Nguyen et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-019-0311-0(2020) 7:1

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Soil salinity assessment by using nearinfrared channel and Vegetation Soil
Salinity Index derived from Landsat 8 OLI
data: a case study in the Tra Vinh Province,
Mekong Delta, Vietnam
Kim-Anh Nguyen1,2,3, Yuei-An Liou1* , Ha-Phuong Tran4, Phi-Phung Hoang3,5 and Thanh-Hung Nguyen6

Abstract
Salinity intrusion is a pressing issue in the coastal areas worldwide. It affects the natural environment and causes
massive economic loss due to its impacts on the agricultural productivity and food safety. Here, we assessed thesalinity intrusion in the Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI image was utilized toderive indices for soil salinity estimate including the single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted
Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index(NDSI). Statistical analysis between the electrical conductivity (EC1:5, dS/m) and the environmental indices derived
from Landsat 8 OLI image was performed. Results indicated that spectral values of near-infrared (NIR) band and VSSI
were better correlated with EC1:5 (r2 = 0.8 and r2 = 0.7, respectively) than the other indices. Comparative results
show that soil salinity derived from Landsat 8 was consistent with in situ data with coefficient of determination, R2= 0.89 and RMSE = 0.96 dS/m for NIR band and R2 = 0.77 and RMSE = 1.27 dS/m for VSSI index. Findings of thisstudy demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring themagnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners,
and farmers by mapping the soil salinity contamination for better selection of accomodating crop types to reduce
economical loss in the context of climate change. Our proposed method that estimates soil salinity using satellitederived variables can be potentially useful as a fast-approach to detect the soil salinity in the other regions withlow cost and considerable accuracy.
Keywords: Soil salinization, Near-infrared (NIR), Climate change, Electrical conductivity, Landsat 8 OLI, Tra Vinh
Province, Mekong Delta

Introduction
Salinity intrusion affects agricultural activities in many
parts of the world (Suarez 1989; Shammi et al. 2019).
It has become a pressing issue in the Mekong Deltaof Vietnam, which has been ranked among top fivecountries most likely to be affected by climate change
coupled with sea level rise. Depending on the* Correspondence: yueian@csrsr.ncu.edu.tw
1
Center for Space and Remote Sensing Research, National Central University,
No. 300, Jhongda Rd., Jhongli District, Taoyuan City 32001, Taiwan, Republicof China
Full list of author information is available at the end of the article

emissions pathway, it is estimated that about 6–12
million people will be potentially affected by coastal
flooding by 2070–2100 and climate change will reduce the national income by up to 3.5% by 2050
(World Bank Group 2018). In particular, Tra Vinh
Province characterized by low-lying coastal and riverdelta region is of very high vulnerability to saline intrusion, fluvial flooding, and cash crops. This area isexperiencing effects of annual monsoon of tropicalclimate zone with long rainy season that source ofsalt brought from the sea by the tides and sea levelrise. This region’s climate is also impacted by the EI© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

(2020) 7:1

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Niño Southern Oscillation (ENSO), which influencesmonsoonal circulation, showing enhanced influence ofsea level rise and drought (World Bank Group 2018).
That is to say, soil salinization is a dynamic phenomena, which can occur through a natural process (primary salinization due to sea level rise) or as a resultof human activities (secondary salinization due tourbanization or irrigation) of enriching the soil withsoluble salts to deteterious levels at or near the soil
surface (Metternicht et al. 2008; Gorji et al. 2017),
resulting in modification of biochemical features ofsoil. Salinity intrusion occurs when salts are dissolvedin water and accumulated in the soil at a level that
affects agricultural production, environment, and economics. During the initial phase, salinity affects themetabolism of soil organisms and reduces the productivity of land. In the next phase, it destroys all theplants and other organisms living in the soil. Salinity
intrusion is thus one of the influencial factors leading
to land degradation and triggering a significant threat
on the sustainable growth and economic benefits(Wiegand et al. 1994; Sertel and Tanik 2017). Globally speaking, it is estimated that farmland affectedby salinity is approximately 45 million hectares withan increased rate from 200,000 to 500,000 hectares ayear (CGIAR 2016). In Vietnam, according to recent
report of Directorate of Water Resources (DWR)Ministry of Agriculture and Rural Development(MARD), the discharge of water in the upstream of
the Mekong River was reduced to 900 m3/s. Meanwhile, the water level in the middle and downstreamof Mekong River was increased by 0.1–1.5 m. The intrusion of seawater increases salinity of water surface
by about 4 g/l spreading through Hau and Tien Rivers, deep into to 45–65 km and 55–60 km from thecoast, respectively (Nguyen and Tanaka 2007; Nguyen
et al. 2008; Nowacki et al. 2015). In contrast, thedrought led to a decline in underground water leveland salinity intrusion in the sweeping 90 years, focusing primarily in Tra Vinh, Ben Tre, and Kien Giang
Provinces. In addition, according to the report of
DWR-MARD in Tra Vinh Province, in the dry season
in 2016, 12,346 ha of rice were damaged due to theimpact of drought and salinity intrusion, mainly in
the Tra Cu, Cau Ngang, Tieu Can, Chau Thanh, andDuyen Hai districts and Tra Vinh City (CGIAR 2016)
Provincial Report of Tra Vinh, 2011-2015.
Because of the devastating impacts of salinization on
soil fertility and agricultural production, finding ways tomitigate the negative impacts on the soil is essential. A
prime step is to map its spatial extent and classify the severity of salinity in the affected areas for sustainable agricultural planning. Ultilizing the satellite data and remotesensing techniques for monitoring soil salinity has

