1. No category

Remote Sensing of Environment 164 (2015) 43–56
Contents lists available at ScienceDirect
Remote Sensing of Environment
journal homepage: www.elsevier.com/locate/rse
Long-term remote monitoring of total suspended matter concentration
in Lake Taihu using 250 m MODIS-Aqua data
Kun Shi a, Yunlin Zhang a,⁎, Guangwei Zhu a, Xiaohan Liu a,b, Yongqiang Zhou a,b, Hai Xu a, Boqiang Qin a,
Ge Liu c, Yunmei Li c
a
Taihu Lake Laboratory Ecosystem Research Station, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,
Nanjing 210008, China
b
University of Chinese Academy of Sciences, Beijing 100049, China
c
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210046, China
a r t i c l e
i n f o
Article history:
Received 13 October 2014
Received in revised form 11 February 2015
Accepted 25 February 2015
Available online xxxx
Keywords:
MODIS
Lake Taihu
Total suspended matter
Wind-induced resuspension
Shallow turbid lake
a b s t r a c t
We have developed and validated a robust empirical model for estimating the concentrations of total suspended
matter (TSM) in Lake Taihu (China), a large turbid inland water body. This model was generated using Moderate
Resolution Imaging Spectroradiometer (MODIS-Aqua) medium-resolution (250 m) data gathered from 2003 to
2013 and in situ data collected from a number of cruise surveys. A strong significant correlate relationship between the in situ TSM data and the atmospherically corrected MODIS-Aqua remote sensing reflectance at the
645 nm band (Rrs(645)) was found (R2 = 0.70, p b 0.001, n = 150). From these data, a local TSM model was developed for Lake Taihu. Long-term TSM distribution maps retrieved from the MODIS-Aqua data demonstrated
marked temporal and spatial variations. Temporally, significant lower TSM was found in summer and autumn
than in winter and spring (p b 0.005, t-test). The significant seasonal variability could be attributed to sediment
resuspension due to changes in the wind speed between different seasons. Lake Taihu also experiences large
inter-annual variations that are primarily caused by changes in wind force over the region. In particular, the
TSM in Lake Taihu from 2006 to 2008 was relatively lower than in other years, which could be explained by
the lower mean wind speed during these years compared to the other years. Spatially, the TSM in the Open
area, especially in the southern part of this region, was consistently higher than in other sub-regions of Lake
Taihu. The coverage of submerged aquatic vegetation (SAV) generally characterized East Lake Taihu as a region
with a relatively lower TSM. Lake topographic conditions, SAV, and runoff discharge jointly contributed to the
spatial variations in TSM.
© 2015 Elsevier Inc. All rights reserved.
1. Introduction
Total suspended matter (TSM) primarily consists of algal and nonalgal organic detritus, living non-algal organisms, degrading phytoplankton cells, and mineral sediments (Binding, Jerome, Bukata, &
Booty, 2008; Shi, Li, Li, & Lu, 2013). TSM plays a critical role in many aspects of lake ecology such as the ecological function of the lake and its
biogeochemical cycle (Doxaran et al., 2014). TSM can directly affect
light attenuation and vertical distribution (Zhang, Zhang, Ma, Feng, &
Le, 2007) and therefore affect phytoplankton productivity (Liu, Zhang,
Yin, Wang, & Qin, 2013) and submerged aquatic vegetation (SAV) distribution and growth (Chen, Hu, & Muller-Karger, 2007; Miller & McKee,
2004). Moreover, TSM also affects nutrient dynamics (Zhu et al., 2013)
and the transport of micropollutants, heavy metals, and other materials
(Nguyen, Leermakers, Osan, Torok, & Baeyens, 2005). Therefore,
⁎ Corresponding author. Tel.: +86 25 86882198; fax: +86 25 57714759.
E-mail address: [email protected] (Y. Zhang).
http://dx.doi.org/10.1016/j.rse.2015.02.029
0034-4257/© 2015 Elsevier Inc. All rights reserved.
acquiring accurate information regarding the TSM distribution patterns
contributes greatly to the understanding of lake ecosystem dynamics
and the development of effective and quantitative monitoring schemes
of aquatic environments, thus improving water quality management
(Zhang, Shi, Liu, Zhou, & Qin, 2014).
Traditionally, TSM in lake waters has been obtained by cruise samples. The use of a limited number of measurements through traditional
point sampling to represent the overall temporal and spatial distribution of the biological parameters has proven to be problematic in lake
waters with high spatial and temporal variations in the biogeochemical
environment, especially for lakes with large spatial scales. Since the
launch of the Coastal Zone Color Scanner (CZCS) in 1973, satellite
image data have been used by the ocean color community to acquire
the distribution of TSM at large spatial scales, with better spatial distribution and temporal resolution than was obtained from the traditional
measurements. Consequently, there is considerable interest in the use
of remote sensing methods to obtain synoptic maps of TSM in lake waters, which has become a popular topic in the field of limnology.
44
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
Over the past three decades, numerous studies have demonstrated
that the TSM distribution in open sea and inland waters can be mapped
from various types of satellite remote sensing data, such as the Seaviewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution
Imaging Spectroradiometer (MODIS), Medium Resolution Imaging
Spectrometer (MERIS), and Landsat TM/ETM/ETM+ (Miller & McKee,
2004; Morel, 2001; Nechad, Ruddick, & Park, 2010; Zhang, Dong, Cui,
Xue, & Zhang, 2014). With very short revisit time (near-daily coverage),
MODIS sensor, on board Aqua platform, launched in May 2002, has
two bands that observe the Earth at relatively fine spatial resolution
(250 m) (Band 1: 645 nm, from 620–670 nm; Band 2: 859 nm,
from 841–876 nm). These two bands have sufficient sensitivity to
detect a wide range of variations in water color properties in
coastal and inland case-II waters (Chen et al., 2007; Shi, Wang, & Jiang,
2013).
The MODIS-Aqua 645 nm band has been widely applied in numerous previous studies to developing TSM estimation models for inland
and coastal turbid waters (Feng, Hu, Chen, & Song, 2014; Miller &
McKee, 2004; Petus et al., 2014). For example, by employing the
MODIS-Aqua 645 nm band, Feng et al. (2014) developed a local TSM estimation model for characterizing the spatial and temporal variations in
TSM in the Yangtze Estuary and its adjacent coastal waters, where TSM
concentrations covered a large range from 1–1000 mg/L. Ondrusek et al.
