Severity of salinity accurately detected and classified on a paddock scale with high resolution multispectral satellite imgery

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dc.contributor.authorSetia, R.
dc.contributor.authorLewis, M.
dc.contributor.authorMarschner, P.
dc.contributor.authorRaja Segaran, R.
dc.contributor.authorSummers, D.
dc.contributor.authorChittleborough, D.
dc.date.issued2013
dc.description.abstract<jats:title>ABSTRACT</jats:title><jats:p>We hypothesised that digital mapping of various forms of salt‐affected soils using high resolution satellite imagery, supported by field studies, would be an efficient method to classify and map salinity, sodicity or both at paddock level, particularly in areas where salt‐affected patches are small and the effort to map these by field‐based soil survey methods alone would be inordinately time consuming. To test this hypothesis, QuickBird satellite data (pan‐sharpened four band multispectral imagery) was used to map various forms of surface‐expressed salinity in an agricultural area of South Australia. Ground‐truthing was performed by collecting 160 soil samples over the study area of 159 km<jats:sup>2</jats:sup>. Unsupervised classification of the imagery covering the study area allowed differentiation of severity levels of salt‐affected soils, but these levels did not match those based on measured electrical conductivity (EC) and sodium adsorption ratio (SAR) of the soil samples, primarily because the expression of salinity was strongly influenced by paddock‐level variations in crop type, growth and prior land management. Segmentation of the whole image into 450 paddocks and unsupervised classification using a paddock‐by‐paddock approach resulted in a more accurate discrimination of salinity and sodicity levels that was correlated with EC and SAR. Image‐based classes discriminating severity levels of salt‐affected soils were significantly related with EC but not with SAR. Of the spectral bands, bands 2 (green, 520–600 nm) and 4 (near‐infrared, 760–900 nm) explained the majority of the variation (99 per cent) in the spectral values. Thus, paddock‐by‐paddock classification of QuickBird imagery has the potential to accurately delineate salinity at farm level, which will allow more informed decisions about sustainable agricultural management of soils. Copyright © 2011 John Wiley &amp; Sons, Ltd.</jats:p>
dc.description.statementofresponsibilityR. Setia, M. Lewis, P. Marschner, R. Raja Segaran, D. Summers, and D. Chittleborough
dc.identifier.citationLand Degradation and Development, 2013; 24(4):375-384
dc.identifier.doi10.1002/ldr.1134
dc.identifier.issn1085-3278
dc.identifier.issn1099-145X
dc.identifier.orcidLewis, M. [0000-0003-1203-6281]
dc.identifier.orcidMarschner, P. [0000-0001-6808-0244]
dc.identifier.orcidRaja Segaran, R. [0000-0002-0484-8194]
dc.identifier.urihttp://hdl.handle.net/2440/70018
dc.language.isoen
dc.publisherJohn Wiley & Sons Ltd
dc.rightsCopyright © 2011 John Wiley & Sons, Ltd.
dc.source.urihttps://doi.org/10.1002/ldr.1134
dc.subjectEC
dc.subjectmultispectral
dc.subjectSAR
dc.subjectsoil properties
dc.subjectsoil salinity
dc.subjectunsupervised classification
dc.subjectAustralia
dc.subjectrangeland salinisation
dc.titleSeverity of salinity accurately detected and classified on a paddock scale with high resolution multispectral satellite imgery
dc.typeJournal article
pubs.publication-statusPublished

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