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

Jiaojiao Huang, Sha Zhang, Jiahua Zhang, Xin Zheng, Xianye Meng, Shanshan Yang and Yun Bai ()
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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
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

Sustainability, 2024, vol. 16, issue 5, 1-24

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.

Keywords: nocturnal evapotranspiration; ecological remote sensing; machine learning; random forest; characteristics analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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