Automated Air Photo Interpretation in the Mapping of Soil Salinization in Cotton-Growing Zones

Golovina, N. N.; Minskiy, D. Ye.; Pankova, Ye. I.; Solovyev, Dmitry Aleksandrovich

doi:10.1080/07493878.1992.10641915

Cited by 6 publications

(2 citation statements)

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“…While it is possible to visually identify saline soils from base maps (Golovina et al, 1992;Dwivedi et al, 1999), this requires interpretation by a skilled technician. As an alternative to visual interpretation, computer aided classification requires more spectral bands but less expert knowledge (Guan et al, 2001;Elnaggar and Noller, 2009;Muller and van Niekerk, 2016).…”

Section: Introductionmentioning

confidence: 99%

Towards decadal soil salinity mapping using Landsat time series data

Fan

Weng

Tao³

2016

International Journal of Applied Earth Observation and Geoinfor

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Section: Introductionmentioning

confidence: 99%

Towards decadal soil salinity mapping using Landsat time series data

Fan

Weng

Tao³

2016

International Journal of Applied Earth Observation and Geoinfor

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“…The findings here showed that this particular drawback from the RS approach can be successfully eased when combining an indirect RS assessment with water and solute transport modeling. It thereby was assumed that for the case study area a single plant-stress factor (soil salinization) occurred and while considering only cotton, a crop that is considered also as a good indicator plant for salinity studies (Golovina et al 1992;Metternicht & Zink 2008). The assumption of cotton cultivation facing only soil salinization as stress was however justified given not only the overall good soil moisture holding capacity at the CRS, but also owing to the same land and water management implemented on the different fields monitored.…”

Section: Discussionmentioning

confidence: 99%

Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin

Ibrakhimov¹,

Awan²,

Sultanov³

et al. 2020

CAJWR

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Accurate assessment of the soil salinization is an important step for mitigation of agricultural land degradation. Remote sensing (RS) is widely used for salinity assessment, but knowledge on prediction precision is lacking. A RS-based salinity assessment in Khorezm allows for modest reliable prediction with weak (R 2 =0.15-0.29) relationship of the salinity maps produced with RS and interpolation of electromagnetic EM38 during growth periods and more reliable (R 2 =0.35-0.56) beyond irrigation periods. Modeling with HYDRUS-1D at slightly, moderately and highly saline sites at various depths showed that irrigation forces salts to move to deeper layers: salts reappear in the upper profile during dry periods. Beyond irrigation events, salts gradually accumulated in the upper soil layers without fluctuations. Coupling RS techniques with numerical modeling provided better insight into salinity dynamics than any of these approaches alone. This should be of interest to farmers and policy makers since the combination of methods will allow for better planning and management.

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