2019
DOI: 10.1088/2515-7620/ab37f0
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Using L-band radar data for soil salinity mapping—a case study in Central Iraq

Abstract: Soil salinization is a critical environmental problem for dryland agriculture. Mapping its distribution and severity in space and time is essential for agricultural management and development. Recently, remote sensing technology has been widely applied in such mapping but mostly using optical remote sensing data. In conjunction with the field surveys, this case study was aimed at developing an operational approach for this purpose by employing ALOS (Advanced Land Observing Satellite) L-band radar data with sup… Show more

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Cited by 14 publications
(14 citation statements)
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“…In this study area, the topography is complex, as there are many zones where the Gobi desert intersects with the vegetated areas; therefore, there is some difficulty in characterizing the surface roughness, which affects the correlation between the backscattering coefficient and the soil salinity. From the above analysis, it can be seen that it is still difficult to invert soil salinity using only a single radar data source [78,79], and in future research, multiple methods or multiple sources of data are needed to reduce the effect of noise.…”
Section: Discussionmentioning
confidence: 99%
“…In this study area, the topography is complex, as there are many zones where the Gobi desert intersects with the vegetated areas; therefore, there is some difficulty in characterizing the surface roughness, which affects the correlation between the backscattering coefficient and the soil salinity. From the above analysis, it can be seen that it is still difficult to invert soil salinity using only a single radar data source [78,79], and in future research, multiple methods or multiple sources of data are needed to reduce the effect of noise.…”
Section: Discussionmentioning
confidence: 99%
“…Including all the remote sensing groups (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13), we assessed a total of 35 indices for significance and relationships to the measured EC values using Pearson correlation analysis [67]. Among them, relationships of 9 indices (B2, B5, SI, SI1, SI3, S8, S9, CRSI, S) were significant at p < 0.05, and 12 (B3, B6, B7, B10, B11, S1, S2, S6, GDVI, NLI, EVI, DEM) were significant at p < 0.01 probability level (Table 5).…”
Section: Selection Of Independent Variablesmentioning
confidence: 99%
“…This leads to abnormal effects of soil moisture and vegetation on salinity estimation. Therefore, the dry season is more suitable for estimation of soil salinity than the wet season [11,31,50,72].…”
Section: Sensitivity Of Water and Vegetation Coveragementioning
confidence: 99%
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“…This can majorly affect spectroscopy absorption features (Hunt, 1980 andWeismiller et al, 1985). Remote sensing datasets and techniques were effectively used in several environmental applications in several countries (Fadhil, 2011(Fadhil, , 2013Wu et al 2019, Gaznayee and Al-Quraishi 2019, Alqasemi et al 2020a, Alqasemi et al 2020b, Kumar et al 2019Wu et al 2018;Yao et al 2011). The surface mineralogical features can be effectively mapping by remote sensing technology, especially Iron oxide, which has significant influences on the soil's spectral reflectance characteristics (Ciampalini et al, 2012).…”
Section: Introductionmentioning
confidence: 99%