(2012) suggested that the 645 nm band had a great potential application for estimating TSM in the Chesapeake Bay, where TSM ranges
from 6–60 mg/L. This band was also successfully used to estimate TSM
in the Northern Gulf of Mexico, the Mississippi River Delta, and the
Mississippi Sound, where TSM was lower than 60 mg/L (Miller &
McKee, 2004). Moreover, this band has also been widely used in inland
and coastal turbid waters to derive Kd(PAR) and turbidity, two measures
that are considered surrogates of TSM (Chen et al., 2007; Hu et al., 2004;
Shi & Wang, 2010; Son & Wang, 2012; Wang, Shi, & Tang, 2011). Wang,
Son, and Harding (2009) developed a new Kd(490) model that combined the current empirical or semi-empirical algorithms for open
oceans and the semi-empirical Kd(490) model for turbid coastal waters
by utilizing the MODIS-Aqua 645 nm band. This model was then widely
used to characterize Kd(490) variations in inland (Wang et al., 2011)
and coastal waters (Shi, Wang, et al., 2013; Son & Wang, 2012). These
studies demonstrate the great potential usability of the MODIS-Aqua
645 nm to develop TSM estimation models from MODIS-Aqua data acquired for turbid inland and coastal waters.
Lake Taihu is a typical large eutrophic shallow lake, with a maximum
depth of no more than 3 m and an average depth of only 1.9 m (Liu,
Zhang, Wang, & Zhou, 2014; Qin, Xu, Wu, Luo, & Zhang, 2007); the
water area of this lake is approximately 2230 km2. The waters of
Taihu Lake are consistently extremely turbid and are characterized by
high and varying TSM due to terrestrial inputs and sediment resuspension (Wang et al., 2011). In a previous study, we developed a TSM estimation model based on a single band of MERIS image data and
investigated the spatial and temporal variations of TSM in Lake Taihu
(Zhang, Shi, et al., 2014). The MERIS imagery had fine spatial (full resolution: 300 m) and spectral resolution and a short revisit time (neardaily coverage), and therefore was thought to be an ideal satellite
image dataset for monitoring TSM in inland lake waters. However, the
sensor has been discontinued since April 2012. Moreover, the amount
of the MERIS image data used in our previous study was so insufficient
that there were some uncertainties for the temporal–spatial pattern of
the TSM distribution because TSM exhibits large variations over very
short time periods (Zhang, Shi, et al., 2014). To continuously monitor
the TSM in Lake Taihu, this study attempted to develop a TSM estimation model using the MODIS-Aqua 645 nm band data because these
data have great potential for monitoring TSM in inland and coastal waters (Feng et al., 2014; Miller & McKee, 2004; Petus et al., 2014). Until
now, no report has been systematically conducted to acquire the longterm TSM distribution patterns for Lake Taihu using MODIS imagery
data. However, the long-term TSM distribution patterns for this lake
are markedly essential for environmental evaluations and ecological
conservation planning.
This study was motivated by the need for an easily operational
model for r remotely sensed estimations of TSM from MODIS imagery
data and by the lack of high spatial and temporal resolution TSM data
from Lake Taihu. Therefore, we sought to address these two needs.
The aims of the present study are to (1) develop a model to estimate
TSM for a shallow and extreme turbid inland lake water based on the
MODIS-Aqua 645 nm band, (2) generate maps of TSM data with high
spatial and temporal resolution, and (3) subsequently use the derived
TSM products from the 2003–2013 MODIS imagery data to characterize
the long-term TSM distribution patterns in Lake Taihu.
2. Materials and methods
2.1. Sampling sites and schedule
The in situ dataset in this study contained 2432 water samples and
62 Rrs(λ) measurements that were collected from two sources
(Table 1). First, from January 2003 to December 2013, a total of 2432
water samples were collected from a long-term monthly observations
plan (LMOP) performed by the Lake Taihu Laboratory Ecosystem
Research (TLLER) team. Second, a total of 62 Rrs(λ) measurements
were collected from Lake Taihu in November 2007 (5 Rrs(λ) measurements), November 2008 (14 Rrs(λ) measurements), May 2010
(7 Rrs(λ) measurements), May (26 Rrs(λ) measurements) and August
2013 (10 Rrs(λ) measurements). The spatial distribution of all sampling
sites in Lake Taihu can be found in Fig. 1. The detailed information about
the sampling is listed in Table 1. To reveal the spatial variations of the
TSM in the varied aquatic environments of Lake Taihu, we divided this
lake into six sub-regions: Meiliang Bay, Zhushan Bai, Gonghu Bay,
Open area, Xukou Bay, and East Lake Taihu (Fig. 1).
2.2. Measurement of bio-optical parameters
Chlorophyll a (Chla) concentration was used to assess the performance of our proposed TSM estimation model. Optical properties of
Lake Taihu could be affected by Chla due to its absorption and scattering
(Shi, Li, et al., 2013), and therefore, Chla may have some impacts on
the performance of our proposed TSM estimation model. We used
Whatman GF/F fiberglass filters (an averaged pore size of 0.7 μm) to filter water samples. Chla were extracted using 90% ethanol at 80 °C from
the filtered fiberglass and spectrophotometrically analyzed to measure
the absorption densities at 750 nm and 665 nm. The Chla content
could be calculated from the absorption densities at the two wavelengths (Jespersen & Christoffersen, 1987). The in situ TSM was obtained by filtering water samples (100–500 ml according to the amount of
particles) using 0.7 μm pore size-Whatman GF/F fiberglass filters
which had been pre-combusted at 550 °C for four hours; the filters
were subsequently dried at 105 °C and weighed using an electrobalance
with an accuracy of 0.01 mg.
Using a Li-Cor 192SA underwater quantum sensor (Li-cor, Inc.,
Lincoln, NE) connected to a Li-Cor 1400 data logger (http://www.licor.
com), the downwelling photosynthetically active radiation (PAR)
Table 1
Information about the sampling cruises.
Sampling period
Number of samples
Measurements
2003–2004 (monthly)
2005–2013 (monthly)
November 11–21, 2007
November 10–21, 2008
May 12, 2010
May 5–14, 2013
August 7–8, 2013
272
2160
5
14
7
26
10
TSM, SDD
TSM, SDD, Kd(PAR)
Rrs
Rrs
Rrs
Rrs
Rrs
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
45
Fig. 1. Distributions of sampling sites. (a) indicates the monthly cruises from 2003 to 2013; THL00–THL09 sites are for odd-numbered months from 2003–2004 and THL00–THL13 sites are
for even-numbered months during these years; sites THL00–THL17 (except sites THL02, THL09, THL11–12, and THL15) are for the second two months of each season from 2005–2013, and
sites THLL00–THLL32 (except THLL02) are for the last month of each season during these years. (b) Indicates the second source of the in situ data set used in this study.
measurements were collected just below the water surface (0 m) and at
six additional depths (0.2, 0.5, 0.75, 1.0, 1.5, and 2.0 m) on the sunny
side of the boat. The Li-Cor 192SA was installed in a stabilizing frame
that assured the proper orientation of the sensor to minimize shading
effects.
