盐生植物中的盐离子是评价植物营养状况的重要指标,反映植物对盐胁迫的适应策略。以艾比湖湿地自然保护区盐生植被为研究对象,利用2016年10月7种盐生植被的叶片盐离子含量数据和对应的叶片光谱反射率数据,分析了盐离子Ca ²⁺、K ⁺、Mg ²⁺和Na ⁺与比值植被指数(RVI)、差值植被指数(DVI)和归一化植被指数(NDSI)之间的相关关系,选出最佳波段组合,建立了12种叶片盐离子与光谱反射率的估算模型,并进行精度验证,从中选出最佳拟合模型。结果表明盐生植被的叶片盐离子含量与原始光谱反射率的相关性较低,且与各波段反射率呈显著负相关的主要是Na ⁺含量。Na ⁺含量与DVI、NDSI和RVI构建的植被指数相关性最高,相关性均达到0.5以上,波段主要位于近红外与中红外区域。以 VDVI(R1750,R1480)、 VDVI(R478,R440)、 VNDSI(R450,R375)和 VRVI(R405,R375)为自变量构建的三次多项式分别是Na ⁺、Ca ²⁺、Mg ²⁺和K ⁺的最佳估算模型。以 VRVI(R1100,R1125)为自变量构建的Na ⁺三次多项式的模型相关系数最大(R=0.806),说明该模型的拟合度较高,预测效果较好,可用来实时监测盐生植被叶片的盐分状况,为盐生植被叶片含盐量的精确诊断提供了技术途径。

The salt ion in halophytes is an important index to evaluate the nutritional status of plants, which reflects the adaptive strategies of vegetation to salt stress. In this study, the halophytes of the Ebinur Lake Wetland Nature Reserve are taken as the target area. The salt ion content data and the leaf spectral reflectivity date of seven types of halophytes in October 2016 are used to analyze the correlativity between salt ion Ca 2+, K +, Mg 2+, Na + and ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference vegetation index (NDSI). The best band combination is selected. The estimation models of 12 salt ions of leaf and spectral reflectivity are built. The best fitting model is selected from accuracy test. The results show that the correlativity between the salt ion content of leaf and the original spectral reflectivity is low. And the significant negative correlation with reflectivity of each band is mainly Na + content. The correlation between Na + content and the vegetation indices of RVI, NDSI and DVI is the best. The correlation is above 0.5, and the band is mainly locating in the region of near infrared and middle infrared. The best estimation models for Na +, Ca 2+, Mg 2+ and K + are VDVI(R1750,R1480), VDVI(R478,R440), VNDSI(R450,R375) and VRVI(R405,R375), which use the independent variables to build the cubic polynomials, respectively. The maximum correction index of the mode is the biggest (R=0.806), and the mode uses VRVI(R1100,R1125) as independent variable to build the cubic polynomial. It shows that the model has high fitting degree and good prediction effect. It can be used to monitor the salt status of halophytic vegetation leaf in real-time and provide a technical approach for the accurate diagnosis of salt content of halophytic vegetation leaf.