不同光温环境下玉米苗期叶片的高光谱特性响应分析

光温环境胁迫是影响作物优质高产的一个主要制约因素，传统的作物胁迫监测，敏锐性不足、耗时费力且多为有损检测。近年来随着信息技术的快速发展，高光谱技术能够快速无损的获取作物生理信息，并对逆境胁迫响应进行动态监测，为现代农业的精准化生产和智能化决策提供了数字化支撑，对实现传统农业向精准化、数字化的现代农业转变具有重要意义。以玉米苗期为研究对象，获取不同光温环境下叶片的高光谱数据和生理参数，探究玉米苗期叶片对不同光温环境的响应规律，进行高光谱差异性分析，并构建生理参数的高光谱反演模型。利用相关分析法筛选光谱敏感波段，采用多元散射校正(MSC)、标准正态变量变换(SNV)、Savitzky-Golaay(S-G)平滑相结合的预处理方法，分别与偏最小二乘回归法(PLS)、主成分回归法(PCR)、逐步多元线性回归法(SMLR)三种建模方法组合，以模型相关系数和均方根误差作为模型效果评价指标，探索高光谱反演叶片生理参数模型的最优方法。结果表明：不同光温环境下玉米的高光谱特性在整体上变化趋势一致，但仍存在差异，在500～700 nm波段内，光谱反射率的升高表明光强的增强；在760～900 nm波段内，光谱反射率的升高表明温度的增强；且光温胁迫环境的变化，均可反映在高光谱特性上，波段760～900 nm内光谱的反射率在高温胁迫环境下较高，在弱光胁迫环境下较低，在低温胁迫环境下反射率显著降低；所构建的SPAD和F_v/F_m的反演模型中，建模最优方法为PLS-MSC-SG，模型验证集相关系数分别为0.958和0.976，训练集相关系数分别为0.979和0.995。模型的预测性精度较高，表明利用高光谱技术，可以实现光温环境胁迫下玉米植株的定量监测，提高田间精细化管理水平，为玉米优质高产的智能化管理提供参考依据。

Response Analysis of Hyperspectral Characteristics of Maize Seedling Leaves Under Different Light and Temperature Environment

Environmental stress of light and temperature is a major restricting factor that affects the quality and yield of crops. Traditional crop stress monitoring is insufficiently sensitive, time-consuming and laborious, and mostly destructive testing. In recent years, with the rapid development of information technology, hyperspectral technology can quickly and non-destructively obtain crop physiological information, and dynamically monitor the response to adversity, providing digital support for the precision production and intelligent decision-making of modern agriculture, and is of great significance for realizing the transformation of traditional agriculture to precision and modern digital agriculture. This paper takes the corn seedling stage as the research object, obtains the hyperspectral data and physiological parameters of leaves under different light and temperature environments, explores the response law of corn leaves to different light and temperature environments, conducts hyperspectral difference analysis, and construct physiological parameters Hyperspectral inversion model. The correlation analysis method is used to screen the spectral sensitive band. The preprocessing method combining Multivariate Scattering Correction (MSC), Standard Normal Variable transformation (SNV), and Savitzky-Golay (SG) smoothing is used, respectively. Partial Least Square regression (PLS), Principal Component Regression (PCR), Stepwise Multiple Linear Regression (SMLR) three modeling methods combination, the model correlation coefficient and root mean square error are used as model effect evaluation indicators to explore the optimal method of hyperspectral inversion of leaf physiological parameter models. The results show that the hyperspectral characteristics of corn under different light and temperature environments have the same changing trend as a whole, but there are still differences. The reflectance of the spectrum in the 500～700 nm band gradually increases with the increase of light intensity, the reflectivity of the spectrum in the 760～900 nm band gradually increases with the increase of temperature, and the changes of the light and temperature stress environment can be reflected in the hyperspectral characteristics. The spectral reflectance in the 760～900 nm band is relatively high in a high temperature stress environment, the spectral reflectance is low in a low light stress environment, and the reflectance is significantly reduced in a low temperature stress environment. The optimal combination of SPAD and F_v/F_m inversion model is PLS-MSC-SG, the correlation coefficients of the model validation set are 0.958 and 0.976, and the correlation coefficients of the training set are 0.979 and 0.995, respectively. The model’s predictive accuracy is high, which indicates that the use of hyperspectral technology can realize quantitative monitoring of maize plants under light and temperature environmental stresses, improve the level of refined management in the field, and provide a reference for the intelligent management of high-quality and high-yield maize.