基于红边优化植被指数的寒地水稻叶片叶绿素含量遥感反演研究

水稻叶片叶绿素含量遥感诊断是实现水稻精准施肥的核心要素。本研究通过分析寒地水稻关键生育期叶片高光谱反射率信息，同时结合PROSPECT模型叶绿素含量吸收系数，参考借鉴现有高光谱植被指数的构造方法和形式，利用相关性分析、连续投影法、遗传算法优化的粗糙集属性简约法进行高光谱特征选择，提出了仅含有695、507和465nm 3个高光谱特征波段的红边优化指数（ORVI）。与Index Data Base数据库中其他用于叶绿素含量反演植被指数，包括ND_528,587、SR_440,690、CARI、MCARI的反演结果进行了对比分析，结果表明：IDB数据库中的已有4种植被指数叶绿素含量反演模型的决定系数R₂分别为0.672、0.630、0.595和0.574；ORVI植被所建立的叶绿素含量反演模型的决定系数R₂为0.726，均方根误差RMSE为2.68，精度高于其他植被指数，说明了ORVI在实际的应用中，能够作为快速反演水稻叶绿素含量的高光谱植被指数。本研究能够为寒地水稻叶绿素含量高光谱遥感诊断及管理决策提供一定的客观数据支撑和模型参考。

Remote sensing inversion of chlorophyll content in rice leaves in cold region based on Optimizing Red-edge Vegetation Index (ORVI)

Rice is one of the important staple crops in China, and the rice planted in Northeast China, such as in Liaoning, Jilin, and Heilongjiang regions, is called cold-region rice. The chlorophyll content in rice leaves is the most direct indicator of the rice growth period and can directly reflect on its nutritional value. Previous research demonstrates that when the chlorophyll content of rice changes, the reflectance of different bands changes at the spectral level. In addition, most of the research studies on the inversion of the rice’s chlorophyll content are based on the complex machine learning algorithms. Although the accuracy of the inversion of the constructed model has been improved, the structure of the model is relatively complex, and the model’s transplantation and universality are poor in the actual application process. Hence, in this study, the inversion of the chlorophyll content of rice leaves in the cold regions was assessed. An ASD ground object spectrometer was employed to procure the hyperspectral information of rice leaves in the critical growth period. On the basis of the feature selection method, the hyperspectral feature subset of the inversion of the chlorophyll content of rice was selected. The characteristic band vegetation index was constructed by combining the chlorophyll content absorption coefficients, and the chlorophyll content of rice was established through using regression analysis. Additionally, by combining the chlorophyll content absorption coefficients in the PROSPECT model, referring to the construction method and form of the existing hyperspectral vegetation index, and using correlation analysis, the continuous projection method and the genetic algorithm optimized the rough set attribute reduction, the hyperspectral features was selected, and the red edge optimization index (ORVI) with only 695, 507, and 465nm hyperspectral feature bands was proposed. Compared with the other vegetation indexes retrieved from the IDB database, namely, ND_528,587, SR_440,690, CARI, and MCARI, the results demonstrated that the determination coefficients of the abovementioned vegetation index inversion models were 0.672, 0.630, 0.595, and 0.574 respectively. The accuracy of the inversion model of chlorophyll content established by ORVI vegetation was higher than that of other vegetation indexes wherein the decision coefficients of the model were R² =^{0.726 and RMSE = 2.68, revealing that ORVI can be used as a hyperspectral vegetation index for the rapid inversion of the rice’s chlorophyll content in practical applications. This research can thereby provide some objective data support and model reference for remote sensing diagnosis and management decision of the rice’s chlorophyll content in the cold regions.}