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Integrating Meteorological and Remote Sensing Data to Simulate Cropland Nocturnal Evapotranspiration Using Machine Learning

Author

Listed:
  • Jiaojiao Huang

    (Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Sha Zhang

    (Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Jiahua Zhang

    (Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Xin Zheng

    (Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Xianye Meng

    (Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Shanshan Yang

    (Space Information and Big Earth Data Research Center, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China)

  • Yun Bai

    (Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, School of Geographic Sciences, Hebei Normal University, Shijiazhuang 050024, China)

Abstract

Evapotranspiration (ET) represents a significant component of the global water flux cycle, yet nocturnal evapotranspiration (ETn) is often neglected, leading to underestimation of global evapotranspiration. As for cropland, accurate modeling of ETn is essential for rational water management and is important for sustainable agriculture development. We used random forest (RF) to simulate ETn at 16 globally distributed cropland eddy covariance flux sites along with remote sensing and meteorological factors. The recursive feature elimination method was used to remove unimportant variables. We also simulated the ETn of C 3 and C 4 crops separately. The trained RF resulted in a determination coefficient (R 2 ) (root mean square error (RMSE)) of 0.82 (7.30 W m −2 ) on the testing dataset. C 3 and C 4 crops on the testing dataset resulted in an R 2 (RMSE) of 0.86 (5.59 W m −2 ) and 0.55 (4.86 W m −2 ) for the two types of crops. We also showed that net radiation is the dominant factor in regulating ETn, followed by 2 m horizontal wind speed and vapor pressure deficit (VPD), and these three meteorological factors showed a significant positive correlation with ETn. This research demonstrates that RF can simulate ETn from crops economically and accurately, providing a methodological basis for improving global ETn simulations.

Suggested Citation

  • Jiaojiao Huang & Sha Zhang & Jiahua Zhang & Xin Zheng & Xianye Meng & Shanshan Yang & Yun Bai, 2024. "Integrating Meteorological and Remote Sensing Data to Simulate Cropland Nocturnal Evapotranspiration Using Machine Learning," Sustainability, MDPI, vol. 16(5), pages 1-24, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1987-:d:1347715
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    References listed on IDEAS

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    1. Wang, Xingwang & Lei, Huimin & Li, Jiadi & Huo, Zailin & Zhang, Yongqiang & Qu, Yanping, 2023. "Estimating evapotranspiration and yield of wheat and maize croplands through a remote sensing-based model," Agricultural Water Management, Elsevier, vol. 282(C).
    2. Kai Xin & Jingyuan Zhao & Tianhui Wang & Weijun Gao, 2022. "Supporting Design to Develop Rural Revitalization through Investigating Village Microclimate Environments: A Case Study of Typical Villages in Northwest China," IJERPH, MDPI, vol. 19(14), pages 1-20, July.
    3. Zhang, Yanqun & Kang, Shaozhong & Ward, Eric J. & Ding, Risheng & Zhang, Xin & Zheng, Rui, 2011. "Evapotranspiration components determined by sap flow and microlysimetry techniques of a vineyard in northwest China: Dynamics and influential factors," Agricultural Water Management, Elsevier, vol. 98(8), pages 1207-1214, May.
    4. Kukal, Meetpal S. & Irmak, Suat, 2022. "Nocturnal transpiration in field crops: Implications for temporal aggregation and diurnal weighing of vapor pressure deficit," Agricultural Water Management, Elsevier, vol. 266(C).
    5. Chen, Dianyu & Wang, Youke & Liu, Shouyang & Wei, Xinguang & Wang, Xing, 2014. "Response of relative sap flow to meteorological factors under different soil moisture conditions in rainfed jujube (Ziziphus jujuba Mill.) plantations in semiarid Northwest China," Agricultural Water Management, Elsevier, vol. 136(C), pages 23-33.
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