become more efficient and economical (Dehni & Lounis
2012; Taghadosi et al., 2018, Seghal et al, 1988. Different
approaches have been developed and utilized such as
spectral reflectivity, combined spectrum channels, statistical analysis, mathematical modeling, and geophysicalmeasurements. Fernandez-Buces et al. (2006) Amongthe various satellite sensors and techniques used to map
salinity and produce EC maps, multispectral satellite imagery has been widely studied in the literature and foundto be a very promising tool for mitigating this type oftask (Richards 1954; Sehgal et al. 1988; Prentice 1972;
Richardson et al. 1976; Dwivedi and Rao 1992; Dwivedi
and Sreenivas 1998). Verma et al. (1994) studied soil
contamination and found that the degree of soil contaminated by salt affects the land cover characteristics,
resulting in different appearances (color, texture, shape,etc.) on the Landsat TM images. Ivits et al. (2013) addressed the limitation of productivity of salt-affectedsoils across the European continent by using soil maps
and SPOT time series satellite images. Scudiero et al.
(2015) used Landsat ETM+ canopy reflectance for assessing soil salinity at region scale. In most cases, multiple
bands were transformed into a single index that is moresensitive to soil salinity than a single band (Madani
2005; Fernandez et al. 2006; Weng et al. 2010; Aldakheel
2011; Allbed et al. 2014; Zhang et al. 2015; Fan et al.
2015). Results of these studies indicated the high possibility of mapping salinity using Landsat data. Recently,
Landsat TM/ETM+ data have been used to monitor avariety of environmental issues for overall vulnerability
assessment (Nguyen et al. 2016; Liou et al. 2017). This
served as a base to develop a global map of ecoenvironmental vulnerability (Nguyen and Liou 2019a)with methodology detailed in Nguyen and Liou (2019b)
and a conceptual framework to further examine vulnerability and adaptive capacity (VAC) using geospatial
techniques at regional scales, applied over Vietnam(Nguyen et al. 2019.) Furthermore, Sommerfeldt et al.
(1984) and Douaoui et al. (2006) investigated the correlation between indices derived from Landsat 8 OLI images and amount of salt obtained from in situ data.
Sommerfeldt et al. (1984) used different techniques toanalyze the salinity of the soil. Their research confirmedthat salty soil can be reasonably well detected by using
Landsat data. Douaoui et al. (2006) compared 11 indicesderived from satellite imagery with 20 m resolution collected during the 1997 summer sampling campaign andfound that regression–kriging systematically provided
the best validation statistics (bias, accuracy, rank ofmethod), while vegetation indices (NDVI) was proven to
be poor predictors of soil salinity within this context. In
contrast, Sumfleth and Duttmann, 2008 modified amethod for predicting the arrangement of land bases inpaddy rice fields by utilizing in situ data and satellite

Nguyen et al. Progress in Earth and Planetary Science(2020) 7:1

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information as an index number. Their correlation analysis showed that the distribution of salt content has aclose relationship with NDVI and that it substantiallycorresponded to the terrain features such as relative altitude, e.g., the altitude of drainage channel, and topographical wetness. Several studies explored the potentialof soil salinity mapping by using very high spatial resolution (VHSR), i.e., QuickBird and IKONOS imageries
through vegetation indices (Setia et al. 2013; Allbed et al.
2014; Sidike et al. 2014). However, the major limitationof VHRS is not freely available and, thus, not economic
to apply for large areas. Additionally, Tao et al. (2012)
retrieved soil salt content (SSC) from the Advanced
Land Imager (ALI) multi-spectral image based on generalized regression neural network (GRNN). Partial leastsquares regression (PLSR) model has been widely usedin estimation of soil parameters (Farifteh et al. 2007;
Zhang et al. 2011; Nawar et al. 2014 ). Goldshleger et al.
(2012) integrated passive and active remote sensing
methods for assessing soil salinity. Yang et al. (2015)
used a dataset of 92 points with salt data at depth of 30–

40 cm that was divided into two subsets for prediction
and validation.
In general, the techniques and methods used in theprevious studies to extract soil salinity informationfrom satellite images have been successful to someextent. However, the study of soil salinity in the lowlying coastal plain where tidal effects associated with
topographical conditions and sea level rise, for instance, Mekong Delta, is relatively poorly investigated.
Hence, we used simultaneous sampling soil data with
Landsat 8 image acquisition. Soil samples were analyzed to obtain EC1:5, and the coefficient of determination between EC1:5 of soil content and spectralreflectance bands at the location of the sampling
points on Landsat 8 were investigated. Based on thetwo sources of data, regression analysis was performed to relate satellite remote sensing data andground measured salinity to derive the soil salinity
map for Tra Vinh Province. This method can rapidly
assess the soil salinity for the whole Mekong Delta
less commonly found in the previous studies,

