International Journal of Advances in Applied Sciences (IJAAS) Vol.
No.
September 2025, pp.
ISSN: 2252-8814.
DOI: 10.
11591/ijaas.
Determination of soil salinization by hyperspectral remote sensing in the Shirvan Plain Sahib Shukurov Khudaverdi1.
Aygun Ismayilova Azer2.
Ramil Sadigov Ali3.
Maya Karimova Javanshir3.
Turkan Hasanova Allahverdi4.
Gunel Asgarova Farhad5 The State Cadastre and Registry of Real Estate Public Legal Entity.
State Service on Property Issues.
Ministry of Economy of Azerbaijan Republic.
Baku.
Azerbaijan Department of Physical Geography.
Faculty of Geography.
Baku State University.
Baku.
Azerbaijan Department of Engineering and Applied Sciences.
Azerbaijan State University of Economics (UNEC).
Baku.
Azerbaijan Faculty of Ecology and Soil Science.
Baku State University.
Baku.
Azerbaijan Department of Soil Genesis.
Geography and Cartography.
Institute of Soil Science and Agrochemistry.
Ministry of Science and Education of Azerbaijan Republic.
Baku.
Azerbaijan Article Info
ABSTRACT
Article history:
The determination of soil salinization in the Shirvan Plain, considered the main agricultural zone of Azerbaijan, negatively affects the productivity of agricultural crops.
Based on 10 m Sentinel-2 images on Google Earth Engine platforms and by examining SI1, green-red band normalized difference vegetation index (GRNDVI), green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), and difference vegetation index of the environment (DVI), four remote sensing salinity monitoring index models.
S1DI1.
S1DI2.
S1DI3, and S1DI4, were constructed to extract soil salinity information in the Shirvan Plain in combination with the measured electrical conductivity.
The results show that the overall classification accuracy of S1DI1 (SI1-GRNDVI).
S1DI2 (SI1GNDVI).
S1DI3 (SI1-NDVI), and S1DI4 (SI1-DVI) models for salinity monitoring are 82.
35%, 83.
10%, 81.
96%, and 79.
25%, respectively.
Received Feb 11, 2024 Revised May 16, 2025 Accepted Jun 8, 2025 Keywords:
Bonitet scale Flatness index Soil salinization Spectral analysis Vegetation cover index This is an open access article under the CC BY-SA license.
Corresponding Author:
Sahib Shukurov Khudaverdi The State Cadastre and Registry of Real Estate Public Legal Entity.
State Service on Property Issues Ministry of Economy Baku.
Azerbaijan Email: sukurov.
sahib@inbox.
INTRODUCTION
As a result of the influence of economic activity, the number of listed anions causes the formation of soil types with different salt compositions in most territories.
Of the easily soluble salts in water, the most toxic for plants are NaCl.
Na2SO4.
MgCl2.
CaCl.
MgSO4, and Na2CO3.
Ions Cl.
SO42.
HCO3.
Na.
Ca2, and Mg2 have a specific effect on plants .
, .
When 0.
5% Cl or 0.
2% Na accumulate in a plant, its leaves burn and necrosis develops.
In the case of a high concentration of NaCl ions in the solution, the transpiration process worsens.
At the same time, the structure of the soil, water, and air regime is disrupted .
That is why it is necessary to flush the soil of salts in the studied areas.
On saline soils with poor water permeability, it is advisable to grow salinity-resistant plants.
One of the factors that has a significant impact on the productivity of the Shirvan Plain agrolandscapes is the amount of humus in the soil.
There are sharp differences in its distribution across the territory .
, .
The salinization of soils in the Shirvan Plain is related to the geological history of land emergence.
Before the fourth period (Oligocen.
Azerbaijan's Kura-Araz lowland, including the Shirvan Plain, was part of the western basin of the present Caspian Sea.
Journal homepage: http://ijaas.
Int J Adv Appl Sci
ISSN: 2252-8814
According to geological data, the area has been submerged and emerged multiple times.
Therefore, soil salinization in this arid and semi-desert zone not only hinders the development of agriculture but also poses a threat to the ecological environment and biosphere.
