Estimation of the Relative Chlorophyll
Content in Spring Wheat Based on an
Optimized Spectral Index
Nijat Kasim, Rukeya Sawut, Abdugheni Abliz, Shi Qingdong, Balati Maihmuti, Ahunaji Yalkun, and Yasenjiang Kahaer
Abstract
The relative chlorophyll content is one of the essential fac-
tors that affect crop growth and yield, and chlorophyll is an
important parameter that reflects the stress and health of
vegetation. The spectral feature parameter method is widely
applied to estimate the relative chlorophyll content of wheat.
To provide a scientific basis for wheat growth monitoring and
agronomic decision-making, we estimated the relative chloro-
phyll content using hyperspectral technology. During the sum-
mer of 2017, we collected canopy reflectance spectra using
field spectroscopy along with the relative chlorophyll content
of wheat. To comprehensively analyze the field-collected
hyperspectral data, various band combinations were used to
calculate a simple spectral index (ratio spectral index,
RSI
),
normalized difference spectral index (
NDSI
) and chlorophyll
index (
CI
). We compared simple spectral indices with 17 dif-
ferent indices from the literature. The relationships between
the indices and relative chlorophyll content were then exam-
ined, and the strongest relationships were demonstrated. The
partial least squares regression (
PLSR
) method was utilized to
develop a predictive model of the relative chlorophyll content.
The newly identified
NDSIs
,
RSIs
, and
CIs
always performed
better than the spectral indices from previous studies, and
the relative chlorophyll content exhibited the highest correla-
tions with
RSI
(R
849 nm
, R
850 nm
),
CI
(R
849 nm
, R
850 nm
), and
NDSI
(R
849
nm
, R
850 nm
), calculated using leaf reflectance spectra (|r|
≥
0.7
).
The -model revealed that the highest
R2Pre
(0.74) and low-
est
RMSEPre
(2.72
SPAD
) were identified with four optimized
chlorophyll indices (
CI
(R
849 nm
, R
850 nm
),
CI
(R539 nm, R553 nm),
CI
(R540 nm, R553 nm), and
CI
(R536 nm, R553 nm)). The spa-
tial information from these parameters will aid the proper nu-
trient management of optimal spring wheat crop growth and
forecasting models for a precision wheat agriculture system.
Introduction
Wheat is the second-largest food crop in the world and is of
great significance to human life. China’s total wheat produc-
tion accounts for more than 25% of the world’s total food
crop production. The planting area is approximately 3 × 10
5
km
2
, and wheat is one of the most important rations in China
(Zhang
et al
., 2015). The development of the wheat industry
will directly affect national food security and social stability
(Sid’ko
et al
., 2017). The production of wheat in arid and semi-
arid regions accounts for more than 50% of the total wheat pro-
duction in the country. It has always played an important role
in safeguarding national food security. Precision agriculture
provides a feasible approach for further increasing the yield of
food crops. Remote sensing, as an important part of precision
agriculture, can meet the requirements of real-time monitoring
of crop growth and guide farmland management and decision-
making. However, the current process of applying remote sens-
ing to precision agriculture still has many problems that need
to be solved, including the low accuracy of the remote sensing
inversion model for crop canopy relative chlorophyll content.
Chlorophyll is a plant pigment that absorbs light energy
during photosynthesis. During the developmental stage and
nitrogen status of crops, leaf relative chlorophyll content
show good correlations with the net photosynthetic rate,
photosynthetic capacity. chlorophyll content has become an
important indicator for evaluating crop growth and nutrition-
al status (Li, 2015) heading period is the most critical period
for production and top-dressing management in all stages of
spring wheat development. During this period, higher rela-
tive chlorophyll content in spring wheat leaves can promote
growth, prolong leaf function, and increase photosynthetic
efficiency and yield (Bannari
et al
., 2007; Cetin
et al
., 2018).
Therefore, it is necessary to monitor the relative chlorophyll
content of the spring wheat canopy accurately and non-de-
structively during this period.
Remote sensing technology is widely used in crop monitor-
ing due to its large scale, high efficiency, and low cost. Hy-
perspectral remote sensing technology, mainly adopts various
methods and indicators based on crop spectral characteristics
Nijat Kasim, Rukeya Sawut, and Yasenjiang Kahaer are
with the College of Resources and Environmental Sciences,
Xinjiang University, Urumqi 830046, China; and the Key
Laboratory of Oasis Ecology under Ministry of Education,
Xinjiang University, Urumqi 830046, China.
Abdugheni Abliz is with the College of Resources and
Environmental Sciences, Xinjiang University, Urumqi
830046, China; and the Institute of Arid Ecology Environment
Xinjiang University, Urumqi 830046, China.
Shi Qingdong is with the College of Resources and
Environmental Sciences, Xinjiang University, Urumqi 830046,
China; the Key Laboratory of Oasis Ecology under Ministry
of Education, Xinjiang University, Urumqi 830046, China;
and the Institute of Arid Ecology Environment Xinjiang
University, Urumqi 830046, China.
Balati Maihmuti is with the College of Resources and
Environmental Sciences, Xinjiang University, Urumqi 830046,
China;the Key Laboratory of Oasis Ecology under Ministry of
Education, Xinjiang University, Urumqi 830046, China; and
the Key Laboratory of Xinjiang General Institutions of Higher
Learning for Smart City and Environment Modeling, Xinjiang
University, Urumqi 830046, China(mahmut_barat@126.com).
Ahunaji Yalkun is with the College of Information Science
and Engineering, Xinjiang University, Urumqi 830046, China.
Photogrammetric Engineering & Remote Sensing
Vol. 84, No. 12, December 2018, pp. 801–811.
0099-1112/18/801–811
© 2018 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.84.12.801
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
December 2018
801