Nguyen et al. Progress in Earth and Planetary ScienceFig. 1 Geographical location of Tra Vinh Province in the Mekong Delta, Vietnam, and DEM of Tra Vinh Province was built from terrain data withscale of 1/5000 (Source: Vietnam Department of Surveying and Mapping, 2008)

Nguyen et al. Progress in Earth and Planetary Science(2020) 7:1

Table 1 Classification of soil salinity based on electricalconductivity values (http://vro.agriculture.vic.gov.au/dpi/vro/
vrosite.nsf/pages/water_spotting_soil_salting_class_ranges#)
EC1 : 5 (dS/m)

Soil salinity classes<2

No salinity

2–4

Low salinity

4–8

Medium salinity

8–16

High salinity> 16

Very high salinity

especially under continuous drought and seawaterconditions rising in this coastal plain.

Methods/Experimental
Study area

The Tra Vinh Province is selected for this study (Fig. 1).
Its geographical location ranges from 9° 31′ 46″ N to
10° 04′ 05″ N and from 105° 57′ 16″ E to 106° 36′ 04″
E, covering an area of 2341.2 km2. Tra Vinh is characterized by the lower delta terrain, belonging to the last pointof downstream of the Mekong River with an average
elevation ranging from 0.6 m to 1.0 m (occupying 66%
of the area of the province). Its soil is attributed byhigh sediments mainly deposited due to annual flooding. Its sedimentations were from the river and sea toform alluvial plains, lowland, swamp, and sand dunes.
The sand dunes have an average height of 1.0 m to
3.0 m, particularly with heights of up to 5.0 m in Long
Son and 10 m in Long Toan. For example, high altitude terrains in the Tra Vinh Province can be seen in
Fig. 1. Located in the tropical monsoon climate, Tra
Vinh’s climate is divided into two distinct seasons,namely rainy and dry seasons. The rainy season starts
from May to October with prevailing wind of southwest monsoon and causes heavy rain. The dry season
starts from November to April. In a year, rainfall isunevenly distributed, forming two distinct seasonscorresponding to the monsoon regime in the area.
The rainy season associated with the southwest
monsoon starts in May and ends in October. At
Cang Long weather station, the average rainfall in
the rainy season is 1617 mm with less annual fluctuation. The dry season is associated with less damping northeast winds, starting in November andending in April of the next year. The average dry
season rainfall is 198 mm and fluctuates significantly
through the season. The temperatures in the area
are relatively high (26–27.6 °C), while their difference is not much during the season. The lowest
temperature in a year falls in January and the highest temperature is in April. The average humidity ina year is 83–85 %. The driest months are in February and March. Average evapotranspiration varies

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from 48 mm in July to 111 mm in March. The highest evapotranspiration occurs in the dry season from
December to April, during which timeframe rainfall
is negligible. The Tra Vinh Province is directly affected by the semi-tide regime of the East Sea
through the Co Chien River and Hau River (Provincial Report in 2011–2015). The tidal effect gradually
decreases from the sea to the inland, mainly incoastal areas. The tide also causes negative consequences of salinity intrusion into the interior, changing the quality of soil in the direction of increased
salinity.
Saline water intrudes into Tra Vinh simultaneouslywith the tide regime, depending on the seasons of
the year, generally highest in March, April, and May.
According to observation data, the salinity is about
25–32 g/l at the gates of Cung Hau and Dinh An.
On the pedology side (Fig. 2), land of Tra Vinh
Province is dominated by alluvial soils and acidsulphate soils, with 56% of saline-affected soils and24.3% of acid soils, including three major soil
groups: (i) sandy soil has an area of 15,169.3 hectares, mainly distributed in Tra Cu, Cau Ngang,
Duyen Hai, and Chau Thanh districts and scatteredin the other districts ; (ii) alluvial land covers an
area of 132,983.4 hectares, primarily distributed
along sand dunes and concentrated in Cau Ngang,
Tra Cu, Duyen Hai, and Chau Thanh; and (iii) acidic
soil area is 55,719.4 hectares, mainly distributed in
Cang Long and Cau Ke with some scattered in Tieu
Can, Chau Thanh, and Tra Vinh town.
Groundwater in coastal areas of Tra Vinh Province exists in two forms, namely shallow groundwater and deep
groundwater. The shallow groundwater is under thesand dunes. The water source is mainly from local rainwater stored as groundwater with depth of less than 100
m and small reserves. The deep groundwater also known
as Pleistocene groundwater is at depth of more than 100
m. The water source is enough, around 97,000 m3/day,for daily life and living with ability to exploit.
Materials(1) Landsat 8 OLI satellite image Level 1T product withradiometrically corrected and co-registered to a cartographic projection and terrain corrections acquired on
February 14, 2017, with a resolution of 30 m.
(2) Field salinity survey was conducted by taking
soil samples over Tra Vinh Province. In total, 44 soil
samples, between 0 and 20 cm depths, were collectedwith 41 of them used in this study since 3 samples
were eliminated due to their locations being underneath of cloudy sky. The locations of samples wereselected based on the soil classification map of Tra
Vinh Province (Fig. 2) to meet the requirement that

