Potential use of satellite observations to detect suspended sediment in delta region: a case study of the Red river delta, Vietnam

Physical sciences | Physics, environmental sciences | Ecology
Vietnam Journal of Science,
Technology and Engineering 3September 2020 • Volume 62 Number 3
Introduction 
Suspended sediment, which includes organic and 
inorganic materials within the water flow, is a natural part of 
a river system. The primary sources of suspended sediment 
come from the erosion of soil, mass movements such as 
landslides, and riverbank erosion or human interventions on 
the landscape [1-3]. High amounts of suspended sediment 
in water can reduce the transmission of light, which not 
only affects the phytoplankton species in short term but 
also the entire ecosystem in the long term. Suspended 
sediment plays an important role in shaping the landscape, 
transporting nutrients to various species, and creating 
ecological habitats [4, 5]. Similarly, pollutants can adhere to 
suspended sediment while in transport and thus suspended 
sediment can influence pollutant movement. Suspended 
sediment is also an indicator of issues occurring in the 
river delta and coastal areas, which include water quality, 
ecological degradation, and soil and/or riverbank erosion. 
To develop a suitable river basin management strategy, 
frequent monitoring of suspended sediment is critical. 
Despite the importance of suspended sediment, it is 
poorly gauged due to the lack of in-situ networks in many 
areas and especially in developing countries. We choose 
the RRD for this research because this region has several 
meteorological stations. However, they have not been 
operated for some time due to lack of budget and thus this 
region is considered to be ungauged basin. Moreover, the 
RRD is one of two largest and most important deltas in 
Vietnam; however, it has not received as much attention as 
the Mekong river delta. Thus, research in this area is central 
to the critical understanding of this important region.
Data quality is also a concern since monitoring suspended 
sediment depends on the number of stations, their locations, 
and the frequency of measurements [6]. There are some 
Potential use of satellite observations 
to detect suspended sediment in delta region:
a case study of the Red river delta, Vietnam
Hue Thi Dao1*, Tung Duc Vu2
1Thuyloi University
2Vietnam Disaster Management Authority, Ministry of Agriculture and Rural Development, Vietnam
Received 4 December 2019; accepted 2 April 2020
*Corresponding author: Email: hue.dao89@gmail.com
Abstract:
Building an integrated river delta basin and coastal 
management plan in the context of climate change 
requires suspended sediments data, which plays 
an important role and is the key component for 
understanding the hydrology regime in the delta 
region. Sediments are responsible for carrying a 
considerable amount of nutrients and contaminants. 
Most sediment discharge data is acquired by surveys/
data collection activities or by mathematical modelling. 
However, these methods are costly, time-consuming, 
and complex. Therefore, in this study, the authors 
investigate the potential use of satellite observations 
(MODIS reflectance) to detect suspended sediment 
flux in the Red river delta (RRD) of Vietnam. The 
relationships between discharge (Q), suspended 
sediment concentration (SSC), and total load (L) 
collected from the three in-situ stations Son Tay station 
(ST), Thuong Cat station (TC), and Hanoi station (HN) 
in the RRD are determined by regression analyses 
of reflectance data (R) obtained from MODIS bands 
1-2 (250-m resolution). The results present a close 
connection between the monthly average of SSC and R 
and a good statistical relationship between the monthly 
average of Q and R in HN. At TC and ST, a lower 
correlation was found compared to HN because of the 
cloud cover and the position where data was collection 
in the river. The coefficient of determination ranged 
from 0.11 to 0.40 for the R-SSC and R-Q relationships. 
A method of estimating SSC and L at a single point 
along the river using data from Q and R was proposed 
based on the relationship correlation results. 
Keywords: delta region, discharge, MODIS, regression 
analysis, suspended sediment.
Classification numbers: 2.1, 5.1
Doi: 10.31276/VJSTE.62(3).03-9
Physical sciences | Physics, environmental sciences | Ecology
Vietnam Journal of Science,
Technology and Engineering4 September 2020 • Volume 62 Number 3
methods to obtain suspended sediment information such 
as using empirical models, physically-based mathematical 
models, and field sampling. Recently, the use of satellite 
images to detect suspended sediment has captured the 
attention of researchers [7-9]. There are studies that use 
Moderate Resolution imaging Spectroradiometer (MoDiS) 
images or Landsat Thematic Mapper (TM) and Enhanced 
Thematic Mapper Plus (ETM+) imagery to characterize 
the spatial and temporal pattern of surface sediments [10-
13] based on the very close relationship between R and 
suspended sediment concentration. Recent results show that 
satellite remote sensing technology is applicable and useful 
to obtain not only suspended sediment information but also 
other hydrological parameters of these ungauged areas [14]. 
This study aims to investigate the potential use of 
satellite observations (MODIS reflectance) to detect the 
seasonal change of suspended sediment flux in the RRD 
region. We first extract the satellite reflectance value at the 
location of the station and then apply simple regression 
analysis to the reflectance, discharge, suspended sediment, 
and total sediment load on the same day. The simple 
regression analysis used in this paper refers to the use of 
single variable (R) for one dependent variable (suspended 
sediment or discharge). We choose the simple regression 
analysis because of limitations in the available data and the 
objective of our research. Regression analysis perfor ...  
