Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semi-arid regions. Multispectral data Sentinel_2 are used to study saline soils in southern Tunisia. 34 soil samples were collected for ground truth data in the investigated region. A moderate correlation was found between electrical conductivity and the spectral indices from SWIR. Different spectral indices were used from original bands of Sentinel_2 data. Statistical correlation between ground measurements of Electrical Conductivity (EC), spectral indices and Sentinel_2 original bands showed that SWIR bands (b11 and b12) and the salinity index SI have the highest correlation with EC. Based on these results and combining these remotely sensed variables into a regression analysis model yielded a coefficient of determination R2 = 0.48 and a n RMSE = 4.8 dS/m.
Soil salinity is widespread in the southern part of Tunisia from the east coast until the desert in the south. It is considered an important component of ecosystem degradation in the world’s dry lands and can lead to desertification [
In recent decades, there has been a widespread application of remote sensing data to map soil salinity, either directly from bare soil or indirectly from vegetation in a real-time and cost-effective manner at various scales [
A regression model based on image enhancement techniques (spectral indices, Principal Components Analysis (PCA) and Tasseled Cap Transformation (TCT)) have also been extensively used to predict soil salinity and to improve the characterised variability of salinity. For example, Tajgardan et al. [
Gabes-Ghannouch is both a Mediterranean and Saharan region. It is located in South-Eastern Tunisia from Jeffara plain into the Gulf of Gabes (
where maximum temperatures reached in the period between June and August (48˚C), while the coldest temperatures are measured between December and February. Due to its proximity to the sea, the climate of the study area slightly differs from the typical arid or semi-arid. The rainfall is irregular and ranges between 150 - 240 mm per year with six months dry season (April-Sept), where the rain does not exceed 4 mm per month.
According to [
The study area includes wetlands and steppe plains as well as areas used for agriculture.
Soil samples are collected within the upper ~10 cm from the soil surface. The campaign of soil sample collection was made in May 2018, which corresponds to the multi-spectral data acquisition date. The choice of dry season to collect the samples was not arbitrarily selected, but aimed at enhancing the detection of spectral characteristics of salt at surface during salt accumulation at that specific time; Salt in the soils, in dry season, is rising up due to capillarity. The signal of salty soil, at this period of the year, is stronger and easier to detect from the optical sensors [
At all sample location, a procedure is used to collect the soil. Each analysed sample in this work is a mix of four soil samples. These 4 samples are collected from 4 corners of a (60 × 60) square, where the center is considered the location of the sample, then the mix of 4 soil collected from 4 corners is the soil sample considered for chemical analysis
Salinity at the top-soil is determined by measuring electrical conductivity (EC). 1/5 soil/water diluted extracts is a convenient method [
The satellite image Sentinel 2 was used to map the soil salinity. This image was acquired in May 2018 and is composed of a multi-spectral imager MSI which provides views in 13 spectral bands from visible to infrared with a resolution varying from 10 to 60 meters. Sentinel 2 spectral bands are incorporated into a spectrum range varying from 443 nm (blue) to 2190 nm in the SWIR.
Bands reflectance, considered as a spectral indices, and the spectral salinity
indices derived from the blue, green, red and near infra-red bands, were used to predict soil salinity from satellite images. After obtaining these indices from the Sentinel_2 image corresponding to the sampling sites, correlation analyzes between the EC measurements and these indices were performed, these correlations are based on the Pearson function. The indices are described in
A linear regression was used to establish relationship between the NIR, SWIR spectra and the reference data from analysis of EC based on the statistical analysis. The highest values of R2 and the lowest value of RMSE (root mean square error) were used to determine the optimal calibrated model. The smallest RMSE indicate the most accurate prediction, this RMSE was derived according to equal of (1). The model will be assessed graphically by analysing the standardized residuals versus the predicted values of EC. By plotting the residuals with the descriptive variable, if a trend is identified, it indicates that the model is not accurate
Spectral indices | Equation | References |
---|---|---|
BI: Brightness index | B 2 + G 2 + R 2 3 | Zhuo Luoa et al., 2008 |
CI: Color index | R − G R + G | Zhuo Luoa et al., 2008 |
HI: Hue index | 2 R − G − B G − B | Zhuo Luoa et al., 2008 |
RI: Redness index | R 2 B + G 2 | Zhuo Luoa et al., 2008 |
SI: Salinity index | R N I R × 100 | Tripathi et al., 1997 |
SI1: Salinity index 1 | B ∗ R | Khan et al., 2005 |
SI2: Salinity index 2 | G ∗ R | Khan et al., 2005 |
SI3: Salinity index 3 | G 2 + R 2 + N I R 2 | Douaoui et al., 2006 |
SI4: Salinity index 4 | G 2 + R 2 | Douaoui et al., 2006 |
SI5: Salinity index 5 | B R | Bannari et al., 2008 |
SI9: salinity index 9 | N I R − R G | Abass and Khan, 2007 |
SI-11: Salinity index 11 | S W I R 1 S W I R 2 | Bannari et al., 2008 |
ASTER_SI: Salinity index ASTER | S W I R 1 − S W I R 2 S W I R 1 + S W I R 2 | Bannari et al., 2008 |
and there is an autocorrelation in the residuals, which is contrary to one of the assumptions of parametric linear regression.
