Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan (original) (raw)
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Evaluation of soil salinity level through using Landsat-8 OLI in Central Fergana valley, Uzbekistan
E3S Web of Conferences
Soil salinity is a major concern in the Uzbekistan. Fergana valleys agricultural lands, it negatively affects plant growth, crop yields, whereas in central part of the valley is semi-desert and desert affects agricultural areas due to subsidence, corrosion and ground water quality, leading to further soil erosion and land degradation. Traditional soil salinity assessments have been doing by collecting of soil samples and laboratory analyzing of collected samples for determining totally dissolved soils (TDS) and electro conductivity, but, Geo-informatic systems (GIS) and Remote Sensing (RS) technologies provides more efficient, economic and rapid tools and techniques for soil salinity assessment and soil salinity mapping. Main goals of this research are to map soil salinity of Fergana valley, to show relation of its result with traditional analysing and analysing withGIS technology As a source of satellite images has been used Landsat-8 OLI. Research areas every arable land validity ...
Using Landsat 8 OLI data to predict and mapping soil salinity for part of An-Najaf governorate
The most important problems of land degradation are represented by the soil salinization that concentrated particularly in arid and semi-arid environments due to high levels of temperature and evaporation. The study focuses on elucidation of the ability of Landsat-8 Operational Land Imager (OLI) sensor data with its visible and infrared bands for detection and mapping of soil salinity (SS) according to salinity levels in dispersed locations of An-Najaf Governorate. Seven soil salinity indices (SSI) have been applied and assessed to detect soil salinity effectually. The multiple linear regression (MLR) has been applied to explore the correlation between various spectral indices and bands with ground measurements of electrical conductivity (ECm) for the sake of prediction of the soil salinity. Strong correlations have been found between the (SSIs) and (ECm). Thematic maps for soil salinity using GIS have been produced and acceptably evaluated.
E3S Web of Conferences
Soil salinity is a serious agricultural concern in Uzbekistan, causing plant growth to be hampered and crop productivity to be diminished. This issue is especially prevalent in semi-desert and desert regions, compounding problems such as soil erosion, land degradation, subsidence, corrosion, and poor groundwater quality. On the other hand, Geographic Information Systems (GIS) and Remote Sensing (RS) technologies provide more efficient, cost-effective, and timely tools and procedures for mapping soil salinity. Different indices and methods can be used to detect and quantify soil salinity levels using the spectral information acquired by the Landsat-8 OLI sensor. Among these are the Normalized Difference Salinity Index (NDSI) and the Normolazed Difference Vegetation Index (NDVI). GIS software integrates satellite imagery with auxiliary data such as soil type and topography, allowing for a thorough assessment of soil salinity distribution over the research area. Compared to traditional...
Ecological Indicators, 2020
Soil salinization is one of the significant soil degradation problems especially faced in arid and semi-arid regions of the world. It poses a high threat to soil productivity in agricultural lands. The demand for economic and rapid detection and temporal monitoring of soil salinity has been rising recently. Satellite imagery and remote sensing approaches are the significant tools for accurate prediction and mapping of soil salinity in various regions of the world. This study aims to compare Landsat-8 OLI and Sentinel-2A derived soil salinity maps of the western part of Urmia Lake in Iran by applying three different salinity indices in conjunction with field measurements. Totally 70 soil samples were collected from top 20 cm of surface soil in October 2016 from an area of 18 km 2. Landsat-8 OLI and Sentinel-2A images were acquired in the same month; both images were atmospherically and radiometrically corrected prior to applying soil salinity indices. After comparing Normalized Difference Vegetation Index (NDVI) value of corresponding pixel for each sample with its electrical conductivity (EC) value, 54 soil samples with various EC ranges were selected for mapping. Among them, 42 samples were used for establishing the regression model and remaining 12 samples were utilized to validate the model. Multiple and linear regression analyses were conducted to correlate the EC data with their corresponding soil salinity spectral index values derived from visible bands of satellite images. The results revealed that soil salinity indices extracted from both Landsat-8 OLI and Sentinel-2A visible bands estimated soil salinity with acceptable accuracy of R 2 0.73 and 0.74, respectively. Multiple linear regression analysis using both Landsat-8 OLI and Sentinel-2A data demonstrated higher accuracy with R 2 value of 0.77 and 0.75, respectively, compared to linear regression. This study proves that various soil salinity classes with different EC ranges can be estimated by correlating ground measurement data with satellite data.