Using the instantaneous mode of the Li-1400 data logger, which was
configured to display 15-second running averages of 1-second measurements, three PAR values were recorded for each depth at 1-minute intervals and the mean value was considered the PAR intensity for that
depth. The diffuse attenuation coefficient of PAR (Kd(PAR)) could be
calculated from nonlinear regression of the underwater irradiance profile (cf. Shi, Zhang, Liu, Wang, & Qin, 2014). Only Kd(PAR) values calculated from regressions with R2 ≥ 0.99 were accepted. We used a
standard 30-cm diameter Secchi disk to measure the Secchi disk depth
(SDD).
2.3. Remote sensing reflectance measurement
We used an ASD (Analytical Spectral Devices) field spectrometer
(Analytical Devices, Inc., Boulder, CO) to measure the total downwelling
and upwelling radiance. The instrument had a viewing field of 25° and a
sensitivity range from 350 to 1050 nm in 1.58 nm increments with 512
wavelengths. The “above-water method” was used to measure the spectra from the water surface (Tang, Tiang, Wang, Wang, & Song, 2004). An
optical fiber was positioned at a height of approximately 0.3 m above
the water surface and each spectrum was sampled 90° azimuthally
from the Sun and at a nadir viewing angle of 40°. The radiance spectra
of the reference panel, the water, and the sky were measured ten
times for each site. The spectra at each sampling site were averaged to
obtain Rrs(λ). In the process of extracting Rrs(λ), the skylight reflectance
at the air–water surface was 2.2% for calm weather, 2.5% for wind
speeds of up to 5 m/s, and 2.6%–2.8% for wind speeds of approximately
10 m/s, and the reflectance of the gray panel was accurately calibrated
to 30% (Mobley, 1994).
2.4. Remote sensing data and meteorological data
Remote sensing data collected by MODIS-Aqua were used to achieve
sufficient spatial and temporal coverage of Lake Taihu. The MODIS-Aqua
data were acquired at high frequency (1 image per day) since 2002 and
are freely available. These data have a maximum spatial resolution of
250 nm (the first two bands) and allow for the monitoring of Lake
Taihu over multi-annual time periods at medium-resolution. The
MODIS-Aqua level 0 data (raw digital counts) from 2003 to 2013 were
obtained from the U.S. NASA Goddard Space Flight Center (GSFC,
http://oceancolor.gsfc.nasa.gov/). We used SeaDAS 6.0 to produce the
calibrated at-sensor radiance from the level 0 MODIS-Aqua data. There
were more than 4000 data granules covering this study region from
January 2003 to December 2013. Among these granules, 1109 scenes
with high quality were selected after visual examination to exclude
those significantly affected by clouds, sun glint, and thick aerosols.
Fig. 2 gives the temporal distribution of the MODIS-Aqua scenes used
in this study. Overall, there was at least one image in any given month
to assure that monthly changes in the TSM could be captured.
The monthly mean wind speed data from 2003 to 2013 were acquired from the nearest meteorological station to Lake Taihu (Dongshan
meteorological station, Fig. 1). The data were downloaded from the
China Meteorological Data Sharing Service System (http://cdc.c.a.gov.
cn/). A detailed introduction to these data and the Dongshan meteorological station can be found in our previous studies (Shi et al., 2014;
Zhang, Shi, et al., 2014).
2.5. Atmospheric correction method
Traditional methods for atmospheric correction of the MODIS data
collected over the water surface are based on the assumption that the
water reflectance in the near-infrared bands can be considered zero
(Wang, Son, Zhang, & Shi, 2013). However, this method may lead to
large errors for optically complex inland waters, especially for extremely turbid waters because reflectance in the near-infrared bands is much
greater than zero (Wang et al., 2011). An approach for atmospheric correction for the MERIS data collected over land was developed based on
dense vegetation targets by Guanter, Gonzalez-Sanpedro, and Moreno
(2007). The application of this approach to retrieving aerosol loading
and water reflectance data from the MERIS data over optically complex
inland waters was previously tested (Guanter et al., 2010). Calculating
the reflectance of close-to-land water pixels through spatial extension
of atmospheric parameters derived over neighboring land pixels can
46
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
Fig. 2. Temporal distributions of the MODIS-Aqua scenes used in this study.
avoid the spectral response of the water constituents (Guanter et al.,
2010). Thus, we selected this atmospheric method, which was originally
developed for land, to derive the water reflectance from the MODISAqua data over Lake Taihu.
Several previous studies (Guanter et al., 2010; Levy, Remer, Mattoo,
Vermote, & Kaufman, 2007) have indicated that the surface reflectance
of dense vegetation at 470 nm (close to 469 nm) is significantly correlated with the reflectance from the top of atmosphere of dense vegetation
at shortwave infrared bands (SWIR) (1240 and 2200 nm). This is because shortwave infrared bands can be free of aerosol effects. Based on
these relationships, the aerosol optical thickness (AOT) at 470 nm and
its contributions to the reflectance from the top of the atmosphere
could be extracted using a radiative transfer model (Guanter et al.,
2010). Finally, the water surface reflectance could be derived by
subtracting the contributions of aerosol from the total reflectance
from the top of the atmosphere. Several steps were needed for this approach in performing the atmospheric correction of the MODIS-Aqua
data over Lake Taihu: (1) the selection of dense dark vegetation targets,
(2) the calculation of the surface reflectance at 469 nm, (3) an inversion
of the aerosol optical depth at 550 nm (AOD550), and (4) the retrieval of
the water Rrs(λ) from the MODIS-Aqua data (Guanter, Gomez-Chova, &
Moreno, 2008; Guanter et al., 2007, 2010; Levy et al., 2007).
2.6. Comparison of satellite and in situ data
Ideally, the satellite data and in situ data should be concurrent within a period determined by the hourly variation of the process being
measured. We set the criterion for matching the satellite data and the
in situ observations to ≤3 h (the time interval between the in situ measurements and their corresponding MODIS-Aqua data) to minimize the
effects of the temporal difference between the in situ and MODIS-Aqua
measurements. Among all the collected samples, our criterion produced
300 in situ TSM measurements-satellite derived Rrs and 62 in situ Rrs
-satellite derived Rrs “matches” of data, i.e., data pairs of the MODISAqua images and the in situ data collected represented by the same
pixel. It should be noted that the match between the MODIS-Aqua derived data and the Rrs(645) value measured in situ were used for
assessing the performance of the atmospheric correction method.
mathematical functions. To further understand the applicability of the
model to estimating the TSM from the MODIS-Aqua data, we assessed
the model's performance using the independent validation dataset
consisting of 150 in situ TSM measurements. To validate the model,
we used values for the in situ measurements of TSM ranging from
13.9 mg/L to 301.3 mg/L, with an average of 48.9 mg/L (S.D. =
36.4 mg/L). The MODIS-Aqua measurements from 2003 to 2013 for
Lake Taihu were acquired to generate the water remote sensing reflectance using the atmospheric correction method mentioned in
Section 2.5. These satellite image data were then used to derive spatial
and temporal TSM distributions using the developed model from
which the monthly mean, seasonal mean and annual mean TSM products for Lake Taihu were produced.