Since soil salinization is characterized by strong spatial heterogeneity, the application of remote sensing technologies is of great importance.
It is necessary to collect and analyze more to predict soil salinization, which requires significant financial resources, but does not provide the necessary speed and accuracy in the era of modern development.
Soil salinization poses a serious threat to the biosphere and ecological environment as a form of soil degradation.
As a result of salinization, valuable soil resources are lost, creating significant ecological problems.
If the amount of salinity in the soil, which is easily soluble in water, affects the normal development of plants, these soils are considered salinized.
In most cases, the amount of salinity in the top meter of these soils exceeds 0.
3% .
Ae.
Remote sensing tools for collecting information about the condition of the land, along with the application of geographic information systems (GIS), numerical simulations, and the use of mathematical models for assessment, analysis, damage calculation, and prevention processes, are modern methods employed.
A variety of data makes a valuable contribution to dynamic environmental monitoring.
This type of survey allows the collection of continuous spectral data at a finer scale, more fully revealing the characteristics of the Earth's surface.
Results show that models established with measured reflectance values are more accurate for pH prediction .
Ae.
The spatial-temporal mapping of soil salinization ensures prompt decision-making for measures to mitigate the negative effects of soil degradation.
For this purpose, satellite technologies ensure the collection of economically beneficial, rapid, high-quality, and accurate spatial data on salinized soils.
Using vegetation indices from vegetation cover is effective in predicting soil texture and moisture content, accurately forecasting the soil's moisture content.
Due to its continuous coverage capability, the hyperspectral remote sensing technology is an important method for monitoring the physical and chemical properties of soil, thanks to its strong dynamics and high discrimination ability.
This method stands out for soil salinization inversion studies in various regions due to its superior modeling, spectral emulation method, and variable selection approach.
However, considering only the spectral information of bare soils without considering the indirect effects of vegetation information on the spectrum of salinized soils could hinder a comprehensive study of soil salinization .
, .
High salinity and alkalinity cause evaporation and drying of vegetation cover.
Therefore, the condition of vegetation and the salinity of the vegetation can be used as an indirect indicator of vegetation cover salinization problems in salt-affected areas .
Conducting soil research based on spectral analysis in Azerbaijan has become very relevant and is considered important for the development of agriculture.
Spectroscopy can be used to analyze the various degrees of soil salinity based on the spectral characteristics of vegetation, particularly red edge and green peak characteristics, in saline areas .
To achieve this, spectral characteristics of vegetation cover at various salinity levels were studied spectroscopically, and based on correlation coefficients, characteristic indicators of soil salinity were determined in relation to the salinity of the soil, and a multi-variant non-linear regression model for soil salinity assessment was established .
The model is validated with measured data, demonstrating that it is a more effective method for obtaining highly accurate salinity information.
The application of remote hyperspectral sensing technology facilitates the operational monitoring of the dynamics of salinization over time .
, .
RESEARCH METHOD
The images were captured in open-air, windy, and cloudless conditions between 10:00-14:00.
As a result of the experiment, 80 data collection units .
plant samples and 40 soil sample.
were obtained.
Soil samples were collected up to a depth of 20 cm.
The permeability of soil samples, as well as the pH and electrical conductivity (EC) of the soil, were measured, and a linear relationship was established between permeability and the total salt content of the soil to calculate soil salinity .
, .
, .
The soil physicochemical parameters include the contents of eight major ions (Na .
K .
Ca2 .
Mg2 .
SO42 Oe.
HCO3Oe.
CO32Oe, and ClO.
, pH.
EC, and particle size distribution.
It is important to calculate the brightness coefficient of hyperspectral satellite images and confirm the results with laboratory analysis.
The coefficient of determination, or R 2, is a measure that provides information about the goodness of fit of a model.
In the context of regression, it is a statistical measure of how well the regression line approximates the actual data.
Important when a statistical model is used either to predict future outcomes or in the testing of hypotheses.
ycI2 = 1 Oe ycycyco ycycycycaycyceycc ycyceycycyceycycycnycuycu .
cIycIycI) ycNycuycycayco ycycyco ycycycycaycyceycc .
cIycIycN) Oc.
cUycnOeycUyc.
