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remote sensing
Article
Application of Sentinel 2 MSI Images to Retrieve
Suspended Particulate Matter Concentrations in
Poyang Lake
Huizeng Liu 1,2 ID , Qingquan Li 2, Tiezhu Shi 2,3, Shuibo Hu 2,3, Guofeng Wu 2, 3, *ID
and Qiming Zhou 1, *ID
1
Department of Geography, Hong Kong Baptist University, Hong Kong, China; huizengliu@life.hkbu.edu.hk
2Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of
Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and
Services, Shenzhen University, Shenzhen 518060, China; liqq@szu.edu.cn (Q.L.); tiezhushi@szu.edu.cn (T.S.);
hsb514@163.com (S.H.)
3College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
*Correspondence: guofeng.wu@szu.edu.cn (G.W.); qiming@hkbu.edu.hk (Q.Z.)
Received: 27 May 2017; Accepted: 19 July 2017; Published: 23 July 2017
Abstract:
Suspended particulate matter (SPM) is one of the dominant water constituents in inland
and coastal waters, and SPM concnetration (C
SPM
) is a key parameter describing water quality. This
study, using in-situ spectral and C
SPM
measurements as well as Sentinel 2 Multispectral Imager
(MSI) images, aimed to develop C
SPM
retrieval models and further to estimate the C
SPM
values of
Poyang Lake, China. Sixty-eight in-situ hyperspectral measurements and relative spectral response
function were applied to simulate Sentinel 2 MIS spectra. Thirty-four samples were used to calibrate
and the left samples were used to validate C
SPM
retrieval models, respectively. The developed
models were then applied to two Sentinel 2 MSI images captured in wet and dry seasons, and the
derived C
SPM
values were compared with those derived from MODIS B1 (
λ
= 645 nm). Results
showed that the Sentinel 2 MSI B4–B8b models achieved acceptable to high fitting accuracies, which
explained 81–93% of the variation of C
SPM
. The validation results also showed the reliability of these
six models, and the estimated C
SPM
explained 77–93% of the variation of measured C
SPM
with the
mean absolute percentage error (MAPE) ranging from 36.87% to 21.54%. Among those, a model
based on B7 (
λ
= 783 nm) appeared to be the most accurate one. The Sentinel 2 MSI-derived C
SPM
values were generally consistent in spatial distribution and magnitude with those derived from
MODIS. The C
SPM
derived from Sentinel 2 MSI B7 showed the highest consistency with MODIS on
15 August 2016
, while the Sentinel 2 MSI B4 (
λ
= 665 nm) produced the highest consistency with
MODIS on
2 April 2017
. Overall, this study demonstrated the applicability of Sentinel 2 MSI for
C
SPM
retrieval in Poyang Lake, and the Sentinel 2 MSI B4 and B7 are recommended for low and high
loadings of SPM, respectively.
Keywords:
suspended particulate matter (SPM); water quality; sentinel 2 MSI; Poyang Lake;
remote sensing
1. Introduction
In water color remote sensing, case-II waters are defined as waters whose optical properties are
significantly influenced by suspended particulate matter (SPM), phytoplankton, and colored dissolved
organic matter (CDOM), and SPM and CDOM concentrations do not covary with phytoplankton [
1
].
Most of inland and coastal waters belong to case-II waters. These waters provide various benefits to
society through commerce, aesthetics, tourism, recreation, and biodiversity conservation, which are
Remote Sens. 2017,9, 761; doi:10.3390/rs9070761 www.mdpi.com/journal/remotesensing
Remote Sens. 2017,9, 761 2 of 19
greatly affected by water quality [
2
–
5
]. SPM is one of the main constituents and optically active matters
in sediment-laden case-II waters. Moreover, SPM concentration (C
SPM
) is a key parameter describing
water quality because of the impacts of SPM on the light transmission, accumulation, and transportation
of nutrients and contaminants in water bodies, and further on an aquatic ecosystem [6–8].
Both natural forces and anthropogenic activities may influence the distribution pattern of SPM,
making the C
SPM
values temporally dynamic and spatially heterogeneous [
9
–
11
]. Therefore, obtaining
the information of C
SPM
at high spatiotemporal resolution is necessary for understanding water
quality dynamics and identifying their driving forces, and further for managing and protecting
aquatic ecosystems [
12
]. In-situ sampling and laboratory analysis can provide accurate measurements;
however, field-based analyses are costly and labor-intensive, and may fail to capture detailed spatial
variation. On the contrary, satellite remote sensing can provide synoptic observations from visible to
near infrared (NIR) spectral regions, which can be used to derive the CSPM values in water.
The spatial, temporal, spectral, and radiometric resolutions of a sensor system determine its
capability in water color remote sensing [
5
]. Ocean color sensors, such as Coastal Zone Color
Scanner (CZCS), Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate-Resolution Imaging
Spectroradiometer (MODIS), and Geostationary Ocean Color Imager (GOCI), have advantages in
more frequent revisits, higher signal-to-noise ratio (SNR), and higher radiometric sensitivity. However,
those sensors hold lower spatial resolutions ranging from several hundred meters to over 1 km,
which are often challenged to monitor fine spatial structure of water parameters in inland and coastal
case-II waters [
13
]. Therefore, land-intended satellites, such as Landsat series sensors, SPOT and
GF-1 [3,14,15], have been applied to retrieve CSPM values of case-II waters [5,11,12,16–18].
Sentinel 2 Multispectral Imager (MSI), a constellation of two satellites in sun-synchronous polar
orbit, targets land and coastal zone monitoring by providing high spatial (10–60 m) and temporal
(2–5 days) resolution images [
19
]. Following the launches and completions of in-orbit commissioning
phase of Sentinel 2A and Sentinel 2B, they turned operational on 28 November 2015 and 7 July 2017,
respectively. MSI has 13 bands from visible to shortwave infrared (SWIR) spectral regions, providing
appropriate data source to document CSPM variations in coastal and inland waters [20].
Some studies have been carried out in retrieving water parameters using Sentinel 2 MSI data.
Gernez et al. [
15
] applied a simulated Sentinel 2 MSI dataset to retrieve C
SPM
values in turbid estuary
waters. Manzo et al. [
21
] investigated the sensitivity of Sentinel 2 MSI to optically active matters
in Italian lakes with the simulated reflectance from bio-optical model. Dörnhöfer et al. [
22
] applied
Sentinel 2 MSI to assess water constituents and bottom characteristics over oligotrophic lake waters.
Kutser et al. [
23
] demonstrated the advantage of Sentinel 2 MSI over Landsat 8 OLI in retrieving
suspended matters in black lakes. Toming et al. [
24
] obtained high fitting accuracy between in-situ
data and remote-sensed reflectance from Sentinel 2 MSI for chlorophyll-a (R
2
= 0.83) and dissolved
organic carbon (R
2
= 0.92). Moreover, the potentials of Sentinel 2 MSI on water quality monitoring in
both inland and coastal waters have been anticipated [5,17,25–28].
