摘要:植被叶片叶绿素是农业遥感反演的重要参数，叶绿素含量的变化与植被生长环境的胁迫程度、生理变化密切相关，故将植被叶绿素进行实时、动态监测对农业生产极为重要。然而，传统经验模型及叶绿素精准测量存在困难。基于高分辨率的Sentinel-2A数据，在机器学习框架下，利用光谱信息、最适光谱指数和基于PROSAIL辐射传输模型的生物协变量构建3种建模方案（方案1：光谱信息和最适光谱指数联合，方案2：光谱信息和物理模型生物协变量联合，方案3：光谱信息、最适光谱指数和物理模型生物协变量联合）。最终基于优选出的建模方案进行棉花叶片叶绿素相对含量的空间数字制图。结果表明：（1）红边波段参与的最适光谱指数比值植被指数（RVI）与棉花叶片SPAD值相关性最高r=0.767，P^**=0.195；（2）将构建的17个变量进行重要性分析可知，构建的最适光谱指数比值植被指数（RVI）与物理模型生物协变量LAI-Cab对估算模型的精度贡献率较大；（3）建模方案构建植被指数时红边波段被确定为最优波段，在增加精度方面起到决定性作用；通过模型评价标准来分析3种方案可知，预测精度大小顺序为模型方案3 > 模型方案1 > 模型方案2，其中方案3的决定系数R²最高为0.826，即估算模型方案3对棉花叶片SPAD值具有最好的预测能力，可以为干旱区农作物的生理参数反演提供新的思路，为农业安全监测，合理水肥配置提供科学数据支持。



Abstract:Vegetation leaf chlorophyll is an important parameter for agricultural remote sensing inversion. The change of Chlorophyll content is intensely associated with the stress degree and physiological changes of vegetation growth environment. Therefore, it is vital for agricultural safety production to conduct real-time and dynamic detect in vegetation chlorophyll. Nevertheless, it is difficult to accurately measure space chlorophyll based on traditionally empirical models. In this study, using high-resolution Sentinel-2A data, three modeling patterns were driven by spectral information, optimal spectral index and biological covariates based on PROSAIL radiation transmission model within the framework of machine learning (random forest) (Scheme 1: the consolidation of spectral information and optimally spectral index combination, Scheme 2: the combination of spectral information and physical model biological covariate, Scheme 3: the merger among spectral information, optimally spectral index and physical model biological covariate). The chlorophyll content of cotton leaves was mapped based on the optimized modeling scheme. The results show that: (1) the correlation between the optimally spectral index Ratio Vegetation Index (RVI) with red edge band and the SPAD value of cotton leaves is the highest r=0.767, P^{* *}=0.195. (2) The importance analysis of the 17 constructed variables shows that the construction between the optimally spectral index Ratio Vegetation Index (RVI) and the physical model biological covariate LAI-Cab imposes a great contribution on the precise of the estimated model. (3) The red-edge band is determined as the optimal band when the vegetation index is constructed by the modeling scheme, which presented main role in constructing vegetation index. Analyzing the three schemes through model evaluation criteria, the order of prediction accuracy was model scheme 3 > model scheme 1 > model scheme 2. The decision coefficient R2 of scheme 3 was the highest at 0.826, which showed model scheme 3 has greater capability in predicting the SPAD value of the cotton leaves. It can provide advanced theory for the inversion of physiological parameters of crops in arid areas. Moreover, it also supplied scientific data support in detecting agricultural safety and allocating reasonable water and fertilizer.





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