2 U .
ycI2 = 1 Oe Oc.
cUycnOeycUyc.
2 The sum squared regression is the sum of the residuals squared, and the total sum of squares is the sum of the distance the data is away from the mean, all squared.
As it is a percentage, it will take values Determination of soil salinization by hyperspectral remote sensing A (Sahib Shukurov Khudaverd.
A ISSN: 2252-8814 between 0 and 1.
To calculate R2 needs to find the sum of the residuals squared and the total sum of squares.
The predicted value is calculated by plugging the x value into the regression line equation.
RESULTS AND DISCUSSION
Mountain ranges surrounding the Shirvan Plain from the north and northwest have influenced its climate, resulting in the implementation of climatic zones in this plain.
This has also affected the spread of vegetation cover across the zones.
The prevailing climate and vegetation cover have also influenced the distribution of soil cover across zones.
Plain is covered by saline soils, while the foothill areas are covered by alluvial soils.
The area, as shown in Figure 1, experiences a semi-arid to arid climate, characterized by dry and warm winters, typical of semi-arid warm steppe and arid desert climates.
This climate type is observed in the western and foothill areas, characterized by low humidity, mild, low-precipitation winters, and relatively hot summers.
The annual duration of sunshine hours in the area varies between 2,100 to 2,400 hours.
With increasing altitude and cloudiness, the annual duration of sunshine hours and total solar radiation The annual radiation balance gradually decreases to 40 kcal/cm 2.
The average annual air temperature in the area ranges from 14 to 15 AC.
Research areaAos soil salinity degrees in 2020 and 2024 are shown in Figures 1.
Apparent decrease (<-3 dS m.
Slightly decrease (-3 to -1 dS m.
No significant (-1 to 1 dS m.
Slight increase ( 1 to 3 dS m.
Apparent increase (>3 dS m.
Figure 1.
Research area in 2020 and 2024 of .
saline soil degree and .
apparent decrease and slight increase After collecting spectral data, undisturbed soil samples from 0 to 20 cm depth were excavated at the same points.
Gravel and plant residues were removed from the collected soil samples.
The moisture content (%) of the soil was determined using the oven-drying and weighing method .
, .
After air-drying and sieving, a soil-water mixture of 1:5 soil to water was prepared for measuring EC using the PNT 3000 COMBI device.
The problem of preserving plant biodiversity requires a deep study of the physiological characteristics of species.
The integral indicator of plant vital activity is the EC of tissues .
The EC of these soils varies between 0 and 25 dS mOe1 in both laboratory and field conditions.
Table 1 shows the EC and pH values measured from the soil samples.
The original reflectivity of the soil and vegetation cover is subjected to differential transformation of the square root using the spectral reflectivity to perform logarithmic differential transformation is used as a spectral index (SI) .
, .
The coefficients of the soil and vegetation cover are calculated.
Measured by the soil salinity according to the rule .
for assessing spectral indicators.
ycyc = Ocycu ycn=1.
cIycnycOeycIy.
cIyaycnOeycIy.
2 ycu ocycu ycn=1.
cIycnycOeycIy.
Ocycn=1.
cIyaycnOeycIy.
Int J Adv Appl Sci.
Vol.
No.
September 2025: 662-670
Int J Adv Appl Sci
ISSN: 2252-8814
Table 1.
Measured EC and pH values of soil samples Depth .
EC .
S mOe.
Temperature indicator (AC) Through field research and the use of the above formulas, specific vegetation parameters were To study vegetation cover in different periods, it is necessary to have information on hyperspectral ability .
, .
Satellite images were used to analyze the spectral brightness coefficient of the areas under The model being developed also depends on the statistical information extracted from the indices .
This includes linear regression, factor analysis, and principal component analysis were Data should be in units of reflectance before creating spectral indices.
The soil SI can reflect the distribution of soil salinity, and using characteristic wavelengths with the highest correlation with soil salinity to establish a high spectral soil SI offers significant advantages in detecting salinization.