2.8. Statistical analysis and accuracy assessment
Statistical analyses that included calculations of the average, maximum, and minimum values and linear and non-linear regressions
were performed using the SPSS 17.0 software (Statistical Program for
Social Sciences). Correlation analysis was used to investigate the relationships between the variables. Significance levels were reported to
be significant (p b 0.05) or not significant (p N 0.05).
The accuracy of the algorithms was assessed by calculating the relative error (RE), the mean absolute percent error (MAPE), and the rootmean-square error (RMSE) between the measured and predicted values
(Shi et al., 2014).
3. Results
3.1. Water optical and biogeochemical conditions
Overall, the concentrations of the water constituents measured from
each sampling site in Lake Taihu presented large spatial and temporal
variability (Fig. 3 and Table 2). The average TSM during the sampling period was 54.2 mg/L (S.D. = 47.7 mg/L), with a maximum of 343.9 mg/L
2.7. Development and validation of the TSM model
We randomly divided the 300 TSM-satellite derived Rrs “matches”
of the data into two parts: 150 pairs were used to develop a TSM estimation model, and 150 pairs were used to validate the model. The calibration dataset contained 150 samples, with the in situ measurements for
TSM ranging from 15.8 mg/L to 218.6 mg/L and averaging 47.6 mg/L
((S.D. (Standard deviation)) = 26.7 mg/L). In this study, we tried
to use the five types of mathematical functions (linear, logarithmic, exponential, power law, and quadratic) to express the relationships between the MODIS-Aqua derived Rrs(645) and the TSM. The optimized
model could be determined by assessing the performance of these
Fig. 3. Frequency distribution and cumulative percentages of TSM measured in situ in Lake
Taihu from 2003–2013.
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
Table 2
Statistics of water quality and optical parameters in Lake Taihu from the in situ data gathered during 2003 and 2013.
Spring
Summer
Autumn
Winter
Overall
Statistics
TSM (mg/L)
Chla (ug/L)
SDD (m)
Kd(PAR) (m−1)
Minimum
Maximum
Mean
S.D.
Minimum
Maximum
Mean
S.D.
Minimum
Maximum
Mean
S.D.
Minimum
Maximum
Mean
S.D.
Minimum
Maximum
Mean
S.D.
2.7
325.9
58.4
47.4
1.7
343.9
50.4
39.9
1.8
299.8
49.7
39.2
3.3
311.4
56.8
50.7
1.7
343.9
54.2
47.7
0.6
189.7
14.9
19.0
0.9
491.2
33.5
46.1
0.8
527.3
28.0
48.1
1.2
70.3
10.4
9.2
0.6
527.3
23.3
87.5
0.0
2.5
0.4
0.3
0.0
2.4
0.4
0.3
0.0
2.1
0.4
0.3
0.1
1.7
0.4
0.2
0.0
2.5
0.4
0.3
0.8
17.1
4.7
2.8
0.7
17.7
4.4
2.4
0.7
21.5
4.5
2.7
0.9
18.4
4.8
3.1
0.7
21.5
4.6
2.8
and a minimum of 1.7 mg/L (Fig. 3 and Table 2). Of all the samples, over
60% of the samples had TSM values of more than 30 mg/L (Fig. 3).
Generally, the average TSM values in the spring (58.4 mg/L, S.D. =
47.4 mg/L) and winter (56.8 mg/L, S.D. = 50.7 mg/L) were larger
than those in the autumn (49.7 mg/L, S.D. = 39.2 mg/L) and summer
(50.4 mg/L, S.D. = 39.9 mg/L). The average values over the four seasons
are considerably higher for Lake Taihu than for other bodies of water
(Park & Latrubesse, 2014; Zhang, Dong, et al., 2014), demonstrating
that the sediments in this large shallow lake are vulnerable to suspension, resulting in relatively high concentrations of TSM in the water
column. Thus, Lake Taihu could be considered an extremely turbid inland water.
Fig. 4. MAPE of MODIS-Aqua atmospheric correction at 412 nm, 443 nm, 469 nm, 488 nm,
531 nm, 555 nm, 645 nm and 859 nm (a), and the comparison between the Rrs(645) measured in situ and derived from the MODIS-Aqua data (b).
47
The concentration of Chla exhibited a very wide range of 0.6 to
527.3 μg/L, with an average of 23.3 μg/L (S.D. = 87.5 μg/L). The highest
average Chla value was reasonably found in the summer (33.5 μg/L,
S.D. = 46.1 μg/L), whereas the lowest average value was observed in
the winter (10.4 μg/L, S.D. = 9.2 μg/L). The highest average Chla value
in Lake Taihu corresponded to strong phytoplankton blooms, occurring
in the summer (Hu et al., 2010; Shi et al., 2014). The high Chla level in
Lake Taihu indicated that this water was under hypertrophic conditions
(Qin et al., 2007). Both Kd(PAR) and SDD showed large variations in Lake
Taihu: Kd(PAR) ranged from 0.7 m−1 to 21.5 m−1, with an average of
4.6 m−1 (S.D.−2.8 m−1), and SDD ranged from 0 m to 2.5 m, with an
average of 0.4 m (S.D. = 0.3 m). The low SDD and high Kd(PAR) values
were characteristics of a typical shallow and extremely turbid inland
body of water, where the high TSM induced by sediment resuspension
noticeably increased the light attenuation and decreased water clarity.
There exhibited strong correlation between TSM and Kd(PAR) (R2 =
0.79; p b 0.001), and between TSM and SDD (R2 = 0.73; p b 0.001),
suggesting a key role of TSM in the optical environment in Lake Taihu.
3.2. Rrs(645) derived from MODIS-Aqua data compared with in
situ measurements
The accuracy of the atmospheric correction was evaluated through
the comparison of the MODIS-Aqua derived and in situ measurements
of Rrs(λ) (Fig. 4). These matches were spectrally resampled using the
spectral response function of the MODIS bands.
Overall, the atmospheric correction performed well for Lake Taihu.
Fig. 4 shows the relative errors between the MODIS-Aqua derived and
in situ measurement of Rrs(λ) at the selected seven MODIS-Aqua
Fig. 5. Calibration (a) and validation (b) of the proposed model for estimating TSM in turbid Lake Taihu. Rrs(645) was derived from the MODIS-Aqua data using atmospheric
correction.
