IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v163y2016icp285-294.html
   My bibliography  Save this article

Mapping soil moisture across an irrigated field using electromagnetic conductivity imaging

Author

Listed:
  • Huang, J.
  • Scudiero, E.
  • Choo, H.
  • Corwin, D.L.
  • Triantafilis, J.

Abstract

The ability to measure and map volumetric soil water (θ) quickly and accurately is important in irrigated agriculture. However, the traditional approach of using thermogravimetric moisture (w) and converting this to θ using measurements of bulk density (ρ—cm3/cm3) is laborious and time consuming. To speed up the process electromagnetic (EM) instruments have been used to assist in mapping average θ along a transect or across a field. This is because the apparent soil electrical conductivity (ECa) measured by EM instruments has been shown to be a function of θ, when other soil properties are uniform. However, mapping depth-specific soil θ has been little explored. One possible approach is to invert the ECa data to calculate estimates of true electrical conductivity (σ) at specific depths (i.e., 0.15, 0.45, 0.75, 1.05 and 1.35m) and couple this to measured θ. This research explores this possibility by using a single frequency multi-coil DUALEM-421 across a centre-pivot irrigated Lucerne field (Medicago sativa L.) in San Jacinto, CA, USA. The first aim is to determine an optimal set of inversion parameters (i.e., forward modelling, inversion algorithm and damping factor—λ) which are appropriate to establish a calibration between σ and θ. In this regard the largest coefficient of determination (R2=0.56) is achieved when we used the FS model, S2 algorithm and a λ=0.3. The second aim is to see if all the coil arrays of the DUALEM-421 are necessary. We conclude that while the DUALEM-1 produces a larger R2 (0.59), the use of the DUALEM-421 data is better (R2=0.56), because the total model misfit (4.70mSm−1) is smaller and because it better accounts for the spatial variation of θ in the subsoil. In terms of predicting θ, the calibration equation (θ=2.751+0.190×σ) was examined using a leave-one-out cross validation. The Lin’s concordance (0.73) between measured and predicted θ was good. The resulting 2-d depth slices and cross-sections gave insights into the spatial distribution of θ which allowed the inference of depth of saturated soil and location of the wetting front and identified areas where deep drainage may be problematic. The approach has applications for water use and management given it can identify inefficiencies in water application rates and use.

Suggested Citation

  • Huang, J. & Scudiero, E. & Choo, H. & Corwin, D.L. & Triantafilis, J., 2016. "Mapping soil moisture across an irrigated field using electromagnetic conductivity imaging," Agricultural Water Management, Elsevier, vol. 163(C), pages 285-294.
  • Handle: RePEc:eee:agiwat:v:163:y:2016:i:c:p:285-294
    DOI: 10.1016/j.agwat.2015.09.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377415300950
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2015.09.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Blonquist, J.M. Jr. & Jones, S.B. & Robinson, D.A., 2006. "Precise irrigation scheduling for turfgrass using a subsurface electromagnetic soil moisture sensor," Agricultural Water Management, Elsevier, vol. 84(1-2), pages 153-165, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shaukat, Hira & Flower, Ken C. & Leopold, Matthias, 2022. "Quasi-3D mapping of soil moisture in agricultural fields using electrical conductivity sensing," Agricultural Water Management, Elsevier, vol. 259(C).
    2. Peng Gao & Jiaxing Xie & Mingxin Yang & Ping Zhou & Wenbin Chen & Gaotian Liang & Yufeng Chen & Xiongzhe Han & Weixing Wang, 2021. "Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM," Agriculture, MDPI, vol. 11(7), pages 1-22, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M. Safdar Munir & Imran Sarwar Bajwa & M. Asif Naeem & Bushra Ramzan, 2018. "Design and Implementation of an IoT System for Smart Energy Consumption and Smart Irrigation in Tunnel Farming," Energies, MDPI, vol. 11(12), pages 1-18, December.
    2. Kelly, T.D. & Foster, T., 2021. "AquaCrop-OSPy: Bridging the gap between research and practice in crop-water modeling," Agricultural Water Management, Elsevier, vol. 254(C).
    3. Hedley, C.B. & Yule, I.J., 2009. "A method for spatial prediction of daily soil water status for precise irrigation scheduling," Agricultural Water Management, Elsevier, vol. 96(12), pages 1737-1745, December.
    4. Wanjiru, Evan M. & Xia, Xiaohua, 2015. "Energy-water optimization model incorporating rooftop water harvesting for lawn irrigation," Applied Energy, Elsevier, vol. 160(C), pages 521-531.
    5. Snyder, R.L. & Pedras, C. & Montazar, A. & Henry, J.M. & Ackley, D., 2015. "Advances in ET-based landscape irrigation management," Agricultural Water Management, Elsevier, vol. 147(C), pages 187-197.
    6. Ali Ajaz & Sumon Datta & Scott Stoodley, 2020. "High Plains Aquifer–State of Affairs of Irrigated Agriculture and Role of Irrigation in the Sustainability Paradigm," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    7. Soulis, Konstantinos X. & Elmaloglou, Stamatios & Dercas, Nicholas, 2015. "Investigating the effects of soil moisture sensors positioning and accuracy on soil moisture based drip irrigation scheduling systems," Agricultural Water Management, Elsevier, vol. 148(C), pages 258-268.
    8. Li, Dazhi & Hendricks Franssen, Harrie-Jan & Han, Xujun & Jiménez-Bello, Miguel Angel & Martínez Alzamora, Fernando & Vereecken, Harry, 2018. "Evaluation of an operational real-time irrigation scheduling scheme for drip irrigated citrus fields in Picassent, Spain," Agricultural Water Management, Elsevier, vol. 208(C), pages 465-477.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:163:y:2016:i:c:p:285-294. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.