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Mapping Soil Salinity Using Calibrated Electromagnetic Measurements
Author(s) -
Lesch S. M.,
Rhoades J. D.,
Lund L. J.,
Corwin D. L
Publication year - 1992
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1992.03615995005600020031x
Subject(s) - soil science , salinity , soil salinity , sampling (signal processing) , environmental science , spatial variability , soil test , dryland salinity , soil water , hydrology (agriculture) , mathematics , statistics , soil organic matter , geology , computer science , soil biodiversity , oceanography , geotechnical engineering , filter (signal processing) , computer vision
A statistical modeling approach is presented that predicts spatial soil salinity patterns from aboveground electromagnetic induction (EM) readings. In this approach, EM readings are obtained from a field sampled on a uniform (centric systematic) grid. A small number of these sample sites are chosen for soil sampling, based on the observed EM field pattern. The salinity levels for these soil samples are determined and then the remaining nonsampled salinity values are predicted from the corresponding EM readings through a multiple linear regression equation. Experimental results suggest that this approach will work well in fields having low to moderate levels of soil textural variability. For example, 95% of the spatial variability in soil salinity within typical 16.2‐ha (40‐acre) cotton ( Gossypium hirsutum L.) fields could be accounted for with only 36 soil samples, as opposed to the 200 to 300 soil samples typically required if no EM readings were available. This approach makes EM readings a more practical and cost‐effective tool by substantially reducing the number of soil samples needed for accurate mapping of spatial salinity patterns at the field scale.