48
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
bands, including the in situ Rrs(λ) data measured in Lake Taihu for five
different time periods: November 11–21, 2007 (11 measurements), November 10–21 (14 measurements), 2008, May 12, 2010 (7 measurements), May 5–14, 2013 (26 measurements), and August 7–8, 2013
(10 measurements). The relative errors at 412 nm, 443 nm, 488 nm,
531 nm, 555 nm, and 645 nm were less than 30%; the largest relative
error appeared at 859 nm (N90%), whereas the lowest relative error
was found at 645 nm (b12%, RMSE = 0.004). Comparisons between
the MODIS-Aqua derived and the in situ measured Rrs(645) data showed
that these values were in good agreement, with a highly significant linear
relationship (R2 = 0.77; p b 0.001) (Fig. 4). The MODIS-Aqua derived and
in situ measured Rrs(645) values were evenly distributed along the 1:1
line (Fig. 4). These results indicated that the atmospheric correction
method is only suitable for wavelengths ≥ 645 nm. For shorter wavelengths, the method requires improvement. In summary, the validation
results demonstrated that the atmospheric correction method based on
land targets can produce reasonably good water Rrs(λ) data for inland
Lake Taihu from the MODIS-Aqua data. The best performance of the atmospheric correction method at 645 nm is encouraging and clearly demonstrates that the MODIS-Aqua derived Rrs(645) data could be used for
remotely sensed estimations of the concentrations of water constituents,
such as the TSM (Doxaran et al., 2014; Wang et al., 2009, 2011; Zhang,
Dong, et al., 2014).
3.3. Model development and validation
Correlation analysis showed that there was a significant positive correlation between the in situ measured TSM and the MODIS-Aqua derived
Rrs(λ). The MODIS-Aqua derived Rrs(645) exhibited a strong correlation
with the in situ TSM measurements (R2 = 0.70; p b 0.001). The noticeably high correlation between the in situ measurement of TSM and the
MODIS-Aqua derived Rrs(645) was similar to the findings of numerous
previous studies (Feng et al., 2014; Miller & McKee, 2004; Ondrusek
et al., 2012; Wang et al., 2011, 2013; Zhang, Dong, et al., 2014). Among
the five types of mathematical functions (linear, logarithmic, exponential, power law, and quadratic) we used to establish the relationships between MODIS-Aqua derived Rrs(645) data and the in situ TSM calibration
dataset, the exponential function gave the best precision with the
highest determination coefficient (R2 = 0.70; p b 0.001) and the lowest
MAPE (23%) and RMSE (14.3 mg/L) (Fig. 5). The developed model based
on the MODIS-Aqua derived Rrs(645) data were defined by Eq. (1):
TSM ¼ 9:65 exp½58:81 ðRrs ð645Þ:
ð1Þ
The proposed model performed well in the validation dataset
(Fig. 5). The RE of the proposed model for the validation dataset varied
from 0.3% to 65%, with a MAPE value of 24.6% (RMSE = 14.0 mg/L). The
Fig. 6. Maps of the MODIS-Aqua derived TSM for four seasons in Lake Taihu generated using the proposed model.
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
relative errors of 46% and 83% for the validation samples used in the proposed model were below 20% and below 40%, respectively. Comparisons between the TSM measured in situ and the TSM estimated using
the model based on the MODIS-Aqua data proposed in this study
49
showed that these values were in good agreement, with a highly significant linear relationship (R2 = 0.80; p b 0.001) (Fig. 5). The in situ and
estimated TSM values were evenly distributed along the 1:1 line
(Fig. 5). These results indicated that the proposed model based on the
Fig. 7. MODIS-Aqua derived (2003–2013) monthly mean TSM in Lake Taihu from January to December.
50
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
MODIS-Aqua derived Rrs(645) data could be used with satisfactory performance to retrieve the TSM for the validation dataset in this extremely
turbid inland water. The relationships between the validation RE of the
model and Chla and between the RE and Chla/TSM were determined
and used to examine the factors affecting the model performance.
There was no statistically significant relationships between the RE and
Chla (R2 = 0.02), or between the RE and Chla/TSM (R2 = 0.002), indicating that the model performance was not sensitive to these factors.
In summary, a TSM retrieval model based on MODIS-Aqua was
established and validated for Lake Taihu, exhibiting good performance.
The proposed model could have satisfactory performance in the extremely turbid waters of other similar shallow turbid lakes (Volpe,
Silvestri, & Marani, 2011). Thus, our proposed model could be used to
map the long-term TSM distribution patterns for Lake Taihu from all
available MODIS-Aqua image data collected from 2003 and 2013, facilitating the study of the spatial and temporal changes of TSM in this water.
3.4. Temporal variations of TSM
3.4.1. Seasonal variations
Figs. 6 and 7 respectively provide the seasonal and monthly means
of the MODIS-Aqua derived TSM products in Lake Taihu using our
proposed model. Generally, the waters in Lake Taihu were characterized
by high TSM in all four seasons. There was strong seasonal variability in
TSM over the entire lake. Overall, the waters in the entire lake were
more turbid (i.e., high TSM) in the spring (March to May) and winter
(December to February) seasons than in the summer (June to August)
and autumn (September to November) seasons. The TSM values significantly increased in spring compared to autumn (p b 0.005, t-test), and
the mean values in the entire lake for spring and autumn were
61.8 mg/L and 47.5 mg/L, respectively. The highest mean TSM value
(65.6 mg/L) for the entire lake was found in March, whereas the lowest
mean TSM value (44.1 mg/L) appeared in September. These results are
in agreement with the findings in the study by Wang et al., 2011, which
demonstrated that the Kd(490) derived from the MODIS-Aqua data
(diffuse attenuation coefficient at 490 nm, an indicator of TSM) for
Lake Taihu in the spring and winter were generally higher than the
Kd(490) value in the summer and autumn.
Time series of the seasonal averages of the MODIS-Aqua derived
TSM values from 2003 to 2013 were constructed for the six regions
(Meiliang Bay, Open area, Xukou Bay, Zhushan Bay, Gonghu Bay, and
East Lake Taihu) and the entire lake by spatially and temporally averaging all valid pixels over the water for each region (Fig. 8). In the spatial
distribution, the highest TSM values appeared in the Open area,
Fig. 8. MODIS-Aqua derived seasonal time series (2003–2013) of TSM for the regions of (a) Meiliang Bay, (b) Zhushan Bay, (c) Open area, (d) Gonghu Bay, (e) Xukou Bay, (f) East Lake
Taihu, and (g) the entire lake.
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
especially in the southern part of this region, while lower values were
found in Meilang Bay, Zhushan Bay, Xukou Bay, Gonghu Bay and particularly East Lake Taihu during all seasons. The seasonal patterns of the
51
MODIS-Aqua derived TSM distribution for the Open area, East Lake
Taihu, Xukou Bay, and Gonghu Bay were generally similar to those of
the entire lake, showing higher values in the winter and spring seasons
Fig. 9. Annual mean TSM distributions of Lake Taihu from 2003 to 2013.
