Progress in Earth andPlanetary Science

Silvestri et al.
Progress in Earth and Planetary Science(2022) 9:45
https://doi.org/10.1186/s40645-022-00490-7Open Access

COMMENT

Comment on “Soil salinity assessment
by using near‑infrared channel and Vegetation
Soil Salinity Index derived from Landsat 8 OLI
data: a case study in the Tra Vinh Province,
Mekong Delta, Vietnam” by Kim‑Anh Nguyen,
Yuei‑An Liou, Ha‑Phuong Tran, Phi‑Phung
Hoang and Thanh‑Hung Nguyen
Sonia Silvestri1* , Diep Ngoc Nguyen2,3 and Emilia Chiapponi1

Abstract
Nguyen et al. (Prog Earth Planet Sci 7:1, 2020. https://​doi.​org/​10.​1186/​s40645-​019-​0311-0) suggest that Landsat 8 OLI
can be used to map and monitor soil salinity in the coastal zone of the Mekong River Delta. The authors use empiricalcorrelations between the near-infrared (NIR) band, or vegetation indexes containing the NIR band, and soil salinity. We
show that within the coastal portion of the Mekong Delta, extensively ponded due to widespread shrimp farming,
about 90% of Landsat 8 pixels are fully or partially covered by water. We then find that, due to strong NIR radiation
absorption, NIR reflectance from ponded pixels decreases linearly with increasing water percentage cover, while nosignificant correlation is found between reflectance and soil salinity. Through detailed new analyses, we conclude that
NIR reflectance attenuation cannot be ascribed to vegetation stress caused by soil salinity, but rather to the presenceof water ponds. We also show that a similar behavior exists in ponded freshwater inland areas, confirming that the NIR
absorption exerted by water is independent of salinity.
Keywords: Soil salinity, Mekong River Delta, Coastal areas, Wetlands, Landsat 8 OLI, Google Satellite, Near-infrared
reflectance reduction, Water absorption, Mixed pixels
1 Introduction
Nguyen et al. (2020) suggest that soil salinity can bemonitored using Landsat 8 OLI (L8 hereafter) in the
coastal zone of the Mekong River Delta. The study
suggests an inverse exponential correlation between*Correspondence: sonia.silvestri5@unibo.it
1
Department of Biological, Geological and Environmental Sciences,
University of Bologna, P.za Porta S. Donato 1, 40126 Bologna, Italy
Full list of author information is available at the end of the article

the near-infrared (NIR) band (i.e., L8 band-5) and theelectrical conductivity ­(EC1:5) (i.e., determined using a
conductivity probe on a mix of 1 part soil with 5 parts
deionized water) determined at several points within
their study site (Tra Vinh Province, Fig. 6 plot d of
Nguyen et al. 2020). Significant correlations between
vegetation indexes that include the NIR band and soil
­EC1:5 are also highlighted in the study (see Fig. 8 plots
SI4, NDSI, NDVI, SAVI and VSSI of Nguyen et al.
2020). The authors underline the good performance of
the Vegetation Soil Salinity Index (proposed by Dehni© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Silvestri et al. Progress in Earth and Planetary Science(2022) 9:45

