Soil Salinity Mapping using Sentinel 2 for Agricultural Land Areas in Lagoon and Coastal Regions of Hue City, Vietnam

The agricultural land has a vital role for the development because it is relevant to 11 of the 17 sustainable development goals Viana et al.,  (2022). Especially in developing countries, agriculture not only ensures food security but also provides the foundation for economic activities via the export of agricultural products Pawlak and Kołodziejczak, (2020). However, agricultural land is currently facing serious problems related to land degradation due to the impacts of climate change and human activities AbdelRahman, (2023). One of the performances of land degradation is salinization. Soil salinization is a sequence from both natural factors and human activities such as the mineralogy of the parent material, topography, water table quality and irrigation during the agricultural production Stavi et al., (2021). In the overall, saline soil is a big problem due to many negative impacts on human life in all aspects related to nature, society and economy Hadj-miloud et al., (2023). In this context, the determination of soil salinity level plays an important role. Remote sensing technologies have emerged as powerful tools for large-scale salinity assessment, offering spatially consistent and cost-effective alternatives to traditional field methods (Kumar et al., 2020; Morgan et al., 2018). Since soil salinity is closely correlated with plant health, most studies use satellite vegetation indices to estimate soil salinity Ramos et al., (2020). The vegetation indices reflect the information of crop via the different of wavebands. In the soil salinity estimation, the Normalized Differential Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Canopy Response Salinity Index (CRSI) are most popular to use for the regression model Ramos et al., (2020). The accuracy of vegetation indices for assessing soil salinity can be inconsistent because they primarily reflect plant health, which may be influenced by other factors than salinity (Allbed and Kumar, 2013). In Vietnam, coastal agriculture is the sector most affected by salinity, due to the effects of high tides, climate change and farming practices Thach et al., (2023). In salinity-affected areas, rice and corn yields are significantly lower compared to other regions (Nguyen and Tran, 2020). However, the establishment of soil salinity maps in Vietnam has been limited, with only a few studies conducted in the Mekong Delta. Consequently, this study aims (i) to evaluate the potential of salinity and vegetation indices derived from Sentinel data for soil salinity mapping in the coastal and lagoon environments of Central Vietnam and (ii) to assess the saline situation within the study area and propose informed recommendations for adaptive agricultural land use practices. The research area encompasses Phu Da town and Vinh Xuan commune, which belongs to Phu Vang district, Hue city, Vietnam, with location at 16024’N to 16029’N and 107042’E to 107048’E. This region is a part of the largest lagoon system in Southeast Asia, called Tam Giang - Cau Hai.

Soil samplings and electrical conductivity measurements
 
There are 50 soil samples that were collected randomly in March of 2024 for five land use types (LUTs), as in Fig 1, at the depth of 0-30 cm. The soil samples were air-dried and ground by 2 mm sieve and then the solution of distillation water to soil mass ratio of 5:1 was used to calculate the EC_1:5 at the Soil Science Laboratory of Hue University of Agriculture and Forestry. The Hana’s EC measurement tool (HI98311) was used to measure the EC value. The suggestion of the United States Department of Agriculture was used for salinity classification based on the EC value, as shown in Table 1. The formula of Sonmez et al., (2008) was applied to convert  to EC value.

 
Spectral indices calculation
 
Sentinel 2A scene of March, 5^th, 2024 was used in order to captured simultaneously with soil sampling time. Accordingly, this study used four bands of Sentinel 2 to calculate eight indices which were widely used in previous research (Salem and Jia, 2024), as shown in Table 2.


 
Regression analysis and accuracy assessment
 
The simple linear regression (SLR) and multiple linear regression (MLR) models are applied for the predicted model. Three indicators to evaluate the accuracy of the predicted model: Root Mean Square Error (RMSE), Coefficient of Determination (R²) and Akaike Information Criterion (AIC). In addition, after identifying the most suitable MLR model, we employed the Variance Inflation Factor (VIF) to assess multicollinearity among the variables within the selected model. Only those equations with VIF values less than 3 (Sarabia and Ortiz, 2009) were deemed acceptable for further evaluation of model fit. We evaluated the statistical significance of differences in mean EC values across different LUTs using Dunn’s post-hoc test. This analysis was conducted with a 95% confidence level. All indicators were calculated using suitable packages available in the RStudio software environment.