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Fig. 2 The soil classification map of Tra Vinh Province (Source: Vietnam Department of Surveying and Mapping, 2008)

samples are located at different soil types, as shownin Fig. 3. The soil samples were taken in the field at
different soil types on 13–14 February 2017 (i.e., during the dry season) and in weather conditions withno rainfall, but sunny (Fig. 4). Each soil sample
weighs 2 kg. Sample preparation for laboratory analysis is addressed as follows: soil samples from thefield must be crushed down in time, picking up theplant, gravel stone, etc., and dried in the air. The
sample room must be ventilated and free of chemicals. Then, the soil samples were analyzed for quantifying
EC1:5(Richards
1954).
The
electricalconductivity is measured at 25 °C from an unfiltered
1:5 soil to deionized water suspension. Soil suspensions were prepared using 35 mL of distilled waterand 7 g of soil into 50 mL plastic centrifuge tubes(Cat. No. 06-443-20, Fisherbrand). Soil suspensions
were continuously shaken by using a mechanicalshaker (132 rpm) at 25 °C for 60 min to dissolve soluble salts. EC1:5 is defined as the first of three steps
to estimate soil salinity (ECe). It was determined bymixing 1 part soil with 5 parts distilled or deionizedwater. After mixing the sample and allowing the sediment to settle, the electrical conductivity of the solution was tested (Slinger and Tension 2005).
Therefore, after 15 min settling time, EC was determined using a conductivity probe (Conductivity Meter
SCM 902A, Apel Instrument). The EC meter was calibrated by KCl standard solution (1.413 dS/m) (Cat.
No. 2974326, Hach Company, Loveland, CO, USA)prior to soil suspensions measurement. Among 41
samples, 29 samples were used for training and 12
samples for validation. The samples used for training
and validation have to meet the condition of being
representative for typical land use types in the regionsof concern with evenly spatial distributed over theProvince (Table 4 in Appendix).
(3) Land use map: The study collected land use map of
Tra Vinh Province with scale of 1/100000 in 2016. The
coordinate system is WGS-84 zone 48.
(4) Map of saline soil classification in Tra Vinh Province: the study collected a saline soil classification map
of Tra Vinh Province with scale of 1/100000. The coordinate system is WGS-84 zone 48.

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Fig. 3 Spatial distribution of soil samples and their measured EC1:5 over Tra Vinh Province overlaid on Google Earth image

Research method

This section presents the processing steps of Landsat 8
image to map soil salinity and the method to assess soil salinity from soil samples (Fig. 5). Firstly, in order toremove the atmospheric effects, the FLAASH model
was applied (Kaufman et al. 1997; Abbas and Feldeet al, 2003 Khan et al. 2005 Khan 2007). Then, further steps to process Landsat 8 images include image
morphology, conversion from digital number to reflectance value, cloud filtering Zanter et al, 2016,
and image enhancement. Subsequently, spatial analysis tools in ArcGIS 10.3 software were used tocompute indices and analyze the indices. The indices
are described in Table 2. Excel software was used
for normalization of the indices. Secondly, 29 samples of EC1:5 data (Table 4 in Appendix) were analyzed with the spectral reflectance of the Landsat 8
OLI image. The soil salinity is estimated by theEC1:5 of the soil as measured in the laboratory.
Based on the results, the analysis is made to determine the relationship between reflectance values andindices of soil salinity to estimate the soil salinityfrom the Landsat 8 image. Various soil types reflectsolar radiation differently. The difference in reflectance allows one to determine the soil type at thesurface layer. Validation samples were taken fromdifferent land use/land cover types, including paddy
rice field, shrimp ponds, bare land, and cropland.
The sample locations are selected at different salinity
intrusion degrees.

Results
Relationship between EC1:5 and spectral reflectancebands

The relationships between EC1:5 and spectral reflectance of Landsat 8 OLI channels are shown in Fig. 6.
Thereby, salinity links to NIR and SWIR1 spectral
channels, in which the NIR channel has the highest
correlation with salinity. The correlation coefficientis 0.80 between EC1:5 (ds/m) and NIR channel (Fig.
6d). Other channels from the Landsat 8 image show
unclear association with soil salinity.

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Fig. 4 Four photos show soil samples taken at different soil types on 13 February 2017 under sunshine condition. Detail characteristics of eachpoint are described as follows: a MP01 is paddy rice field after harvest with 6 m elevation; ,b L11 is of former use for aquaculture for shrimp at 5m elevation; c MP05 is land use to grow banana surrounded by paddy field at 5 m elevation; and d P9 is paddy rice field at harvest time at
3 m elevation

Fig. 5 The workflow of salinity retrieval using Landsat 8 OLI image

Nguyen et al. Progress in Earth and Planetary Science(2020) 7:1

Table 2 The formulas used to derive the indices, and theirreferences
No. Indices

FormulaSources

1

Salinity index - SI1

SI1 = sqrt(green2 + red2)

Douaoui et al.
(2006)

2

Salinity index - SI2

SI2 = sqrt(green × red)

Douaoui et al.
(2006)

3

Salinity index - SI3

SI3 = sqrt(blue × red)

Khan et al. (2001)