and Lounis (2012) as VSSI = 2 × green − 5 × (red + NIR), where “green” and “red” are the L8 band-3 and theL8 band-4, respectively).
The Tra Vinh Province covers a large territory that
includes inner agricultural lands as well as a coastal
wetland zone, characterized by an extensive presenceof ponds for shrimp farming (Wassmann et al. 2019).
Focusing exclusively on the latter, we will show in thiscomment article that (1) L8 spatial resolution is notsuitable to distinguish between ponded and non-ponded areas; therefore, (2) the decreased NIR reflectanceascribed to increased soil salinity is instead due to thepresence of water in L8 mixed pixels. Finally, (3) weshow that NIR reflectance is equally reduced independently of whether the water ponding area is saltwateror freshwater.
Salinity affects vegetation development and its
health status. Some vegetation species, as halophytes,
can grow on salt marsh salted soil because they developed specific adaptation measures (Silvestri andMarani 2004). Mangroves are also highly tolerant tosalinity, thanks to a range of adaptations (Parida andJha 2010). However, in areas other than coastal saline
wetlands, salt is toxic to vegetation and affects thedevelopment of natural vegetation as well as crop production (Montanarella et al. 2015; Vargas et al. 2018).
Hence, in these cases, remote sensing can be used todetect the stress that salt produces on plants, causing lower photosynthetic activity (Tilley et al. 2007;
Zhang et al. 2015; Scudiero et al. 2015). Specifically,
decreased near-infrared (NIR) reflectance is observed
when vegetation is subjected to salinity stress (Wanget al. 2002; Tilley et al. 2007; Zhang et al. 2011). L8 has
been used for this purpose in other studies (Scudiero
et al. 2015; Gorji et al. 2020). However, factors otherthan salinity stress may be responsible for a reductionin NIR reflectance, as the presence of water around or
underneath the vegetation, a well-known effect in wetlands (Silva et al. 2008; Hestir et al. 2008; Adam et al.
2010). Therefore, the conclusion that a reduction in
the NIR reflectance is due to vegetation stress caused
by increased salinity must be carefully tested in coastal
wetlands, and the influence of water absorption mustbe accounted for (Dekker et al. 2002).
Here, we focus on the same L8 OLI image analyzed
by Nguyen et al. (2020), acquired on February 14, 2017,and available in USGS EarthExplorer. We selected theCollection-2 Level-2 product (see detailed characteristics in Additional file 1: Table S1), which is geometrically, radiometrically and atmospherically corrected
to surface reflectance by USGS (2022). We masked theareas covered by clouds.

Page 2 of 8

2 Main text
2.1 Landsat 8 OLI spatial resolution is not adequate
to study soil salinity in the ponded coastal zoneof the Mekong River Delta

Wetlands are characterized by partly emerged and submerged areas. The coastal portion of the Mekong River
Delta is characterized by an almost continuous presence of artificial ponds, mainly used for shrimp farming(Fig. 1). Ponds are surrounded by embankments withlittle streets, small villages, bare soil as well as vegetatedareas. In order to identify emerged vegetation and study
its health status, the spatial resolution allowing pixels
to be mostly “pure” (i.e., either exclusively composed offully emerged or fully submerged areas) should first be
determined. Too coarse resolution, in fact, gives a large
percentage of mixed pixels, i.e., pixels comprising both
emerged and flooded areas, with a reflectance that isintermediate between those from both categories. To
understand if Landsat data can be used to discriminate
ponded vs non-ponded surfaces, we must determine
the probability that pixels contain portions of standing waters. We can perform such analysis by generatinga grid with cells of 30 m × 30 m, overlaying on L8 pixels, and by counting the number of cells that fully fallon water ponds, land and mixed water/land areas. Since
the coastal ponded area within the Tra Vinh Province isvery large (more than 275 k­ m2, see Fig. 1), we performed
these analyses by generating 20 grids of 10 × 10 cells (of30 m × 30 m each) distributed across the ponded area.
Twelve grids were created around the sampling points
described in Nguyen et al. (2020; points with IDs 30–41
listed in Table 4), while other eight grids were randomly
generated in QGIS across the remaining ponded area
(Fig. 1). The detection of the grid cells falling on water
ponds or on emerged land was determined with a visualinspection method in QGIS using Google Earth satelliteas base image. Google Earth is the most accurate highresolution virtual globe currently available and covers
almost the entire land surface of the Earth (Yu and Gong
2012; Guo et al. 2020). It integrates aerial photographswith medium to high spatial resolution satellite images
mainly collected by WorldView-1/2/3, GeoEye-1, Airbus’
Pleiades, QuickBird, Landsat and SPOT5. Such integration allows the operator to zoom to around 0.3–0.5 m
resolution (see Additional file 1: Table S2 for details)
depending on the availability of satellite images in different locations of the world. Therefore, the advantage
of using Google Earth is that it provides a recent cloudfree base image with an extremely high spatial resolutionthat allows the operator to clearly detect water ponds
and land. The horizontal accuracy of Google Earth varies for different locations of the world and is strongly
affected by topography, being lower on mountainous

