11 citations as recorded by crossref.

1. Enhancing data reliability for photovoltaic ecological assessment: A machine learning approach to carbon flux gap-filling Y. Liu et al. https://doi.org/10.1080/15567036.2026.2734254
2. Attentional BiLSTM with Ecological Process Constraints for Carbon–Water Flux Prediction in Cold, Temperate Coniferous Forests X. Wang et al. https://doi.org/10.3390/f17030307
3. An explainable machine learning consensus framework for robust estimations of environmental effects on population dynamics A. Dhananjanie et al. https://doi.org/10.1016/j.ecoinf.2026.104118
4. Efficient Swell Risk Prediction for Building Design Using a Domain-Guided Machine Learning Model H. Alharbi https://doi.org/10.3390/buildings15142530
5. Data-driven approach to robust spatio-temporal assessment of carbon fluxes using Earth observation and ground-based data A. Gorbarenko et al. https://doi.org/10.1016/j.envsoft.2026.106881
6. Organic aerosol enhances boreal forest photosynthesis under cumulus clouds E. Ezhova et al. https://doi.org/10.1038/s43247-025-02539-z
7. Explainable machine learning highlights the role of diffuse radiation in ecosystem carbon uptake of boreal forest T. Laanti et al. https://doi.org/10.1017/eds.2026.10040
8. Hybrid forest models in the age of abundant monitoring data and powerful data-driven algorithms: back to the future Y. Lo & J. Blanco https://doi.org/10.1016/j.ecolmodel.2026.111799
9. Modeling CO2 fluxes in coastal wetlands of China using explainable sequence-based deep learning N. Nguyen et al. https://doi.org/10.1016/j.ecoinf.2026.103766
10. A Machine Learning Framework for Daily Mangrove Net Ecosystem Exchange Prediction from 2000 to 2025 L. Ruan et al. https://doi.org/10.3390/rs18040667
11. Hydrology and Carbon Flux Interconnections in a Hemiboreal Forest: Impacts of Heatwaves in Järvselja, Estonia F. Civitate et al. https://doi.org/10.3390/f17030297