4

Salinity index - SI4

SI4 = (red × NIR)/green

Abbas and Khan
(2001)

5

Salinity index - SI5

SI5 = blue/red

Abbas and Khan
(2001)

6

Normalized Difference
Salinity Index (NDSI)NDSI = (red − NIR)/(red+ NIR)

Khan et al. (2001)

7

Normalized Difference
Vegetation Index (NDVI)NDVI = (NIR − red)/(red+ NIR)

Khan et al. (2001)

8

Soil Adjusted Vegetation
Index (SAVI) (L = 0.5)

SAVI = (1 + L) × NIR −red/L + NIR + red

Alhammadi andGlenn (2008)

9

Vegetation Soil Salinity Index(VSSI)VSSI = 2 × green − 5 ×(red + NIR)

Dehni and Lounis(2015)

Figure 7 shows a great difference between red,green, blue, NIR, and SWIR1 spectral channels corresponding to the strong reflective zones of theplants. The soil types with low salinity correspond tohigh vegetation areas that exhibit high NIR values.
The red, green, blue, and SWIR1 channels show different reflections for different salinity types, but withless reflectance for the NIR channel. Therefore, theNIR channel can be used to derive reliable results insalt estimation. Different salt thresholds will receive

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different reflectivity curves, which will serve as abasis for selecting the estimation model.

Relationship between EC1:5 and indices derived fromLandsat data

We also investigate the relationship between EC1:5 andremote sensing indices as shown in Fig. 8. The indices
are described in Table 2.
From the scatter plots in Fig. 8, it shows that the SI1,
SI2, SI3, and SI5 indices are weakly correlated with thesalinity. In contrast, the SI4, NDSI, NDVI, SAVI, and
VSSI have a clear relationship with the salinity with coefficient of determination (r2) higher than 0.41.

The salinity estimation and validation

After using the regression method for five indices derived from Landsat data including SI4, NDVI, NDSI,SAVI, VSSI, and NIR for the soil salinity, it is found thatthe regression models have a well-defined coefficient(R2) between salty value obtained from the field measurement and salty values derived from remote sensing
data. The highest coefficient is 0.80 between EC1:5 andNIR channel reflectance as shown in Table 3. Table 3 reviews the regression models for estimating EC from remote sensing data, characterized by various indicesincluding NIR, SI4, NDSI, NDVI, SAVI, and VSSI. It alsopresents R2, P value, and standard errors of EC (dS/m).

Fig. 6 Correlations between electrical conductivity (EC1:5) and spectral reflectance at different bands of Landsat 8 OLI image acquired on 14/02/2017:
between EC and a blue band, b green band, c red band, d NIR band, e shortwave infrared 1 (SWIR1) band, and f shortwave infrared 2 (SWIR2) band

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Fig. 7 Spectral reflectance profiles of the major salt-affected lands extracted from Landsat 8 image in the Tra Vinh Province

Estimation of soil salinity and accuracy
assessment

five levels based on Chhabra (1996) and Skaggs et al.
(2014).

Accuracy assessment

The statistical analysis results are shown in Table 3. Allof six regression models have a high statistical coefficient(R2) with P value less than 0.05. Therefore, the study
adopts all of these models to estimate the salinity of soil
based on the remote sensing indices for the 12 validation
samples (Table 4 in Appendix). Figure 9 shows that
there is a strong correlation between estimated EC1:5
and observed EC1:5 when using the first and sixthmodels. It is found that the correlation between EC1:5
and NIR and between EC1:5 and VSSI model are 0.89
and 0.77, respectively, and the corresponding lowest gradient values (bias) are 0.38 and 0.28, respectively, indicating that the two models were the most suitable ones
while comparing with the salinity values.

Results of estimated salinity mapping

Accuracy assessment indicates that salinity (EC1:5, dS/m)shows its high correlation with NIR and VSSI indices.
Therefore, this study generates salinity maps for the entire Tra Vinh Province using the NIR model (Fig. 10)
and VSSI model (Fig. 11). Soil salinity is classified into

Discussion
Tra Vinh is characterized by lowland and located in
the coastal plain of downstream Mekong River so that
it is seriously affected by tides and sea level rising.
Seawater intrudes into the inland through the riverwhen high tide occurs or sea dikes were broken bystorms. Salt water can also be followed by capillaries,
cracks in the soil, to penetrate deep into the interior
through the sea dikes. This is the main mechanism of
the salinization process in Tra Vinh Province. The
purpose of this study is to map the spatial distribution of soil salinity to assist management and agricultural planning. Hence, if the samples were taken at a
depth of less than 5 cm corresponding to most of theroot zone of the plants where the topsoil layers only
show the organic part of the roots. This topsoil is ofa plowed area, raised for cultivation with fertilizer,
lime, organic residue, and other chemicals. Therefore,
the samples at a depth less than 5 cm have limitation
to identify the content of natural salty in the soils.
In agricultural irrigation, salts are not absorbed at thesurface, instead of that salts are accumulated in theNguyen et al. Progress in Earth and Planetary Science(2020) 7:1

