Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan

Autor: Elif Günal, Mesut Budak, Marian Brestic, Mohammad Javed Ansari, Xiukang Wang, Sami Al Obaid, Orhan Mete Kiliç
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Multivariate statistics
Salinity
Social Sciences
Reflection
Soil Chemistry
Physical Chemistry
Remote Sensing
Soil
Mathematical and Statistical Techniques
Spectrum Analysis Techniques
Soil functions
Agricultural Soil Science
Land Use
Multidisciplinary
Geography
Physics
Statistics
Classical Mechanics
near-Infrared Spectroscopy
Regression analysis
Agriculture
Vegetation
Terrestrial Environments
Chemistry
Physical Sciences
Medicine
Soil Salinity
Engineering and Technology
Regression Analysis
Research Article
Soil salinity
Soil test
Science
Soil Science
Soil science
Infrared Spectroscopy
Linear Regression Analysis
Research and Analysis Methods
Human Geography
Linear regression
Environmental Chemistry
Statistical Methods
Turkmenistan
Ecology and Environmental Sciences
Electric Conductivity
Biology and Life Sciences
Agricultural Land
Chemical Properties
Multivariate Analysis
Linear Models
Earth Sciences
Environmental science
Mathematics
Zdroj: PLoS ONE
PLoS ONE, Vol 16, Iss 11 (2021)
PLoS ONE, Vol 16, Iss 11, p e0259695 (2021)
ISSN: 1932-6203
Popis: Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.
Databáze: OpenAIRE
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