Author
Listed:
- Li, Yao
- Peng, Xiongbiao
- Liu, Zhunqiao
- Lu, Xiaoliang
- Gu, Xiaobo
- Yu, Lianyu
- Xu, Jiatun
- Cai, Huanjie
Abstract
Accurate estimation of actual crop evapotranspiration (ETc act) is essential for optimizing water resource management and irrigation strategies, particularly in arid and semi-arid agricultural regions. Traditional models rely on extensive meteorological data, limiting their applicability in data-scarce areas. This study used on-site ground observation data with a 30-minute temporal resolution from a winter wheat field at the Yangling Station on the Guanzhong Plain, China, to evaluate the performance of machine learning-driven semi-mechanistic models driven by three machine learning methods (Ridge regression, Random Forest, and Support Vector Machine) in estimating ETc act. These machine learning-driven semi-mechanistic models integrate photosynthetic indicators (Gross Primary Production, GPP; solar-induced chlorophyll fluorescence, SIF; near-infrared reflectance of vegetation, NIRv) with the square root of vapor pressure deficit (VPD0.5) to enhance ETc act estimation accuracy. The results showed that among the photosynthetic indicators, GPP and SIF exhibited a strong correlation with ETc act. When combined with VPD0.5, their correlation with ETc act further increased by 0.10 and 0.05, respectively, while their response time to ETc act variations was reduced by 2 hours and 1 hour. Notably, NIRv exhibited the weakest correlation with ETc act, with a Pearson correlation coefficient of only 0.31, significantly lower than SIF (0.78) and GPP (0.69), indicating its limited effectiveness as an independent predictor. Furthermore, machine learning-driven semi-mechanistic models driven by machine learning achieved higher accuracy in ETc act estimation than single-factor machine learning models and the Penman-Monteith equation incorporating the single crop coefficient method. Among them, the RF model based on SIF × VPD0.5 achieved the best performance, with an R2 of 0.86 and an RMSE of 0.69 mm/day. This study demonstrates that machine learning-driven semi-mechanistic models can significantly improve ETc act estimation accuracy while reducing dependence on meteorological data. The proposed approach provides a new theoretical framework for improving water resource management and irrigation efficiency in arid and semi-arid agricultural regions, while also offering a scientific basis for future ETc act estimation methods integrating remote sensing data.
Suggested Citation
Li, Yao & Peng, Xiongbiao & Liu, Zhunqiao & Lu, Xiaoliang & Gu, Xiaobo & Yu, Lianyu & Xu, Jiatun & Cai, Huanjie, 2025.
"A machine learning-driven semi-mechanistic model for estimating actual evapotranspiration: Integrating photosynthetic indicators with vapor pressure deficit,"
Agricultural Water Management, Elsevier, vol. 315(C).
Handle:
RePEc:eee:agiwat:v:315:y:2025:i:c:s037837742500277x
DOI: 10.1016/j.agwat.2025.109563
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
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:315:y:2025:i:c:s037837742500277x. 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.
We have no bibliographic references for this item. You can help adding them by using 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.