Estimation of daily apple tree transpiration in the Loess Plateau region of China using deep learning models

Xing, Liwen; Cui, Ningbo; Liu, Chunwei; Zhao, Lu; Guo, Li; Du, Taisheng; Zhan, Chesheng; Wu, Zongjun; Wen, Shenglin; Jiang, Shouzheng

doi:10.1016/j.agwat.2022.107889

Cited by 9 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It appears that models perform better when applied to trees compared to low-growing plants. This observation is supported by Xing et al [7] and Liu et al [27]. Similar to the approach proposed by Tu et al in 2019 [28], our model could potentially be improved by introducing a phenological function.…”

Section: Resultssupporting

confidence: 84%

See 1 more Smart Citation

Comparative Assessment of Sap Flow Modeling Techniques in European Beech Trees: Can Linear Models Compete with Random Forest, Extreme Gradient Boosting, and Neural Networks?

Nalevanková

Fleischer

Mukarram

et al. 2023

Water

View full text Add to dashboard Cite

Transpiration and sap flow are physiologically interconnected processes that regulate nutrient and water uptake, controlling major aspects of tree life. They hold special relevance during drought, where wrecked sap flow can undermine overall tree growth and development. The present study encompasses five-year (2012–2015 and 2017) sap flow datasets on European beech (Fagus sylvatica). Four different techniques were used for sap flow modeling, namely, a linear model (LM), random forest (RF), extreme gradient boosting machine (XGBM), and neural networks (NN). We used six variants (Variants 1–6) differing in the captured conditions and the dataset size. The ‘prediction power’ was the ratio of the predicted and observed sap flow. We found the LM had the maximum prediction power for the overall sap flow in beech trees with 1 h shift of global radiation. In the reaming variants, the LM provided comparable prediction power to RF and XGBM. At the same time, NN exhibited relatively poor prediction power over other machine learning models. The study supports an easier-to-apply and computationally simpler approach (LM) to assess sap flow over more sophisticated machine learning approaches (RF, XGBM, and NN).

show abstract

Section: Resultssupporting

confidence: 84%

“…Transpiration is primarily controlled by stomata, internal plant resistances, local hydrological conditions, and climatic variations, especially evaporative demands [4][5][6]. Although it is crucial to quantify plant transpiration, accurate and direct measuring is technically demanding, time and money consuming, and labor intensive [7].…”

Section: Introductionmentioning

confidence: 99%