11 citations as recorded by crossref.

1. On the use of machine learning methods to improve the estimation of gross primary productivity of maize field with drip irrigation H. Guo et al. 10.1016/j.ecolmodel.2022.110250
2. Challenges in studying water fluxes within the soil-plant-atmosphere continuum: A tracer-based perspective on pathways to progress N. Orlowski et al. 10.1016/j.scitotenv.2023.163510
3. Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods D. Garcia-Rodriguez et al. 10.1016/j.ecoinf.2024.102638
4. Gap Filling Method and Estimation of Net Ecosystem CO2 Exchange in Alpine Wetland of Qinghai–Tibet Plateau X. Wang et al. 10.3390/su15054652
5. Monitoring Energy Balance, Turbulent Flux Partitioning, Evapotranspiration and Biophysical Parameters of Nopalea cochenillifera (Cactaceae) in the Brazilian Semi-Arid Environment A. Jardim et al. 10.3390/plants12132562
6. Using automated machine learning for the upscaling of gross primary productivity M. Gaber et al. 10.5194/bg-21-2447-2024
7. Sink or carbon source? how the Opuntia cactus agroecosystem interacts in the use of carbon, nutrients and radiation in the Brazilian semi-arid region A. Jardim et al. 10.1016/j.jhydrol.2023.130121
8. A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach C. Zhang et al. 10.1016/j.jhydrol.2021.127047
9. Partitioning of water vapor and CO fluxes and underlying water use efficiency evaluation in a Brazilian seasonally dry tropical forest (Caatinga) using the Fluxpart model C. Borges et al. 10.1016/j.jsames.2024.104963
10. Understanding interactive processes: a review of CO2 flux, evapotranspiration, and energy partitioning under stressful conditions in dry forest and agricultural environments A. da Rosa Ferraz Jardim et al. 10.1007/s10661-022-10339-7
11. Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops T. Zenone et al. 10.1186/s13717-022-00400-1