Silvestri et al. Progress in Earth and Planetary Science(2022) 9:45

Page 3 of 8

Fig. 1 The upper left panel shows the study area: the yellow polygon indicates the coastal zone with ponds used for shrimp farming, which ismagnified in the A panel; the B panel shows the freshwater area. Blue points in the A panel indicate the location of the grids created around thetwelve sampling points provided by Nguyen et al. (2020); red points in the A panel indicate the location of the eight additional grids randomly
generated; orange points in the B panel indicate the location of the grids created on freshwater ponds. The images of the grids of 10 × 10 cells (of30 m × 30 m each) in the bottom right of the figure correspond to the grids created around point with ID 32 and around point with ID 36 overlaid
on the most recent and on the historic Google Earth satellite images (see Table 1 for the exact dates of the images). Coordinate reference system:
WGS84 UTM Zone 48. Maps were produced in QGIS and Google Earth Pro using Google Earth satellite images

terrain and higher on plains. Several studies have shownthat on plains and lowlands the absolute positioning
accuracy generally varies between 0.1 and 5.0 m (Benker et al. 2011; Paredes-Hernandez et al. 2013; Goudarzi
and Landry Jr 2017; Guo et al. 2020). The disadvantage
is that Google Earth is a collage of images collectedvery recently but in different periods, and some landuse changes may have occurred since 2017, when the L8
image used in this study was acquired. Some differences
may also be due to seasonal variations or differences inwater levels. Moreover, given the typically high humidity of the Mekong delta, some areas are still covered byclouds. Therefore, to further verify the method, we also
used Google Earth Pro to explore the database of available images, including those collected before February
2017, by overlaying the same 20 grids generated in QGIS.
Figure 1 shows an example of two generated grids, while
Table 1 shows the number of cells that, within each gridof 10 × 10 cells, fully fall within water ponds, land or on
mixed surfaces using the recent Google Earth image aswell as cloud-free images collected before February 2017.
The table includes the dates of the analyzed images. We
notice that, on average, more than 86% of the cells contain both water and emerged areas, and almost 4% fall on

17

83

J15

Mix

Image date

M18

Image date

Land

88

Mix

0

12

Land

Water

0

Water

G30

A14

93

7

0

M18

91

7

2

G31

J15

69

14

17

F18

85

0

15

G32

A14

100

0

0

D19

100

0

0

G33

A14

94

1

5

M18

92

3

5

G34

J15

95

4

1

D19

96

2

2

G35

M11

91

3

6

F18

94

0

6

G36

J15

64

32

4

D19

64

34

2

G37

J15

64

33

3

F18

74

25

1

G38

A14

96

0

4

D19

78

17

5

G39

A14

92

6

2

D19

87

10

3

G40

A14

100

0

0

F18

98

2

0

G41

A14

99

1

0

D19

93

6

1

R1

J15

74

26

0

D19

72

28

0

R2

J06

75

14

11

D19

69

18

13

R3

A14

96

4

0

D19

93

5

2

R4

A14

95

0

5

D19

96

1

3

R5

A14

89

5

6

D19

89

5

6

R6

J15

77

14

9

M18

78

17

5

R7

A14

94

3

3

D19

94

2

4

R8

87

9.2

3.8

86.55

9.7

3.75

Mean %

Cells sum to 100 for each grid. G30 to G41 are grids created around the corresponding field points of Nguyen et al. (2020) (see IDs of Table 4). R1 to R8 are grids created around random points falling within the coastal
ponded zone. Google Earth satellite images are used as base images. The first part of the table is produced using the recent base images (collected on December 2019, March 2018 and February 2018, reported in thetable as D19, M18 and F18, respectively). The second part is produced using historical cloud-free base images collected before February 14, 2017 (collected on January 2015, August 2014, March 2011 and January 2006,
reported in the table as J15, A14, M11 and J06, respectively)

Historical images

Recent images

ID

Table 1 Number of grid cells fully falling on water, land or mixed water/land

Silvestri et al. Progress in Earth and Planetary Science(2022) 9:45
Page 4 of 8

Silvestri et al. Progress in Earth and Planetary Science(2022) 9:45

water ponds, confirming that the large majority of L8 pixels are affected by the presence of water.
Pixels that fully or partially fall on water bodies areaffected by the water ability to absorb incident energy
in the infrared portion of the electromagnetic spectrum,
attenuating the reflected NIR signal (see Dekker et al.
2002) and therefore interfering with any possible signal attenuation linked to vegetation stress. Our results
indicate that in the ponded coastal area of the Tra Vinh
Province, about 90% of the Landsat pixels are affectedby the presence of water and should be masked to avoid
the NIR reflectance attenuation exerted by water. Only
the remaining 10% can be used to study a possible correlation between soil salinity and NIR reflectance, using
salinity field measurements performed exclusively withinpixels that fully fall on emerged land. This method would
provide a map with more than 90% of masked pixels.
Therefore, we conclude that L8 spatial resolution is notsuitable for this purpose in the ponded coastal area of
the Tra Vinh Province. High spatial resolution satellites,such as WorldView-3 or Planet, would provide a muchlarger number of pixels falling on emerged land, allowing
a more effective analysis.
2.2 The presence of ponds influences the NIR signal (andthe vegetation indexes containing NIR), masking
possible relations with soil E­ C1:5 in the coastal areaof the Tra Vinh Province