52
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
and lower values in the summer and autumn seasons. Some interesting
characteristics are shown in the seasonal pattern of MODIS-Aqua derived TSM for Meiliang Bay and Zhushan Bay, where the waters were
dominated by algae particles. In fact, these two regions have different
seasonal patterns of TSM; in particular, the two regions have opposite
highs in TSM variations. For Meiliang Bay and Zhushan Bay, peak concentrations of TSM appeared in the summer and autumn seasons,
while the peak concentrations of TSM for the other four regions in
Lake Taihu occurred in the spring and winter seasons, reflecting the effects of algae on TSM variations in Meiliang Bay and Zhushan Bay. This is
because the primary mechanisms for significant TSM seasonal variations are different: the TSM in the waters of Meiliang Bay and Zhushan
Bay were dominated by phytoplankton and thus the phytoplankton biomass was a main factor controlling the TSM variations; by contrast, the
main contributor to the TSM in the other four regions was windinduced sediment re-suspension. Peaks in the amount of phytoplankton
biomass in Meiliang Bay and Zhushan Bay generally appeared in the
summer and autumn seasons (Hu et al., 2010; Wang et al., 2011,
2013), eventually leading to a high TSM in the summer and autumn seasons for these two regions.
3.4.2. Inter-annual variations
The annual means of the MODIS-Aqua derived TSM maps for Lake
Taihu are presented in Fig. 9, demonstrating the inter-annual changes
in the TSM from 2003 to 2013. The lowest annual mean TSM value was
48.5 mg/L in 2003 and the highest value was 57.1 mg/L in 2005, revealing that Lake Taihu experienced moderate spatial and inter-annual variations in the TSM. The TSM in Lake Taihu from 2003 to 2013 experienced
three markedly different inter-annual variations: TSM simply increased
from 2003 to 2005, decreased sharply in 2006 to 2008, and then started
to increase from 2009 to 2013 (Fig. 10). Lower wind speeds in 2006 to
2008 than usual (Hu et al., 2010) resulted in the significantly lower
TSM values in Lake Taihu from 2006 to 2008 than in the periods of
2003 to 2005 and 2009 to 2013 (t-test, p b 0.005). The low TSM could result in more light penetration into the water column and thus was conducive to cyanobacteria growth in Lake Taihu. The time-series presented
both significant spatial and inter-annual variability from 2003 to 2013
(t-test, p b 0.005) (Fig. 10). Overall, the TSM in the entire lake varied
widely, from 11.7 mg/L in October, 2006 to 108 mg/L in December,
2012, with a long-term mean of 54.1 mg/L (S.D. = 19.2 mg/L).
The TSM in East Lake Taihu was generally the lowest among the six
regions from 2003 to 2006; however, from 2007 to 2013, Zhushan
Bay was the region with the lowest TSM. The TSM in the Open area
was consistently higher than in other five regions, ranging from
10.7 mg/L in January, 2003 to 131.9 mg/L in November, 2013, with a
long-term mean of 59.9 mg/L (S.D. = 23.3 mg/L). The long-term mean
values for TSM in Meiliang Bay, Xukou Bay, Zhushan Bay, Gonghu Bay,
and East Lake Taihu were 45.6 mg/L (S.D. = 24.4 mg/L), 46.9 mg/L
(S.D. = 23.3 mg/L), 37.1 mg/L (S.D. = 24.0 mg/L), 44.0 mg/L (S.D. =
21.2 mg/L), and 37.0 mg/L (S.D. = 23.9 mg/L), respectively.
Fig. 11. TSM spatial distribution in Lake Taihu averaged from all TSM estimates from the
MODIS-Aqua data gathered from 2003–2013 (a) and spatial S.D. distribution in Lake
Taihu based on all TSM estimates from MODIS-Aqua data gathered from 2003–2013 (b).
3.5. Spatial distribution characterization of TSM
The MODIS-Aqua derived TSM data from 2003 to 2013 were
averaged to calculate the regional TSM distribution for Lake Taihu
Fig. 10. MODIS-Aqua derived annual mean TSM for the six sub-regions of Lake Taihu and the entire lake.
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
53
(Fig. 11(a)). The TSM was highest in the southern part of the Open area,
followed by the middle part of the Open area. In Meiliang Bay, the TSM
increased from the inner to the outer regions. These results are in agreement with several previous studies (Liu et al., 2013, 2014; Wang et al.,
2013).
The spatial distribution of the S.D. values was also calculated
from the MODIS-Aqua derived TSM values between 2003 and 2013
(Fig. 11(b)). The S.D. of the TSM values ranged from 10 mg/L in East
Lake Taihu to 80 mg/L in the Open area, with an average of 41.3 mg/L,
suggesting a substantial spatial variation in the TSM. The largest S.D.
values were found in the southern parts of the Open area and East
Lake Taihu, whereas the lowest values were found in the northern
part of the Open area, near Meiliang Bay and Xukou Bay. The variations
of the TSM in the southern parts of the Open area and East Lake Taihu
were larger than in the other regions of Lake Taihu.
4. Discussion
4.1. Characteristics of TSM and its role in the water optical environment
from the in situ data
During the sampling periods evaluated in the present study, the in
situ TSM values were high, ranging from 1.7 mg/L to 343.9 mg/L and
averaging 54.2 mg/L (S.D. = 47.7 mg/L). These results were similar to
previous studies in Lake Taihu (Wu et al., 2013; Zhang, Shi, et al.,
2014) and indicate that Lake Taihu is an extremely turbid body
of water. TSM in Lake Taihu was obviously higher than that of
open sea waters (Binding et al., 2008) and slightly turbid estuaries
(Vantrepotte, Loisel, Dessailly, & Mériaux, 2012), though the composition of TSM may differ between Lake Taihu and open sea water.
The TSM in Lake Taihu is dominated by inorganic suspended matter
(Qin et al., 2007), whereas in open sea, the primary TSM component is
phytoplankton particles (Brown, Huot, Werdell, Gentili, & Claustre,
2008). These differences would result in the apparent difference in optical properties between open sea and inland turbid waters, consequently leading to the conclusion that the models developed for
estimating TSM in open sea water are not suitable for turbid inland
waters.