RMSE = 1 N ∑ 1 N [ Z * ( x i ) − Z ( x i ) ] 2 (1)
where: N; Number of points, Z*(xi) is estimated value at point xi Z(xi) and is observation value at point xi.
Based on the data set collected from the fieldwork, the investigation area is considered as highly affected by salinity according to the results obtained from the Department of primary industries in Australia [
The main statistical parameters for EC data are given in
A Pearson correlation between the electrical conductivity values and the Sentinel 2 spectral bands was conducted
The most correlated bands are the band 11 and 12 of SWIR, the empiric equation A = log(1/R) which transform the reflectance to absorbance improve the correlation by 3% that’s why bands absorbance will be considered as spectral indices and will be integrated to construct the model. The salinity index SI provides the highest correlation 49%
Color indices showed a low correlation with EC varying between 13% and 21%. Salinity indices show a moderate correlation with the EC, varying between 8% and 49%.
Max | Min | Average | Standard optimization | |
---|---|---|---|---|
EC (ds/m) | 31.7 | 0.251 | 3.811 | 6.405 |
Variables | EC | band2 | band3 | band4 | band8 | band11 | band12 |
---|---|---|---|---|---|---|---|
EC | 1 | 0.172 | 0.083 | 0.145 | −0.232 | −0.409 | −0.436 |
band2 | 1 | 0.956 | 0.859 | 0.454 | 0.542 | 0.497 | |
band3 | 1 | 0.942 | 0.630 | 0.627 | 0.570 | ||
band4 | 1 | 0.610 | 0.610 | 0.569 | |||
band8 | 1 | 0.520 | 0.474 | ||||
band11 | 1 | 0.934 | |||||
band12 | 1 |
Variables | EC | BI | CI | HI | RI | SI | SI1 | SI2 | SI3 | SI4 | SI5 | SI9 | SI11 | ASTER |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
EC | 1 | 0.136 | 0.212 | 0.061 | 0.210 | 0.493 | 0.159 | 0.121 | −0.088 | 0.127 | −0.103 | −0.376 | 0.237 | 0.241 |
BI | 1 | 0.777 | 0.192 | 0.734 | 0.519 | 0.998 | 0.999 | 0.835 | 0.998 | −0.908 | −0.438 | −0.172 | −0.184 | |
CI | 1 | 0.245 | 0.992 | 0.558 | 0.780 | 0.783 | 0.619 | 0.807 | −0.932 | −0.462 | −0.192 | −0.202 | ||
HI | 1 | 0.225 | 0.135 | 0.208 | 0.188 | 0.157 | 0.190 | −0.224 | −0.094 | 0.018 | 0.023 | |||
RI | 1 | 0.532 | 0.733 | 0.740 | 0.601 | 0.768 | −0.907 | −0.420 | −0.173 | −0.183 | ||||
SI | 1 | 0.543 | 0.498 | −0.010 | 0.506 | −0.439 | −0.964 | −0.065 | −0.057 | |||||
SI1 | 1 | 0.996 | 0.814 | 0.994 | −0.902 | −0.465 | −0.176 | −0.188 | ||||||
SI2 | 1 | 0.848 | 0.999 | −0.919 | −0.418 | −0.171 | −0.184 | |||||||
SI3 | 1 | 0.848 | −0.817 | 0.121 | −0.113 | −0.134 | ||||||||
SI4 | 1 | −0.931 | −0.420 | −0.173 | −0.186 | |||||||||
SI5 | 1 | 0.352 | 0.170 | 0.183 | ||||||||||
SI9 | 1 | 0.125 | 0.115 | |||||||||||
SI11 | 1 | 0.999 | ||||||||||||
ASTER | 1 |
The most correlated is spectral salinity index SI
The linear regression is used to predict the spatial variability of soil salinity based on remote sensing and ground truth measurements. The prediction of the EC values from Sentinel_2 bands and the spectral indices is associated with the identification of 3 variables shown in Equation (2). A significant coefficient of determination R2 indicates that the predictor variables used in the model can explain 48% of the total variation of the predicted EC values. The regression empirical relationship is given by the following formula:
EC = − 0.3 + 0.4 × SI − 5.2 E − 03 × band 11 − 4.9 E − 03 × band 12 (2)
The standard error RMSE (root mean square error) of the estimation is about 4.8 dS/m. This error decreases with increasing soil salinity, which means the higher the electrical conductivity is, the closer the predicted conductivity will lie to the ground truth measurement.
The empirical relationship between measured and estimated EC values showed an overestimation of the predicted electrical conductivity values.
The plot of the standardized residuals versus the predicted values of EC shown in
The present study demonstrates that combining the Sentinel_2 SWIR bands and the salinity index into a regression model offers a potentially quick and inexpensive method to model the spatial variation in soil salinity. The combination of these remotely sensed variables into one model was able to explain 48% of the spatial variation in the soil salinity of the study area.
Although this study demonstrates that soil salinity mapping and modelling can be undertaken with good accuracy based on high spatial resolution multispectral images, further research is needed to focus on investigating the possibility of hyperspectral data in mapping and modelling soil salinity.
The authors declare no conflicts of interest regarding the publication of this paper.
Hihi, S., Rabah, Z.B., Bouaziz, M., Chtourou, M.Y. and Bouaziz, S. (2019) Prediction of Soil Salinity Using Remote Sensing Tools and Linear Regression Model. Advances in Remote Sensing, 8, 77-88. https://doi.org/10.4236/ars.2019.83005