ST had smaller correlation results than HN. An interesting point in these results is that using 
the reflectance value to predict SSC is better than predicting Q by R. Both the scaling factors 
and exponents in the R-SSC equations were not much different for the three stations, but they 
did vary significantly in case of the R-Q relationship equations. The R-SSC relationship (see 
Fig. 8) displayed a similar trend for all stations, but there were more outlier points in TC than 
in HN and ST. 
0
1000
2000
3000
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5000
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7000
8000
9000
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0.09 100.09 200.09 300.09 400.09
M
on
th
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 m
ea
n 
di
sc
ha
rg
e,
 Q
 (m
3 /s
) 
Monthly mean suspended sediment concentration, SSC (g/m3) 
TC HN ST
Power (TC) Power (HN) Power (ST)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0.09 0.11 0.13 0.15 0.17
M
on
th
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 m
ea
n 
di
sc
ha
rg
e,
 Q
 (m
3 /s
) 
Monthly reflectance, R 
TC HN ST
Power (TC) Power (HN) Power (ST)
Fig. 7. Scatter plots of monthly reflectance, R, and monthly 
mean discharge, Q, at the three stations TC, HN, and ST.
A close relationship between R-Q and R-SSC were 
recorded at the HN station. The R2 value was 0.40 and 0.33 
for R-Q and R-SSC, respectively, for this station. However, 
TC and ST had smaller correlation results than HN. An 
interesting point i these results is that using the reflectance 
value to predict SSC is better than predicting Q by R. Both 
the scaling factors and exponents in the R-SSC equations 
were not much different for the three stations, but they did 
vary significantly in case of the R-Q relationship equations. 
The R-SSC relationship (see Fig. 8) displayed a similar 
trend for all stations, but there were more outlier points in 
TC than in HN and ST.
one possible reason to explain the outlier points is the 
effect of clouds. The cloud cover is different at each station 
and it influences the reflectance value of the pixel where the 
observation data was taken. 
Inter-relationship between regression parameters
As shown in Figs. 5, 6, and 7, the relationship of L-Q, 
R-SSC, and R-Q can be expressed as
L=aQb (2)
SSC=αRβ (3)
Q=γRδ (4)
Substituting Eq. (2) and Eq. (3) into Eq. (1) reveals
aQb = Q*αRβ (5)
Then,
8 
one possible reason to explain the outlier points is the effect of clouds. The cloud cover 
is different at each station and it influences the reflectance value of the pixel where the 
observation data was taken. 
Inter-relationship between regression parameters: 
As shown in Figs. 5, 6, and 7, the relationship of L-Q, R-SSC, and R-Q can be 
expressed as (2) (3) (4) 
Substituting Eq. (2) and Eq. (3) into Eq. (1) reveals aQb = Q*αRβ (5) 
Then, ( 
)
 (6) 
Comparing Eq. (6) with Eq. (4) gives 
 ( 
)
and (7) 
 ( 
) (8) 
Depending on Eq. (7) and Eq. (8), it is possible to estimate the parameters for one of the 
three equations (Eq. (2) Eq. (3), or Eq. (4)) from the parameters of the other equations. For 
example, if we observed Q at a specific point of river section, we can correlate Q with 
satellite-observed R and then γ and δ parameter in Eq. (4) could be obtained. in addition, the 
parameters a and b could be possibly estimated from hydro-geological characteristics and 
land cover in the upstream area using a regionalization scheme [18]. once the parameters γ, 
δ, a, and b are identified through the above procedure, α and β in Eq. (3) can be obtained 
from Eqs. (7) and (8) without using observed SSC data. Then, Eq. (3) could be applied for 
near-real-time SSC monitoring using satellite observed water-surface reflectance, R, and 
identified parameters α and β. 
 (6)
Comparing Eq. (6) with Eq. (4) gives
8 
one possible reason to explain the outlier points is the effect of clouds. The cloud cover 
is different at each station and it influences the reflectance value of the pixel where the 
observation data was taken. 
Inter-relationship between regression parameters: 
As shown in Figs. 5, 6, and 7, the relationship of L-Q, R-SSC, and R-Q can be 
expressed as (2) (3) (4) 
Substituting Eq. (2) and Eq. (3) i to Eq. (1) reveals aQb = Q*αRβ (5) 
en, ( 
)
 (6) 
paring Eq. (6) with Eq. (4) gives 
 ( 
)
and (7) 
 ( 
) (8) 
Depending on Eq. (7) and Eq. (8), it is possible to estimate the parameters for one of the 
three equations (Eq. (2) Eq. (3), or Eq. (4)) from the parameters of the other equations. For 
example, if we observed Q at a specific point of river section, we can correlate Q with 
satellite-observed R and then γ and δ parameter in Eq. (4) could be obtained. in addition, the 
parameters a and b could be possibly estimated from hydro-geological characteristics and 
la d cover i the upstr am area using a regionalization scheme [18]. once the parameters γ, 
δ, a, and b are identified through the above procedure, α and β in Eq. (3) can be obtained 
from Eqs. (7) and (8) without using observed SSC data. Then, Eq. (3) could be applied for 
near-real-time SSC monitoring using satellite observed water-surface reflectance, R, and 
identified parameters α and β. 
