IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v287y2023ics0378377423003311.html
   My bibliography  Save this article

Mapping super high resolution evapotranspiration in oasis-desert areas using UAV multi-sensor data

Author

Listed:
  • Wei, Jiaxing
  • Dong, Weichen
  • Liu, Shaomin
  • Song, Lisheng
  • Zhou, Ji
  • Xu, Ziwei
  • Wang, Ziwei
  • Xu, Tongren
  • He, Xinlei
  • Sun, Jingwei

Abstract

High spatial resolution maps of evapotranspiration (ET) for precision agricultural irrigation, water resource management are increasingly important in the context of climate change. Here, we conducted extensive unmanned aerial vehicle (UAV) experiments in oasis-desert areas with three 2 km × 1 km regions during two growing seasons. The spatially distributed land surface temperature (LST), albedo and leaf area index (LAI) were retrieved from thermal infrared, multispectral and LiDAR (light detection and ranging) multi-sensor data, and the surface energy balance system (SEBS) model with an optimized surface roughness length for heat transfer was utilized to estimate ET with a super high resolution (SHR) of 0.2 m. The model’s outputs were validated using the measurements from the eddy covariance (EC) with a source area of hundred meters and optical-microwave scintillometer (OMS) with a source area of approximately 2 km × 1 km. They yielded mean root mean square error (RMSE) values of 53 and 45 W/m2 for sensible heat flux (H) and 70 and 65 W/m2 for latent heat flux (LE), respectively. The seasonal characteristics of LE distributions indicated the difference of LE in oasis-desert areas varied with the growing period. These sub-pixel differences in the cropland, wetland, and desert experimental areas were caused mainly by the agricultural activities, the vegetation coverage, and the topographic peaks and valleys, respectively. This study can potentially provide spatially sub-pixel information on agricultural and ecological water management and genuinely bridge the spatio-temporal scale gap between field observations and satellite remote sensing.

