Comparative Analysis of Two Machine Learning Algorithms in Predicting Site-Level Net Ecosystem Exchange in Major Biomes

Remote Sensing

Lihua Zhang¹  Xiaofeng Xu²  Fenghui Yuan³  Xia Song⁴  Yuedong Guo⁵  Yunjiang Zuo⁵  Nannan Wang⁵  Jingwei Zhang⁵  Changchun Song⁵  Jianzhao Liu⁵  Ying Sun⁵  Xinhao Zhu⁵  Ziyu Guo⁵ 

[1] Biology Department, College of Life and Environmental Science, Minzu University, Beijing 100081, China;Biology Department, San Diego State University, San Diego, CA 92182, USA;CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China;CoStar Group, San Diego, CA 92122, USA;Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;

关键词: machine learning;    NEE;    random forest;    terrestrial ecosystem;    XGBoost;   

DOI  :  10.3390/rs13122242

来源: DOAJ

【 摘 要 】

The net ecosystem CO₂ exchange (NEE) is a critical parameter for quantifying terrestrial ecosystems and their contributions to the ongoing climate change. The accumulation of ecological data is calling for more advanced quantitative approaches for assisting NEE prediction. In this study, we applied two widely used machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to build models for simulating NEE in major biomes based on the FLUXNET dataset. Both models accurately predicted NEE in all biomes, while XGBoost had higher computational efficiency (6~62 times faster than RF). Among environmental variables, net solar radiation, soil water content, and soil temperature are the most important variables, while precipitation and wind speed are less important variables in simulating temporal variations of site-level NEE as shown by both models. Both models perform consistently well for extreme climate conditions. Extreme heat and dryness led to much worse model performance in grassland (extreme heat: R² = 0.66~0.71, normal: R² = 0.78~0.81; extreme dryness: R² = 0.14~0.30, normal: R² = 0.54~0.55), but the impact on forest is less (extreme heat: R² = 0.50~0.78, normal: R² = 0.59~0.87; extreme dryness: R² = 0.86~0.90, normal: R² = 0.81~0.85). Extreme wet condition did not change model performance in forest ecosystems (with R² changing −0.03~0.03 compared with normal) but led to substantial reduction in model performance in cropland (with R² decreasing 0.20~0.27 compared with normal). Extreme cold condition did not lead to much changes in model performance in forest and woody savannas (with R² decreasing 0.01~0.08 and 0.09 compared with normal, respectively). Our study showed that both models need training samples at daily timesteps of >2.5 years to reach a good model performance and >5.4 years of daily samples to reach an optimal model performance. In summary, both RF and XGBoost are applicable machine learning algorithms for predicting ecosystem NEE, and XGBoost algorithm is more feasible than RF in terms of accuracy and efficiency.

【 授权许可】

Unknown