Poyang Lake is the largest freshwater lake in China, which provides important habitats for
Siberian cranes [
29
] and the Yangtze Finless Porpoise [
30
]. As a floodpath lake, SPM in Poyang Lake
supplements sediment losses due to sand dredging and scouring. However, high C
SPM
may deteriorate
the water quality and silt channels. Therefore, some studies have been focused on deriving C
SPM
in this
lake. The C
SPM
monitoring in Poyang Lake has been largely dependent on the images from Landsat
and MODIS Terra and Aqua sensors [
9
,
11
,
12
,
31
–
34
]. Recently, Li et al. [
14
] investigated the capability
of GF 1 WFV in C
SPM
estimations; however, the atmospheric correction over turbid waters is hindered
by lacking SWIR bands, limiting its applications on water quality monitoring. Red and NIR bands
have been frequently used to retrieve C
SPM
due to their sensitivity to C
SPM
[
3
,
12
,
35
]. Sentinel 2 MSI has
seven bands in red to NIR spectral regions, which may provide additional potential in C
SPM
retrieval.
Considering the advantages in spatial, temporal, and spectral resolutions of Sentinel 2 MSI, developing
Sentinel 2 MSI-based retrieval models should facilitate the C
SPM
recording in Poyang Lake. This study
aimed to: (i) develop Sentinel 2 MSI-based C
SPM
retrieval models, (ii) compare Sentinel 2 MSI with
Remote Sens. 2017,9, 761 3 of 19
MODIS in C
SPM
estimations to validate the developed models, and (iii) to identify appropriate spectral
bands for CSPM retrieval in Poyang Lake.
2. Materials and Methods
2.1. Study Area
Poyang Lake (115
◦
47
0
–116
◦
45
0
E, 28
◦
22
0
–29
◦
45
0
N) is located on the southern bank of the middle
Yangtze River (Figure 1). It is a floodpath lake with large fluctuations of seasonal water level variation.
Its area ranges from <1000 km
2
in dry season to about 4000 km
2
in wet season. The lake receives water
from tributaries of five rivers (Raohe, Xinjiang, Fuhe, Ganjiang, and Xiushui), and drains into the
Yangtze River through the lake mouth in the north. Suspended sediment is one of the dominant factors
affecting the water quality of Poyang Lake, and its concentration fluctuates largely due to discharges
from the five rivers, sand dredging activities, and backflows from Yangtze River [11,36,37].
Remote Sens. 2017, 9, 761 3 of 20
MSI with MODIS in CSPM estimations to validate the developed models, and (iii) to identify
appropriate spectral bands for CSPM retrieval in Poyang Lake.
2. Materials and Methods
2.1. Study Area
Poyang Lake (115°47′–116°45′E, 28°22′–29°45′N) is located on the southern bank of the middle
Yangtze River (Figure 1). It is a floodpath lake with large fluctuations of seasonal water level
variation. Its area ranges from <1000 km2 in dry season to about 4000 km2 in wet season. The lake
receives water from tributaries of five rivers (Raohe, Xinjiang, Fuhe, Ganjiang, and Xiushui), and
drains into the Yangtze River through the lake mouth in the north. Suspended sediment is one of the
dominant factors affecting the water quality of Poyang Lake, and its concentration fluctuates largely
due to discharges from the five rivers, sand dredging activities, and backflows from Yangtze River
[11,36,37].
Figure 1. True color composite of atmospherically corrected Sentinel 2 MSI images captured on 15
August 2016 (a) and 2 April 2017 (b), respectively, showing Poyang Lake, and the sampling sites in
2010 (×) and 2011(+) are illustrated in (a).
2.2. In-Situ Data
Fieldworks were carried out on 16–18 October 2010 and 8–10 August 2011. Eighty-five sampling
sites (47 in 2010 and 38 in 2011) were distributed along the main channel of Poyang Lake (Figure 1a).
At each sampling site, the wind speed and direction were measured using a wind velocity indicator
to determine the water–air interface reflectance rate used in remote sensing reflectance calculation.
The location was recorded using a global positioning system receiver (Garmin Ltd., Lenexa, USA).
Water spectra were measured using a portable ASD Fieldspec Pro Dual VNIR spectrometer (ASD
Inc., Longmont, USA). The ASD Fieldspec Pro Dual VNIR spectrometer has a spectral range of 350–
1050 nm with a spectral sampling interval of 1.4 nm, and it was used to measure the water spectra
following the Ocean Optics Protocols For Satellite Ocean Color Sensor Validation [38] and steps
described by Ma et al. [39].
Figure 1.
True color composite of atmospherically corrected Sentinel 2 MSI images captured on
15 August 2016
(
a
) and 2 April 2017 (
b
), respectively, showing Poyang Lake, and the sampling sites in
2010 (×) and 2011(+) are illustrated in (a).
2.2. In-Situ Data
Fieldworks were carried out on 16–18 October 2010 and 8–10 August 2011. Eighty-five sampling
sites (47 in 2010 and 38 in 2011) were distributed along the main channel of Poyang Lake (Figure 1a).
At each sampling site, the wind speed and direction were measured using a wind velocity indicator
to determine the water–air interface reflectance rate used in remote sensing reflectance calculation.
The location was recorded using a global positioning system receiver (Garmin Ltd., Lenexa, KS,
USA). Water spectra were measured using a portable ASD Fieldspec Pro Dual VNIR spectrometer
(ASD Inc., Longmont, CO, USA). The ASD Fieldspec Pro Dual VNIR spectrometer has a spectral range
of 350–1050 nm with a spectral sampling interval of 1.4 nm, and it was used to measure the water
spectra following the Ocean Optics Protocols For Satellite Ocean Color Sensor Validation [
38
] and
steps described by Ma et al. [39].
The C
SPM
value of each water sample was measured gravimetrically with steps: (1) the water
sample was filtered using a pre-dried and weighted Whatman GF/F glass fiber filter with a 0.45
µ
m
pore size; (2) the filter was dried for 2 h at 110
◦
C and reweighted after cooling to room temperature;
Remote Sens. 2017,9, 761 4 of 19
and (3) the C
SPM
was obtained by dividing the difference in weight before and after filtering by the
water sample volume [39].
2.3. Satellite Images and Pre-Processing
2.3.1. Satellite Images
The Sentinel 2 MSI, with a 290 km field of view and a radiometric quantization of 12-bit, provides
a total of 13 spectral bands spanning from visible and near infrared to SWIR spectral region, and it
has four bands with a spatial resolution of 10 m, six bands with 20 m, and three bands with 60 m [
40
].
Two Sentinel 2 MSI L1C images captured on 15 August 2016 and 2 April 2017 were downloaded
from the website provided by European Space Agency (https://scihub.copernicus.eu/dhus/).
The inundation areas of Poyang Lake were appropriately 3200 and 1700 km
2
on
15 August 2016
and
2 April 2017
, respectively, which represent the characteristics of Poyang Lake in the wet season
and in the dry season.
One MODIS Terra image captured on 15 August 2016 and one MODIS Aqua L1A image captured
on 2 April 2017 were downloaded from NASA OceanColor website (http://oceancolor.gsfc.nasa.gov/).
The MODIS Terra image, instead of MODIS Aqua, was used for the MODIS Aqua image on 15 August
2016 because it was affected by clouds. The MODIS images were atmospherically corrected by firstly
removing the Rayleigh reflectance and then subtracting the reflectance at 1240 nm following the
procedures applied by Wu et al. [
12
]. The Sentinel 2 MSI images were resampled to 20 m to facilitate
further processing and analyses.