In turbid inland and coastal waters, the TSM, photosynthetic pigments, and colored dissolved organic matter (CDOM) are responsible
for the inherent optical properties of the water, such as total absorption
and scattering (Babin, Morel, Fournier-Sicre, Fell, & Stramski, 2003;
Babin, Stramski, et al., 2003; Zhang et al., 2007). For instance, TSM contributes to the total absorption coefficient at 550 nm in the waters, from
a minimum of less than 27% in Lake Dianchi (China) to a maximum of
approximately 79% in Lake Taihu, where phytoplankton is prevalent
and where TSM originates from wind-induced bottom resuspension
(Shi, Li, et al., 2013). Previous studies of inland and coastal turbid waters
have shown that TSM has noticeable impacts on water transparency,
Kd(PAR), euphotic depth, and remote sensing reflectance that are directly related to the inherent optical properties of water in inland turbid
waters (Wang, Sun, Li, Le, & Huang, 2010; Zhang et al., 2007). For example, the Kd(PAR) for the Florida Bay, USA (a shallow inner shelf lagoon)
was more closely related to the TSM than to the concentration of Chla
(Phlips, Lynch, & Badylak, 1995). The significantly correlated relationships between TSM and SDD and between TSM and Kd(PAR), which
was derived from a large number of field measurements taking in
Lake Taihu, confirmed that TSM plays a key role in Kd(PAR) and SDD
variations in this body of water (Fig. 12). Understanding the relationships between TSM and the optical parameters can facilitate the development of models for estimating these optical parameters from remote
sensing data. After determining the role of TSM in the water optical
properties, the corresponding appropriate models for estimating related
optical parameters such as Kd(PAR) and SDD can be developed using the
TSM model.
Fig. 12. Relationships between TSM and Kd(PAR) (a) and between TSM and SDD (b).
4.2. Factors affecting the spatial and temporal changes of TSM
We have shown that TSM in Lake Taihu has obvious spatial and temporal variations that can be explained by a few environmental factors
and meteorological conditions. With a very large dynamic ratio (the
square root of the surface area divided by the average water depth) of
25.6 km/m (Shi et al., 2014), TSM in Lake Taihu is greatly determined
by wind-induced sediment resuspension. The spatial distribution pattern of TSM in Lake Taihu is affected by several factors that could significantly affect the impact of the wind in this lake, such as the lake
topography and SAV (Zhang, Shi, et al., 2014). In addition, sediment deposited by the major input river is another important factor affecting
the spatial distribution pattern of TSM in Lake Taihu.
To investigate the effect of wind-driven sediment resuspension on
the spatial distribution of TSM in Lake Taihu, we calculated the distance
between each pixel and the lake shore in four directions (N, E, S, and
W) and then performed a correlated analysis between the MODIS derived TSM and the distance. The result (Fig. 13) showed the existence
of a significant positive linear relationship between the TSM and the
sum of the distance to the north and to the east shore, meaning that
the TSM in the western and southern parts of Lake Taihu (corresponding to the Open area) is higher than the TSM in the north and east
parts of the lake. This result is in agreement with our findings that
higher TSM was found in the Open area compared to the other regions
of Lake Taihu. The dominant wind directions in Lake Taihu were found
to be NNW and ESE, resulting in longer wind fetches for north and
west parts of Lake Taihu than for other parts. Longer wind fetches result
in stronger wind forces and thus more sediment resuspension in the
north and west parts of Lake Taihu. In the littoral zones and lake bays,
such as Zhushan Bay and Meiliang Bay, the wind fetches were shorter
than in the Open area, decreasing the power of wind waves and therefore causing the lower TSM in these areas relative to the Open area.
Therefore, wind-driven sediment resuspension expressing using disturbance index could largely explain the spatial variations in the TSM in
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K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
Fig. 13. Relationship of TSM to the distance sum between each pixel to the north and to the
east shore.
Lake Taihu. The findings are in agreement with our previous study
(Zhang, Shi, et al., 2014).
Several studies have suggested that SAV could affect the TSM spatial
distribution in Lake Taihu, with most of the SAV being distributed in
Gonghu Bay, Xukou Bay, and East Lake Taihu (Liu et al., 2013; Qin
et al., 2007; Zhang, Shi, et al., 2014). As shown in Fig. 12(b), the TSM
(b 40 mg/L) in Gonghu Bay, Xukou Bay, and East Lake Taihu was much
lower than that in the Open area (N60 mg/L). The SAV has the functions
of water filtration, water purification, wind-induced wave abatement,
and restraining sediment resuspension (Zhang, Shi, et al., 2014), and
therefore would cause the low TSM in the waters with large amounts
of SAV. Gonghu Bay, Xukou Bay, and East Lake Taihu are three typical
macrophyte-dominated water regions in Lake Taihu, characterized by
abundant submerged vegetation communities and thus have reasonably lower TSM than the Open area.
Extremely high TSM was found in the southern part of the Open
area, which could be explained in part by sediment brought by the
Tiaoxi River (Fig. 12(b)). The Tiaoxi River is the greatest inflowing
river, and its outlet mouth into Lake Taihu is near the southern part of
the Open area. In a single year, the inflowing discharge from the Tiaoxi
River can reach 2.7 billion m3, contributing approximately 60% of the
total inputs from inflowing rivers into Lake Taihu (Liu et al., 2011).
This indicates that a large amount of materials may be brought into
the southern part of the Open area in Lake Taihu via the Tiaoxi River,
which would significantly increase the TSM in this area (Fig. 12(b)).
The impacts of inflowing discharge from the Xiaoxi River on the TSM
in Lake Taihu were particularly obvious after strong rainfall events occurred in this area. For example, Fig. 14 demonstrates the TSM spatial
distribution in Lake Taihu on October 10, 2013, showing that an extremely high amount of TSM (128 mg/L) was found in the southern
part of the Open area, which was highly associated to one strong rainfall
event occurring on October 8, 2013. This strong rainfall event could
markedly increase the runoff discharge from the Tiaoxi River and thus
lead to the extremely high TSM in the southern part of the Open area.
It should be noted that the input of sediments from the Tiaoxi River
seem to influence the TSM only in the southern part of the Open area,
but has a minimal or negligible impact in the other regions of Lake
Taihu.
Clearly, lake topography, SAV, and inflowing rivers are the three
most important factors determining the TSM spatial variations in Lake
Taihu, though the impacts of lake topography and SAV on the spatial
distribution of TSM arise through manipulating wind force distributions
by these two factors. Of course, other factors such as physical properties
and the composition of bottom sediment may also affect the TSM spatial
distribution in Lake Taihu. However, these factors were not discussed in
this study due to a lack of studies regarding the physical properties and
composition of the bottom sediment in Lake Taihu. These will be further
investigated in a future study.
Fig. 14. MODIS-Aqua derived TSM spatial distribution in Lake Taihu on October 10, 2013
after a strong rainfall event occurring in this area.
Many previous studies have suggested that winds are the most important factor affecting the temporal variations of TSM through sediment resuspension in large and shallow lakes and estuaries (Chen
et al., 2007; Feng et al., 2014). In Tampa Bay (USA), the stronger wind
speed resulted in higher TSM resuspension, leading to higher turbidity
in the dry season and especially during March–April (Chen et al.,
2007). The significant seasonal fluctuations in the TSM of Lake Taihu
can also be attributed to seasonal variations of wind force or winddriven sediment resuspension (Zhang, Shi, et al., 2014). The strongest
and weakest wind forces in Lake Taihu were found in the spring and autumn seasons, respectively (Shi et al., 2014). Stronger winds led to more
sediment resuspension in the spring season, which would lead to a
higher TSM in the spring than in the other seasons. The MODIS-Aqua derived TSM images showed that Lake Taihu experienced significant interannual variations in TSM. The observed inter-annual variations in the
TSM of Lake Taihu are consistent with changes in wind force (Fig. 15),
and a lower TSM coincided with lower wind speeds. The TSM in Lake
Taihu from 2006–2008 was relatively lower than in other years, which
could be explained by the slightly lower mean wind speed during this
period compared to other years (Fig. 15).