 (7) 
and 
 ( ) (8)
Dependi g on Eq. (7) and Eq. (8), it is p ssible to 
estimate the parameters for one of the three equations (Eq. 
(2) Eq. (3), or Eq. (4)) from the parameters of the other 
equation . For example, if we observed Q at a specific p int 
of river section, we can correlate Q with satellite-observed
R and then γ and δ parameter in Eq. (4) could b obtaine . In 
addition, the parameters a and b could be possibly estimated 
rom hydro-geological characteristics and land cover in the 
upstream area using a regionalization scheme [18]. once 
the parameters γ, δ, a, and b are identified through the above 
procedure, α and β in Eq. (3) can be obtained from Eqs. 
(7) and (8) without using observed SSC data. Then, Eq. 
(3) could be applied for near-real-time SSC monitoring 
using satellite observed water-surface reflectance, R, and 
identified parameters α and β.
9 
Fig. 8. Scatter plots of the monthly mean suspended sediment concentration, SSC, and 
monthly reflectance, R, at the three stations TC, HN, and ST. 
Table 2. Relationship equation and performance of regression of L-Q, Q-SSC, R-Q, R-
SSC at the three stations. 
Correlation Station Relationship 
equation 
R2 
L-Q 
TC 0.94 
HN 0.82 
ST 0.87 
Q-SSC 
TC 0.76 
HN 0.37 
ST 0.43 
R-Q 
TC 0.11 
HN 0.40 
ST 0.13 
R-SSC 
TC 0.21 
HN 0.33 
ST 0.18 
0
50
100
150
200
250
300
350
0.09 0.11 0.13 0.15 0.17M
on
th
ly
 su
sp
en
de
d 
se
di
m
en
t c
on
ce
nt
at
io
n,
 S
SC
(g
/m
3 )
Monthly reflectance, R 
TC HN ST
Power (TC) Power (HN) Power (ST)
Fig. 8. Scatter plots of the monthly mean suspended sediment 
concentration, SSC, and monthly reflectance, R, at the three 
stations TC, HN, and ST.
Physical sciences | Physics, environmental sciences | Ecology
Vietnam Journal of Science,
Technology and Engineering8 September 2020 • Volume 62 Number 3
Table 2. Relationship equation and performance of regression of 
L-Q, Q-SSC, R-Q, R-SSC at the three stations.
Correlation Station Relationshipequation R
2
L-Q
TC L=0.23Q1.86 0.94
HN L=1.03Q1.55 0.82
ST L=1.26Q1.49 0.87
Q-SSC
TC Q=19.87SSC0.87 0.76
HN Q=116.53SSC0.66 0.37
ST Q=75.42SSC0.86 0.43
R-Q
TC Q=1575R1.19 0.11
HN Q=64678R2.90 0.40
ST Q=22716R2.23 0.13
R-SSC
TC SSC=3427.1R1.60 0.21
HN Q=7926.8R2.38 0.33
ST Q=2927R1.92 0.18
Conclusions
This study explored the possibility of detecting a seasonal 
change of suspended sediment flux by using remotely 
sensed reflectance of MODIS imagery. At first, we extracted 
R from MoDiS (band 1, 250-m resolution, Surface Daily 
L2G Global) and then analysed the relationship between 
R-SSC and R-Q. We also estimated the relationship between 
L-Q and Q-SSC. 
The results indicate a significant relationship in L-Q 
and Q-SSC and a possible connection in R-SSC and R-Q. 
Although there were some error sources that affected 
the accuracy of the suspended sediment and discharge 
estimation, the results showed a potential of using MoDiS 
satellite reflectance to detect SSC in the delta region. A set 
of equations that calculate the sediment depending on Q 
and R was built in this study. This set has a potential for 
application in other study areas where the change in Q and 
R corresponds to the characteristics of each area.
The approach introduced here illustrates the possible 
use of satellite images and the information of Q in SSC 
monitoring in a data-poor basin. one limitation in this 
study is using only R extracted from satellites, which 
cannot exactly detect the value of suspended sediment 
without Q data. However, a combination of other satellite 
observations such as the EoMAP (Earth observation and 
Environmental Services) water quality monitoring services 
and R from MoDiS images can solve the problem of 
monitoring suspended sediment in ungauged river basins 
in future research. Moreover, using hydrological results 
obtained from remote sensing can be used in combination 
with a numerical model for a deeper understanding about 
the basin. 
ACKNOWLEDGEMENTS 
The authors would like to acknowledge the University of 
Yamanashi, Ministry of Education, Culture, Sports, Science 
and Technology, Japan (MEXT) for supporting this study; 
and Vietnam Academy for Water Resources (VAWR), 
Ministry of Agriculture and Rural Development (MARD) 
for providing data and information.
The authors declare that there is no conflict of interest 
regarding the publication of this article. 
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