2.3.2. Sentinel 2 MSI Atmospheric Correction
Sentinel 2 MSI L1C product provides the top of atmosphere reflectance (
ρTOA
), which is assumed
to be the sum of Rayleigh reflectance (ρr), aerosol reflectance (ρa) and water-leaving reflectance (ρw):
ρTOA =ρr+ρa+t·ρw
where tis the two-way diffuse atmospheric transmittance [41].
The reflectance caused by Rayleigh scattering was estimated with the 6S radiative transfer
code [
42
] for each band, meanwhile the diffuse atmospheric transmittance (t) was also
derived. A mid-latitude Summer atmosphere condition was adopted, and the mean values of solar and
view zenith and azimuth across the whole granule were calculated for the sun and sensor geometry.
Cloud and land masking was performed with a threshold on the reflectance centered at 1610 nm, and
pixels were classified as not being water when the Rayleigh-corrected reflectance (
ρc
=
ρTOA −ρr
) of
B11 was greater than 0.0215 [28,43].
The SWIR-based aerosol correction method [
28
,
44
] was implemented using the following steps:
(1) a 15
×
15 moving average filter was applied to improve the SNR of two SWIR bands; (2) the aerosol
type (
ε
) was determined from the ratio of two SWIR bands (
ε
=
ρc11
/
ρc12
) for each pixel; (3) the
εi,12
of
a given band iover band 12 was extrapolated exponentially as follow:
εi, 12 =(ε)δiand δi=λ12 −λi
λ12 −λ11
(λis the wavelength);
and (4) the ρwat the ith band was derived with the following formula:
ρi
w=1
tiρi
c−εi, 12 ·ρ12
c,
where tiis the two-way diffuse atmospheric transmittance for band i.
Remote Sens. 2017,9, 761 5 of 19
2.4. In-Situ Water-Leaving Reflectance Calculation
The abnormal radiance measurements from grey diffuse reflectance standard, water, and sky were
first removed considering their spectral characteristics, and the remote sensing reflectance for each
sampling site was then calculated using the remaining measurements with the following equation [
39
]:
Rrs =Lw−rLsky
πLp/ρp
where the R
rs
(sr
−1
) is the remote sensing reflectance; L
w
,L
sky
and L
p
(W
·
m
−2·
sr
−1
) are the measured
radiances from water, sky, and grey diffuse reflectance standard, respectively; rindicates water–air
interface reflectance rate, and its value is 0.022, 0.025, or 0.028 for a wind velocity of 0, 5, or 10 m/s,
respectively;
ρp
is the reflectance rate of grey diffuse reflectance standard, and its value is 0.25; and
π
(sr) is solid angle. Only the R
rs
at the wavelengths of 400–920 nm were employed for further
analysis because of low SNR in the edges of the spectral regions and strong absorptions in longer
spectral regions.
2.5. Sentinel 2 MSI Spectral Simulation
The in-situ R
rs
spectra were integrated using the relative spectral response (RSR) function of
Sentinel 2 MSI (downloaded from the website provided by European Space Agency, https://earth.esa.
int/web/sentinel/user-guides/sentinel-2-msi/document-library; accessed on 4 July 2017) to obtain
simulated water-leaving reflectance according to following formula:
ρw=πRλ2
λ1RSR(λ)Rrs(λ)dλ
Rλ2
λ1RSR(λ)dλ
where
λ
is the wavelength,
λ1
and
λ2
are the lower and upper wavelength bound for each band and
ρw
is the simulated water-leaving reflectance. The
ρw
values of the first nine bands were obtained for
Sentinel 2 MSI.
2.6. Model Development
The samples with incorrect spectrum (non-typical case-II water spectrum or negative values),
wrong water constituent concentration (negative value), or unstable measurement conditions were
removed. The remaining samples were recorded and numbered from 1 to n based on C
SPM
value
from high to low. The odd-numbered samples were used for model calibration, the others for model
validation. The C
SPM
values of both datasets were statistically described. The R
rs
spectra were
visualized and analyzed. The correlation analysis between C
SPM
and R
rs
was implemented. The
widely used linear, quadratic, exponential, and power models of C
SPM
against the simulated
ρw
spectra
were calibrated using the least squares technique, respectively. The calibrated models were applied to
estimate the C
SPM
values of validation dataset. The coefficient of determination (R
2
), mean absolute
percentage error (MAPE), and root mean square error (RMSE) between the measured and estimated
values were calculated to assess the fitting and validation accuracy. Six models with better fitting and
validation accuracies were selected for further analyses.
2.7. CSPM Estimation and Comparison
The selected six C
SPM
retrieval models were applied to Sentinel 2 MSI images to retrieve
C
SPM
values. To evaluate these models’ applicability, the C
SPM
values deriving from MODIS using
an empirical model were used as the quasi-reference values and compared with those deriving from
Sentinel 2. The C
SPM
retrieval model based on MODIS B1 (C
SPM
= 0.43
×
exp(31.46
×
B1), B1 is centered
at 645 nm), which was calibrated and validated with synchronous satellite and in-situ observations [
12
],
was applied to MODIS images to retrieve C
SPM
values. The C
SPM
values derived from these sensors
Remote Sens. 2017,9, 761 6 of 19
were mapped and compared to illustrate their consistence and difference. Moreover, the C
SPM
values
retrieved from each band of Sentinel 2 MSI were downsampled to 250 m by averaging, and plotted
against those from MODIS. The correlation coefficient and simple linear regression line were calculated
to evaluate the reliability of Sentinel 2 MSI-derived C
SPM
values. For expression convenience, MODIS
was used to represent MODIS Terra and Aqua B1 in the following sections, since only this band was
used for CSPM retrieval from MODIS images.
The image processing and mapping were implemented with Python and ArcGIS (ESRI, Inc.,
Redlands, CA, USA), except that the Rayleigh correction for MODIS images was carried out using
SEADAS (http://seadas.gsfc.nasa.gov/). Model calibration and validation were implemented using
Matlab (Mathworks, Inc., Natick, MA, USA).
3. Results
3.1. In-Situ Data
Thirteen samples in 2010 were removed from following statistics and analyses, one with a negative
C
CHL
value, one with obviously higher reflectance than others, one with extremely low reflectance,
and 10 collected under high wind velocities (>5 m/s) or unstable light illumination condition on
15 October 2010. Four samples in 2011 were identified from their spectral shape and removed.
After removing outliers, a total of 68 in-situ samples were left. The statistical results (Table 1) for
C
SPM
showed that the C
SPM
values of all the samples have a mean value of 77.75 mg/L, a standard
deviation of 60.56 mg/L and a coefficient of variation of 77.89%, with the C
SPM
ranging from 17.16 to
294.50 mg/L. The calibration dataset (average C
SPM
= 79.45 mg/L, standard deviation = 63.22 mg/L,
CV = 79.57%) and validation dataset (average C
SPM
= 76.05 mg/L, standard deviation = 58.67 mg/L,
CV = 77.15%) hold similar statistical characteristics.
Table 1.