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Fig. 8 Correlations between electrical conductivity (EC1:5) and salinity indices derived from different bands of Landsat 8 OLI and environmental
indices, including Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Vegetation Soil Salinity Index (VSSI)

lower portion of the root-zone (Mougenot et al.
1993). When salinity does exist at the surface, it canbe successfully detected with remote sensing techniques, but only very saline soils, where the influence on vegetation is minimal (Mulla et al. 2013). In
general, when the plants experience biotic and abiotic stress (including salinity), their photosynthetic
activity decreases, causing increased visible reflectance and reduced NIR reflectance from the vegetation (U. S. Salinity Laboratory Staff 1954).
Essentially, this study intended to determine the indirect relationship between the salinity of the topsoil
and spectral reflectance by measurement methods

and to determine the actual salinity from the samples at thedepth of 20 cm.
According to FAO (1976), saline soil is defined as
amount of salt in ECe > 4mmho/cm (ECe is EC measured by saturation extraction method). This is thedefinition only concerning the amount of salt that
presents in the soil, without considering the otherchemical properties. The U.S. Salinity Laboratory(U.S. Salinity Laboratory Staff 1954) classifies agricultural soil salinities as 0–2 dS/m = non-saline; 2–4
dS/m = slightly saline; 4–8 dS/m = moderately saline; 8–16 dS/m = strongly saline; and > 16 dS/m =extremely saline, where salinity is quantified as the

Table 3 Regression models for estimating EC from remote sensing data
P valueStandard error of EC (dS/m)

1

EC1:5 = 28.013 × e

0.80

2.71E−08

1.29

2

EC1:5 = 26.068 × e−14.73 × SI4

0.48

0.00761

2.44

3

EC1:5 = 2.485 × e3.2449 × NDSI

0.46

0.00019

1.88

No.

Regression formula−13.39 × NIR−4.984 × NDVI

R2

4

EC1:5 = 5.671 × e

0.41

0.00034

2.15

5

EC1:5 = 7.406 × e−9.16 × SAVI

0.62

3.78E−07

1.69

6

EC1:5 = 72.86 × e2.8802 × VSSI

0.71

1.39E−05

1.76

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Appendix
Table 4 Salinity information of 41 samples, among which 29 (~ 70%) samples are used for training and 12 (~ 30%) samples
highlighted in gray color are used for validation
No. Code X (UTM WGS84_Zone48)(m)

Y (UTM WGS84_Zone48)(m)

Electrical conductivity (d/Sm)

Elevation(meter)Soil types

1

L.01

6495311098282

0.05

0Sand dunes’ soil

2

P4N

6543721081554

0.10

0Sand dunes’ soil

3

P5N

65317310815620.21

0Less salty soil

4P865969010893140.22

0Less salty soil

5

MP06 634243

10922380.30

6Alluvial soil has yellowish reddish layer

6

MP01 650832

1097403

0.31

6Less salty soil

7

MP05 640473

1095595

0.35

5Alluvial soil has yellowish reddish layer

8

P2N

6564261070927

0.36

0Salty soil average

9

P7N

6587181082446

0.37

0Sand dunes’ soil

10

MP13 644985

1078898

0.42

2Less salty soil

11

MP08 630202

1085956

0.45

2Alluvial soil has yellowish reddish layer

12

P5N2 653218

1081402

0.45

0Less salty soil

13

MP11 638932

1083668

0.48

3Less salty soil

14

L.02

6515901094664

0.51

3

Deep alkaline soils, saline soil average

15

MP02 650585

1099018

0.57

3Less salty soil

16

P9-2

6641371085331

0.60

0Salty soil average

17

MP10 632954

1077177

0.62

3Less salty soil

18

P10

6642071082773

0.62

0Salty soil

19

L.06

6528011092571

0.63

3

Deep alkaline soils, saline soil average

20

MP03 649479

1100892

0.64

3Less salty soil

21

P2N2 656415

1070929

0.64

0Salty soil average

22

MP07 629148

1096882

0.69

1Alluvial soil has yellowish reddish layer

23

MP14 647257

1071374

0.73

1Salty soil under mangrove forest

24

MP04 648527

1100904

0.75

4Less salty soil

25

L.03

6515901094664

0.75

3Deep alkaline soils, saline soil average

26

L.05

65446610920210.80

3Less salty soil

27

L.04

65468910950020.83

3Potential of deep alkaline soil, saline soil average

28

P96641211085370

1.46

Soil disturbance by cultivation

29

L.07

6547261087204

1.60

Sand dunes’ soil

30

P3N

6579431076231

3.01

Less salty soil

31

L.13

6541281061941

3.45

Salty soil

32

P12

6616961069167

3.65

Salty soil

33

L11
N

6649551072706

3.88

Salty soil average

34

P13

6522291066825

4.36

Less salty soil

35

L.12

6624321063649

4.66

Salty soil

36

L.14

6499231061131

5.19

Salty soil under mangrove forest

37

L.11

662631

1058792

5.26

Soil disturbance by cultivation

38

L.15

6468141062193

5.36

Soil disturbance by cultivation

39

L.10

6695451062763

7.53

Potential of shallow alkaline soil, under mangrove
forest40

L.08

6673201069881

8.20

Potential of shallow alkaline soil, under mangrove
forest41

L.09

6714601068779

9.31

Potential of shallow alkaline soil, under mangrove
forestNguyen et al. Progress in Earth and Planetary Science(2020) 7:1

