Abstract Suitable spectral monitoring models of canopy water content provide a scientific basis for real-time dynamic, accurate, non-destructive diagnosis over large acreage. This work investigates winter wheat under different water treatments to examine the relationship between canopy water content and spectral reflectance. Principal component regression spectral monitoring models are developed based on the combination of growth stages. The growth stage constraints are divided, and the influence of other background noises is removed to achieve accurate and stable spectral monitoring results of canopy water content at all growth stages. The following main conclusions are derived. (1) At the stem elongation–booting, booting–milking, and milking–ripening stages and during the entire growth period, the spectral transforms with the highest correlation with winter wheat canopy water content are the first-order derivative, division by R930, division by R450-750, and division by R930, respectively; the corresponding sensitivity bands are 758, 759, 690, and 759 nm, respectively. At the stem elongation–booting, booting–milking, and milking–ripening stages and during the entire growth period, the “three-edge” parameters with the highest correlation with winter wheat canopy water content are Rg/Rr, SDr/Sdy, (Rg − Rr)/(Rg + Rr), and (SDr-SDb), respectively. (2) In accordance with the rationale that the spectral parameters should have the highest correlation coefficients with canopy water content at each growth stage, combinational models of canopy water content that are specific to individual growth stages are developed based on spectral transforms or “three-edge” parameters. Compared with the optimal single-parameter regression model, the combinational models significantly improve the estimation accuracy of canopy water content at each growth stage. (3) Monitoring models based on principal component analysis are constructed with comprehensive spectral information. These models can improve the monitoring accuracy at other growth stages, especially at the stem elongation–booting stage, compared with combinational models developed based on spectral transforms or “three-edge” parameters.

中文翻译：

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基于光谱变换和“三边”参数的冬小麦冠层含水量监测

摘要 适宜的冠层含水量光谱监测模型为大面积实时动态、准确、无损诊断提供了科学依据。这项工作调查了不同水处理下的冬小麦，以检查冠层含水量与光谱反射率之间的关系。基于生长阶段的组合开发了主成分回归光谱监测模型。划分生长阶段约束条件，去除其他背景噪声的影响，实现各生长阶段冠层含水量光谱监测结果准确稳定。得出以下主要结论。(1) 在茎伸长-孕穗、孕穗-挤奶、挤奶-成熟阶段和整个生育期，与冬小麦冠层含水量相关性最高的光谱变换为一阶导数，分别为R930除法、R450-750除法和R930除法；相应的灵敏度波段分别为 758、759、690 和 759 nm。在茎伸长-孕穗、孕穗-挤奶和挤奶-成熟阶段以及整个生育期，与冬小麦冠层含水量相关性最高的“三边”参数为Rg/Rr、SDr/Sdy，（ Rg - Rr)/(Rg + Rr) 和 (SDr-SDb) 分别。(2) 根据光谱参数应与每个生长阶段冠层含水量具有最高相关系数的原理，特定于各个生长阶段的冠层含水量组合模型是基于光谱变换或“三边”参数开发的。与最优单参数回归模型相比，组合模型显着提高了各生长阶段冠层含水量的估计精度。(3) 综合光谱信息构建基于主成分分析的监测模型。与基于光谱变换或“三边”参数开发的组合模型相比，这些模型可以提高其他生长阶段的监测精度，特别是在茎伸长-启动阶段。组合模型显着提高了每个生长阶段冠层含水量的估计精度。(3) 综合光谱信息构建基于主成分分析的监测模型。与基于光谱变换或“三边”参数开发的组合模型相比，这些模型可以提高其他生长阶段的监测精度，特别是在茎伸长-启动阶段。组合模型显着提高了每个生长阶段冠层含水量的估计精度。(3) 综合光谱信息构建基于主成分分析的监测模型。与基于光谱变换或“三边”参数开发的组合模型相比，这些模型可以提高其他生长阶段的监测精度，特别是在茎伸长-启动阶段。