数字农科院2.0

Enhanced cotton chlorophyll content estimation with UAV multispectral and LiDAR constrained SCOPE model

文献类型： 外文期刊

作者： Puchen Yan;Yangming Feng;Qisheng Han;Zongguang Hu;Xi Huang;Kaikai Su;Shaozhong Kang

作者机构：

关键词： Cotton leaf chlorophyll content;Multi-temporal analysis;Multispectral-LiDAR fusion;SCOPE model;UAV remote sensing

期刊名称： International Journal of Applied Earth Observation and Geoinformation

ISSN： 1569-8432

年卷期： 2024 年 132 卷

页码：

收录情况： SCIE（2024版）

摘要： Accurate and non-destructive estimation of leaf chlorophyll content (LCC) is crucial for optimizing cotton production. This study enhances the SCOPE model by integrating unmanned aerial vehicle (UAV)-derived multispectral data with leaf area index (LAI) from LiDAR data, significantly improving precision of LCC estimation, particularly during crucial growth stages of cotton. We construct and analyze three cost functions: COST1, which relies solely on spectral data; COST2, which incorporates direct LAI inputs; and COST3, which adjusts for LAI measurement uncertainties by combining spectral term with an error term representing the squared relative error between measured and model-estimated LAI. Our findings indicate that while COST1 establishes a baseline, COST2 and COST3 provide more accurate LCC estimations. COST3, validated against theoretical data, field-measured cotton datasets, and an additional maize dataset, proves most robust, maintaining consistent accuracy across all growth stages especially when considering input data uncertainties. This highlights the importance of integrating appropriate forms of LAI in cost functions to refine LCC estimation. Future research should focus on improving data acquisition quality and developing more advanced cost functions to advance LCC estimation further.

分类号：

  • 相关文献

[1]A Machine Learning Model and Multi-Temporal Remote Sensing for Sustainable Soil Management in Egypt's Western Nile Delta. Metwaly, Mohamed M.,AbdelRahman, Mohamed A. E.,Mohamed, Sayed A.. 2024

[2]Identification of Pine Wilt Disease Infected Wood Using UAV RGB Imagery and Improved YOLOv5 Models Integrated with Attention Mechanisms. Peng Zhang,Zhichao Wang,Yuan Rao,Jun Zheng,Ning Zhang,Degao Wang,Jianqiao Zhu,Yifan Fang,Xiang Gao. 2023

[3]Flood Disaster Monitoring and Emergency Assessment Based on Multi-Source Remote Sensing Observations. Lei, Tianjie,Wang, Jiabao,Li, Xiangyu,Wang, Weiwei,Shao, Changliang,Liu, Baoyin. 2022

[4]Sunflower-YOLO: Detection of sunflower capitula in UAV remote sensing images. Rui Jing,Qinglin Niu,Yuyu Tian,Heng Zhang,Qingqing Zhao,Zongpeng Li,Xinguo Zhou,Dongwei Li. 2024

[5]Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images. Qiuyan Huang,Juanjuan Feng,Maofang Gao,Shuangshuang Lai,Guangping Han,Zhihao Qin,Jinlong Fan,Yuling Huang. 2024

[6]A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments. Xin Xu,Li Zhang,Jibo Yue,Heming Zhong,Ying Wang,Jie Liu,Yanhui Lu,Hongbo Qiao. 2024

[7]Cross-scale estimating of forage nitrogen in alpine grassland integrating UAV imagery and Sentinel-2 data. Jinlong Gao,Mengwei Han,Dongmei Zhang,Zhanping Ma,Yongkang Zhang,Shuai Fu,Tiangang Liang. 2025

[8]Diagnosis of nitrogen nutrition in winter wheat across years based on multi-source remote sensing data from unmanned aerial vehicles. Deshan Chen,Yitian Chen,Hui Zhang,Jinrui Liu,Qian Cheng,Fuyi Duan,Xiaohui Kuang,Wanna Fu,Jie Liu,Zhen Chen. 2025

作者其他论文 更多>>