Ratoon rice production has been an emerging cropping system to increase food quality and productivity worldwide. Efficient monitoring of ratoon rice aboveground biomass (AGB) over large areas is valuable for precision agriculture, as AGB is closely related to crop grain yield and quality. Unmanned aerial vehicle (UAV) remote sensing has opened an unprecedented opportunity to efficiently monitor crop AGB instead of labor-intensive ground measurements. Vegetation indices (VIs)-based approach for estimating AGB is easily affected by background materials and often suffers saturation problems at high AGB levels. Although the combined use of UAV-collected structural and spectral features can alleviate these problems to some extent, uncertainties on the AGB estimation still exist for crop with significant difference in canopy architecture and AGB composition throughout the growing season (e.g., ratoon rice). There is a hypothesis that the combination of spectral, textural and structural features can improve ratoon rice AGB estimations across different developmental stages. Therefore, the utility of UAV-based spectral, textural and structural features were quantified in estimating ratoon rice AGB at field level with contrasting agronomic treatments (i.e., nitrogen fertilizer and stubble height), in which multiple linear regression (MLR) and gaussian process regression (GPR) methods were applied and compared. Results showed that (1) each feature had its own respective limitation: specifically, spectral and textural features exhibited insufficient sensitivity to AGB variability of remaining stubbles or stem at early stage and suffered saturation problem at grain filling stage; structural features were difficult to detect the emergence of panicles from panicle initiation to heading stages; (2) the combination of three types of features can complement each other and achieved the highest accuracy using GPR method: the combination of spectral, structural and textural features achieved the best estimation accuracy for estimating ratoon rice AGB with an R² of 0.94 and RMSE of 81.4 g m⁻² across different developmental stages, which significantly improved the model performance compared to the combination of spectral and textural features (R² = 0.56, RMSE = 170.2 g m⁻²) and the combination of spectral and structural features (R² = 0.86, RMSE = 138.8 g m⁻²). In summary, this study provides a novel approach for efficiently estimating ratoon rice AGB at field level, which is critical for timely decision making (e.g., determine when and where to apply fertilizer or pesticide) in precision agriculture.

再生稻生产已成为一种新兴的种植系统，可提高全球粮食质量和生产力。有效监测大面积再生稻地上生物量（AGB）对于精准农业很有价值，因为AGB与作物产量和质量密切相关。无人驾驶飞行器 (UAV) 遥感为有效监测作物 AGB 而不是劳动密集型的地面测量提供了前所未有的机会。基于植被指数 (VIs) 的 AGB 估计方法很容易受到背景材料的影响，并且在高 AGB 水平下经常遇到饱和问题。虽然结合使用无人机采集的结构和光谱特征可以在一定程度上缓解这些问题，对于在整个生长季节冠层结构和 AGB 组成存在显着差异的作物（例如，宿根稻），AGB 估计仍然存在不确定性。有一个假设是光谱、纹理和结构特征的组合可以改善不同发育阶段的再生稻 AGB 估计。因此，基于 UAV 的光谱、纹理和结构特征的效用在估计田间水平的再生稻 AGB 时被量化，并采用对比农艺处理（即氮肥和留茬高度），其中多元线性回归 (MLR) 和高斯过程回归(GPR) 方法被应用和比较。结果表明（1）每个特征都有其各自的局限性：具体而言，光谱和纹理特征对早期残茬或茎秆的AGB变异敏感性不足，在灌浆期出现饱和问题；从穗开始到抽穗阶段的穗出现的结构特征难以检测；(2) 三种特征的组合可以相互补充，使用 GPR 方法达到最高的精度：光谱、结构和纹理特征的组合在用 R 估计再生稻 AGB 时达到了最好的估计精度²的 0.94 和 81.4 g m ^{-2的 RMSE 在不同的发育阶段，与光谱和纹理特征的组合（R 2}  = 0.56，RMSE = 170.2 g m ^-2）和光谱和结构的组合相比，显着提高了模型性能特征（R ²  = 0.86，RMSE = 138.8 g m ^-2）。总之，本研究提供了一种在田间有效估算再生稻 AGB 的新方法，这对于精准农业中的及时决策（例如，确定何时何地施用化肥或农药）至关重要。