and EOS
extracted from the software showed a difference of up to
two weeks compared to GT. The phenology metrics images
extracted and the optical temporal image used earlier were
classified to label the fields as plant or ratoon using the GT
points. The GT points were again split into 80% and 20%
for training and testing, respectively. Ultimately, the sug-
arcane fields were mapped and classified into the plant or
ratoon crops, and their acreage was estimated. The acreage
was compared with the sugarcane crop area dataset avail-
able from the mill.
Results and Discussion
The present study was undertaken to classify sugarcane
crop fields in the KSCM mill catchment area in Uttar
Pradesh. The discrimination of the plant and ratoon crop
using satellite data was also achieved in a two-step process.
First, the sugarcane pixels are identified, and secondly, they
are classified as ratoon or plant. Table 2shows a compar-
ison of the accuracies of the different classifiers over dif-
ferent datasets and the best outcome is highlighted in bold.
It is seen that the optical datasets offered better results than
the SAR data regardless of the classifier used. The SVM
classifier on the optical multi-temporal dataset gave the
highest accuracy of 95.9% and a Kappa coefficient of 0.95.
Figure 3shows the results for the extraction of sugarcane
crop mask using the SVM classifier over temporal optical
data.
It is also seen that contrary to many other studies that
claim object-based classification is better than the pixel-
based classification (Makinde et al., 2016; Whiteside et al.,
2011), the pixel-based classification performed better in
classifying the small sugarcane fields. This is because of
the scale of objects being mapped and the resolution of the
data used. Since the field parcels in this region are rela-
tively small and the marginal pixels are influenced by the
neighbouring land-use classes, only a few pure pixels in
the farm’s interior represent the actual spectral characters
of the sugarcane crop. While the pixel-based classification
is spot on solely based on every individual pixel’s spectral
characters, the object-based classification takes the average
spectral response of a group of pixels considered an object
based on their visual qualities as shape, size and colour. .
As explained by Cai and Liu (2013), the most meaningful
objects might have fewer pixels. Thus the accuracy of the
classifiers based on objects is compromised. The Sentinel-2
dataset still stands competent in pixel-based classification
for mapping small farms. The results are also comparable
to the sugarcane mapping using Sentinel-2 data carried out
by Wang et al. (2019). With higher resolution datasets, the
object-based classification may work better to delineate the
small farms. The sugarcane crop mask was extracted from
the SVM classifier results, and the mask was used to further
classify the fields as plant or ratoon.
Table 3shows that the RF classifier’s performance over
the temporal optical images gave the best results (high-
lighted in bold) for segregation of plant and ratoon fields. It
was noticed that the phenology-based classification did not
provide expected results in terms of classifying plant and
ratoon crops.
Figure 4shows that the Random Forest classifier over
the optical dataset shows a very satisfying classification
with significantly less noisy pixels. The field boundaries
Journal of the Indian Society of Remote Sensing
123
Table 2 Comparison of classification accuracies of sugarcane in Nanauta mill catchment
Image input Type of
classification
Classifier Producer accuracy
(%)
User’s accuracy
(%)
Overall accuracy
(%)
Kappa
coefficient
Sentinel-1 Multi-
temporal
Object-based RF 86.21 75.76 70.89 0.66
SVM 82.76 80.00 71.42 0.66
Sentinel-2 Single date RF 72.41 70.00 79.89 0.76
SVM 65.52 67.86 76.71 0.73
Sentinel-2 Multi-
temporal
RF 86.21 75.76 86.77 0.84
SVM 89.66 78.79 87.30 0.85
Sentinel-2 Multi-
temporal
Pixel-based RF 94.89 91.69 93.44 0.92
SVM 97.53 93.55 95.91 0.95
Fig. 3 Optical Temporal Classification using SVM classifier
Table 3 Comparison of classification accuracies to discriminate sugarcane plant and ratoon crop type
Image input Classifier Average producer accuracy
(%)
Average user’s accuracy
(%)
Overall accuracy
(%)
Kappa
coefficient
Phenology parameters RF 75.52 75.29 76.63 0.50
SVM 77.19 77.75 78.84 0.54
Sentinel-2 multi-
temporal
RF 90.55 90.71 90.75 0.81
SVM 84.38 83.54 83.47 0.73
Journal of the Indian Society of Remote Sensing
123
are also prominently visible. The phenology-based classi-
fication’s lower accuracy stems from the staggered SOS
and EOS dates in the catchment area. Phenology is cal-
culated from images spanning over the entire year, pro-
viding a wide range for SOS and EOS dates. This extensive
range of SOS and EOS could overlap for the plant and
ratoon crops, thus confusing the classifier. Meanwhile, the
ratoon crops having a much voluminous canopy and better
vigour are spectrally distinguishable from the plant crops
using the optical satellite images. Another factor that plays
Fig. 4 Plant and Ratoon discrimination using RF and SVM classifiers over optical data and phenology parameters
Fig. 5 A comparison of Plant vs Ratoon classification against the GPS survey in the Gudamb village, KSCM catchment
Journal of the Indian Society of Remote Sensing
123
an important role is the inevitability of clouds in optical
data. Unlike spectral image-based classification, the cloud
cannot be eliminated entirely from the time series of
optical images while extracting phenology. The spectral
image-based classification thus shows higher accuracy. The
SVM and RF demonstrated outstanding performance in
classifying the sugarcane crop types as discussed by
Everingham et al., (2007). Figure 5compares the two
classifiers over the Gudamb village in the mill catchment
area against the GPS based field survey data.
