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Soil Salinity Research and Mapping using Remote Sensing GIS
Authored by Sahib Shukurov
Soil
salinity in irrigated soils in underground rocks accumulation of soluble salts
in the root zone to adversely affect the growth of most crops [1]. This results
in increasing impact on crop yields and agricultural production in both dry and
irrigated areas due to poor land and water management and expansion of the
agricultural frontier into marginal dry lands. Arid area crop jeld be achieved
with irrigation and this reason expand the salinization hazard.
Guidelines
for irrigation water quality generally focus on the physical, chemical, and
microbiological factors that may affect crop growth or the soil environment.
Trigger values or thresholds are provided for [2].
Furthermore,
salinity also affects other major soil degradation phenomena such as soil
dispersion, increased soil erosion, and engineering problems. When soil
salinization Advances in Environmental and Agricultural Science is assessed in
economic terms, reasons to be concerned about it become more apparent. To keep
track of changes in salinity and anticipate further degradation, operational
research is needed so that proper and timely decisions can be made to modify
the management practices or undertake reclamation advances in the application
of remote sensing technology in mapping and monitoring degraded lands,
especially in salt-affected soils, have shown great promise of enhanced speed,
accuracy and cost-effectiveness.
The
approach to the problem of delineating saline soils using remote sensing data
and GIS techniques has been proved in many recent studies to be most efficient
[3].
Methods
and Techniques Used
Geographical
location
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 [4,5].
The
study was conducted in the coastal areas of Salyan and Neftchala districts of
the Republic of Azerbaijan. Development of agriculture in the coastal areas of
the Caspian Sea, which is below the world sea level (Figure 1).
The
territory is a dry and semi-arid, seaside main agricultural zone of Azerbaijan.
The
soils of the arid and semi-arid regions are generally deficient in organic
matter where saline and sodic soils are commonly found. The dispersed sodium in
soil degrades the soil structure and restricts root growth and water movement
in soil [4]
Climate
The
climate of the area is temperate-hot semi-desert and dry-steppe type with dry
summers. Semi-desert and saline, gray-meadow, carbonate alluvial-meadow and
meadow soils are widespread. The Kura River flows into the Caspian Sea, forming
a delta in this area. There is gray-meadow, carbonate alluvialmeadow and meadow
wetlands. Salinity is found. Vegetation is desert and semi-desert.
Remotely sensed data is an efficient data source to produce variety of salt-affected soil maps in conjunction with field measurements. As such, soil analysis by utilizing modern technological tools of Remote Sensing (RS) and Geographical Information System (GIS) provides a valuable resource inventory related to the well-being of land especially those allocated for agricultural production [6] (Figure 2).
The
main goal is to confirm the remote sensing information with the results of
laboratory analysis. These are the results of a joint study of Azercosmos LLC
and the Institute of Soil Science and Agro chemistry of ANAS base on Azersky
satellite images. The methodology used to research the salt affected soil area
and soil pattern. In dry and irrigated areas, salts tend to concentrate on the
soil surface. As salinity increases, more salts will appear at the soil
surface, favoring the use of conventional remote sensing tools. To keep track
of changes in salinity and anticipate further degradation, operative research
method is needed so that proper and timely decisions can be made to modify the
management practices or undertake reclamation and rehabilitation. The fields of
Remote Sensing and Geographical Information Systems (GIS) are expanding very fast
and the methods are constantly adapted to new fields of application.
Our
work was carried out by means of space and ground soil surveys of the research
area that were synchronous in time and location. For this purpose, by means of
conducting ground reconnaissance survey of the area, were have chosen sub
satellite areas with contrasting soil salinization, and satellite images have
been ordered from Azersky 1A/1B in panchromatic mode with spatial resolution of
1.5m on the site, and in multispectral mode (4-channel mode) with spatial
time-resolution about 2.0m on the site. In addition, Azersky satellite data for
done aimed at exploring of soil salinization. In regard to satellite data
processing methods and decoding of soil salinity state, we can outline the
following main approaches:
•
Calculating vegetation indexes that facilitate identification of soil salinity.
•
Using statistical models and methods (multiple regression, method of principal
components, maximum probability method, and regression of partial least
squares).
•
Using geostatistical techniques (kriging, co-kriging, modified kriging).