Statistics describing the concentrations of suspended particulate matter (C
SPM
, mg/L) of the
calibration and validation dataset as well as all the samples. Std. Dev. means standard deviation, and
CV is coefficient of variation (%).
Dataset Number Minimum Maximum Average Std. Dev. CV
Calibration 34 19.00 294.50 79.45 63.22 79.57
Validation 34 17.16 282.25 76.05 58.67 77.15
All 68 17.16 294.50 77.75 60.56 77.89
The R
rs
spectra show typical spectral characteristics of case-II waters (Figure 2a), in which R
rs
values increase over 400–550 nm and are much higher than zero in NIR spectral regions. Normally,
a higher C
SPM
value tends to produce a higher reflectance curve. There are two SPM reflectance
peaks around 580 and 810 nm. Figure 2b shows the correlations between CSPM and Rrs of all samples
as well as samples with C
SPM
greater than and lower than 50 mg/L. When taking all samples into
consideration, there exists significantly positive correlations between C
SPM
and R
rs
over 400–920 nm
(r > 0.6) and strong correlations between 707–900 nm (r
≥
0.85) at a significance level of 0.05 (Figure 2b).
For samples with C
SPM
greater than 50 mg/L, the correlations were obviously lower in visible spectral
region and higher in NIR spectral region than those with C
SPM
greater than 50 mg/L. However, all of
the correlation curves peaked at around 750 nm.
Remote Sens. 2017,9, 761 7 of 19
Remote Sens. 2017, 9, 761 7 of 20
Figure 2. Remote sensing reflectance (Rrs) spectra and their corresponding suspended particulate
matter concentrations (mg/L), and the relative spectral response function of the Sentinel 2 MSI (black
dash curve) (a); and the correlation coefficient (r) between the Rrs and suspended particulate matter
concentration (CSPM) (b).
3.2. Model Development
The calibration results of CSPM retrieval models are shown in Table 2, and the B4- and B7-based
models are illustrated in Figure 3. The models based on Sentinel 2 MSI B6 or longer wavelengths
achieved very good fitting performance with R2 over 0.9, RMSE less than 20 mg/L, and F-value of the
model’s significance test ranging from 151.14 to 205.49. The B7-based power model had the best
fitting accuracy (R2 = 0.93, MAPE = 16.58%, RMSE = 16.50 mg/L, F = 205.49). The B4-based exponential
model explained 81% of the variation of CSPM with a RMSE value of 27.95 mg/L and a MAPE value of
30.32%, while B5-based model produced better calibration accuracy (R2 = 0.88, MAPE = 20.99%, RMSE
= 21.51 mg/L, F = 114.47). However, B1-, B2- and B3-based models produced poor fitting accuracy
with R2 smaller than 0.60. Therefore, the models calibrated with B4–B8b were selected for further
validations.
Table 2. Regression models with the goodness of fitting between suspended particulate matter
concentration (CSPM, mg/L) and the simulated water-leaving reflectance at Sentinel-2 MSI B1–B8b.
Model
R2
MAPE
RMSE
F
CSPM = 2.335 × exp(47.62 × B1)
0.57
40.89
41.02
20.24
CSPM = 1.769 × exp(37.38 × B2)
0.56
41.36
41.87
18.81
CSPM = 1.808 × exp(25.08 × B3)
0.53
38.34
42.83
17.29
CSPM = 4.044 × exp(19.53 × B4)
0.81
30.32
27.95
61.47
CSPM = 8.385 × exp(16.49 × B5)
0.88
20.99
21.51
114.47
CSPM = 3329 × B61.375
0.91
16.61
18.20
151.14
CSPM = 2950 × B71.357
0.93
16.58
16.50
205.49
CSPM = 2887 × B81.223
0.91
16.23
18.14
167.15
CSPM = 2520 × B8b1.42
0.90
18.30
19.59
141.14
Note: MAPE is mean absolute percentage error (%), RMSE is the root mean square error (mg/L), F is
the F-value of the model’s significance test.
Figure 2.
Remote sensing reflectance (R
rs
) spectra and their corresponding suspended particulate
matter concentrations (mg/L), and the relative spectral response function of the Sentinel 2 MSI (black
dash curve) (
a
); and the correlation coefficient (r) between the R
rs
and suspended particulate matter
concentration (CSPM) (b).
3.2. Model Development
The calibration results of C
SPM
retrieval models are shown in Table 2, and the B4- and B7-based
models are illustrated in Figure 3. The models based on Sentinel 2 MSI B6 or longer wavelengths achieved
very good fitting performance with R
2
over 0.9, RMSE less than 20 mg/L, and F-value of the model’s
significance test ranging from 151.14 to 205.49. The B7-based power model had the best fitting accuracy
(R
2
= 0.93, MAPE = 16.58%, RMSE = 16.50 mg/L, F = 205.49). The B4-based exponential model explained
81% of the variation of C
SPM
with a RMSE value of 27.95 mg/L and a MAPE value of 30.32%, while
B5-based model produced better calibration accuracy (R
2
= 0.88, MAPE = 20.99%, RMSE = 21.51 mg/L,
F = 114.47
). However, B1-, B2- and B3-based models produced poor fitting accuracy with R
2
smaller than
0.60. Therefore, the models calibrated with B4–B8b were selected for further validations.
Table 2.
Regression models with the goodness of fitting between suspended particulate matter
concentration (CSPM, mg/L) and the simulated water-leaving reflectance at Sentinel-2 MSI B1–B8b.
Model R2MAPE RMSE F
CSPM = 2.335 ×exp(47.62 ×B1) 0.57 40.89 41.02 20.24
CSPM = 1.769 ×exp(37.38 ×B2) 0.56 41.36 41.87 18.81
CSPM = 1.808 ×exp(25.08 ×B3) 0.53 38.34 42.83 17.29
CSPM = 4.044 ×exp(19.53 ×B4) 0.81 30.32 27.95 61.47
CSPM = 8.385 ×exp(16.49 ×B5) 0.88 20.99 21.51 114.47
CSPM = 3329 ×B61.375 0.91 16.61 18.20 151.14
CSPM = 2950 ×B71.357 0.93 16.58 16.50 205.49
CSPM = 2887 ×B81.223 0.91 16.23 18.14 167.15
CSPM = 2520 ×B8b1.42 0.90 18.30 19.59 141.14
Note: MAPE is mean absolute percentage error (%), RMSE is the root mean square error (mg/L), F is the F-value of
the model’s significance test.
The six selected models were applied to the validation dataset to estimate the C
SPM
values,
and validation results were shown in Figure 4. Similar to the calibration results, the B6 or longer
wavelength-based models produced better estimation accuracies, which explained 88–93% of the
variation of measured C
SPM
, and the B7-based model produced the best validation performance
(R
2
= 0.93, MAPE = 21.54%, RMSE = 16.06 mg/L). The B4-based model had the lowest estimation
accuracy among the six selected, however, it still obtained acceptable performance (R
2
= 0.77,
MAPE = 36.87%, RMSE = 32.38 mg/L). Therefore, all the six selected models were applied to retrieve
CSPM values of Poyang Lake.
