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Simulating ecosystem water and carbon fluxes by integrating remote sensing indices with multiple machine learning models
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DOI：10.1016/j.agwat.2026.110622.png)
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Abstract
En 中文
• Fusing SIF and LAI robustly enhanced semi-arid water-carbon flux modeling. • Site-level evaluation quantified predictive bottlenecks across the four fluxes. • SHAP extracted quantitative non-linear thresholds for key environmental drivers. • Sensitivity analysis mapped feasible LAI and SWC ranges optimizing ecosystem WUE.
Keywords:
Interpretable machine learning
Ecosystem water and carbon fluxes
Solar-induced chlorophyll fluorescence
Leaf area index
SHAP attribution
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