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Article

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

1
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
4
Biology Department, San Diego State University, San Diego, CA 92182, USA
5
Biology Department, College of Life and Environmental Science, Minzu University, Beijing 100081, China
6
CoStar Group, San Diego, CA 92122, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this study as correspondence author.
Academic Editor: Magaly Koch
Remote Sens. 2021, 13(12), 2242; https://doi.org/10.3390/rs13122242
Received: 19 April 2021 / Revised: 1 June 2021 / Accepted: 4 June 2021 / Published: 8 June 2021
(This article belongs to the Section Environmental Remote Sensing)
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. View Full-Text
Keywords: machine learning; NEE; random forest; terrestrial ecosystem; XGBoost machine learning; NEE; random forest; terrestrial ecosystem; XGBoost
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MDPI and ACS Style

Liu, J.; Zuo, Y.; Wang, N.; Yuan, F.; Zhu, X.; Zhang, L.; Zhang, J.; Sun, Y.; Guo, Z.; Guo, Y.; Song, X.; Song, C.; Xu, X. Comparative Analysis of Two Machine Learning Algorithms in Predicting Site-Level Net Ecosystem Exchange in Major Biomes. Remote Sens. 2021, 13, 2242. https://doi.org/10.3390/rs13122242

AMA Style

Liu J, Zuo Y, Wang N, Yuan F, Zhu X, Zhang L, Zhang J, Sun Y, Guo Z, Guo Y, Song X, Song C, Xu X. Comparative Analysis of Two Machine Learning Algorithms in Predicting Site-Level Net Ecosystem Exchange in Major Biomes. Remote Sensing. 2021; 13(12):2242. https://doi.org/10.3390/rs13122242

Chicago/Turabian Style

Liu, Jianzhao, Yunjiang Zuo, Nannan Wang, Fenghui Yuan, Xinhao Zhu, Lihua Zhang, Jingwei Zhang, Ying Sun, Ziyu Guo, Yuedong Guo, Xia Song, Changchun Song, and Xiaofeng Xu. 2021. "Comparative Analysis of Two Machine Learning Algorithms in Predicting Site-Level Net Ecosystem Exchange in Major Biomes" Remote Sensing 13, no. 12: 2242. https://doi.org/10.3390/rs13122242

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