988 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 50, NO. 3, MARCH 2012
Estimation of Chlorophyll a Concentration Using
NIR/Red Bands of MERIS and Classification
Procedure in Inland Turbid Water
Yunmei Li, Qiao Wang, Chuanqing Wu, Shaohua Zhao, Xing Xu, Yanfei Wang, and Changchun Huang
Abstract—The classification criteria are established to classify
the water of Taihu Lake into four classes based on above-water
remote sensing reflectance (R
rs
), i.e., types A to D. Among the
four water types, type A spectra represented the case of waters
where algal blooms or aquatic plants appeared, while type B is
referred to the water with high suspended matter concentration
and low chlorophyll a concentration (C
chla
). Both types A and B
were not suitable for retrieving C
chla
from image data. Hence,
three-band, four-band, and two-band ratio algorithms were
constructed to retrieve C
chla
from water types C and D. The
obtained results showed that the relation trends between C
chla
and R
rs
were different between type C and type D waters. By
using Medium Resolution Imaging Spectrometer images, acquired
on November 11, 2007 and November 20, 2008, the C
chla
of Taihu
Lake was mapped by band 9/band 7 models; it could be concluded
that the C
chla
mainly ranged from 0 to 20 mg · m
−3
, accounting
for 83.70% of the whole lake area in 2007 image, while the area
was 86.63% in 2008 image. The estimation accuracies varied from
different C
chla
ranges. The mean absolute percent errors obtained
by band 9/band 7 models were 106.23%, 56.79%, 38.04%, 33.80%,
and 58.74% for the ranges 0 mg · m
−3
<C
chla
<=5 mg · m
−3
,
5 mg · m
−3
<C
chla
<= 10 mg · m
−3
, 10 mg · m
−3
<
C
chla
<= 20 mg · m
−3
, 20 mg · m
−3
<C
chla
<= 30 mg · m
−3
,
and 30 mg · m
−3
<C
chla
, respectively. Correspondingly,
the root-mean-square errors were 5.02, 4.45, 5.59, 8.72, and
32.55 mg · m
−3
, respectively.
Index Terms—Chlorophyll a concentration, Medium Resolution
Imaging Spectrometer (MERIS), near infrared (NIR)/red model,
Taihu Lake, water classification.
I. I NTRODUCTION
A
S THE THIRD largest freshwater lake in China, Taihu
Lake is very important for water supply to major cities
along the lakeshore. However, water pollution of the lake is
Manuscript received November 1, 2010; revised February 26, 2011 and
May 1, 2011; accepted July 17, 2011. Date of publication September 15, 2011;
date of current version February 24, 2012. This work was supported in part
by the National Natural Science Foundation of China under Grant 40971215,
by National Doctoral Fund under Grant 20093207110011, and by the Special
Project of High-Resolution under Grant E0203/1112.
Y. Li, X. Xu, Y. Wang, and C. Huang are with the Key Laboratory of Virtual
Geographic Environment, Ministry of Education, Nanjing Normal University,
Nanjing 210046, China (e-mail: liyunmei@njnu.edu.cn; 68698500@qq.com;
357008144@qq.com; huangchangchun_aaa@163.com).
Q. Wang, C. Wu, and S. Zhao are with the Satellite Environment Application
Center, Ministry of Environmental Protection, Beijing 100029, China (e-mail:
wangqiao@sepa.gov.cn; wu.chuanqing@sepa.gov.cn; zshyytt@126.com).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TGRS.2011.2163199
becoming more and more serious in recent years, which has
caused lake eutrophication. This problem severely threatens the
normal functioning of the lake. As such, it is essential that
the water quality should be regularly monitored for tracking
the anthropogenic effects and sustainably managing water re-
source. Fortunately, as a key indicator of water quality, chloro-
phyll a concentration (C
chla
) can be inversed from remotely
sensed data, which is crucial for the continuous monitoring
of water quality. Generally speaking, the C
chla
can be derived
from the blue and green spectral bands [1], [2] in open-ocean
Case-1 water. However, the method is not very suitable for
inland productive turbid waters, because of the overlapping
and uncorrelated absorptions by dissolved organic matter and
nonalgal particles in the blue spectral region [3]–[6]. Thus,
spectral algorithms which are based on reflectance in the red
and the near-infrared (NIR) spectral regions are regarded to
be preferable for estimating C
chla
in turbid productive waters
[6]–[9]. Dall’Olmo et al. [6] validated the potential of ap-
plying NIR/red reflectance ratios to estimate C
chla
via Sea-
viewing Wide Field-of-view Sensor and Moderate Resolution
Imaging Spectroradiometer data. Gons [8] developed a simple
semianalytical method using wavebands centered at 665, 705,
and 775 nm and later reexamined the performance by setting
wavelength 708.75 nm instead of 705 nm [10]. Dall’Olmo
and Gitelson [11] developed a three-band semianalytical al-
gorithm for estimating C
chla
. Moses et al. [12] validated a
three-band model and a two-band model which used Medium
Resolution Imaging Spectrometer (MERIS) reflectances in the
red and NIR spectral regions for estimating C
chla
in inland,
estuarine, and coastal turbid productive waters. Some studies
show that three-band model is better than the two-band ratio
[13], [14].
Three-band algorithm is based on radiative transfer (RT)
theory according to absorption and scattering properties of
water constituents. The relationship between C
chla
and three
band is as follows:
C
chla
∝
R
−1
rs
(λ
1
) − R
−1
rs
(λ
2
)
× R
rs
(λ
3
) (1)
where R
rs
(λ
i
) stands for remote sensing water reflectance at
wavelength λ
i
(i =1, 2 or 3), usually in the visible range of
400–800 nm.
The efficiency of the algorithm has been proved in various
lakes and reservoirs with variable optical properties [12]–[15],
and the algorithm is also validated in Taihu Lake [16], [17].
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