生态与农村环境学报2026，Vol.42Issue(4)：561-573,13.DOI:10.19741/j.issn.1673-4831.2025.0128
基于机器学习的广域净生态系统碳交换分析与模拟
Analysis and Simulation of Widespread Net Ecosystem Exchange Utilizing Machine Learning
摘要
Abstract
The Net Ecosystem Exchange(NEE)serves as a critical metric for assessing ecosystem carbon budgets,offer-ing valuable insights into carbon cycling mechanisms and informing strategies for climate change mitigation.This study le-verages ground-based ChinaFLUX data collected from seven widespread forest and grassland ecosystems between 2003 and 2010.Five machine learning models—Random Forest(RF),Gradient Boosting Decision Tree(GBDT),Extreme Gradi-ent Boosting(XGBoost),LightGBM model and linear regression,were employed in conjunction with Pearson correlation analysis and geographic detector analysis to systematically investigate the key environmental drivers influencing the interan-nual and seasonal variations of NEE.The primary objectives of this study are to evaluate the applicability of these models in predicting NEE variations across different temporal scales and to provide theoretical foundations for model optimization.The findings reveal a significant decline in the overall ecosystem carbon sink capacity,as indicated by NEE,from 2003 to 2010(Slope=17.14,P＜0.05).However,the carbon sink capacities at the Xishuangbanna and Haibei sites exhibited an upward trend(SlopeXSBN=-2.61 and Slope HBGCT=-5.64).Seasonal analysis highlighted pronounced disparities in NEE during summer compared to other seasons.Notably,grassland ecosystems demonstrated a marked increase in carbon se-questration capacity during spring,with a slope of-0.74 and a P-value less than 0.05.The primary drivers of interannual variability were identified as atmospheric pressure,soil moisture,radiation,and wind speed,while seasonal fluctuations were predominantly influenced by temperature,soil moisture and soil temperature.Among the models evaluated,the RF model demonstrated the highest accuracy and precision in predicting interannual NEE(R2=0.94).For seasonal predic-tions,the RF model also exhibited strong performance,while the LightGBM model and XGBoost model were particularly accurate for spring and winter predictions,respectively.By integrating spatial statistics from the geographical detector with key factor identification techniques from machine learning models,this study offers a novel perspective and methodological framework for elucidating the spatiotemporal patterns of NEE and its underlying driving mechanisms.关键词
净生态系统碳交换量(NEE)/机器学习模型/地理空间探测器/环境因子Key words
net ecosystem exchange(NEE)/machine learning/geographical detector/environmental factor分类
资源环境引用本文复制引用
杜欣怡,袁换欢,张建亮,徐网谷,钱者东,张银龙,王智..基于机器学习的广域净生态系统碳交换分析与模拟[J].生态与农村环境学报,2026,42(4):561-573,13.基金项目
国家重点研发计划(SQ2020YFF0426320) （SQ2020YFF0426320）
生态环境部管理支撑项目(2110199201502) （2110199201502）
中央级公益性科研院所基本科研业务专项(GYZX210507) （GYZX210507）
国家社会科学基金(20BFX176) （20BFX176）
