Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass

Remote Sensing

Changhui Li¹  Xuanlong Ma²  Jing Xu³  Weiye Huang⁴  Wenlong Li⁴  Chenli Liu⁴ 

[1] Agriculture and Animal Husbandry Collage, Qinghai University, Xining 810016, China;College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730020, China;School of Agriculture and Forestry Economic and Management, Lanzhou University of Finance and Economics, Lanzhou 730020, China;State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Lanzhou University, Lanzhou 730000, China;

关键词: hyperspectral;    alpine grassland;    above-ground biomass;    machine learning;    feature selection;    interpretability;   

DOI  :  10.3390/rs14092086

来源: DOAJ

【 摘 要 】

Above-ground biomass (AGB) is a key indicator for studying grassland productivity and evaluating carbon sequestration capacity; it is also a key area of interest in hyperspectral ecological remote sensing. In this study, we use data from a typical alpine meadow in the Qinghai–Tibet Plateau during the main growing season (July–September), compare the results of various feature selection algorithms to extract an optimal subset of spectral variables, and use machine learning methods and data mining techniques to build an AGB prediction model and realize the optimal inversion of above-ground grassland biomass. The results show that the Lasso and RFE_SVM band filtering machine learning models can effectively select the global optimal feature and improve the prediction effect of the model. The analysis also compares the support vector machine (SVM), least squares regression boosting (LSB), and Gaussian process regression (GPR) AGB inversion models; our findings show that the results of the three models are similar, with the GPR machine learning model achieving the best outcomes. In addition, through the analysis of different data combinations, it is found that the accuracy of AGB inversion can be significantly improved by combining the spectral characteristics with the growing season. Finally, by constructing a machine learning interpretable model to analyze the specific role of features, it was found that the same band plays different roles in different records, and the related results can provide a scientific basis for the research of grassland resource monitoring and estimation.

【 授权许可】

Unknown