We selected twelve L8 pixels corresponding to the fieldmeasurement points described in Nguyen et al. (2020)
across the coastal area (points with IDs 30–41 listed inTable 4). The location of the twelve points can be seenin Additional file 1: Fig. S1. We notice that the points
fall on embankments surrounded by ponds and channels and that eleven out of the twelve L8 OLI pixels corresponding to the sampling points fall on mixed water/
land surfaces (Additional file 1: Fig. S1). We notice that

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two points (point G30 and point G41) fall at the edge
between land and water, and point G40 falls on water,even though very close to the embankment. This may
be due to a low precision of the Global Navigation Satellite System (GNSS) receiver used by the authors or toa low absolute positioning accuracy of the Google Earthimages as discussed in the previous chapter. Nevertheless, we underline that an error of a few meters in the relative positioning of GNSS points is small with respect tothe size of the L8 pixels themselves. We notice that in allcases, pixels and their nearest neighbors contain a very
high proportion of water. In order to quantify the water
percentage cover within each selected pixel, we generated 100 regularly spaced points (see Additional file 1:
Fig. S2). As we did for the analysis described at paragraph 2.1, we used recent and historical Google Earthsatellite images (see Table 1 for image dates) to visually
determine the number of points falling on water ponds(Additional file 1: Fig. S2). This number is the best available proxy of actual water percentage cover. Figure 2ashows that there is a significant linear correlation (Pvalue < 0.01) between the water percentage cover andthe L8 NIR band reflectance: Reflectance decreases
with increasing water percentage cover, confirming thatshrimp farming ponds interfere with the reflectance signal, by absorbing near-infrared electromagnetic energy.
This correlation is slightly stronger if we consider thevalues of water percentage cover determined using historical images in Google Earth. No significant correlation
could be found between ­EC1:5 and the L8 NIR reflectance(Fig. 2). We also performed a multiple regression of L8
NIR reflectance on ­EC1:5 and water percentage cover as
independent variables. We find that L8 NIR reflectancevariability is almost totally explained by water percentage cover, while the correlation with EC1:5 is not significant (P value > 0.05). A similar behavior is observed when

Fig. 2 Relationship of the L8 NIR reflectance of the G30–G41 pixels with a the water percentage cover and b the E­ C1:5 provided by Nguyen et al.
(2020)

Silvestri et al. Progress in Earth and Planetary Science(2022) 9:45

considering the VSSI as a dependent variable instead of
the NIR reflectance.
2.3 The presence of ponds reduces NIR reflectancewhether they contain saltwater or freshwater

In order to explore the possible influence of salt on theprevious analysis, we compared the effect of the water
presence on the NIR reflectance in freshwater and saline
ponded environments. Therefore, we randomly selected
20 pixels falling on freshwater ponds in the inner MekongRiver Delta, at a distance higher than 60 km from themouth (Fig. 1), so that the ponds are not affected bysalinity (Eslami et al. 2019; Wassmann et al. 2019). As forthe coastal saline area, in order to match the 20 observations selected in the freshwater area, to the original 12
pixels corresponding to the field observations performedby Nguyen et al. (2020), we added 8 pixels randomly
selected within the grids used to determine if the L8 spatial resolution were adequate (Fig. 1). We generated 100
points regularly spaced within all the created pixels. Figure 3 shows that in (a) saltwater ponded areas as well asin (b) freshwater ponded areas, there is a significant linear correlation (P value < 0.01) between water percentage cover and reflectance of the L8 NIR band. Similarly,
Fig. 3 shows that there is a significant linear correlation

Page 6 of 8

(P value < 0.01) also between water percentage coverand the VSSI for (c) saltwater and (d) freshwater ponded
areas. Our results confirm that NIR reflectance is gradually reduced by an increasing water surface, in both salted
and freshwater environments. This result confirms thatsalinity does not influence the absorption of the incident
energy exerted by water.

