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Deep Learning-Based Prediction of Multi-Species Leaf Pigment Content Using Hyperspectral Reflectance

文献类型：期刊论文

作者Wang, Ziyu2,3,4; Xu, Duanyang1
刊名REMOTE SENSING
出版日期2025-09-25
卷号17期号:19页码:3293
关键词leaf pigment content leaf reflectance one-dimensional convolutional neural network two-dimensional convolutional neural network genetic algorithm
DOI10.3390/rs17193293
产权排序4
文献子类Article
英文摘要Highlights What are the main findings? CNN models combined with genetic algorithm-based spectral band selection achieved high-accuracy estimation of leaf pigment content across tree species. The 2D CNN outperformed the 1D CNN, with optimal results obtained using 3-4 convolutional layers. What is the implication of the main finding? The study provides a non-destructive and robust approach for monitoring leaf pigments across different tree species. The CNN-based approach improved remote sensing applications in vegetation health assessment and forest ecosystem management.Highlights What are the main findings? CNN models combined with genetic algorithm-based spectral band selection achieved high-accuracy estimation of leaf pigment content across tree species. The 2D CNN outperformed the 1D CNN, with optimal results obtained using 3-4 convolutional layers. What is the implication of the main finding? The study provides a non-destructive and robust approach for monitoring leaf pigments across different tree species. The CNN-based approach improved remote sensing applications in vegetation health assessment and forest ecosystem management.Abstract Leaf pigment composition and concentration are crucial indicators of plant physiological status, photosynthetic capacity, and overall ecosystem health. While spectroscopy techniques show promise for monitoring vegetation growth, phenology, and stress, accurately estimating leaf pigments remains challenging due to the complex reflectance properties across diverse tree species. This study introduces a novel approach using a two-dimensional convolutional neural network (2D-CNN) coupled with a genetic algorithm (GA) to predict leaf pigment content, including chlorophyll a and b content (Cab), carotenoid content (Car), and anthocyanin content (Canth). Leaf reflectance and biochemical content measurements taken from 28 tree species were used in this study. The reflectance spectra ranging from 400 nm to 800 nm were encoded as 2D matrices with different sizes to train the 2D-CNN and compared with the one-dimensional convolutional neural network (1D-CNN). The results show that the 2D-CNN model (nRMSE = 11.71-31.58%) achieved higher accuracy than the 1D-CNN model (nRMSE = 12.79-55.34%) in predicting leaf pigment contents. For the 2D-CNN models, Cab achieved the best estimation accuracy with an nRMSE value of 11.71% (R2 = 0.92, RMSE = 6.10 mu g/cm2), followed by Car (R2 = 0.84, RMSE = 1.03 mu g/cm2, nRMSE = 12.29%) and Canth (R2 = 0.89, RMSE = 0.35 mu g/cm2, nRMSE = 31.58%). Both 1D-CNN and 2D-CNN models coupled with GA using a subset of the spectrum produced higher prediction accuracy in all pigments than those using the full spectrum. Additionally, the generalization of 2D-CNN is higher than that of 1D-CNN. This study highlights the potential of 2D-CNN approaches for accurate prediction of leaf pigment content from spectral reflectance data, offering a promising tool for advanced vegetation monitoring.
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WOS关键词CHLOROPHYLL CONTENT ; PLS-REGRESSION ; RETRIEVAL ; MODEL ; SELECTION ; PROSPECT ; CANOPY ; CAROTENOIDS ; PARAMETERS ; INDEXES
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001593893200001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/217452]  
专题陆地表层格局与模拟院重点实验室_外文论文
通讯作者Wang, Ziyu
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Zhejiang A&F Univ, Key Lab Carbon Sequestrat & Emiss Reduct Agr & For, Hangzhou 311300, Peoples R China;
3.Zhejiang A&F Univ, Coll Environm & Resources, Coll Carbon Neutral, Hangzhou 311300, Peoples R China;
4.Peking Univ, Sch Urban Planning & Design, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China;
推荐引用方式
GB/T 7714		 Wang, Ziyu,Xu, Duanyang. Deep Learning-Based Prediction of Multi-Species Leaf Pigment Content Using Hyperspectral Reflectance[J]. REMOTE SENSING,2025,17(19):3293.
APA Wang, Ziyu,&Xu, Duanyang.(2025).Deep Learning-Based Prediction of Multi-Species Leaf Pigment Content Using Hyperspectral Reflectance.REMOTE SENSING,17(19),3293.
MLA Wang, Ziyu,et al."Deep Learning-Based Prediction of Multi-Species Leaf Pigment Content Using Hyperspectral Reflectance".REMOTE SENSING 17.19(2025):3293.

入库方式： OAI收割

来源：地理科学与资源研究所

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