Suggested Citation

  • Wei, Jiaxing & Dong, Weichen & Liu, Shaomin & Song, Lisheng & Zhou, Ji & Xu, Ziwei & Wang, Ziwei & Xu, Tongren & He, Xinlei & Sun, Jingwei, 2023. "Mapping super high resolution evapotranspiration in oasis-desert areas using UAV multi-sensor data," Agricultural Water Management, Elsevier, vol. 287(C).
  • Handle: RePEc:eee:agiwat:v:287:y:2023:i:c:s0378377423003311
    DOI: 10.1016/j.agwat.2023.108466
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377423003311
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2023.108466?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. Chen, Assaf & Orlov-Levin, Valerie & Meron, Moshe, 2019. "Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management," Agricultural Water Management, Elsevier, vol. 216(C), pages 196-205.
    2. Feng, Jiaojiao & Wang, Weizhen & Che, Tao & Xu, Feinan, 2023. "Performance of the improved two-source energy balance model for estimating evapotranspiration over the heterogeneous surface," Agricultural Water Management, Elsevier, vol. 278(C).
    3. Ramírez-Cuesta, J.M. & Intrigliolo, D.S. & Lorite, I.J. & Moreno, M.A. & Vanella, D. & Ballesteros, R. & Hernández-López, D. & Buesa, I., 2023. "Determining grapevine water use under different sustainable agronomic practices using METRIC-UAV surface energy balance model," Agricultural Water Management, Elsevier, vol. 281(C).
    4. Ortega-Salazar, Samuel & Ortega-Farías, Samuel & Kilic, Ayse & Allen, Richard, 2021. "Performance of the METRIC model for mapping energy balance components and actual evapotranspiration over a superintensive drip-irrigated olive orchard," Agricultural Water Management, Elsevier, vol. 251(C).
    5. He, Xinlei & Liu, Shaomin & Xu, Tongren & Yu, Kailiang & Gentine, Pierre & Zhang, Zhe & Xu, Ziwei & Jiao, Dandan & Wu, Dongxing, 2022. "Improving predictions of evapotranspiration by integrating multi-source observations and land surface model," Agricultural Water Management, Elsevier, vol. 272(C).
    6. Bwambale, Erion & Abagale, Felix K. & Anornu, Geophrey K., 2022. "Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review," Agricultural Water Management, Elsevier, vol. 260(C).
    7. Garcia-Vasquez, Ana Cristina & Mokari, Esmaiil & Samani, Zohrab & Fernald, Alexander, 2022. "Using UAV-thermal imaging to calculate crop water use and irrigation efficiency in a flood-irrigated pecan orchard," Agricultural Water Management, Elsevier, vol. 272(C).
    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. Licheng Liu & Wang Zhou & Kaiyu Guan & Bin Peng & Shaoming Xu & Jinyun Tang & Qing Zhu & Jessica Till & Xiaowei Jia & Chongya Jiang & Sheng Wang & Ziqi Qin & Hui Kong & Robert Grant & Symon Mezbahuddi, 2024. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Wang, Wendi & Straffelini, Eugenio & Tarolli, Paolo, 2023. "Steep-slope viticulture: The effectiveness of micro-water storage in improving the resilience to weather extremes," Agricultural Water Management, Elsevier, vol. 286(C).
    3. Masoud Derakhshandeh & Mustafa Tombul, 2022. "Calibration of METRIC Modeling for Evapotranspiration Estimation Using Landsat 8 Imagery Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 315-339, January.
    4. Guilherme Jesus & Martim L. Aguiar & Pedro D. Gaspar, 2022. "Computational Tool to Support the Decision in the Selection of Alternative and/or Sustainable Refrigerants," Energies, MDPI, vol. 15(22), pages 1-20, November.
    5. Yeboah, Samuel, 2023. "Unlocking the Potential of Technological Innovations for Sustainable Agriculture in Developing Countries: Enhancing Resource Efficiency and Environmental Sustainability," MPRA Paper 118215, University Library of Munich, Germany, revised 26 Jul 2023.
    6. Li Bin & Muhammad Shahzad & Hira Khan & Muhammad Mehran Bashir & Arif Ullah & Muhammad Siddique, 2023. "Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    7. Leonardo D. Garcia & Camilo Lozoya & Antonio Favela-Contreras & Emanuele Giorgi, 2023. "A Comparative Analysis between Heuristic and Data-Driven Water Management Control for Precision Agriculture Irrigation," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    8. Konstantina Ragazou & Alexandros Garefalakis & Eleni Zafeiriou & Ioannis Passas, 2022. "Agriculture 5.0: A New Strategic Management Mode for a Cut Cost and an Energy Efficient Agriculture Sector," Energies, MDPI, vol. 15(9), pages 1-17, April.
    9. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
    10. Stavros Sakellariou & Marios Spiliotopoulos & Nikolaos Alpanakis & Ioannis Faraslis & Pantelis Sidiropoulos & Georgios A. Tziatzios & George Karoutsos & Nicolas R. Dalezios & Nicholas Dercas, 2024. "Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems," Sustainability, MDPI, vol. 16(3), pages 1-16, February.
    11. He, Ruyan & Jin, Yufang & Jiang, Jinbao & Xu, Meng & Jia, Sen, 2022. "Sensitivity of METRIC-based tree crop evapotranspiration estimation to meteorology, land surface parameters and domain size," Agricultural Water Management, Elsevier, vol. 271(C).
    12. Bogdan Kulig & Jacek Waga & Andrzej Oleksy & Marcin Rapacz & Marek Kołodziejczyk & Piotr Wężyk & Agnieszka Klimek-Kopyra & Robert Witkowicz & Andrzej Skoczowski & Grażyna Podolska & Wiesław Grygierzec, 2023. "Forecasting of Hypoallergenic Wheat Productivity Based on Unmanned Aerial Vehicles Remote Sensing Approach—Case Study," Agriculture, MDPI, vol. 13(2), pages 1-21, January.
    13. Maged Mohammed & Ramasamy Srinivasagan & Ali Alzahrani & Nashi K. Alqahtani, 2023. "Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres," Sustainability, MDPI, vol. 15(17), pages 1-24, August.
    14. Awais Ali & Tajamul Hussain & Noramon Tantashutikun & Nurda Hussain & Giacomo Cocetta, 2023. "Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    15. Wasi Ul Hassan Shah & Yuting Lu & Gang Hao & Hong Yan & Rizwana Yasmeen, 2022. "Impact of “Three Red Lines” Water Policy (2011) on Water Usage Efficiency, Production Technology Heterogeneity, and Determinant of Water Productivity Change in China," IJERPH, MDPI, vol. 19(24), pages 1-23, December.
    16. Luis Vargas Tamayo & Christopher Thron & Jean Louis Kedieng Ebongue Fendji & Shauna-Kay Thomas & Anna Förster, 2020. "Cost-Minimizing System Design for Surveillance of Large, Inaccessible Agricultural Areas Using Drones of Limited Range," Sustainability, MDPI, vol. 12(21), pages 1-25, October.
    17. Yeboah, Samuel, 2023. "Unlocking the Potential of Technological Innovations for Sustainable Agriculture in Developing Countries: Enhancing Resource Efficiency and Environmental Sustainability," MPRA Paper 118216, University Library of Munich, Germany, revised 04 Aug 2023.

    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:287:y:2023:i:c:s0378377423003311. 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.