International Journal of Applied Earth Observation and Geoinformation, 2019
Soil salinization is one of the most serious environmental issues degrading land resources globally, particularly in arid and semi-arid regions. Therefore, regional and precise monitoring of soil salinity is required to prevent and mitigate salinization. This study aimed to specify an effective monitoring method with remote sensing techniques using the Dakhla Oasis, central Western Desert of Egypt as a case study area. For ground-truthing, electrical conductivity, pH, reflectance spectra, and mineral compositions were measured for top soil samples from 31 points. Spectral data from a Landsat8 OLI image of one scene acquired close to the ground sampling time was used to estimate soil salinity using a variety of methods, including single band, band ratio and combination, spectral index, linear spectral unmixing (LSU), and mixture tuned matched filtering (MTMF). After estimating the salinity over the study area through the best regression model between the spectral data and measured salinity data, the image was classified into five salinity classes. The classified salinized zones were verified by the resistivity and thickness of the near-surface layers and depth to the groundwater table, using vertical electrical sounding (VES) at 46 profiles. The most salinized zones in the southern area were congruent with the lowest VES resistivity. The surface layer thickness and clay content were specified as the main cause of the salinity difference between the southern and northern areas. The land surface temperature (LST) retrieved from the thermal band data of the OLI image and another Landsat ETM + image in 2001 was identified as increasing salinization. Finally, urban and vegetation land covers along with the five soil salinity classes were characterized by the influencing factors of elevation, slope, LST, soil pH, top layer resistivity and thickness, and depth to the groundwater table. LSU proved to predict salinity more accurately with 76% correctness than the MTMF model (67%) and the band combination and spectral indices (55% at most). The proposed methods will be useful for soil salinity mapping from satellite imagery in similar environments to this study.
Soil Salinity Mapping Using Multitemporal Landsat Data
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016
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...
Journal of the Indian Society of Remote Sensing, 2019
Soil salinization from arid to semiarid climate is a serious environmental problem. In some countries, salinization is considered a real threat to food security and food quality because it lowers crop yields and can irretrievably damage land. In the irrigated area of the plain of Tadla (Central Morocco), the intensive use of groundwater and saline surfaces lead to the degradation of soil quality. Experimental methods of monitoring soil salinity by direct real-time measurements are in high demand, but also very limited in terms of spatial coverage. The objective of this paper is to map soil salinity in the arid and semiarid zone of the Tadla plain in Morocco, based on optical remote sensing data and field measurements of sodium absorption ratio. The first results of work were devoted to the evaluation and validation of salinity models in the study area. Observations on the site, correlation, verification and validation of the model enabled us to map the salinity. All these use the soil salinity map based on non-saline soil content, light saline soil, moderate saline soil and highly saline soil. The map obtained from our model allowed us to identify the distribution of salinity in the study area. The values of the electrical conductivity in the study area range from less than 2 ds/m (non-saline soil) to more than 8 ds/m (highly saline soil) with a significant variation between the different levels of soil salinity in the study area.
2015
Soil salinity occurs on over 50% of the irrigated land and is one of the limiting factors for food crop production in the Central Asian region. Identification and mapping of salt-affected areas is a first step to coping with soil salinity. Traditional methods of mapping soil salinity, based on soil sampling and laboratory analyses, are time consuming and costly. Remote sensing-based approach has been developed to track historical changes in occurrences of soil salinity during the period 2000-2011. The method was tested for Syrdarya Province, Uzbekistan, where rapid salinity build-up has been recorded since 1965 when 300,000 ha of virgin land were developed based on canal irrigation. The seasonal Landsat images for the period 2000-2011 were used to calculate the radiance, reflectance and Normalized Difference Vegetation Index (NDVI) raster layers. The seasonal NDVI values were used to estimate the maximum annual NDVI values for three periods: 2000-2003, 2004-2007 and 2008-2011. The m...
Soil salinity mapping using Landsat 8 OLI data and regression modeling in the Great Hungarian Plain
SN Applied Sciences
Salt's deposition in the subsoil is known as salinization. It is caused by natural processes such as mineral weathering or human-made activities such as irrigation with saline water. This environmental issue has grown more critical and is frequently occurring in the Hungarian Great Plain, adversely influencing agricultural productivity. This study aims to predict soil salinity in the Great Hungarian Plain, located in the east of Hungary, using Landsat 8 OLI data combined with four state-of-the-art regression models, i.e., Multiple Linear Regression, Partial Least Squares Regression, Ridge Regression, and Feedforward Artificial Neural Network. For this purpose, seventy-six soil samples were collected during a field survey conducted by the Research Institute for Soil Sciences and Agricultural Chemistry between the 15 of September and the 15 of October, 2016. We used the min–max accuracy, the root-mean-square error (RMSE), and the mean squared error (MSE) to evaluate and compare th...
Detection and modeling of soil salinity variations in arid lands using remote sensing data
Open Geosciences, 2021
Soil salinization is a ubiquitous global problem. The literature supports the integration of remote sensing (RS) techniques and field measurements as effective methods for developing soil salinity prediction models. The objectives of this study were to (i) estimate the level of soil salinity in Abu Dhabi using spectral indices and field measurements and (ii) develop a model for detecting and mapping soil salinity variations in the study area using RS data. We integrated Landsat 8 data with the electrical conductivity measurements of soil samples taken from the study area. Statistical analysis of the integrated data showed that the normalized difference vegetation index and bare soil index showed moderate correlations among the examined indices. The relation between these two indices can contribute to the development of successful soil salinity prediction models. Results show that 31% of the soil in the study area is moderately saline and 46% of the soil is highly saline. The results support that geoinformatic techniques using RS data and technologies constitute an effective tool for detecting soil salinity by modeling and mapping the spatial distribution of saline soils. Furthermore, we observed a low correlation between soil salinity and the nighttime land surface temperature.