Page 12 of 16

Fig. 9 Correlation of EC1:5 derived from Landsat 8 OLI and observed EC1:5 from field survey data for the 12 test samples over Tra Vinh Province

electrical conductivity of a saturated soil paste extract (ECe with units of dS/m). At ECe < 20 dS/m,
the ability to directly assess soil salinity from bar soil
reflectance is limited (He et al. 1992; Metternicht
and Zinck 2003; Allbed and Kumar 2013) He et al.
(2012) Metternicht & Zinck. (2012).
According to He et al. (1992), electrical conductivity(EC1:5) of a soil extract is the most widely used parameter for describing soil salinity (Rayment and Higginson 1992). Electrical conductivity is estimated by
the concentration of ions in the soil that predominantly consists of the cations Na+, Ca2+, K+, and Mg2+
and the anions Cl-, SO42- and HCO3- (Rhoades et al.
1989; Sumner and Naidu 1998). In soil, ions present
mainly in soil solution and are adsorbed in soil colloids. The standard laboratory method for determining the EC1:5 of a soil is by using a saturated paste
extract (ECe) (Rayment and Higginson 1992). Due tothe difficulties encountered in determining the appropriate water saturation point when preparing a saturated paste extract (Hogg and Henry 1984), soil towater ratios of 1:1, 1:2, 1:2.5, 1:5, and 1:10 have beenused to determine the EC1:5 values of soils (Slavich
and Petterson 1993; Franzen 2007; Sonmez et al.
2008; Rayment & Lyons, 2011; Wang et al. 2011).
The 1:5 ratio has the advantages of simplicity andreduced time and cost as compared to saturation
paste extracts (Reitemeier 1946) and also dissolves
larger amount of solutes than the saturation paste

extracts, especially for sparingly soluble salts (Metternicht Zinck 2003). Therefore, this study has
chosen the EC1:5 index to determine the soil salinity.
The soil salinity map with five salty levels serves forland management and agricultural planning. Remotesensing technology has been proven its efficiency inthe investigation of soil salinity on a large area. Results show that using Landsat image 8 OLI images
allows one to extract and monitor the features ofthe salty land. They indicate that the salinity of thesoil has strong relationship with the reflectance at
NIR channel. This is in line with the results of research presented by Elnaggar and Noller (2009).
However, the SWIR channel does not have a highcorrelation with EC1:5, which has been found in the
study (Csillag 1993), reflecting higher values of thevisible and infrared channels near the surface of
non-salty soils. The relationship is unclear for thesand patterns with a high salinity value and low reflectance in the NIR channel. This may be due tothe influence of the naturally hot dry climate conditions of the area of research, such as Saudi Arabia
in the Middle East. He et al. (1992) found a strong
relationship between the SI and salinity indicator in
the red channel, which has a high correlation with
EC. A similar result is seen in the study by Noroozi
et al. (2012). In the case of Tra Vinh Province, it isaffected by salt brought from the sea by the tide andrising sea levels. For this reason, the areas

Nguyen et al. Progress in Earth and Planetary Science(2020) 7:1

Page 13 of 16

Fig. 10 Soil salinity map with depth from 0 cm to 20 cm in Tra Vinh Province, derived from Landsat 8 OLI using NIR channel (equation 1 in Table3). Soil samples are displayed on the map with in situ photos (right) showing land cover conditions. The numbering of the locations is to classify
the soil sample site, while the initial letters, M, P, and L indicate the names of the person who took the soil samples

contaminated by salt due to low elevation andflooded by tide flow exhibited lower reflectance on
the NIR channel in comparison with those of theother regions.
Results of the evaluation model to estimate soil
salinity by using NIR channel show the signatures ofsalinity dynamics in the research area. They werevalidated with simultaneous field data with coefficient of determination, R2 = 0.89 and RMSE = 0.96
dS/m. The same was found for the VSSI index, with
R2 = 0.77 and RMSE = 1.27 dS/m. This shows that
the NIR channel and VSSI index are suitable for soil
salinity estimation (Fig. 8 and Fig. 9). However, theunderestimation is common in existing remote sensing studies. For this case, the underestimates are increased with the degree of soil salinization. The
major contribution for the underestimation may result from the data inaccuracy other than model ineffectiveness. Bias analysis confirmed that improper
atmospheric correction contributed to underestimation or field sampling representativeness within remote sensing pixels was probably the major cause
for the underestimation. Notably, despite of thislimitation, this research achieved a reasonably good
performance in retrieving soil salinity from newgeneration multi-spectral sensors. This has been
done in the various studies by using environmental

indicators. By taking for example the SI, NDVI,
NDMI, and NDSI derived from Landsat satellite images to study environmental conditions (Abdul-Qadir
and Benni 2010), it has been proven of the high potential in enhancement and extraction of soil salinityfrom remote sensing data. Results of this study indicate that the NIR channel demonstrates a higher potential to detect the salinity intrusion in the Mekong
Delta with a higher degree of confidence. Our findings match with outcomes of Allbed and Kumar(2013) in that the information on the salinity of thesoil can be found in the NIR and SWIR channels.
Allbed and Kumar (2013) found that the Red channel in the series Landsat ETM+ images has a highcorrelation with EC1:5 for Iraq. In the future, there isa need to extend the research scope based on longterm spatiotemporal monitoring of soil salinity tohave a better understanding of interaction between
natural conditions (soil moisture, soil type, natural
salt content, etc.) and human impacts (irrigation,
land use, etc.). These nature and human interaction
should result in soil contamination as it can be observed by using spectral reflectance of the remotesensing images. This is very valuable for agriculturalsoil salinity mapping worldwide especially in the developing countries since this method is of low costand acceptable accuracy.

