干旱区生态环境知识资源中心(Arid): Machine-learned actual evapotranspiration for an irrigated pecan orchard in Northwest Mexico

Arid

DOI	10.1016/j.agrformet.2023.109825
	Machine-learned actual evapotranspiration for an irrigated pecan orchard in Northwest Mexico
	Stoffer, Robin; Hartogensis, Oscar; Rodriguez, Julio Cesar; van Heerwaarden, Chiel
通讯作者	Stoffer, R
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
ISSN	0168-1923
EISSN	1873-2240
出版年	2024
卷号	345
英文摘要	Accurate field-scale estimates of the actual evapotranspiration (ETact) based on readily available input data is indispensable to optimize irrigation in (semi-)arid regions. In this study, at an irrigated pecan orchard in Northwest Mexico we explored the potential for three different machine learning algorithms to improve upon the 30-min ETact estimates provided by the FAO Penman-Monteith method (FAO-PM) when trained and tested on multi-year eddy-covariance measurements. We found that all three machine learning algorithms performed well (R-2 > 0.9), with the Random Forest (RF) giving the best overall performance. RF consistently outperformed FAO-PM on sub-daily scales in the growing season, showing its potential to further improve sub-daily ET(act )estimates. In addition, RF 1) shows a clear improvement (R-2 increase up to 0.168) compared to a typical FAO-PM estimate based on the procedures described in FAO report 56, and 2) mostly performed similar as FAO-PM with a highly accurate locally fitted crop factor on the seasonal scale. The latter highlights its ability to infer the correct seasonal trend through input variables that reflect the underlying physical processes, giving potential for application at other similar fields in the region with limited local tuning. To assess whether incorporating existing (semi-)physical parameterizations could improve the performance, we additionally combined our machine learning models with FAO-PM both with and without locally fitted crop factor. Overall, we found this resulted in a modest improvement in performance in most cases (R-2 increase up to 0.01). Notably, incorporating the locally calibrated crop factor and FAO-PM each reduced the errors made by the RF in the second half of the growing season. This implies that it is still useful to incorporate FAO-PM and a crop factor in the machine learning model, especially when the crop factor can be determined with high accuracy.
英文关键词	Actual evapotranspiration Machine learning Irrigation Semi-arid Eddy-covariance
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001129101100001
WOS关键词	SUPPORT VECTOR MACHINE ; ENERGY IMBALANCE ; WATER
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402627
推荐引用方式 GB/T 7714	Stoffer, Robin,Hartogensis, Oscar,Rodriguez, Julio Cesar,et al. Machine-learned actual evapotranspiration for an irrigated pecan orchard in Northwest Mexico[J],2024,345.
APA	Stoffer, Robin,Hartogensis, Oscar,Rodriguez, Julio Cesar,&van Heerwaarden, Chiel.(2024).Machine-learned actual evapotranspiration for an irrigated pecan orchard in Northwest Mexico.AGRICULTURAL AND FOREST METEOROLOGY,345.
MLA	Stoffer, Robin,et al."Machine-learned actual evapotranspiration for an irrigated pecan orchard in Northwest Mexico".AGRICULTURAL AND FOREST METEOROLOGY 345(2024).