Remote Sens. 2017,9, 761 8 of 19
Remote Sens. 2017, 9, 761 8 of 20
Figure 3. Suspended particulate matter concentration (CSPM) retrieval models based on Sentinel 2 MSI
B4 (a) and B7 (b).
The six selected models were applied to the validation dataset to estimate the CSPM values, and
validation results were shown in Figure 4. Similar to the calibration results, the B6 or longer
wavelength-based models produced better estimation accuracies, which explained 88–93% of the
variation of measured CSPM, and the B7-based model produced the best validation performance (R2 =
0.93, MAPE = 21.54%, RMSE = 16.06 mg/L). The B4-based model had the lowest estimation accuracy
among the six selected, however, it still obtained acceptable performance (R2 = 0.77, MAPE = 36.87%,
RMSE = 32.38 mg/L). Therefore, all the six selected models were applied to retrieve CSPM values of
Poyang Lake.
Figure 3.
Suspended particulate matter concentration (C
SPM
) retrieval models based on Sentinel 2 MSI
B4 (a) and B7 (b).
Remote Sens. 2017, 9, 761 8 of 20
Figure 3. Suspended particulate matter concentration (CSPM) retrieval models based on Sentinel 2 MSI
B4 (a) and B7 (b).
The six selected models were applied to the validation dataset to estimate the CSPM values, and
validation results were shown in Figure 4. Similar to the calibration results, the B6 or longer
wavelength-based models produced better estimation accuracies, which explained 88–93% of the
variation of measured CSPM, and the B7-based model produced the best validation performance (R2 =
0.93, MAPE = 21.54%, RMSE = 16.06 mg/L). The B4-based model had the lowest estimation accuracy
among the six selected, however, it still obtained acceptable performance (R2 = 0.77, MAPE = 36.87%,
RMSE = 32.38 mg/L). Therefore, all the six selected models were applied to retrieve CSPM values of
Poyang Lake.
Remote Sens. 2017, 9, 761 9 of 20
Figure 4. Scatter plots of estimated against measured suspended particulate matter concentration
(CSPM) for the validation dataset: Sentinel 2 MSI B4 (a), B5 (b), B6 (c), B7 (d), B8 (e), and B8b (f). The
solid line is the regression line between the estimated and measured values, and the dashed line is 1:1
line.
3.3. CSPM Estimation and Comparison
The CSPM values derived from Sentinel 2 MSI B4, B7, and B8b, as well as those from MODIS
captured on 15 August 2016 were shown in Figure 5. The distribution patterns of CSPM obtained from
the three bands of Sentinel 2 MSI and MODIS Terra were generally consistent across the whole
Poyang Lake, while Sentinel 2, with obviously higher spatial resolutions, could resolve more detailed
spatial variations than MODIS. The eastern and western parts of the lake had lower CSPM values, while
a turbid plume with high loadings of CSPM was observed in the middle part from south to north.
However, the Sentinel 2 MSI B4 tended to produce slight lower CSPM values than the other three maps
in the very high CSPM regions, while the B8b-derived map showed noise patterns in waters with low
CSPM values.
Figure 6 shows the scatter plots of the CSPM values derived from Sentinel 2 MSI B4–B8b against
those of MODIS on 15 August 2016. The CSPM values derived from Sentinel 2 MSI were highly and
significantly correlated with those from the MODIS at a significance level of 0.05 (r > 0.9, p < 0.05),
however, the slope of the regression line varied a lot. The slope of the regression line for Sentinel 2
MSI B4 and B5 against MODIS were 0.76 and 0.75, respectively, which means that these two bands
tended to produce lower CSPM values than MODIS for high CSPM values. However, the CSPM values
retrieved from Sentinel 2 MSI B6–B8b are relatively more consistent with those from MODIS with a
higher slope of the regression line between them. Overall, the Sentinel 2 MSI B7 showed the highest
consistence with a slope of the regression line of 1.00, though it tended to produce slightly higher
CSPM values than MODIS with an intercept of 9.87 mg/L. Figure 7 illustrates the mean and standard
deviation values of CSPM across the whole lake derived from Sentinel 2 MSI and MODIS. The mean
CSPM values of Sentinel 2 MSI B4–B8b were 22.31, 28.10, 25.51, 29.86, 24.25, and 37.76 mg/L,
respectively, which were higher than that of MODIS (19.51 mg/L). The standard deviation values
were obviously larger than their corresponding mean values except Sentinel 2 MSI B5, which
indicates high spatial variations across the lake. The Sentinel 2 MIS B4 and B5 had lower standard
variations than other bands.
Figure 4.
Scatter plots of estimated against measured suspended particulate matter concentration
(C
SPM
) for the validation dataset: Sentinel 2 MSI B4 (
a
), B5 (
b
), B6 (
c
), B7 (
d
), B8 (
e
), and B8b (
f
).
The solid line is the regression line between the estimated and measured values, and the dashed line
is 1:1 line.
Remote Sens. 2017,9, 761 9 of 19
3.3. CSPM Estimation and Comparison
The C
SPM
values derived from Sentinel 2 MSI B4, B7, and B8b, as well as those from MODIS
captured on 15 August 2016 were shown in Figure 5. The distribution patterns of C
SPM
obtained from
the three bands of Sentinel 2 MSI and MODIS Terra were generally consistent across the whole Poyang
Lake, while Sentinel 2, with obviously higher spatial resolutions, could resolve more detailed spatial
variations than MODIS. The eastern and western parts of the lake had lower C
SPM
values, while a turbid
plume with high loadings of C
SPM
was observed in the middle part from south to north. However, the
Sentinel 2 MSI B4 tended to produce slight lower C
SPM
values than the other three maps in the very
high C
SPM
regions, while the B8b-derived map showed noise patterns in waters with low C
SPM
values.
Remote Sens. 2017, 9, 761 10 of 20
Figure 5. Suspended particulate matter concentrations (CSPM) retrieved from Sentinel 2 MSI B4 (a), B7
(b), B8b (c), and MODIS Terra B1 (d) captured on 15 August 2016. The areas in the red rectangle are
zoomed in to show the detailed CSPM variations.
Figure 5.
Suspended particulate matter concentrations (C
SPM
) retrieved from Sentinel 2 MSI B4 (
a
),
B7 (
b
), B8b (
c
), and MODIS Terra B1 (
d
) captured on 15 August 2016. The areas in the red rectangle are
zoomed in to show the detailed CSPM variations.
Remote Sens. 2017,9, 761 10 of 19
Figure 6shows the scatter plots of the C
SPM
values derived from Sentinel 2 MSI B4–B8b against
those of MODIS on 15 August 2016. The C
SPM
values derived from Sentinel 2 MSI were highly and
significantly correlated with those from the MODIS at a significance level of 0.05 (r > 0.9, p< 0.05),
however, the slope of the regression line varied a lot. The slope of the regression line for Sentinel 2 MSI
B4 and B5 against MODIS were 0.76 and 0.75, respectively, which means that these two bands tended
to produce lower C
SPM
values than MODIS for high C
SPM
values. However, the C
SPM
values retrieved
from Sentinel 2 MSI B6–B8b are relatively more consistent with those from MODIS with a higher slope
of the regression line between them. Overall, the Sentinel 2 MSI B7 showed the highest consistence
with a slope of the regression line of 1.00, though it tended to produce slightly higher C
SPM
values
than MODIS with an intercept of 9.87 mg/L. Figure 7illustrates the mean and standard deviation
values of C
SPM
across the whole lake derived from Sentinel 2 MSI and MODIS. The mean C
SPM
values
of Sentinel 2 MSI B4–B8b were 22.31, 28.10, 25.51, 29.86, 24.25, and 37.76 mg/L, respectively, which
were higher than that of MODIS (19.51 mg/L). The standard deviation values were obviously larger
than their corresponding mean values except Sentinel 2 MSI B5, which indicates high spatial variations
across the lake. The Sentinel 2 MIS B4 and B5 had lower standard variations than other bands.
