A Novel Hybrid-DCNN-Based Framework for Enhanced Rice Aboveground Biomass Estimation Under Limited Samples
文献类型：期刊论文
| 作者 | Liu, Yibo8; Pei, Jie8; Zou, Yaopeng8; Tan, Shaofeng8; He, Yinan7; Zheng, Xiaopo8; Wang, Tianxing8; Fang, Huajun5,6; Wang, Li4; Huang, Jianxi1,2,3 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:4405716 |
| 关键词 | Data models Crops Autonomous aerial vehicles Estimation Training Biomass Predictive models Fertilizers Deep learning Biological system modeling Aboveground biomass (AGB) deep convolutional neural network (DCNN) multisource uncrewed aerial vehicle (UAV) data PROSAIL-PRO rice |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3544343 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Aboveground biomass (AGB) of rice is crucial for monitoring growth and predicting yields. While deep learning algorithms, such as deep convolutional neural networks (DCNNs), show compelling performance in estimating crop parameters, gathering sufficient ground-truth samples for model training poses a significant challenge, leading to the small sample problem. To address this, we propose a framework that utilizes a hybrid inversion model based on the PROSAIL-PRO radiative transfer model (RTM) combined with machine learning techniques [XGBoost and random forest (RF)]. This framework incorporates active learning optimization and the spectral angle mapper (SAM) method to select simulated samples that closely match real-world conditions, simultaneously assigning geographic location information to the samples. Using these qualified samples, we constructed both single-branch and multibranch DCNN models that integrate uncrewed aerial vehicle (UAV)-based hyperspectral principal components (PCs), canopy height (CH) information from the canopy surface model (CSM), and canopy temperature derived from thermal infrared (TIR) images. The effectiveness of this approach was validated across two experimental sites. The single-branch DCNN achieved the highest accuracy at site A ( R-2=0.816 and root-mean-square error (RMSE) =61.608 g/m(2)) with PCs, TIR, and CSM as inputs, while the multibranch DCNN performed best at site B ( R-2=0.784 and RMSE =65.533 g/m(2)), using PCs and TIR as inputs. Results indicate that simulated samples have considerable potential for practical applications. PCs were the primary contributors to the model, with TIR playing a more significant role than CSM. Overall, this study demonstrates high-precision estimation of rice AGB despite limited measured samples, offering valuable insights for crop monitoring under small sample conditions. |
| URL标识 | 查看原文 |
| WOS关键词 | LEAF-AREA INDEX ; RADIATIVE-TRANSFER ; YIELD ESTIMATION ; WINTER-WHEAT ; GRAIN-YIELD ; REGRESSION ; GROWTH ; REFLECTANCE ; INVERSION ; VARIABLES |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001447530000016 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213350]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Pei, Jie; Wang, Tianxing |
| 作者单位 | 1.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agrihazards, Beijing 100083, Peoples R China 2.China Agr Univ, Coll Land Sci & Technol, Beijing 100107, Peoples R China; 3.Southwest Jiaotong Univ, Fac Geosci & Engn, Chengdu 611756, Peoples R China; 4.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Remote Sensing & Digital Earth, Beijing 100101, Peoples R China; 5.Zhongke Jian Inst Ecoenvironm Sci, Jian 343000, Peoples R China; 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China; 7.Lawrence Berkeley Natl Lab, Berkeley, CA 94720 USA; 8.Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu, Yibo,Pei, Jie,Zou, Yaopeng,et al. A Novel Hybrid-DCNN-Based Framework for Enhanced Rice Aboveground Biomass Estimation Under Limited Samples[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:4405716. |
| APA | Liu, Yibo.,Pei, Jie.,Zou, Yaopeng.,Tan, Shaofeng.,He, Yinan.,...&Huang, Jianxi.(2025).A Novel Hybrid-DCNN-Based Framework for Enhanced Rice Aboveground Biomass Estimation Under Limited Samples.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,4405716. |
| MLA | Liu, Yibo,et al."A Novel Hybrid-DCNN-Based Framework for Enhanced Rice Aboveground Biomass Estimation Under Limited Samples".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):4405716. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。