Determination of soil salinity based on WET measurements using the concept of salinity index

2018 ◽  
Vol 181 (4) ◽  
pp. 600-605 ◽  
Author(s):  
George Kargas ◽  
Petros Kerkides
Keyword(s):  
Soil Salinity ◽  
Salinity Index

Change on detection of vegetation cover and soil salinity using GIS technique in Diyala Governorate, Iraq

2021 ◽  
Vol 30 (1) ◽  
pp. 148-158
Author(s):  
Haneen Adeeb ◽  
Yaseen Al-Timimi
Keyword(s):  
Soil Salinity ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Inverse Correlation ◽  
Salinity Changes ◽  
Gis Technique ◽  
Salinity Index ◽  
Remote Sensing Techniques ◽  
Strong Inverse Correlation

Soil salinity is one of the most important problems of land degradation, that threatening the environmental, economic and social system. The aim of this study to detect the changes in soil salinity and vegetation cover for Diyala Governorate over the period from 2005 to 2020, through the use of remote sensing techniques and geographic information system. The normalized difference vegetation index (NDVI) and salinity index (SI) were used, which were applied to four of the Landsat ETM+ and Landsat OLI satellite imagery. The results showed an increase in soil salinity from 7.27% in the period 2005–2010 to 27.03% in 2015–2020, as well as an increase in vegetation from 10% to 24% in the same period. Also the strong inverse correlation between the NDVI and the SI showed that vegetation is significantly affected and directly influenced by soil salinity changes

Soil salinity assessment using temporal series of Sentinel2 satellite images in irrigated paddy fields of Western Africa

2020 ◽  
Author(s):  
Moussa Issaka ◽  
Walter Christian ◽  
Michot Didier ◽  
Pichelin Pascal ◽  
Nicolas Hervé ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Ground Truth ◽  
Growing Season ◽  
Bare Soil ◽  
Atmospheric Effects ◽  
Ground Truth Data ◽  
Multi Temporal ◽  
Salinity Index ◽  
Niger River

<p>Salinization and alkalinization are worldwide among the soil degradation threats in irrigated schemes affecting soil productivity. Niger River basin irrigated schemes in the Sahel arid zone are no exception (ONAHA, 2011). The use of remote sensing for identifying and evaluating the level of these phenomena is an interesting tool. The launching of the Sentinel2 satellite constellation (2015) brings new perspectives with high spectral and temporal resolutions images. The aim of this study was to develop a methodology for detection of salt-affected soils in this climatic condition.</p><p>To achieve our goal, we used two types of data: remote sensing and ground truth data.</p><p>Two complementary approaches were used: one by observing salinity on bare soil by the use of salinity index (SI) and the other by observing the indirect effects of salinity on the vegetation during eight (8) rice growth phases  using vegetation index NDVI.</p><p>Remote sensing data were acquired from multi temporal sentinel2 images over 4 years (from 11/12/2015 to 30/11/2019). One hundred and fifty seven (157) images were downloaded (one image each 5 days) and corrected from atmospheric effects and some bands resampled to 5 m using python software. The salinity and vegetation indices were calculated. NDVI index was calculated and NDVI integral between NDVI curve and the threshold of 0.21 NDVI calculated for the eight growing cycles.</p><p>Ground truth data were collected in 2019 during the dry growing season (January – may 2019) from 24 calibration plots and 40 validation plots. One hundred and twenty (120) soil samples collected and analyzed for pH and electrical conductivity and finally forty six (46) biomass samples were collected, air dried and weighed for biomass yield and 46 grains samples collected for grain yield.</p><p>NDVI integral proved to be good indicator for yield variations and could distinguish crops behavior according to the growing period. It also makes it possible to distinguish plots which were not cultivated or with weak growth due to strong constraints of which the main one is salinity. It showed also that the effect of salinity on growth differs according to the growing season and the possibility of managing irrigation. Bare soil analysis distinguishes fields with different salinity indexes despite the low number of dates for which bare soil can be observed.</p><p>Ascending Hierarchical Classification (AHC) enabled to identify four classes of NDVI dynamics over time and bare soil salinity index. High saline soils according to direct soil measurements were related to the class characterized by high frequency of no-cultivation during the dry season and low NDVI integral during the wet season. Multi-temporal Sentinel2 images analysis enabled therefore to detect rice crop fields affected by salinity through its influence on crop behavior. This approach will be tested over the whole paddy schemes of the Niger River valley.</p>