Where, 
y_o = Measured.
EC value = Predicted.
EC value = Mean of all the measured.
EC value = Number of samples.
K = Number of independent variables used (include dependent variable); 
L is = log-likelihood estimate.

Electrical conductivity and spectral indices of ground points
 
The maximum EC recorded was 7.8 dS/m, while the minimum value was 2.8 dS/m. The average EC across all sample points was found to be 4.25 dS/m, with a standard deviation of 1.30 dS/m. Eight maps describing vegetation and salinity indices are presented in Fig 2. The data from Fig 2 shows that the SI_T, SI1 and SI3 indices are significantly lower in vegetated areas compared to other land types, while the SI2 index is lowest in water-covered areas. The vegetation indices (NDVI, SAVI, EVI and CRSI) indicate the extent of vegetation cover across the study area, highlighting that the southwestern region-primarily used for double-cropped rice cultivation-has the highest vegetation density.


       
The data corresponding to these eight indices collected from 50 sampling points are detailed in Table 3. The standard deviation (SD) values, which have been calculate based on packages in R studio software, of the indices for rice land use are consistent. This consistency can be attributed to the synchronized implementation of the crop calendar, which has been applied for rice cultivation. In contrast, other LUTs exhibit higher SD values due to the diversity of crops and varying planting times.


       
The correlation between the EC values and the salinity and vegetation indices is illustrated in Fig 3. The Pearson correlation indicates that the EC index is positively correlated with the salinity indices (SI_T, SI1, SI2, SI3) and negatively correlated with the vegetation indices (NDVI, SAVI, EVI, CRSI). Most correlation values are greater than 0.7, with the exception of the SI2 index, which has a correlation value of 0.35.


       
The nature of soil salinity is divided into two types: primary and secondary, in which primary is the salinity process generated by the nature of the elements that make up the soil, while secondary is mainly formed from land use, especially for agricultural land Gojiya et al., (2023). Our study area is located near the sea and lagoons and is characterized by active agricultural practices. As a result, the mechanisms of soil salinization are quite complex, involving both primary and secondary processes. In addition, ecological conditions also affect the EC concentration in soil. Some recent studies have shown that vegetation indices can be used to determine EC content in agricultural land (Haq et al., 2023), while others have suggested that SI indices can be used to determine EC content in bare, grass land and uncovered areas (Luo et al., 2025). Recent study used NDVI or EVI as referent classification for soil salinity levels (Djuraev et al., 2021). On the contrary, others (Zhang et al., 2011) stated that these indices completely unable to detect soil salinity. In our viewpoint, the lack of significant vegetation cover in their arid study area likely explains this finding. Our results also found that the SI3 index has a strongest correlation to EC value, so it is a good indicator to estimate soil EC. This finding was confirmed by the previous research (Douaoui et al., 2006a).
 
The regression models to estimate soil saline for coastal and lagoon areas
 
Fig 4 presents scatter plots comparing predicted and measured EC values using various salinity and vegetation indices. Each plot includes a regression equation, coefficient of determination (R²), Akaike Information Criterion (AIC) and root mean square error (RMSE), which collectively assess model accuracy and fit. SI3 yielded the highest prediction accuracy, with an R² of 0.82, AIC of 88.22 and RMSE of 0.55, indicating a strong correlation between predicted and observed EC value while SI2 exhibited the poorest model fit (R² = 0.12, RMSE = 1.22), indicating it is not a reliable indicator for predicting EC in this study area.