3 Conclusions
Nguyen et al. (2020) has the merit of exploring the useof L8 OLI to study the influence of salinity on vegetation in the Mekong River Delta. Even though the L8 spatial resolution is suitable to perform the analysis on the
inner agriculture land of the Tra Vinh Province considered by the authors, we show that the method cannot beapplied to the coastal area where shrimp farming ponds
are widely present. We have shown that L8 spatial resolution is not suitable to distinguish between ponded andnon-ponded areas in the coastal portion of the Tra Vinh
Province, because about 90% of the L8 pixels fully or partially fall on ponds and canals. We have also shown thatthe attenuation of the NIR reflectance is primarily due tothe water presence and decreases linearly with increasingwater percentage cover, while no significant correlation
between soil salinity and NIR reflectance could be found.

Fig. 3 Relationship of the L8 NIR reflectance with the water percentage cover in a the coastal area and b in the freshwater area (plot A and plot B of
Fig. 1, respectively). Relationship of the VSSI with the water percentage cover (c) in the coastal area and d in the freshwater area

Silvestri et al. Progress in Earth and Planetary Science(2022) 9:45

The attenuation of the NIR reflectance due to the presence of water ponds is similar in the coastal saline area
and in the inner freshwater area.

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Strategies Division, Euro-Mediterranean Center on Climate Change, Via della
Libertà, 12, 30175 Venice, VE, Italy.
Received: 27 January 2022 Accepted: 17 May 2022

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​022-​00490-7.
Additional file 1. Supplementary figures and tables.
Acknowledgements
Diep Nguyen Ngoc and the collaboration among the authors were supportedby the European Commission under the Erasmus Mundus Joint Master Degree
Programme in Water and Coastal Management [Grant Number 586596-EPP1-2017-1-IT-EPPKA1-JMDMOB]. The authors would like to acknowledge theanonymous reviewers of this article who gave an important contribution to
improve it.
Author contributions
SS proposed the topic, conceived and designed the study, carried out theexperimental study, analyzed the data and interpreted the results, and wrote
the manuscript. DNN and EC collaborated in analyzing the data and helpedin their interpretation. All authors read and approved the final manuscript.
Sonia Silvestri is a Senior Assistant Professor at the University of Bologna (Italy),
Department of Biological, Geological, and Environmental Sciences. She holds
a position as an Adjunct Associate Professor at Duke University, USA (Divisionof Earth & Ocean Sciences, Nicholas School of the Environment). She is also a
fellow of the Venice International University, Venice, Italy. Her research focuseson active and passive remote sensing applied to vegetation mapping, soil
studies, hydrology, tidal environments morphology, water quality in coastal
environments; hyperspectral imagery analysis; peatland detection throughremote sensing and airborne geophysical methods; ecology of wetlands; andwetlands ecogeomorphology. Diep Nguyen Ngoc holds a research contract at
the Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University (Italy), in collaboration with the Risk Assessment and Adaptation Strategies Division, Euro-Mediterranean Center on Climate Change. She iscurrently working at a project that aims at assessing the impacts of multi-risks
induced by multiple climatic stressors in the Metropolitan city of Venice. Emilia
Chiapponi is a Ph.D. student at the University of Bologna (Italy), Departmentof Biological, Geological, and Environmental Sciences. She is currently working
on greenhouse gas emissions and carbon balance along a salinity gradient incoastal wetlands. All authors read and approved the final manuscript.
Funding
European Commission under the Erasmus Mundus Joint Master
Degree Programme in Water and Coastal Management [Grant Number
586596-EPP-1-2017-1-IT-EPPKA1-JMDMOB].
Availability of data and materials
The dataset supporting the conclusions of this article is available in the
ZENODO repository at: https://​zenodo.​org/​depos​it/​58535​16 with https://​doi.​
org/​10.​5281/​zenodo.​58535​16.

Declarations
Ethical approval
This article does not contain any studies with human participant or animals
performed by any of the authors.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Biological, Geological and Environmental Sciences, Universityof Bologna, P.za Porta S. Donato 1, 40126 Bologna, Italy. 2 Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice,
Via Torino 155, 30172 Mestre, VE, Italy. 3 Risk Assessment and Adaptation

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