This index is similar to the normalized difference vegetation index (NDVI), but it suppresses the effects of soil pixels.
It uses a canopy background adjustment factor.
L, which is a function of vegetation density and often requires prior knowledge of vegetation amounts optimal value of L=0.
5 is used to account for first-order soil background ycIyaycOya = 5y.
cAyaycIOeycIyceyc.
cAyaycI ycIyceycc 0.
In this experiment, general vegetation indices with simple ratios are selected.
These include the radar vegetation index (RVI).
NDVI, and difference vegetation index (DVI).
yaycOya = ycAyaycI Oe ycIyceycc ycCycIyaycOya = .
ycAyaycIOeycIyceycc ycAyaycI ycIyceycc 0.
ycAycIyaycOya2 = 2yycAyaycI 1OeOo.
yycAyaycI .
2 Oe8.
cAyaycIOeycIyceyc.
The correlation coefficient between soil salinity measured with the salinity index and the SI is R 2=-0.
which passes the significance test at the 0.
01 level and indicates that it can be used to invert soil salinity.
ycIya = 2 y OoycI2 1050 ycI2 850 Creating a remote sensing model.
Statistics are used to analyze the statistical relationship between variables .
It can help us accurately understand the degree of influence of variables on each other, conducting a multivariate linear regression .
nalysis of trend surfac.
by considering soil salinity as the dependent variable and the salt index and vegetation index as independent variables.
Figure 2 shows the scatter plot of the different indices.
The results show that there is a good non-linear relationship between soil salinity and the two indices (Figures 2.
), with an R-squared value of 0.
755 for the determined model and an F-value of 6.
ycU = 15330000.
35 y 12 Oe 270.
215 y 22 Oe 33546.
02 y 1 y 2 300.
Determination of soil salinization by hyperspectral remote sensing A (Sahib Shukurov Khudaverd.
A ISSN: 2252-8814 .
Figure 2.
Scatter plot of different indices .
SI1-green-red band normalized difference vegetation index (GRNDVI), .
SI1-green normalized difference vegetation index (GNDVI), .
SI1-NDVI, and .
SI1-DVI In tests of significance at the 0.
01 level, where x1 is the salinity index and x2 is the vegetation index.
An inversion model has been tested based on twelve soil sample units that did not participate in modeling.
The model samples are clustered around the diagonal, as shown mainly, with a proximity of 0.
Figure 3 shows the spatial distribution of soil salinity levels in cotton fields in the Shirvan region.
There is good agreement between simulated and measured values, indicating that the model established here is effective and validated (Figures 3.
Therefore, it can be used to predict the overall salt content of the soil.
Using the remote sensing salinity monitoring index model to invert the soil salinity of cotton fields in the Shirvan Plain.
S1DI1 has the highest number of correctly classified sample points, while S1D3 and S1DI4 have relatively few correctly classified sample points.
The overall accuracy of S1DI1.
S1DI2.
S1DI3, and S1DI4 is 94.
49%, 95.
79%, and 95.
763%, respectively.
Figure 4 shows the agreement with the measured conductivity.
Among the sample classification errors, the classification errors of sample points of model S1DI1, model S1DI2, model S1DI3, and model S1DI4 were mainly caused by non-saline soil (Figures 4.
Long-term anthropogenic impact .
eep melioration plowing, afforestation, cultivation of agricultural, and horticultural crops using irrigatio.
has caused significant changes in the properties of solonetz soils of the Shirvan Plain, which persist even after a long .
ore than 32 year.
post-irrigation period.
The chemistry of salinA soils in the area with smoothed microrelief is predominantly chloride-sulfatesodium, only in the upper part .
to 35 c.
is NaCl.
Vertical distribution of the activity of Na .
ClAe, and Ca2 ions measured in pastes with 35% humidity in sample wells.
The distribution of gypsum in the vertical profile of soils in the areas forms three distinct maxima: the first is in the middle part of the upper meter layer, the second is at a depth of about 2 m, and the third is at the depth of the lower part of the fourth and upper fifth meter layers.
