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A Three-Dimensional Conceptual Model for Estimating the Above-Ground Biomass of Winter Wheat Using Digital and Multispectral Unmanned Aerial Vehicle Images at Various Growth Stages

文献类型： 外文期刊

作者： Zhu, Yongji 1 ; Liu, Jikai 1 ; Tao, Xinyu 1 ; Su, Xiangxiang 1 ; Li, Wenyang 2 ; Zha, Hainie 3 ; Wu, Wenge 1 ; Li, Xinwei 1 ;

作者机构： 1.Anhui Sci & Technol Univ, Coll Resources & Environm, Chuzhou 233100, Peoples R China

2.Anhui Sci & Technol Univ, Coll Agr, Chuzhou 233100, Peoples R China

3.Anqing Normal Univ, Sch Comp & Informat, Anqing 246133, Peoples R China

4.Anhui Yigang Informat Technol Co Ltd, Anqing 246003, Peoples R China

5.Anhui Acad Agr Sci, Rice Res Inst, Hefei 230001, Peoples R China

6.Agr Waste Fertilizer Utilizat & Cultivated Land Qu, Chuzhou 233100, Peoples R China

7.Anhui Engn Res Ctr Smart Crop Planting & Proc Tech, Chuzhou 233100, Peoples R China

关键词： AGB; UAV; winter wheat; spectral features; three-dimensional conceptual model

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 13 期

页码：

收录情况： SCI

摘要： The timely and accurate estimation of above-ground biomass (AGB) is crucial for indicating crop growth status, assisting management decisions, and predicting grain yield. Unmanned aerial vehicle (UAV) remote sensing technology is a promising approach for monitoring crop biomass. However, the determination of winter wheat AGB based on canopy reflectance is affected by spectral saturation effects. Thus, constructing a generic model for accurately estimating winter wheat AGB using UAV data is significant. In this study, a three-dimensional conceptual model (3DCM) for estimating winter wheat AGB was constructed using plant height (PH) and fractional vegetation cover (FVC). Compared with both the traditional vegetation index model and the traditional multi-feature combination model, the 3DCM yielded the best accuracy for the jointing stage (based on RGB data: coefficient of determination (R-2) = 0.82, normalized root mean square error (nRMSE) = 0.2; based on multispectral (MS) data: R-2 = 0.84, nRMSE = 0.16), but the accuracy decreased significantly when the spike organ appeared. Therefore, the spike number (SN) was added to create a new three-dimensional conceptual model (n3DCM). Under different growth stages and UAV platforms, the n3DCM (RGB: R-2 = 0.73-0.85, nRMSE = 0.17-0.23; MS: R-2 = 0.77-0.84, nRMSE = 0.17-0.23) remarkably outperformed the traditional multi-feature combination model (RGB: R-2 = 0.67-0.88, nRMSE = 0.15-0.25; MS: R-2 = 0.60-0.77, nRMSE = 0.19-0.26) for the estimation accuracy of the AGB. This study suggests that the n3DCM has great potential in resolving spectral errors and monitoring growth parameters, which could be extended to other crops and regions for AGB estimation and field-based high-throughput phenotyping.

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