全球定位系统2025，Vol.50Issue(2)：22-30,9.DOI:10.12265/j.gnss.2024209
基于BorutaShap的多参数CYGNSS森林生物量反演模型研究
Research on multi-parameter CYGNSS forest biomass inversion model based on BorutaShap
摘要
Abstract
Aiming at three key indicators of aboveground biomass(AGB),vegetation optical depth(VOD)and canopy height model(CHM)in biomass estimation,a regression composite model BorutaShap-CNN-LSTM-GRU(BCLG)based on Cyclone Global Navigation Satellite System(CYGNSS)data was proposed.The model combines the advantages of convolutional neural network(CNN),long short-term memory(LSTM)and gated recurrent unit(GRU),and optimizes the performance of the model through BorutaShap feature selection technology to improve the inversion accuracy.By comparing the inversion results of the validation set of AGB and VOD at 6 months and CHM at 12 months,compared with the full-parameter CLG model without BorutaShap,the BCLG model increased the R2 of AGB,VOD and CHM from 0.78,0.96 and-0.08 to 0.83,0.97 and 0.21,respectively,and the RMSE decreased from 25.08 t/ha,0.05 m and 8.34 m to 22.29 t/ha,0.04 m and 7.15 m.Compared with the single CNN,LSTM and GRU models after BorutaShap,the BCLG composite model showed significant advantages in the inversion accuracy of AGB,VOD and CHM,which proves that the proposed BCLG model has good performance in biomass index inversion based on CYGNSS,and provides a new technical means for remote sensing biomass monitoring and evaluation.关键词
旋风全球导航卫星系统(CYGNSS)/深度学习/森林地上生物量(AGB)/植被光学深度(VOD)/冠层高度模型(CHM)Key words
CYGNSS/deep learning/above ground biomass(AGB)/vegetation optical depth(VOD)/canopy height model(CHM)分类
天文与地球科学引用本文复制引用
张云,徐瑞爽,杨树瑚,韩彦岭,洪中华..基于BorutaShap的多参数CYGNSS森林生物量反演模型研究[J].全球定位系统,2025,50(2):22-30,9.基金项目
国家自然科学基金面上项目(42271335,42176175) （42271335,42176175）
