Can Machine Learning Algorithms Successfully Predict Grassland Aboveground Biomass?

文献类型： 外文期刊

第一作者： Wang, Yue

作者： Wang, Yue;Qin, Rongzhu;Zhang, Kaiping;Chai, Ning;Zhang, Feng;Cheng, Huzi;Liang, Tiangang;Gao, Jinlong;Feng, Qisheng;Hou, Mengjing;Liu, Jie;Liu, Chenli;Zhang, Wenjuan;Fang, Yanjie;Huang, Jie;Zhang, Feng

作者机构：

关键词： MODIS; Google Earth Engine; biomass inversion; spatio-temporal scalability; model building

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 16 期

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收录情况： SCI

摘要： The timely and accurate estimation of grassland aboveground biomass (AGB) is important. Machine learning (ML) has been widely used in the past few decades to deal with complex relationships. In this study, based on an 11-year period (2005-2015) of AGB data (1620 valid AGB measurements) on the Three-River Headwaters Region (TRHR), combined with remote sensing data, weather data, terrain data, and soil data, we compared the predictive performance of a linear statistical method, machine learning (ML) methods, and evaluated their temporal and spatial scalability. The results show that machine learning can predict grassland biomass well, and the existence of an independent validation set can help us better understand the prediction performance of the model. Our findings show the following: (1) The random forest (RF) based on variables obtained through stepwise regression analysis (SRA) was the best model (R-vad(2) = 0.60, RMSEvad = 1245.85 kg DW (dry matter weight)/ha, AIC = 5583.51, and BIC = 5631.10). It also had the best predictive capability of years with unknown areas (R-indep(2) = 0.50, RMSEindep = 1332.59 kg DW/ha). (2) Variable screening improved the accuracy of all of the models. (3) All models' predictive accuracy varied between 0.45 and 0.60, and the RMSE values were lower than 1457.26 kg DW/ha, indicating that the results were reliably accurate.

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