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

Verification of soil salinity index model based on 0.02–3 GHz complex dielectric permittivity spectrum measurements

2019 ◽  
Vol 574 ◽  
pp. 517-528 ◽  
Author(s):  
Agnieszka Szypłowska ◽  
Justyna Szerement ◽  
Arkadiusz Lewandowski ◽  
Marcin Kafarski ◽  
Andrzej Wilczek ◽  
...  
Keyword(s):  
Dielectric Permittivity ◽  
Soil Salinity ◽  
Complex Dielectric Permittivity ◽  
Index Model ◽  
Model Based ◽  
Spectrum Measurements ◽  
Salinity Index ◽  
Permittivity Spectrum

scholarly journals Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kim-Anh Nguyen ◽  
Yuei-An Liou ◽  
Ha-Phuong Tran ◽  
Phi-Phung Hoang ◽  
Thanh-Hung Nguyen
Keyword(s):  
Soil Salinity ◽  
Vegetation Index ◽  
Near Infrared ◽  
Mekong Delta ◽  
The Other ◽  
Landsat 8 ◽  
Salinity Intrusion ◽  
Landsat 8 Oli ◽  
Environmental Indices ◽  
Salinity Index

AbstractSalinity 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. Here, we assessed the salinity intrusion in the Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI image was utilized to derive indices for soil salinity estimate including the single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index (NDSI). Statistical analysis between the electrical conductivity (EC1:5, dS/m) and the environmental indices derived from Landsat 8 OLI image was performed. Results indicated that spectral values of near-infrared (NIR) band and VSSI were better correlated with EC1:5 (r2 = 0.8 and r2 = 0.7, respectively) than the other indices. Comparative results show that soil salinity derived from Landsat 8 was consistent with in situ data with coefficient of determination, R2 = 0.89 and RMSE = 0.96 dS/m for NIR band and R2 = 0.77 and RMSE = 1.27 dS/m for VSSI index. Findings of this study demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring the magnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners, and farmers by mapping the soil salinity contamination for better selection of accomodating crop types to reduce economical loss in the context of climate change. Our proposed method that estimates soil salinity using satellite-derived variables can be potentially useful as a fast-approach to detect the soil salinity in the other regions with low cost and considerable accuracy.

scholarly journals An Investigation of the Relationship between the Electrical Conductivity of the Soil Saturated Paste Extract ECe with the Respective Values of the Mass Soil/Water Ratios 1:1 and 1:5 (EC1:1 and EC1:5)

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 661 ◽  
Author(s):  
George Kargas ◽  
Iakovos Chatzigiakoumis ◽  
Athanasios Kollias ◽  
Dimitrios Spiliotis ◽  
Petros Kerkides
Keyword(s):  
Electrical Conductivity ◽  
Soil Water ◽  
Correlation Coefficient ◽  
Soil Salinity ◽  
Water Mass ◽  
Soil Samples ◽  
Standard Methodology ◽  
Mass Ratios ◽  
The Relationship

The standard methodology for the soil salinity assessment is provided through the determination of the electrical conductivity (EC) of the soil saturated paste extract, ECe. This approach is cumbersome and tedious. Instead of this, it appears easier to measure the EC of various soil over water mass ratios, (soil:water), such as 1:1, 1:5. In the present study an attempt is made to compare the ECe methodology with the methods providing the EC1:1 and EC1:5. ECe, and EC1:1 or EC1:5 values were obtained from 198 soil samples from 5 different locations in Greece. It was shown that the methods providing EC1:1 and EC1:5 values are linearly correlated to the ECe methodology with a high correlation coefficient (R2 > 0.93).

Development of Comprehensive Soil Salinity Index

2002 ◽  
Vol 128 (3) ◽  
pp. 185-188 ◽  
Author(s):  
R. M. Patel ◽  
S. O. Prasher ◽  
R. B. Bonnell ◽  
R. S. Broughton
Keyword(s):  
Soil Salinity ◽  
Salinity Index
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