XAI finds signs of clouds in the Net Ecosystem Exchange of boreal forest

XAI finds signs of clouds in the Net Ecosystem Exchange of boreal forest 

Laanti, Lintunen, Noe, Miles, Heljanko, Kulmala, Ezhova  

We applied three distinct machine learning models (random forest, LightGBM, and XGBoost) to predict net ecosystem exchange (NEE) in boreal forests using site-level information and climatic variables from two Finnish stations, SMEAR I and II as well as one Estonian station, SMEAR Estonia. Our study focuses on explainable artificial intelligence (XAI) technique called Shapley values, to interpret how radiation and meteorological and biospheric variables influence NEE.  

Using XAI, we found that diffuse radiation enhancement of NEE is linked to type of cloudiness. Our Shapley value analysis revealed that at the same diffuse radiation level, NEE can be enhanced more under overcast sky than under clear-sky or broken cloudiness conditions. Under a certain parameter range, this seems to counterbalance the negative effect of reduction in PAR on photosynthesis under overcast sky. Furthermore, visualizing the interplay between PAR, cloudiness, and NEE based on seasonality highlighted subtle differences in how these parameters interact at northern versus southern sites. Importantly, the use of three distinct machine learning models that all showed similar results demonstrate that these observed relationships are consistent.  

Although the discovered relationships between radiation, cloudiness and NEE do not necessarily reflect true causality, they can guide further testing of possible causal hypotheses. By integrating XAI into NEE modeling with machine learning, we gain deeper insights into the physical and ecological processes shaping carbon fluxes. Such interpretability is vital for understanding NEE dynamics in boreal forests, particularly in the face of evolving climate scenarios where cloud cover, temperature, and moisture regimes shift and introduce complex feedback mechanisms. Integrating XAI thus provides a valuable framework for interpreting complex, potentially nonlinear drivers behind NEE and for exploring new avenues of causal investigation in ecosystem research.