Unmanned aerial vehicle (UAV) system is an emerging remote sensing tool for profiling crop phenotypic characteristics, as it distinctly captures crop real-time information on field scales. For optimizing UAV agro-monitoring schemes, this study investigated the performance of single-source and multi-source UAV data on maize phenotyping (leaf area index, above-ground biomass, crop height, leaf chlorophyll concentration, and plant moisture content). Four UAV systems [i.e., hyperspectral, thermal, RGB, and Light Detection and Ranging (LiDAR)] were used to conduct flight missions above two long-term experimental fields involving multi-level treatments of fertilization and irrigation. For reducing the effects of algorithm characteristics on maize parameter estimation and ensuring the reliability of estimates, multi-variable linear regression, backpropagation neural network, random forest, and support vector machine were used for modeling. Highly correlated UAV variables were filtered, and optimal UAV inputs were determined using a recursive feature elimination procedure. Major conclusions are (1) for single-source UAV data, LiDAR and RGB texture were suitable for leaf area index, above-ground biomass, and crop height estimation; hyperspectral outperformed on leaf chlorophyll concentration estimation; thermal worked for plant moisture content estimation; (2) model performance was slightly boosted via the fusion of multi-source UAV datasets regarding leaf area index, above-ground biomass, and crop height estimation, while single-source thermal and hyperspectral data outperformed multi-source data for the estimation of plant moisture and leaf chlorophyll concentration, respectively; (3) the optimal UAV scheme for leaf area index, above-ground biomass, and crop height estimation was LiDAR + RGB + hyperspectral, while considering practical agro-applications, optical Structure from Motion + customer-defined multispectral system was recommended owing to its cost-effectiveness. This study contributes to the optimization of UAV agro-monitoring schemes designed for field-scale crop phenotyping and further extends the applications of UAV technologies in precision agriculture.

无人机（UAV）系统是一种用于描述农作物表型特征的新兴遥感工具，因为它可以在田间尺度上清晰地捕获农作物的实时信息。为了优化无人机农业监测方案，本研究调查了单源和多源无人机数据在玉米表型（叶面积指数，地上生物量，作物高度，叶片叶绿素浓度和植物水分含量）方面的表现。四个无人机系统（即高光谱，热，RGB和光检测与测距（LiDAR））用于执行两个长期实验领域以上的飞行任务，这两个领域涉及施肥和灌溉的多层次处理。为了减少算法特征对玉米参数估计的影响并确保估计的可靠性，多变量线性回归 使用反向传播神经网络，随机森林和支持向量机进行建模。过滤高度相关的无人机变量，并使用递归特征消除程序确定最佳无人机输入。主要结论是：（1）对于单源无人机数据，LiDAR和RGB纹理适合叶面积指数，地上生物量和作物高度估计；高光谱在叶片叶绿素浓度估算方面表现优于大光谱；通过热加工来估算植物的水分含量；（2）通过融合有关叶面积指数，地上生物量和作物高度估计的多源无人机数据集，模型性能得到了略微提升，而单源热和高光谱数据在植物估计方面的表现优于多源数据水分和叶绿素浓度分别；（3）用于叶面积指数，地上生物量和作物高度估计的最佳UAV方案是LiDAR + RGB +高光谱，在考虑实际农业应用时，建议使用Motion +客户定义的多光谱系统的光学结构成本效益。这项研究为优化用于田间作物表型的无人机农业监测计划做出了贡献，并进一步扩展了无人机技术在精准农业中的应用。