Nguyen et al. Progress in Earth and Planetary Science(2020) 7:1

Page 14 of 16

Fig. 11 Soil salinity map with depth from 0 to 20 cm, using model VSSI index in Tra Vinh Province (equation 6 in Table 3)

Conclusions
This study investigates salinity intrusion in Tra Vinh
Province, which is the most affected place among sevencoastal provinces in the Mekong Delta. A method of
spatially monitoring soil salinity is proposed by using
satellite data-derived variables in order to support the
management of land resources and to determine thesuitable crops to cultivate. Results of the study showthat use of Landsat 8 OLI images allows one to estimate
the salinity value of the soil surface with an acceptableaccuracy. It is found that the values of NIR band and
VSSI indices are highly correlated with EC1:5 (dS/m)with coefficient of determinations 0.89 and 0.77, respectively, with corresponding lowest gradient values(biases) of 0.38 and 0.28, respectively. Error analysis
showed that soil salinity was relatively accurate for low
saline soils and underestimated for highly saline soils.
The underestimation may result from the poor representativeness of soil samples. That is to say, the NIR
and VSSI models’ results most fit the observed salinity
values in comparison with the other models implemented in our study. It is inevitable that climate change
is affecting Tra Vinh Province by manifesting the sea
level rise, salt water intrusion, and drought that lead toincreased soil salinity in the study areas, especially inthe lower-elevation terrains, such as coastal areas andestuaries. As a result, the agricultural cultivation in Tra
Vinh Province will be altered. Proper measures to mitigate the impacts of increased soil salinity must be designed in order to sustain the agriculture and economic
development of Tra Vinh Province.
In addition, there is a need to further develop and improve the skills in the monitoring and forecasting ofsalinization in the agricultural areas of Tra Vinh Province and neighboring regions with potential to be vulnerable to salinity intrusion. For example, adopting a
newly developed normalized difference latent heat index(NDLI) that could enhance the sensitivity on surface
water availability over land surface may lead to an improved monitoring of the salinization (Liou et al. 2019).
Furthermore, although the proposed methodology of thestudy has been demonstrated to perform well in termsof extracting soil salinity information by using the Landsat OLI data, an improved version of the proposed
methodology especially in spatial domains will be evenmore beneficial to achieve a greater efficiency in contributing to the orientation and planning of agricultural production areas of the salinity intrusion vulnerable regions.

(2020) 7:1

Page 15 of 16

Nevertheless, the current version of the proposed methodology is ready to be applied to the other coastal provinces of the Mekong Delta in order to assess theterrains’ vulnerability at a regional scale with low costand acceptable accuracy for the planning of land use.

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Nguyen et al. Progress in Earth and Planetary Science

Acknowledgements
The authors appreciate the United States Geological Survey (USGS) for
providing the satellite data used in this study.
Authors’ contributions
Conceptualization was done by HPT and KAN. Data curation was done by
HPT, PPH, and THN. Formal analysis was done by KAN. Funding acquisition
was done by YAL and THN. Investigation was done by HPT and PPH.
Methodology was done by HPT. Project administration was done by HPT.
Supervision was done by KAN and YAL. Writing of the original draft andreviewing, editing, revising, and finalization of the manuscript was done by
YAL. All authors read and approved the final manuscript.
Funding
This research was financially supported by the VAST under the code
VAST05.04/16-17 and MOST of Vietnam under code KHCN_TNB/14-19/C23
and partially supported by MOST of Taiwan under the codes 107-2111-M008-036 and 105-2221-E-008-056-MY3.
Availability of data and materials
Please contact authors for the data supporting this study.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Center for Space and Remote Sensing Research, National Central University,
No. 300, Jhongda Rd., Jhongli District, Taoyuan City 32001, Taiwan, Republicof China. 2Institute of Geography, Vietnam Academy of Science andTechnology (VAST), No.18 Hoang Quoc Viet Rd., Cau Giay Dist.,, Hanoi City,
Vietnam. 3Graduate University of Science and Technology, VAST, No. 18
Hoang Quoc Viet Rd., Cau Giay Dist.,, Hanoi City, Vietnam. 4Ho Chi Minh City
Institute of Resources Geography, VAST, No. 1, Mac Dinh Chi St., 1st District,
Ho Chi Minh City, Vietnam. 5Ho Chi Minh City Space Technology Application
Center, Vietnam National Space Center, VAST, Ho Chi Minh City, Vietnam.
6
Representative Office of VAST in Ho Chi Minh City, Ho Chi Minh City,
Vietnam.
Received: 26 June 2019 Accepted: 1 November 2019

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