•
Carrying out laboratory analysis
Regression
analysis of the correlation between magnitude of results of laboratory tests,
and space images, was used for decoding satellite images. During the
classification, a total of 24 bands were used, including 4 different time
satellite images (4*4 = 16), each with 4 bands, and 4 NDVI and 4 SVSI indexes
(4 + 4 = 8), each with 1 band. The following predictors were used for
regression analysis: image was utilized to derive indices for soil salinity
estimate including the single bands, Vegetation Soil Salinity Index (VSSI),
Normalized Difference Vegetation Index (NDVI), and Normalized Difference
Salinity Index (NDSI).
Two
salinity indices based on the concept of spectral response to salt-affected
soils were calculated. It is noted that spectral response in terms of digital
number (DN) of salt-affected soils is relatively higher than other categories
in band-1 (B1) and band-3 (B3). The following two salinity indices were used:
1)
Salinity Index (S.I.). This index which gives relatively adequate results in the
re-classification of salt-affected soils.
1) S.
İ. = (Band1 x Band3)1/2
Normalized
Differential Salinity Index (NDSI). The NDSI is basically the difference
between the red and near infrared band combination divided by the sum of the
red and near infrared band combination. The algorithm used was:
2)
NDSI = (𝐵𝑎𝑛𝑑3−𝐵𝑎𝑛𝑑4)
/ (𝐵𝑎𝑛𝑑3−𝐵𝑎𝑛𝑑4)
Reflectance
variations of vegetation on the image are attributed to the different species
of vegetation and their densities, which together provide evidence of shallow
ground water table conditions and saline agricultural areas.
Normalized
Differential Vegetation Index (NDVI) which easily grasps the state of
vegetation. NDVI was the most common form of vegetation index and was basically
the difference between the red and near infrared band combination divided by
the sum of the red and near infrared band combination or (Figure 3&4):
3)
NDVI = (𝑁İ𝑅−𝑅) / (𝑁İ𝑅+𝑅)
Was
executed isolate clustering algorithm to determine the characteristics of the
natural groupings of cells in multidimensional attribute space. Maximum
probability classification has been performed on a number of raster bands and
creates a raster classified as output (Figures 5).
Statistical
analysis between results of laboratory tests and the environmental indices
derived from Azersky image was performed. Results indicated that spectral
values of near-infrared (NIR) band and VSSI were better correlated with severe
salinity and salinity indicators than the other indices. Comparative results
show that soil salinity derived from Azersky was consistent with in situ data
with coefficient of determination, R2 = 0.89 and RMSE = 1.06 dS/m for NIR band
and R2 = 0.80 and RMSE = 1.50 dS/m for VSSI index. Findings of this study
demonstrate that Azersky images reveal a high potential for spatiotemporally monitoring
the magnitude of soil salinity at the topsoil layer.
Results
and Discussions
Our
work began with the creation of the basic GIS layers within the Salyan and
Neftchala rural districts (research objects), including borders of irrigated
sites, settlements, roads, lakes, rivers, irrigation network, etc. All the
layers that make up the GIS were obtained by decoding Azersky space imagery for
2018-2019, and also land use maps of local farmers. After conducting fieldwork,
field observation points and results of satellite imagery of the research
object were added into GIS. Then the image of NDSI indices was divided into
three classes corresponding to non-saline, slightly saline, and medium-saline
soils. Quintile counted on imagery for day 250 in 2018-2019 was used as class
boundaries.
The
main goal of our research was to investigate the potential of thermal imagery
as a rapid, non-destructive landscape-level method for assessing the soil
salinity of areas under crops. Our results confirm that remotely sensed canopy
temperature at the landscape level is significantly related to soil salinity. Statistical
analysis showed significant differences between salinity classes.
The
assessments were confirmed by laboratory tests. IN the laboratory, the iCAP
7200 ICP-OES Duo device was used to measure the amount of Na+, K+, Mg+, Ca
+cations. Soil layer analysis (0- 20cm, 20-50cm, 50-100cm) was analyzed at 250
points (Figure 6).
Controlled
Classification (MLC) operation was performed for soil analysis. A salinity map
has been compiled based on salinity prices and type (Figures 7&8).
Conclusion
Using
regression analysis of linkage between spectral properties of Azersky space
image and laboratory analysis of irrigated soils, it became possible to build,
it became possible to build salinization regression models only for particular
soil layers. Salinity maps were built on a semi-quantitative level using
automated image classification on learning selection using and dividing soil
imagery based on NDSI salinity index values. Strongly saline soils were identified
most accurately, while soils with other salinity degrees were outlined with
less precision
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