From the final classified image using the optical images,
the plant and ratoon fields’ area in the catchment area were
computed. According to the data collected from the KSC
mill’s GPS survey, the catchment area had around 520
acres and 540 acres of plant and ratoon crops, respectively.
The classification results showed a slightly higher estima-
tion of approximately 535 acres of sugarcane plant crop
and 560 acres of the ratoon crop.
Conclusions
Sentinel-2 images showed promising capabilities for field-
level classification as well as discrimination between sug-
arcane plant and ratoon crops in the mill catchments with
the help of ground truth points. It is desirable to use pixel-
based classification to delineate the small field parcels
using Sentinel data. The sugarcane fields were isolated
from other land cover classes with an overall accuracy of
95.91% and 0.95 kappa coefficient using the SVM classi-
fier. The plants and ratoon fields were separated with an
accuracy of 90.75% and 0.81 kappa coefficient using the
Random Forest classifier. The sugarcane plant and ratoon
crops were estimated to cover around 520 acres and 540
acres, respectively, of the catchment area. Sentinel-2 data
delivered convincing results with SVM and RF classifiers
to delineate small-sized farms and map the different crop
types. Outcomes of this study suggest that the Sentinel-2
datasets can be exploited conveniently in crop mapping and
monitoring at the farm scale.
Acknowledgements Authors sincerely express their gratitude to
General Manager, Kisan Sahkari Chini Mills Ltd (KSCM) and Mr.
Varshney, IT cell, KSCM for providing necessary information and
GPS based field survey of sugarcane crop besides other logistic
support. We are also thankful to Director, IIRS for providing neces-
sary facilities and encouragement to carry out research for farmer-
centric applications.
Declarations
Conflict of interest The authors declare no conflict of interest.
References
Abdel-Rahman, E. M., & Ahmed, F. B. (2008). The application of
remote sensing techniques to sugarcane (Saccharum spp. hybrid)
production: A review of the literature. International Journal of
Remote Sensing, 29(13), 3753–3767. https://doi.org/10.1080/
01431160701874603
Belgiu, M., & Dra
˘gu, L. (2016). Random forest in remote sensing: A
review of applications and future directions. ISPRS Journal of
Photogrammetry and Remote Sensing, 114, 24–31. https://doi.
org/10.1016/j.isprsjprs.2016.01.011
Cai, S., & Liu, D. (2013). A comparison of object-based and
contextual pixel-based classifications using high and medium
spatial resolution images. Remote Sensing Letters, 4(10),
998–1007. https://doi.org/10.1080/2150704X.2013.828180.
Eklundh, L., & Jo
¨nsson, P. (2017). TIMESAT 3.3 with seasonal trend
decomposition and parallel processing Software Manual. Lund
and Malmo University, Sweden, 1–92. Retrieved July 29, 2020,
from http://web.nateko.lu.se/timesat/timesat.asp.
Everingham, Y. L., Lowe, K. H., Donald, D. A., Coomans, D. H., &
Markley, J. (2007). Advanced satellite imagery to classify
sugarcane crop characteristics. Agronomy for Sustainable Devel-
opment, 27(2), 111–117.
Jiang, H., Li, D., Jing, W., Xu, J., Huang, J., Yang, J., & Chen, S.
(2019). Early season mapping of sugarcane by applying machine
learning algorithms to sentinel-1A/2 time series data: A case
study in Zhanjiang City, China. Remote Sensing.https://doi.org/
10.3390/RS11070861
Makinde, E. O., Salami, A. T., Olaleye, J. B., & Okewusi, O. C.