The C
SPM
values derived from Sentinel 2 MSI B4, B7, and B8b as well as those from MODIS
on 2 April 2017 were shown in Figure 8. The C
SPM
values were relatively low in most parts of
Poyang Lake on 2 April 2017, except that the central to southern Poyang Lake showed slightly higher
SPM loading. Sentinel 2 MSI B4 and B7 produced similar C
SPM
distribution patterns with MODIS.
However, the B8b-derived maps showed noise patterns and obviously held higher C
SPM
values than
the other three maps. Figure 9illustrates the scatter plots of C
SPM
values derived from Sentinel 2
MSI B4–B8b against those of MODIS on 2 April 2017. The C
SPM
values derived from Sentinel 2 MSI
were strongly and significantly correlated with those from the MODIS at a significance level of 0.05
(r > 0.8, p< 0.05), however, all of B4–B8b tended to produce higher C
SPM
values than MODIS. Sentinel
2 MSI B4 showed the highest consistence with MODIS with a correlation coefficient of 0.91, a slope
of 0.97, and an intercept of 6.42 mg/L. Sentinel 2 MSI B8b produced the lowest consistence with
an intercept of 19.27 mg/L, while B7 produced acceptable results with a correlation coefficient of 0.86
and a regression line of y = 1.11x + 7.63 with MODIS. The mean and standard deviation values of
C
SPM
values from Sentinel 2 MSI and MODIS were illustrated in Figure 10. Similar to results shown in
Figure 9, the Sentinel 2 MSI B4 with mean = 13.43 mg/L and standard deviation = 5.21 mg/L was the
closest to MODIS (mean = 7.19, standard deviation = 4.87 mg/L), while Sentinel 2 MSI B8b produced
an obviously higher mean value (27.18 mg/L) than the other bands. The low standard deviations,
ranging from 4.87 to 6.48 mg/L, also indicated low spatial variations of CSPM values on 2 April 2017.
Remote Sens. 2017,9, 761 11 of 19
Remote Sens. 2017, 9, 761 11 of 20
Figure 6. Scatter plots of CSPM values derived from Sentinel 2 MSI B4 (a), B5 (b), B6 (c), B7 (d), B8 (e),
and B8b (f) against those from MODIS Terra B1 on 15 August 2016. The solid line is the regression
line and the dashed line is 1:1 line. The number along the color ramp indicates the pixel number after
log transformation (y = log1.05(x)).
Figure 6.
Scatter plots of C
SPM
values derived from Sentinel 2 MSI B4 (
a
), B5 (
b
), B6 (
c
), B7 (
d
), B8 (
e
),
and B8b (
f
) against those from MODIS Terra B1 on 15 August 2016. The solid line is the regression line
and the dashed line is 1:1 line. The number along the color ramp indicates the pixel number after log
transformation (y = log1.05(x)).
Remote Sens. 2017, 9, 761 12 of 20
Figure 7. The mean and standard deviation (Std) of CSPM derived from Sentinel 2 MSI B4–B8b and
MODIS Terra B1 on 15 August 2016.
The CSPM values derived from Sentinel 2 MSI B4, B7, and B8b as well as those from MODIS on 2 April
2017 were shown in Figure 8. The CSPM values were relatively low in most parts of Poyang Lake on 2
April 2017, except that the central to southern Poyang Lake showed slightly higher SPM loading.
Sentinel 2 MSI B4 and B7 produced similar CSPM distribution patterns with MODIS. However, the
B8b-derived maps showed noise patterns and obviously held higher CSPM values than the other three
maps. Figure 9 illustrates the scatter plots of CSPM values derived from Sentinel 2 MSI B4–B8b against
those of MODIS on 2 April 2017. The CSPM values derived from Sentinel 2 MSI were strongly and
significantly correlated with those from the MODIS at a significance level of 0.05 (r > 0.8, p < 0.05),
however, all of B4–B8b tended to produce higher CSPM values than MODIS. Sentinel 2 MSI B4 showed
the highest consistence with MODIS with a correlation coefficient of 0.91, a slope of 0.97, and an
intercept of 6.42 mg/L. Sentinel 2 MSI B8b produced the lowest consistence with an intercept of 19.27
mg/L, while B7 produced acceptable results with a correlation coefficient of 0.86 and a regression line
of y = 1.11x + 7.63 with MODIS. The mean and standard deviation values of CSPM values from Sentinel
2 MSI and MODIS were illustrated in Figure 10. Similar to results shown in Figure 9, the Sentinel 2
MSI B4 with mean = 13.43 mg/L and standard deviation = 5.21 mg/L was the closest to MODIS (mean
= 7.19, standard deviation = 4.87 mg/L), while Sentinel 2 MSI B8b produced an obviously higher mean
value (27.18 mg/L) than the other bands. The low standard deviations, ranging from 4.87 to 6.48 mg/L,
also indicated low spatial variations of CSPM values on 2 April 2017.
Figure 7.
The mean and standard deviation (Std) of C
SPM
derived from Sentinel 2 MSI B4–B8b and
MODIS Terra B1 on 15 August 2016.
Remote Sens. 2017,9, 761 12 of 19
Remote Sens. 2017, 9, 761 13 of 20
Figure 8. Suspended particulate matter concentrations (CSPM) retrieved from Sentinel 2 MSI B4 (a), B7
(b), B8b (c), and MODIS Aqua B1 (d) on 2 April 2017. The areas in the red rectangle are zoomed in to
show the detailed CSPM variations.
Figure 8.
Suspended particulate matter concentrations (C
SPM
) retrieved from Sentinel 2 MSI B4 (
a
),
B7 (
b
), B8b (
c
), and MODIS Aqua B1 (
d
) on 2 April 2017. The areas in the red rectangle are zoomed in
to show the detailed CSPM variations.
Remote Sens. 2017,9, 761 13 of 19
Remote Sens. 2017, 9, 761 14 of 20
Figure 9. Scatter plots of CSPM values derived from Sentinel 2 MSI B4 (a), B5 (b), B6 (c), B7 (d), B8 (e),
and B8b (f) against those from MODIS Aqua on 2 April 2017. The solid line is the regression line and
the dashed line is 1:1 line. The number along the color ramp indicates the pixel number after log
transformation (y = log1.05(x)).
Figure 9.
