文献类型： 外文期刊

作者： Xu, Zhanghua ¹ ; Zhang, Chaofei ¹ ; Xiang, Songyang ¹ ; Chen, Lingyan ¹ ; Yu, Xier ¹ ; Li, Haitao ¹ ; Li, Zenglu ³ ; Guo, Xiaoyu ³ ; Zhang, Huafeng ⁵ ; Huang, Xuying ⁶ ; Guan, Fengying ⁷ ;

作者机构： 1.Fuzhou Univ, Coll Environm & Safety Engn, Fuzhou 350108, Peoples R China

2.Fuzhou Univ, Acad Digital China, Fuzhou 350108, Peoples R China

3.Fujian Prov Key Lab Resources & Environm Monitorin, Sanming 365004, Peoples R China

4.SEGi Univ, Fac Educ, KotaDamansara 47810, Malaysia

5.Xiamen Adm Ctr Afforestat, Xiamen 361004, Peoples R China

6.Guangdong Acad Agr Sci, Inst Agr Econ & Informat, Guangzhou 510000, Peoples R China

7.Int Ctr Bamboo & Rattan, Key Lab Natl Forestry & Grassland Adm, Beijing 100102, Peoples R China

关键词： Canopy chlorophyll content (CCC); hybrid method; leaf area index (LAI); Moso bamboo forests; PROSAIL RTM; Canopy chlorophyll content (CCC); hybrid method; leaf area index (LAI); Moso bamboo forests; PROSAIL RTM

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （ 影响因子：5.3； 五年影响因子：5.6 ）

ISSN： 1939-1404

年卷期： 2025 年 18 卷

页码：

收录情况： SCI

摘要： Leaf area index (LAI) and chlorophyll content are crucial variables in photosynthesis, respiration, and transpiration, playing a vital role in monitoring vegetation stress, estimating productivity, and evaluating carbon cycling processes. Currently, physical models are widely adopted for estimating LAI and canopy chlorophyll content (CCC). However, the main challenges of physical model-based methods for estimating LAI and CCC are the high computational cost and the fact that different combinations of canopy variables result in similar spectral reflectance for local minima. To address this limitation, a hybrid model was proposed to invert the LAI and CCC in Moso bamboo (Phyllostachys pubescens) forests. This approach utilized the PROSAIL canopy radiation transfer model, established look-up table (LUT) for LAI and CCC, and employed the Stacking ensemble learning framework. Compared with the PROSAIL LUT method, the hybrid model demonstrated higher performance in predicting LAI and CCC by incorporating the strengths of different models within the hybrid framework. The R-2 values between predicted and measured values were improved by 3.28% and 7.15%, while the RMSE values were reduced by 19.71% and 16.14%, respectively. Moreover, the hybrid model based on Stacking ensemble learning achieved an 86% reduction in running time. Therefore, the hybrid model, which integrates the PROSAIL model with the Stacking ensemble learning framework, offers a more efficient and accurate approach for remotely estimating the LAI and CCC in Moso bamboo forests. The high efficiency of this method makes it promising and suitable for application to other types of vegetation.