Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods-Reference-Cited by-同舟云学术

Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods

Published:2024-07 Issue: Volume:81 Page:102638
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Garcia-Rodriguez David,Ruber Pablo Catret,Iglesias Fuente Domingo J.,Durá Juan José Martínez,Baeza Ernesto López,Celda Antonio Garcia

Funder

Espana Ministerio de Ciencia e Innovacion

Agencia Estatal de Investigación

Publisher

Elsevier BV

Reference60 articles.

1. Machine learning approach to simulate soil CO2 fluxes under cropping systems;Adjuik;Agronomy,2022

2. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. Food and Agriculture Organization, Rome, Italy (FAO Irrigation and Drainage Paper, No. 56. 300 p.);Allen,1998

3. Basic principles for soil carbon sequestration and calculating dynamic country-level balances including future scenarios;Andrén;Assess. Methods Soil Carbon,2001

4. Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods;Bayram;Environ. Monit. Assess.,2023

5. Spatiotemporal modeling and prediction of solar radiation;Bodas-Salcedo;J. Geophys. Res. Atmos.,2003

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DEAF: An adaptive feature aggregation model for predicting soil CO2 flux;Ecological Informatics;2024-09

2. Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning;Land;2024-07-17