The formation of these gypsum maxima indicates that the process of salt accumulation proceeded with three distinct interruptions, which exactly correspond to the three cycles of river water retreat noted above, whereas in the area, the connection between gypsum accumulation and these later cycles was already weaker or lost.
Int J Adv Appl Sci.
Vol.
No.
September 2025: 662-670
Int J Adv Appl Sci
ISSN: 2252-8814
Figure 3.
Spatial distribution of soil salinity levels in cotton fields of the Shirvan region: .
SIDI1, .
SIDI2, .
SIDI3, and .
SIDI4 .
Figure 4.
Conformity with measured conductivity of .
S1DI1, .
S1DI2, .
S1DI3, and .
S1DI4
Determination of soil salinization by hyperspectral remote sensing A (Sahib Shukurov Khudaverd.
A ISSN: 2252-8814
CONCLUSION
The purpose is to highlight the potential regressions between the synthetic image of the indices used here as an explanatory variable and each of the vegetation and soil indices used as variables to explain.
studies have shown, in the soils of the studied area, under the influence of plowing and long-term irrigation, the original solonetz soils were transformed into soils that are not found in virgin conditions.
Long-term sustainable changes in vegetation as a result of phytomelioration can be judged by the fact that the planted forest stands have partially survived, but their species composition has changed: Ribes aureum.
Cotinus coggygria.
Ulmus pumila.
Ulmus laevis, and Lonicera tatrica grow among the tree species.
Pyrus communis, and individual specimens of Quercus robur exist in a sparse and depressed state.
The resulting tree species are covered by grass species of the local flora, characteristic of the solonetz complex vegetation: Galatella villosa.
Limonium caspium.
Festuca valesiaca.
Elaeagnus angustifolia.
Poa bulbosa.
Artemisia lerchiana.
Stipa capillata.
Chorispora tenella.
Descurainia sophia.
Tanacetum achilleifolium.
Artemisia santonica.
Kochia prostrata.
Galium verum.
Limonium caspium.
Limonium gmelinii.
Lamium amplexicaule.
Tanacetum vulgare.
Tulipa gesneriana.
Polygonum aviculare.
Over most of the area, dead trees remain (GNDVI).
Plant communities on the fallow land are characterized by a higher density and species richness than on virgin solonetz, and annual ephemeral species predominate (DVI).
former forest plantations, wormwood overgrowth is more successful than on former arable land.
The species composition and structure of the modern vegetation cover on reclaimed lands indicate that restorative succession is developing in the grass cover.
ACKNOWLEDGEMENTS
This work was supported by the equipment provided by the Institute of Soil Science and Agrochemistry.
Baku State University (Faculty of Geography.
Ecology and Soil Scienc.
, and Azerbaijan State University of Economics.
Our gratitude to the colleagues of the Registry of Real Estate Public Legal Entity.
FUNDING INFORMATION
The authors state no funding is involved.
AUTHOR CONTRIBUTIONS STATEMENT
This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author contributions, reduce authorship disputes, and facilitate collaboration.
Name of Author Sahib Shukurov Khudaverdi Aygun Ismayilova Azer Ramil Sadigov Ali Maya Karimova Javanshir Turkan Hasanova Allahverdi Gunel Asgarova Farhad C : Conceptualization M : Methodology So : Software Va : Validation Fo : Formal analysis ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue I : Investigation R : Resources D : Data Curation O : Writing - Original Draft E : Writing - Review & Editing ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue ue Vi : Visualization Su : Supervision P : Project administration Fu : Funding acquisition CONFLICT OF INTEREST STATEMENT The authors state no conflict of interest.
INFORMED CONSENT
This study received permission from the Azerbaijan Republic Government.
All informed consents are based on the recommendations of the Ministry of Science and Education of Azerbaijan on soil research.
Int J Adv Appl Sci.
Vol.
No.
September 2025: 662-670
Int J Adv Appl Sci
ISSN: 2252-8814
ETHICAL APPROVAL
This study complies with the research ethics code, ensures that all procedures are carried out by established ethical standards, and has received approval from the relevant ethics committee.
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding author, [SSK], upon reasonable request.
REFERENCES