Scatter plots of C
SPM
values derived from Sentinel 2 MSI B4 (
a
), B5 (
b
), B6 (
c
), B7 (
d
), B8 (
e
),
and B8b (
f
) against those from MODIS Aqua on 2 April 2017. The solid line is the regression line and
the dashed line is 1:1 line. The number along the color ramp indicates the pixel number after log
transformation (y = log1.05(x)).
Remote Sens. 2017, 9, 761 15 of 20
Figure 10. The mean and standard deviation (Std) of CSPM derived from Sentinel 2 MSI B4–B8b and
MODIS Aqua B1 sensed on 2 April 2017.
4. Discussion
This study developed CSPM retrieval models with simulated Sentinel 2 MSI spectra and in-situ
CSPM values in Poyang Lake. Several models were reported with in-situ optical and CSPM values, and
they were successfully applied to satellite images for CSPM retrieval [14,45,46]. The models developed
in this study should be reliable considering the following two reasons: (i) the Rrs derived from in-situ
measurements are less affected by atmospheric interference and are frequently used for spaceborne
sensor calibration and validation [5,47], and the NASA protocol [38] was strictly followed to ensure
high quality optical measurements; and (ii) the synchronous in-situ CSPM values and Rrs are more
consistent in spatial and temporal scale, while the spatiotemporal scale gap problem always exists
between satellite and in-situ observations [3].
This study found that the Sentinel 2 MSI B4–B8b band obtained acceptable to high fitting
accuracy and validation performance, and the models derived from Sentinel 2 MSI B1–B3 were less
accurate. These calibration results were similar to the models develop for Dongting Lake [3], which
is also a large sediment-laden floodpath lake along the middle section of the Yangtze River.
According to the bio-optical model [39,48], Rrs can be expressed as a function of absorption and
backscattering coefficients. The reflectance of short wavelength bands, like Sentinel 2 MSI B1–B3, are
affected by the absorption and backscattering characteristics of the water optically active matters
(including SPM, chlorophyll, and CDOM) and pure water together, therefore the correlations
between CSPM and these three bands are weak (Figure 2). The absorption coefficients of these optically
active matters decrease with increasing wavelength, and turn close to zero in NIR regions [49].
Therefore, the magnitude and shape of Rrs are mainly determined by the particulate backscattering
coefficient and pure water absorption in longer spectral regions [50], which explains the better fitting
results obtained by Sentinel 2 MSI B6–B8b. The best calibration and validation results obtained by
Sentinel 2 MSI B7 (centered at 783 nm) may be explained by the fact that it lies in the left shoulder of
the SPM reflectance peak around 810 nm (Figure 2a), which is a result of particulate backscattering
and slight decrease in water absorption [51]. Such a result is consistent with that of Kutser et al. [23],
who demonstrated the capability of Sentinel 2 MSI B7 in retrieving CSPM values over strong absorbing
black waters due to its adjacency to 810 reflectance peak.
The absorptions of the optically active matters could still contribute to the reflectance of Sentinel
2 MSI B4 (centered at 665 nm), especially for waters with high loading of SPM or the presence of
absorbing substances [3,23]. Therefore, the relationships between CSPM and Rrs at this band are
complicated, and the calibration samples scattered sparsely around the fitting curve in Figure 3,
which explains the relatively lower performance obtained by Sentinel 2 MSI B4. Such a statement
could also be supported by the findings of Wu et al. [49], in which an obvious phytoplankton
Figure 10.
The mean and standard deviation (Std) of C
SPM
derived from Sentinel 2 MSI B4–B8b and
MODIS Aqua B1 sensed on 2 April 2017.
Remote Sens. 2017,9, 761 14 of 19
4. Discussion
This study developed C
SPM
retrieval models with simulated Sentinel 2 MSI spectra and in-situ
C
SPM
values in Poyang Lake. Several models were reported with in-situ optical and C
SPM
values, and
they were successfully applied to satellite images for C
SPM
retrieval [
14
,
45
,
46
]. The models developed
in this study should be reliable considering the following two reasons: (i) the R
rs
derived from in-situ
measurements are less affected by atmospheric interference and are frequently used for spaceborne
sensor calibration and validation [
5
,
47
], and the NASA protocol [
38
] was strictly followed to ensure
high quality optical measurements; and (ii) the synchronous in-situ C
SPM
values and R
rs
are more
consistent in spatial and temporal scale, while the spatiotemporal scale gap problem always exists
between satellite and in-situ observations [3].
This study found that the Sentinel 2 MSI B4–B8b band obtained acceptable to high fitting accuracy
and validation performance, and the models derived from Sentinel 2 MSI B1–B3 were less accurate.
These calibration results were similar to the models develop for Dongting Lake [
3
], which is also
a large sediment-laden floodpath lake along the middle section of the Yangtze River. According to
the bio-optical model [
39
,
48
], R
rs
can be expressed as a function of absorption and backscattering
coefficients. The reflectance of short wavelength bands, like Sentinel 2 MSI B1–B3, are affected by the
absorption and backscattering characteristics of the water optically active matters (including SPM,
chlorophyll, and CDOM) and pure water together, therefore the correlations between C
SPM
and these
three bands are weak (Figure 2). The absorption coefficients of these optically active matters decrease
with increasing wavelength, and turn close to zero in NIR regions [
49
]. Therefore, the magnitude
and shape of R
rs
are mainly determined by the particulate backscattering coefficient and pure water
absorption in longer spectral regions [
50
], which explains the better fitting results obtained by Sentinel
2 MSI B6–B8b. The best calibration and validation results obtained by Sentinel 2 MSI B7 (centered
at 783 nm) may be explained by the fact that it lies in the left shoulder of the SPM reflectance peak
around 810 nm (Figure 2a), which is a result of particulate backscattering and slight decrease in water
absorption [
51
]. Such a result is consistent with that of Kutser et al. [
23
], who demonstrated the
capability of Sentinel 2 MSI B7 in retrieving C
SPM
values over strong absorbing black waters due to its
adjacency to 810 reflectance peak.
The absorptions of the optically active matters could still contribute to the reflectance of Sentinel
2 MSI B4 (centered at 665 nm), especially for waters with high loading of SPM or the presence of
absorbing substances [
3
,
23
]. Therefore, the relationships between C
SPM
and R
rs
at this band are
complicated, and the calibration samples scattered sparsely around the fitting curve in Figure 3, which
explains the relatively lower performance obtained by Sentinel 2 MSI B4. Such a statement could
also be supported by the findings of Wu et al. [
49
], in which an obvious phytoplankton absorption
peak, a correlation peak between a
d
and suspended particulate inorganic matter concentration, and
a correlation peak between a
g
and C
DOC
were observed in this spectral region, while the correlation
between a
p
and C
SPM
plummeted around 665 nm. However, Sentinel 2 MSI B4 still holds advantage
for waters with low CSPM values, which has been widely used for case-II waters [13,16,25].
