Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery

Taghadosi, Mohammad Mahdi; Hasanlou, Mahdi; Eftekhari, Kamran

doi:10.1080/01431161.2018.1512767

Cited by 77 publications

(42 citation statements)

References 24 publications

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“…The U.S. Salinity Laboratory (U.S. Salinity Laboratory Staff 1954) classifies agricultural soil salinities as 0-2 dS/m = non-saline; 2-4 dS/m = slightly saline; 4-8 dS/m = moderately saline; 8-16 dS/m = strongly saline; and > 16 dS/m = extremely saline, where salinity is quantified as the He et al (1992), electrical conductivity (EC 1:5 ) of a soil extract is the most widely used parameter for describing soil salinity (Rayment and Higginson 1992). Electrical conductivity is estimated by the concentration of ions in the soil that predominantly consists of the cations Na+, Ca 2+ , K + , and Mg 2+ and the anions Cl -, SO 4 2and HCO 3 - (Rhoades et al 1989;Sumner and Naidu 1998). In soil, ions present mainly in soil solution and are adsorbed in soil colloids.…”

Section: Discussionmentioning

confidence: 99%

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

Nguyen

Liou

Tran³

et al. 2020

Prog Earth Planet Sci

126

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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. 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 (EC 1:5 , 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 EC 1:5 (r 2 = 0.8 and r 2 = 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, R 2 = 0.89 and RMSE = 0.96 dS/m for NIR band and R 2 = 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 satellitederived variables can be potentially useful as a fast-approach to detect the soil salinity in the other regions with low cost and considerable accuracy.

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Section: Discussionmentioning

confidence: 99%

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

Nguyen

Liou

Tran³

et al. 2020

Prog Earth Planet Sci

126

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“…Soil salinity is a major problem in certain areas, due to the build-up of minerals in the soil due to evaporation of groundwater, irrigation and reduced leaching due to low precipitation. There is considerable interest in mapping areas of saline soils, and recent research by Taghadosi et al (2018) has examined the potential of using C-band SAR data to do this. As the relative permittivity of saline water differs from pure water, the relative permittivity of soils is expected to vary with salinity.…”

Section: Salinitymentioning

confidence: 99%

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

Beale

Snapir

Waine

et al. 2019

Preprint

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Abstract. Soil Moisture is a key variable in hydrology, weather and climate modelling. Research has been directed to the estimation of soil moisture over wide areas through a combination of modelling, in-situ measurement and remote sensing to improve the accuracy of hydrological and meteorological forecasting. For monitoring and controlling irrigation and other agricultural purposes, there is also a need to capture local variability. Significant soil moisture differences are observed between and within fields due to land use, soil properties, drainage, tillage, vegetation, solar radiation, air temperature, wind, rain and other factors. Taking the United Kingdom as an example, the average area of agricultural fields is about 12 hectares, requiring a mapping resolution of less than 100 m. Satellite-based remote sensing, including the use of C-band SAR (such as on Sentinel-1), has the potential to satisfy this requirement, but many current data products are aggregated to a spatial resolution of at least 1km and/or provide soil moisture in relative units or indices. Both strategies mitigate the uncertainties introduced by field-scale variability in soil hydrological and vegetation properties. Geospatial datasets of soil properties and land use, crop modelling and other remote sensing techniques may provide an alternative approach to mitigating this variability and allow finer scale products to be produced with acceptable errors. This paper looks at the role of soil properties in the estimation of soil moisture from C-band SAR. We show that information on the soil texture, organic matter content, surface temperature, land use and crop modelling should be important inputs to the success of retrieving soil moisture at the field scale. Previously published data provides guidance in setting soil roughness parameters, based on soil properties, following farming operations such as primary tillage. Beyond soil moisture retrieval, there is exciting potential in SAR remote sensing data to improve the spatial resolution and mapping accuracy of some soil properties.

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“…Using the potential of satellite data and remote sensing techniques, it becomes possible to monitor soil salinity more efficiently and more economically (Allbed & Kumar, 2013;Garcia, Eldeiry, & Elhaddad, 2005;Morshed, Islam, & Jamil, 2016;Taghadosi, Hasanlou, & Eftekhari, 2018). Determining the spectral characteristics of saline surface soils and the spatial distribution of affected areas can be achieved by analyzing data from newly launched satellites with good spatial and spectral resolution, which helps to map salinity over large scales and with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of soil salinity from Sentinel-2 multispectral imagery

Taghadosi

Hasanlou

Eftekhari³

2019

European Journal of Remote Sensing

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Soil salinity is a widespread environmental hazard and the main causes of land degradation and desertification, especially in arid and semi-arid regions. The first step in finding such a solution is providing accurate information about the severity and extent of the salinity spread in affected areas; this can be done by mapping the electrical conductivity (EC) of the soil. Utilizing the potential of high-resolution satellite imagery along with remote sensing techniques is a promising method to map salinity, as it allows for large-scale monitoring and provides high accuracy and efficiency. This paper, therefore, aims at assessing soil salinity by mapping the EC of soils, using satellite imagery from the newly launched Sentinel-2 satellite as well as Landsat-8 data. A field study was carried out using those data, and various salt features were extracted that relate the EC values of field samples to satellite-derived salt features. The study used two different regression approaches MLP and SVR. Additionally, two feature selection algorithms, GA and SFS, were implemented on the data to improve model performance. The study concludes that the proposed method for modeling salinity and the mapping of soil EC can be considered an effective approach for soil salinity monitoring.

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Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery

Cited by 77 publications

References 24 publications

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

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

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

Retrieval of soil salinity from Sentinel-2 multispectral imagery

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