IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v260y2022ics037837742100576x.html
   My bibliography  Save this article

Quantifying irrigation water use with remote sensing: Soil water deficit modelling with uncertain soil parameters

Author

Listed:
  • Bretreger, David
  • Yeo, In-Young
  • Hancock, Greg

Abstract

Water sharing plans have been implemented to allocate water fairly between irrigated agriculture and other stakeholders. Water accounting is an important feature of successful water management and sharing. Remote regions are often neglected with metering infrastructure and therefore remote sensing is an option for the quantification of irrigation water use. The guides published in FAO Irrigation and Drainage paper No. 56 (FAO56) can provide some information in data poor regions although it has its own limitations without direct observations. This study uses the constellation of Landsat satellites (5–8) to monitor crop conditions via the vegetation index to assess crop growth through a crop coefficient (Kc) based on an actual field condition. This remotely sensed input is then used in soil water deficit modelling based on the FAO56 approach over two fields, an almond plantation and a vineyard, located in South Australia. Soil parameters, such as readily available water (RAW), are taken from in-situ observations and digital soil maps for comparison. The results closely matched metered irrigation time series with only small changes in results when interchanging in-situ soil properties or digital soil maps. Following this an uncertainty analysis using a Monte Carlo approach was performed using the range of parameter values for RAW. A small period of this study (9 months of 2015/16) overlapped with the Sentinel-2a operational period, which was investigated for its improved spatial resolution and differences in spectral band width of key vegetation observation bands. When comparing these results to previous studies, which did not consider soil water deficits, the improvements are substantial (improvements ranged from 3% to 15% monthly and 56% to 68% annually). These improvements require extra data, which has the limitation of comprehensive field data being difficult to obtain and digital soil maps being potentially unreliable. The choice to include soil water deficit modelling or not is dependent on the required accuracy to effectively use the quantified irrigation for the intended use.

Suggested Citation

  • Bretreger, David & Yeo, In-Young & Hancock, Greg, 2022. "Quantifying irrigation water use with remote sensing: Soil water deficit modelling with uncertain soil parameters," Agricultural Water Management, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:agiwat:v:260:y:2022:i:c:s037837742100576x
    DOI: 10.1016/j.agwat.2021.107299
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037837742100576X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2021.107299?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. Phogat, V. & Pitt, T. & Cox, J.W. & Šimůnek, J. & Skewes, M.A., 2018. "Soil water and salinity dynamics under sprinkler irrigated almond exposed to a varied salinity stress at different growth stages," Agricultural Water Management, Elsevier, vol. 201(C), pages 70-82.
    2. Bretreger, David & Yeo, In-Young & Quijano, Juan & Awad, John & Hancock, Greg & Willgoose, Garry, 2019. "Monitoring irrigation water use over paddock scales using climate data and landsat observations," Agricultural Water Management, Elsevier, vol. 221(C), pages 175-191.
    3. Minhas, P.S. & Ramos, Tiago B. & Ben-Gal, Alon & Pereira, Luis S., 2020. "Coping with salinity in irrigated agriculture: Crop evapotranspiration and water management issues," Agricultural Water Management, Elsevier, vol. 227(C).
    4. Garrido-Rubio, Jesús & González-Piqueras, Jose & Campos, Isidro & Osann, Anna & González-Gómez, Laura & Calera, Alfonso, 2020. "Remote sensing–based soil water balance for irrigation water accounting at plot and water user association management scale," Agricultural Water Management, Elsevier, vol. 238(C).
    5. Xue, Jingyuan & Bali, Khaled M. & Light, Sarah & Hessels, Tim & Kisekka, Isaya, 2020. "Evaluation of remote sensing-based evapotranspiration models against surface renewal in almonds, tomatoes and maize," Agricultural Water Management, Elsevier, vol. 238(C).
    6. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
    7. Krishna, Gopal & Sahoo, Rabi N. & Singh, Prafull & Bajpai, Vaishangi & Patra, Himesh & Kumar, Sudhir & Dandapani, Raju & Gupta, Vinod K. & Viswanathan, C. & Ahmad, Tauqueer & Sahoo, Prachi M., 2019. "Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing," Agricultural Water Management, Elsevier, vol. 213(C), pages 231-244.
    8. Kullberg, Emily G. & DeJonge, Kendall C. & Chávez, José L., 2017. "Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients," Agricultural Water Management, Elsevier, vol. 179(C), pages 64-73.
    Full references (including those not matched with items on IDEAS)