(2016). Object based and pixel based classification using
rapideye satellite imager of ETI-OSA, Lagos, Nigeria. Geoin-
formatics FCE CTU, 15(2), 59–70. https://doi.org/10.14311/gi.
15.2.5
Mather, P., & Tso, B. (2009). Classification methods for remotely
sensed data (2nd ed.). CRC Press. https://doi.org/10.1201/
9781420090741
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines
in remote sensing: A review. ISPRS Journal of Photogrammetry
and Remote Sensing, 66(3), 247–259. https://doi.org/10.1016/j.
isprsjprs.2010.11.001
Nihar, A., Mohammed Ahamed, J., Pazhanivelan, S., Kumaraperu-
mal, R., & Ganesha Raj, K. (2019). Estimation of cotton and
maize crop area in Perambalur district of Tamil Nadu using
multi-date sentinel-1A SAR data. International Archives of the
Photogrammetry, Remote Sensing and Spatial Information
Sciences - ISPRS Archives, 42(3/W6), 67–71. https://doi.org/
10.5194/isprs-archives-XLII-3-W6-67-2019
Pandey, S., Patel, N. R., Danodia, A., & Singh, R. (2019).
Discrimination of sugarcane crop and cane yield estimation
using Landsat and IRS resourcesat satellite data. International
Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences - ISPRS Archives, 42(3/W6), 229–233.
https://doi.org/10.5194/isprs-archives-XLII-3-W6-229-2019
Patel, N. R., Bhattacharjee, B., Mohammed, A. J., Tanupriya, B., &
Saha, S. K. (2006). Remote sensing of regional yield assessment
of wheat in Haryana, India. International Journal of Remote
Sensing, 27(19), 4071–4090. https://doi.org/10.1080/
01431160500377188
Sheykhmousa, M., Kerle, N., Kuffer, M., & Ghaffarian, S. (2019).
Post-disaster recovery assessment with machine learning-derived
land cover and land use information. Remote Sensing.https://doi.
org/10.3390/rs11101174
Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadi-
manesh, F., Ghamisi, P., & Homayouni, S. (2020). Support
Vector Machine Versus Random Forest for Remote Sensing
Journal of the Indian Society of Remote Sensing
123
Image Classification: A Meta-Analysis and Systematic Review.
IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing, 13(October), 6308–6325. https://doi.org/
10.1109/JSTARS.2020.3026724
Singh, R., Patel, N. R., & Danodia, A. (2020). Mapping of sugarcane
crop types from multi-date IRS-Resourcesat satellite data by
various classification methods and field-level GPS survey.
Remote Sensing Applications: Society and Environment, 19,
100340.
Singla, S. K., Garg, R. D., & Dubey, O. P. (2018). Sugarcane ratoon
discrimination using LANDSAT NDVI temporal data. Spatial
Information Research, 26(4), 415–425. https://doi.org/10.1007/
s41324-018-0184-0
Ustuner, M., Sanli, F. B., & Abdikan, S. (2016). Balanced vs
imbalanced training data: Classifying rapideye data with support
vector machines. International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences - ISPRS
Archives, 41(July), 379–384. https://doi.org/10.5194/isprsarc
hives-XLI-B7-379-2016
Verma, A. K., Garg, P. K., & Hari Prasad, K. S. (2017). Sugarcane
crop identification from LISS IV data using ISODATA, MLC,
and indices based decision tree approach. Arabian Journal of
Geosciences.https://doi.org/10.1007/s12517-016-2815-x
Wang, J., Xiao, X., Liu, L., Wu, X., Qin, Y., Steiner, J. L., & Dong, J.
(2020). Mapping sugarcane plantation dynamics in Guangxi,
China, by time series Sentinel-1, Sentinel-2 and Landsat images.
Remote Sensing of Environment, 247(December 2019), 111951.
https://doi.org/10.1016/j.rse.2020.111951
Wang, M., Liu, Z., Ali Baig, M. H., Wang, Y., Li, Y., & Chen, Y.
(2019). Mapping sugarcane in complex landscapes by integrat-
ing multi-temporal Sentinel-2 images and machine learning
algorithms. Land Use Policy, 88(July), 104190. https://doi.org/
10.1016/j.landusepol.2019.104190
Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing
object-based and pixel-based classifications for mapping savan-
nas. International Journal of Applied Earth Observation and
Geoinformation, 13(6), 884–893. https://doi.org/10.1016/j.jag.
2011.06.008
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Journal of the Indian Society of Remote Sensing
123
