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Mapping subsurface tile drainage systems with thermal images

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  • Woo, Dong Kook
  • Song, Homin
  • Kumar, Praveen

Abstract

In Midwestern agricultural fields, subsurface tile drainage has been widely used to remove excess water from the soil through perforated tubes installed beneath the ground surface. While it plays an important role in enabling agricultural activities in wet but productive areas, this system is a major driving factor affecting water and nutrient dynamics, and water quality in this region. However, despite its critical role, the specific locations of subsurface tile drainage structures are not generally available nor well captured by conventional optical image processing due to soil surface features, such as topographic depressions and tillage. To overcome these challenges, in this study, we have explored the potential of using thermal images to identify the location of a subsurface drainage pipe. The hypothesis is that the unique spatial distribution of soil moisture set up by tile drains can result in the difference in surface soil temperature between areas near and away from drainage pipes. Toward this objective, we designed and developed an experimental device based on a dimensionless analysis at a scale of 1:20, which was deployed in the open air for 4.5 months. The experimental results demonstrate that (1) there is an ideal time for thermal image acquisition that maximizes the contrast between the regions close to and distant from subsurface drainage systems, and (2) the thermal image processing approach proposed in this study is a promising tool that has advantages of higher accuracy and stability in localizing subsurface drainage pipes over optical image-based approaches

Suggested Citation

  • Woo, Dong Kook & Song, Homin & Kumar, Praveen, 2019. "Mapping subsurface tile drainage systems with thermal images," Agricultural Water Management, Elsevier, vol. 218(C), pages 94-101.
  • Handle: RePEc:eee:agiwat:v:218:y:2019:i:c:p:94-101
    DOI: 10.1016/j.agwat.2019.01.031
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    References listed on IDEAS

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    1. Naz, B.S. & Ale, S. & Bowling, L.C., 2009. "Detecting subsurface drainage systems and estimating drain spacing in intensively managed agricultural landscapes," Agricultural Water Management, Elsevier, vol. 96(4), pages 627-637, April.
    2. Allred, Barry & Eash, Neal & Freeland, Robert & Martinez, Luis & Wishart, DeBonne, 2018. "Effective and efficient agricultural drainage pipe mapping with UAS thermal infrared imagery: A case study," Agricultural Water Management, Elsevier, vol. 197(C), pages 132-137.
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    Cited by:

    1. Allred, Barry & Martinez, Luis & Fessehazion, Melake K. & Rouse, Greg & Koganti, Triven & Freeland, Robert & Eash, Neal & Wishart, DeBonne & Featheringill, Robert, 2021. "Time of day impact on mapping agricultural subsurface drainage systems with UAV thermal infrared imagery," Agricultural Water Management, Elsevier, vol. 256(C).
    2. Deuss, Kirstin Ella & Almond, Peter C. & Carrick, Sam & Kees, Lawrence John, 2023. "Identification, mapping, and characterisation of a mature artificial mole channel network using ground-penetrating radar," Agricultural Water Management, Elsevier, vol. 288(C).
    3. Song, Homin & Woo, Dong Kook & Yan, Qina, 2021. "Detecting subsurface drainage pipes using a fully convolutional network with optical images," Agricultural Water Management, Elsevier, vol. 249(C).
    4. Allred, Barry & Martinez, Luis & Fessehazion, Melake K. & Rouse, Greg & Williamson, Tanja N. & Wishart, DeBonne & Koganti, Triven & Freeland, Robert & Eash, Neal & Batschelet, Adam & Featheringill, Ro, 2020. "Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes," Agricultural Water Management, Elsevier, vol. 232(C).
    5. Kratt, C.B. & Woo, D.K. & Johnson, K.N. & Haagsma, M. & Kumar, P. & Selker, J. & Tyler, S., 2020. "Field trials to detect drainage pipe networks using thermal and RGB data from unmanned aircraft," Agricultural Water Management, Elsevier, vol. 229(C).

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