       
Table 4 presents the MLR models developed using stepwise selection based on the AIC. The MLR models exhibit significantly higher accuracy compared to SLR models. Although the model combining SI3, EVI and NDVI (Model 6) shows an approximately 2% higher RMSE and a 1.1% lower R² compared to the others, it achieves the lowest AIC score of 83.43 and uses the fewest variables. The technique to mitigate multicollinearity among the variables in the model for the model of SI3, NDVI and EVI variables.


       
Fig 5 shows six scatter plots, each representing a different model (Model 1 to Model 6). Each plot compares predicted EC (X-axis) values to measured EC values (Y-axis). As illustrated in the figure, the differences among the models are not markedly significant. A notable feature across the models is that data points corresponding to the highest values deviate substantially from the 1:1 reference line. This suggests that for extreme EC values, factors beyond those captured by remote sensing data may be influencing the results, indicating the need to incorporate additional variables or contextual information to improve model accuracy.


       
Fig 6 shows that only two of four models, those combining SI3 with NDVI and SI3 with EVI, exhibited a multicollinearity index of less than 3, which meets the criteria for acceptable use. The model combining SI3 with NDVI, (EC= 2.22+11.47*SI3-1.78*NDVI) has the lowest AIC and RMSE coefficients (84.6 and 0.52), as well as the highest R2 value with a value of 0.84. We recommend this model for estimating salinity in the coastal sandy areas and lagoons of Hue city. Linear regression models are widely utilized in soil salinity mapping Al-Ali et al., (2021), among them MLR models tend to be more accurate than SLR models Al-Khuzaie et al., (2022). This is because MLR models take into account a greater number of factors, reducing the likelihood of overlooking important variables that affect soil salinity compared to simpler models. However, in certain situations, simple models can still yield satisfactory results, serving as a foundation for further analysis or the application of more complex models Hihi et al., (2019).


 
Agricultural land management adapt to soil saline situation
 
Fig 7 illustrate the spatial distribution of soil salinity across agricultural land areas, as determined by the selected MLR model (EC= 2.22+11.47*SI3-1.78*NDVI). The findings indicate that all agricultural land within the study site exhibits salinity.


       
A Dunn depth analysis was conducted at a 95% confidence level on all points (where each pixel represents a point) across various LUTs as shown in Fig 8. The results indicated a statistically significant difference in the mean EC values among the LUTs.


       
Irrigation and fertilization significantly impact the soil EC content (Thiam et al., 2021). Our results show that two-seasons paddy rice areas have the lowest EC due to frequent flooding in November, which washes salts away, consistent with recent study Shirokova et al., (2024).  Single-season rice fields, adjacent to lagoons with higher salinity water, have higher EC than two-seasons paddy rice areas. Coastal forests dominated by Acacia, Melaleuca and Casuarina show the highest salinity, as these species tolerate saline soils (Khalil et al., 2016). We suggest planting coconut trees in these areas for economic and salinity tolerance benefits (Sun et al., 2024). Abandoned lands accumulate surface salts with high EC; revegetation with native grasses is recommended to improve the soil gradually. Vegetable and annual crop fields show varied EC due to diverse crops and irrigation, but crop selection often ignores salinity tolerance. Current crops such as sweet potato, chili pepper and corn are not suitable for saline soil, so the shifting to more salt-tolerant crops like garlic and pitaya should be considered (Cavalcante et al., 2008). Overall, varying salinity across land uses highlights the need for tailored management to minimize salinity impacts and support sustainable agriculture.

In the research site, the linear regression models using eight spectral indices revealed that a combination of SI3 and NDVI (EC= 2.22+11.47*SI3-1.78*NDVI) provided the most accurate salinity estimates. The entire agricultural area was affected: nearly 50% slightly saline, 5% strongly saline and the remainder moderately saline. Coastal forests showed the highest EC, while two-season paddy rice had the lowest. The study suggests cropping pattern adjustments and highlights the potential of multi-temporal/multi-satellite data, along with stakeholder involvement, to improve salinity mapping.

The present study was supported by Hue University via grant number NCTB.DHH.2024.06. 
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.