Proximal Sensing Methods for Mapping Soil Water Status in an Irrigated Maize Field

  • C. B. HedleyEmail author
  • I.J. Yule
  • M.P. Tuohy
  • B.H. Kusumo
Part of the Progress in Soil Science book series (PROSOIL)


Approximately 80% of allocated freshwater in New Zealand is used for irrigation, and the area irrigated has increased by 55% every decade since 1965. The research described in this chapter therefore focuses on developing new techniques to map and monitor soil attributes relevant to irrigation water use efficiency. The apparent electrical conductivity (ECa) of soils under a 33-ha irrigated maize crop was mapped using a mobile electromagnetic induction (EM) and RTK-DGPS system, and this map was used to select three contrasting zones. Within each zone, further ECa values were recorded at a range of volumetric soil water contents (θ) to develop a relationship between ECa, soil texture, soil moisture, and available water-holding capacity (AWC) (R 2 = 0.8). This allowed spatial prediction of AWC, showing that these sandy and silty soils had similar AWCs (∼160 mm/m). High-resolution digital elevation data obtained in the EM survey were also co-kriged with TDR-derived θ to produce soil moisture prediction surfaces, indicating drying patterns and their relationship to topography and soil texture. There was a 12.5–13.1% difference in soil moisture to 45 cm soil depth between the wettest and the driest sites at any one time (n = 47). Spatial and temporal variability of soil moisture, indicated by these co-kriged prediction surfaces, highlights the need for a rapid high-resolution method to assess in situ soil moisture. The potential of soil spectral reflectance (350–2,500 nm range; 1.4–2 nm resolution) for rapid field estimation of soil moisture was therefore investigated. Soil spectra were pre-processed and regressed against known soil moisture values using partial least squares regression (R 2 calibration = 0.79; R 2 prediction using leave-one-out cross-validation = 0.71). These proximal sensing methods facilitate spatial prediction of soil moisture, information which could then be uploaded to a variable rate irrigator.


Available water-holding capacity Co-krige Digital elevation map EM mapping 



The authors would like to thank Hew and Roger Dalrymple for use of their farm. The research has been funded by the Agricultural and Marketing Research and Development Trust, New Zealand (AGMARDT), the New Zealand Vice-Chancellors’ Committee, William Georgetti Trust, and The Sustainable Land Use and Research Initiative (SLURI), New Zealand.


  1. Cook SE, Bramley RGV (1998) Precision agriculture – opportunities, benefits and pitfalls of site-specific crop management in Australia. Austr J Exp Agric 38:753–763CrossRefGoogle Scholar
  2. DeJonge CK, Kaleita AL, Thorp KR (2007) Simulating the effects of spatially variable irrigation on corn yields, costs, and revenue in Iowa. Agric Water Manage 92:99–109CrossRefGoogle Scholar
  3. Huth NI, Poulton PL (2007) An electromagnetic induction method for monitoring variation in soil moisture in agroforestry systems. Austr J Soil Res 45:63–72CrossRefGoogle Scholar
  4. Hezarjaribi A, Sourell H (2007) Feasibility study of monitoring the total available water content using non-invasive electromagnetic induction-based and electrode-based soil electrical conductivity measurements. Irrigat Drainage 56:53–65CrossRefGoogle Scholar
  5. Hueni A, Tuohy M (2006) Spectroradiometer data structuring, pre-processing and analysis - an IT based approach. J Spat Sci 51(2):93–102CrossRefGoogle Scholar
  6. Hedley CB, Yule IJ, Bradbury S (2005) Using electromagnetic mapping to optimise irrigation water use by pastoral soils. Proceedings of the joint international conference of the New Zealand Hydrological Society, International Association of Hydrogeologists, and New Zealand Society of Soil Science, Auckland, 28 November–2 December, New Zealand Hydrological Society, Wellington, New Zealand, CD-ROMGoogle Scholar
  7. Hedley CB, Yule IJ, Eastwood CR, Shepherd TG, Arnold G (2004) Rapid identification of soil textural and management zones using electromagnetic induction sensing of soils. Austr J Soil Res 42:389–400CrossRefGoogle Scholar
  8. Kaleita AL, Tian LF, Hirschi MC (2005) Relationship between soil moisture content and soil surface reflectance. Trans Am Soc Agric Eng 48(5):1979–1986Google Scholar
  9. Milne JDG, Clayden B, Singleton PL, Wilson AD (1995) Soil description handbook. Manaaki Whenua Press, Landcare Research, New Zealand, 157 ppGoogle Scholar
  10. Mouazen AM, De Baerdemaeker J, Ramon H (2005) Towards development of on-line soil moisture content sensor using a fibre-type NIR spectrophotometer. Soil Tillage Res 80:171–183CrossRefGoogle Scholar
  11. Waine TW, Blackmore BS, Godwin RJ (2000) Mapping available water content and estimating soil textural class using electromagnetic induction. EurAgEng 2000, Warwick, UK, Paper 00-SW-044, European Society of Agricultural Engineers, UKGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • C. B. Hedley
    • 1
    Email author
  • I.J. Yule
    • 2
  • M.P. Tuohy
    • 3
  • B.H. Kusumo
    • 4
    • 4
  1. 1.Landcare ResearchManawatu Mail CentrePalmerston NorthNew Zealand
  2. 2.New Zealand Centre for Precision AgricultureInstitute of Natural Resources, Massey UniversityPalmerston NorthNew Zealand
  3. 3.Institute of Natural Resources, College of Science, Massey UniversityNorth Shore CityNew Zealand
  4. 4.Faculty of Agriculture, Department of Soil ScienceUniversity of MataramLombokIndonesia

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