4.3. Implications for Lake Taihu water quality monitoring and
ecosystem restoration
Regrettably, in the past, the significantly major challenge in satellite
remote sensing of water quality of Lake Taihu, a large and shallow turbid
Lake, was the lack of a reliable model to relate the satellite signal to the
water quality parameters. The atmospheric correction method used in
SeaDAS is not suitable for turbid inland waters as the water-leaving reflectance in the near-infrared bands (MODIS-Aqua 748 nm and 869 nm
bands) is significantly more than zero (Li et al., 2012). An iterative atmospheric correction method based on the assumption of black water in
SWIR at 1240 nm, 1640 nm, and 2130 nm developed by Wang et al.
(2011) could work for this lake. This method uses a large amount of
black strips that appear in the MODIS 1640 nm band, meaning that it
is impossible to obtain TSM data in the black strips of the daily images.
Our results indicated that the atmospheric correction method based on
land performed well for the MODIS-Aqua data acquired from this turbid
lake, especially in the MODIS-Aqua 645 nm band that has been widely
used to measure the TSM from turbid inland and coastal waters (Feng
et al., 2014; Miller & McKee, 2004; Petus et al., 2014).
K. Shi et al. / Remote Sensing of Environment 164 (2015) 43–56
55
Fig. 15. Monthly mean MODIS-Aqua derived TSM and wind speeds measured at Dongshan meteorological station from 2003–2013.
For the first time, marked temporal and spatial variations in the TSM
of Lake Taihu and their driving factors were clearly revealed and qualified via the rigorous analysis of an 11-year MODIS-Aqua time series
used in this study. It is difficult to characterize these TSM variations
using traditional ship-based field surveys due to the size and complex
water environments of Lake Taihu. Overall, the success of this effort
can be attributed to three factors: (I) the frequent and synoptic available
coverage of the MODIS-Aqua images at 250 m spatial resolution for Lake
Taihu; (II) a valid atmospheric correction that led to the validated TSM
estimation model for a large, dynamic TSM range; and (III) the sufficient
number of in situ measurements that allowed for the successful development of the TSM estimation.
Our study clearly suggested that the temporal and spatial variations
in TSM were controlled by one external factor (discharge sediment from
inflowing rivers), and two internal factors (lake topography and SAV)
that are related to winds or wind-induced sediment resuspension.
Thus, to fully understand the variations in TSM, both the sediment
load from inflowing river runoff and wind-driven resuspension events
need to be considered in developing an optimal management plan for
Lake Taihu. Understanding the impacts of river runoff on TSM in Lake
Taihu has an important significance for the assessment of the role of external pollution sources brought by river inflow into Lake Taihu, because
inflowing rivers can introduce a large amount of sediment containing
high amounts of nutrients and other pollutants into Lake Taihu. On
the other hand, SAV could improve the water quality by providing nutrient uptake and decreasing the TSM. Thus, exploring the relationship between TSM and SAV distributions is very important for ecosystem
restoration efforts in Lake Taihu that rely on an aquatic vegetation
planting method. The long-term TSM monitoring provided in this
study will allow us to acquire enough data to study this relationship.
The findings and the approach in this study demonstrated significant
implications for the long-term monitoring of TSM in Lake Taihu. Not
only can these results help management decision-making, they can be
used for assessing management effectiveness. This is particularly important in a changing climate, as it is usually difficult to investigate the
causal factors leading to environmental changes without continuous
and long-term assessment. Therefore, we recommend that future Lake
Taihu monitoring plans include satellite water color remote sensing to
aid the interpretation of spatial and temporal patterns of some important water quality parameters, such as SDD and Chla.
5. Summary and conclusions
This study addressed two technical challenges in documenting and
understanding the long-term TSM distribution variations in Lake
Taihu, from 2003 to 2013. The technical challenges are that (a) there
isn't a good atmospheric correction for inland waters and (b) there
isn't a good empirical model for TSM in Lake Taihu. The goal of this
study was to address these issues, which was achieved by (I) the application of an atmospheric correction to the MODIS-Aqua medium-
resolution data and (II) the construction of a TSM estimation model
for deriving the temporal and spatial distribution patterns of TSM.
A customized MODIS-Aqua 645 nm band based model was firstly
developed and validated to retrieve the concentration of TSM in Lake
Taihu. The model was then implemented to derive the spatial and temporal TSM distribution patterns in Lake Taihu and to quantify their daily,
seasonal and inter-annual variations from 2003 to 2013. First, marked
temporal and spatial variations were found in this study. The significant
seasonal variability between the summer and autumn seasons (lower
TSM) and the spring and winter seasons (higher TSM) could be attributed to changes in the wind speeds between the different seasons. Lake
Taihu also experiences large inter-annual variations that are primarily
caused by the changes in wind force over the region. In particular, the
TSM in Lake Taihu from 2006–2008 was relatively lower than in the
other years, which could be explained by the slightly lower mean
wind speed during these years compared to the other years. Spatially,
TSM in the Open area, especially in the southern part of this region,
was consistently higher than in other sub-regions of Lake Taihu. Lake topographic conditions, SAV, and runoff discharge from the Tiaoxi River
contributed to the spatial variations in TSM.
The experience learned from this study could be used in the next
generation of satellite data, such as the Sentinel-3 OLCI (Ocean Land
Color Imager) data, to derive TSM spatial and temporal distributions
in Lake Taihu. The characteristics of the upcoming Sentinel-3 OLCI are
similar to those of MERIS (e.g., spatial full resolution 300 m), with numerous improvements (21 bands, high SNR, and shorter revisit time).
The Sentinel-3 OLCI bands are optimized to measure water color over
the open ocean, coastal zones, and inland waters. Up to eleven bands
(central wavelength N 700 nm) are appropriate for developing TSM estimation models, which hopefully demonstrate new potentials and
challenges for assessing TSM variations in Lake Taihu and other turbid
inland waters.
Acknowledgments
This study was supported by grants from the National Natural Science Foundation of China (Nos. 41325001, 41301376 and 41271355),
the Key Program Nanjing Institute of Geography and Limnology,
the Chinese Academy of Sciences (No. NIGLAS2012135003), and the
Provincial Natural Science Foundation of Jiangsu in China (Nos.
BK2012050 and BK20141515). The authors thank all the members of
Taihu Lake Laboratory Ecosystem Research Station (TLLER) for their
participation in the field experiments.
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