By applying the models calibrated in this study, the Sentinel 2 MSI B4–B8b produced generally
consistent C
SPM
maps with MODIS. However, compared with MODIS images, the most frequently
used data for C
SPM
monitoring in Poyang Lake, Sentinel 2 MSI can resolve finer variations over small
scales. It is understandable that one single MODIS pixel with a 250 m resolution is corresponding to
more than 12
×
12 pixels with a 20 m resolution. Therefore, Sentinel 2 MSI can provide more accurate
estimations especially in spatially heterogeneous regions. Similar phenomena were also found for
Landsat 8 OLI and GF 1 WFV OLI over the MODIS Terra and Aqua [
14
,
28
,
52
]. More importantly
for the floodpath lake, many parts of Poyang Lake can be narrow in dry seasons, which makes low
resolution images unusable, while higher spatial resolution image, such as Landsat 8 OLI and Sentinel
2 MSI, can still work well to resolve small water bodies [53,54].
The Sentinel 2 MSI B8b (centered at 865 nm) might not be suitable for C
SPM
retrieval because
of the noise patterns in the low C
SPM
waters. This could be explained by the low R
rs
for waters
Remote Sens. 2017,9, 761 15 of 19
with low C
SPM
values due to high water absorption in this band [
55
]. For example, a R
rs
value of
0.00046 was measured at 865 nm for the water sample with a C
SPM
value of 23.32 mg/L. Therefore, the
water-leaving radiances only contribute to a very small part of the satellite-sensed signal at this band,
which could not be separated accurately from the atmospheric interference. Moreover, the relative
lower SNR value held by Sentinel 2 MSI B8b may also partially account for the noise pattern.
The C
SPM
values derived from MODIS were cross-compared with those from Sentinel 2 MSI to
determine the reliability and applicability of Sentinel 2 MSI-based models developed in this study,
because of lacking synchronous Sentinel image and in-situ C
SPM
values. The time differences between
MODIS and Sentinel 2 MSI overpasses are within 3 h. Therefore, it is reasonable to omit the influence
of SPM loadings dynamics on cross-comparisons between Sentinel 2 and MODIS. The MODIS-based
C
SPM
retrieval model was used because it was calibrated and validate with two independent datasets
collected in 2007 and 2012, respectively, and found to have acceptable and stable performance [
12
].
Compared with MODIS, Sentinel 2 MSI B4 and B5 tended to underestimate in the regions with higher
C
SPM
values, which might be partially explained by the models’ uncertainty and the discrepancy in RSR
and SNR of the two sensors [
14
]. The Sentinel 2 MSI B7-derived C
SPM
values were the most consistent
with those from MODIS for turbid waters, which further demonstrates its capability in C
SPM
retrieval
for Poyang Lake. The cross-comparisons between MODIS and other sensors for C
SPM
retrieval could
be found in literatures [
14
,
25
]. For examples, Wu et al. developed Landsat-5 TM-based C
SPM
model
for Poyang Lake by assuming the MODIS-derived to be the true values [
56
]; and Tian et al. developed
GF-1 WFV-based CSPM model for Deep Bay with assistance of MODIS-derived CSPM values [52].
The high quality SWIR bands of Sentinel 2 MSI also partially contributed to the successful C
SPM
retrieval in this study, because they facilitate the atmospheric correction over case-II waters like
Poyang Lake. The SWIR-based atmospheric correction method has been proven to be reliable and
integrated into SEADAS for the operational product generations over turbid waters from MODIS
images [57,58]. Recently, it is also modified and successfully applied to Landsat 8 OLI, a precursor of
Sentinel 2 MSI [
28
,
59
,
60
]. Although the Sentinel 2 MSI SWIR bands is about one third of the Landsat 8
OLI SWIR bands in terms of SNR specification, the SNR can be improved through average filtering [
44
].
Therefore, we infer that the SWIR-based atmospheric correction method should produce acceptable
performance for Sentinel 2 MSI.
The advantage of high temporal resolution is obvious, since frequent observations would enable
C
SPM
monitoring over short periods. For example, GF 1 WFV with a 4-day revisit period obtained
monthly cloud-free observations for Poyang Lake in 2015, while Landsat 8 OLI only had 4 clear scenes
over the same period. Sentinel 2 MSI provides a revisit time of 5-day at the equator with the full
operations of the two satellites [
40
]. Considering its capability in C
SPM
retrieval demonstrated in our
study, Sentinel 2 MSI should be an ideal data source for the operational C
SPM
monitoring in Poyang
Lake. Novoa et al. [
53
] developed a switching model for low-to-high turbidity waters, which used red
band for low-to-medium turbid waters and NIR band for high turbid waters to retrieve C
SPM
values.
Developing a switching model for Poyang Lake based on Sentinel 2 MSI B4 and B7 using this modeling
strategy might work better to capture the complex spatiotemporal C
SPM
patterns of this floodpath lake.
Moreover, further elaborate evaluation of the atmospheric correction and C
SPM
retrieval accuracy with
concurrent in-situ and satellite observations would be a meaningful task.
5. Conclusions
The Sentinel 2 MSI B4–B8b-based C
SPM
retrieval models were developed, and they were
found acceptable and applicable in estimating C
SPM
values of Poyang Lake. The Sentinel 2 MSI
B4–B8b-derived C
SPM
maps revealed clear spatial distribution patterns, and a riverine induced turbid
plume was observed on 15 August 2016, while the C
SPM
values were relatively lower across the whole
Poyang Lake on 2 April 2017. The consistent results of cross comparisons between MODIS and Sentinel
2 MSI also proved the applicability of the models developed. The Sentinel 2 MSI B7-based power model
(C
SPM
= 2950
×
B7
1.357
) with the highest calibration and validation accuracy, and high consistency
Remote Sens. 2017,9, 761 16 of 19
with MODIS, is recommended for sediment-laden waters, while the Sentinel 2 MIS B4-based model
(4.044
×
exp(19.53
×
B4)) works better for clear waters. This study demonstrated that the Sentinel
2 MSI, with higher spatial resolution than MODIS, more band configurations than GF 1 WFV, and
shorter revisiting time than Landsat 8 OLI, should be an appropriate data source for monitoring C
SPM
over case-II waters such as Poyang Lake.
Acknowledgments:
The research was supported by the Basic Research Program of Shenzhen Science and
Technology Innovation Committee (No. JCYJ20151117105543692), Scientific Research Foundation for Newly
High-End Talents of Shenzhen University, Natural Science Foundation of China (NSFC) General Research Grant
(41471340), Shenzhen Future Industry Development Funding Program (No. 201507211219247860), Research Grants
Council (RGC) of Hong Kong General Research Fund (GRF) (HKBU 203913) and Hong Kong Baptist University
Faculty Research Grant (FRG2/14-15/073). We thank the Nanjing Institute of Geography and Limnology, Chinese
Academy of Sciences for their help and assistance in the in-situ optical measurements and laboratory-based
determinations. We are grateful to the two anonymous reviewers for their helpful comments and suggestion.
Author Contributions:
All authors conceived and designed the study. Huizeng Liu, Shuibo Hu and Guofeng Wu
made substantial contributions to experiments design. Huizeng Liu implemented the experiments. Teizhu Shi
made substantial contributions to the field campaigns and spectral measurements. All authors discussed the basic
structure of the manuscript, and Huizeng Liu finished the first draft. Qingquan Li, Qiming Zhou, Guofeng Wu,
Shuibo Hu reviewed and edited the draft. All authors read and approved the submitted manuscript, agreed to be
listed and accepted the version for publication.
Conflicts of Interest: The authors declare no conflict of interest.
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