    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. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    2. Nakabuye, Hope Njuki & Rudnick, Daran & DeJonge, Kendall C. & Lo, Tsz Him & Heeren, Derek & Qiao, Xin & Franz, Trenton E. & Katimbo, Abia & Duan, Jiaming, 2022. "Real-time irrigation scheduling of maize using Degrees Above Non-Stressed (DANS) index in semi-arid environment," Agricultural Water Management, Elsevier, vol. 274(C).
    3. Katimbo, Abia & Rudnick, Daran R. & DeJonge, Kendall C. & Lo, Tsz Him & Qiao, Xin & Franz, Trenton E. & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Crop water stress index computation approaches and their sensitivity to soil water dynamics," Agricultural Water Management, Elsevier, vol. 266(C).
    4. Katimbo, Abia & Rudnick, Daran R. & Liang, Wei-zhen & DeJonge, Kendall C. & Lo, Tsz Him & Franz, Trenton E. & Ge, Yufeng & Qiao, Xin & Kabenge, Isa & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Two source energy balance maize evapotranspiration estimates using close-canopy mobile infrared sensors and upscaling methods under variable water stress conditions," Agricultural Water Management, Elsevier, vol. 274(C).
    5. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2021. "The mean value of gaussian distribution of excess green index: A new crop water stress indicator," Agricultural Water Management, Elsevier, vol. 251(C).
    6. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    7. Ezenne, G.I. & Jupp, Louise & Mantel, S.K. & Tanner, J.L., 2019. "Current and potential capabilities of UAS for crop water productivity in precision agriculture," Agricultural Water Management, Elsevier, vol. 218(C), pages 158-164.
    8. Bhatti, Sandeep & Heeren, Derek M. & Evett, Steven R. & O’Shaughnessy, Susan A. & Rudnick, Daran R. & Franz, Trenton E. & Ge, Yufeng & Neale, Christopher M.U., 2022. "Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska," Agricultural Water Management, Elsevier, vol. 274(C).
    9. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).
    10. Ramos, Tiago B. & Darouich, Hanaa & Oliveira, Ana R. & Farzamian, Mohammad & Monteiro, Tomás & Castanheira, Nádia & Paz, Ana & Alexandre, Carlos & Gonçalves, Maria C. & Pereira, Luís S., 2023. "Water use, soil water balance and soil salinization risks of Mediterranean tree orchards in southern Portugal under current climate variability: Issues for salinity control and irrigation management," Agricultural Water Management, Elsevier, vol. 283(C).
    11. DeJonge, Kendall C. & Thorp, Kelly R. & Brekel, Josh & Pokoski, Tyler & Trout, Thomas J., 2024. "Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado," Agricultural Water Management, Elsevier, vol. 299(C).
    12. Amazirh, Abdelhakim & Er-Raki, Salah & Ojha, Nitu & Bouras, El houssaine & Rivalland, Vincent & Merlin, Olivier & Chehbouni, Abdelghani, 2022. "Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions," Agricultural Water Management, Elsevier, vol. 260(C).
    13. Rigas Giovos & Dimitrios Tassopoulos & Dionissios Kalivas & Nestor Lougkos & Anastasia Priovolou, 2021. "Remote Sensing Vegetation Indices in Viticulture: A Critical Review," Agriculture, MDPI, vol. 11(5), pages 1-20, May.
    14. Ramos, Tiago B. & Liu, Meihan & Paredes, Paula & Shi, Haibin & Feng, Zhuangzhuang & Lei, Huimin & Pereira, Luis S., 2023. "Salts dynamics in maize irrigation in the Hetao plateau using static water table lysimeters and HYDRUS-1D with focus on the autumn leaching irrigation," Agricultural Water Management, Elsevier, vol. 283(C).
    15. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).
    16. Li, Yunfeng & Yu, Qihua & Ning, Huifeng & Gao, Yang & Sun, Jingsheng, 2023. "Simulation of soil water, heat, and salt adsorptive transport under film mulched drip irrigation in an arid saline-alkali area using HYDRUS-2D," Agricultural Water Management, Elsevier, vol. 290(C).
    17. Gonçalves, Ivo Zution & Mekonnen, Mesfin M. & Neale, Christopher M.U. & Campos, Isidro & Neale, Michael R., 2020. "Temporal and spatial variations of irrigation water use for commercial corn fields in Central Nebraska," Agricultural Water Management, Elsevier, vol. 228(C).
    18. Peddinti, Srinivasa Rao & Kisekka, Isaya, 2022. "Estimation of turbulent fluxes over almond orchards using high-resolution aerial imagery with one and two-source energy balance models," Agricultural Water Management, Elsevier, vol. 269(C).
    19. El-Naggar, A.G. & Hedley, C.B. & Horne, D. & Roudier, P. & Clothier, B.E., 2020. "Soil sensing technology improves application of irrigation water," Agricultural Water Management, Elsevier, vol. 228(C).
    20. Jovanovic, N. & Pereira, L.S. & Paredes, P. & Pôças, I. & Cantore, V. & Todorovic, M., 2020. "A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods," Agricultural Water Management, Elsevier, vol. 239(C).

    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:260:y:2022:i:c:s037837742100576x. 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.