尝试应用灰色关联分析方法（GRA）分析典型的水分植被指数（WVI）和水分含量(LWC)间的关联度, 然后选择对冬小麦叶片水含量敏感的指数, 比较SRM-PLS（逐步回归-偏最小二乘）方法和PLS方法估算LWC的精度。 首先, 对冬小麦WVI与LWC进行灰色关联分析, 筛选出对冬小麦LWC敏感的WVI; 其次, 利用筛选出的敏感WVI, 分别用PLS-SRM方法和PLS两种方式估算冬小麦LWC; 然后对两种方式进行比较, 选择最高决定系数（R2）和最小均方根误差（RMSE）的LWC估算模型来估算冬小麦LWC。 结果表明: 在整个生育期用PLS和PLS-SRM方法估算LWC, R2和RMSE分别为0.605和0.575, 4.75%和7.35%。 研究表明: 先使用GRA对WVI和LWC进行关联度分析, 再用PLS或PLS-SRM方法可以提高冬小麦的LWC估算精度。

The objective of the present study was to compare two methods for the precision of estimating leaf water content (LWC) in winter wheat by combining stepwise regression method and partial least squares (SRM-PLS) or PLS based on the relational degree of grey relational analysis (GRA) between water vegetation indexes (WVIs) and LWC. Firstly, data utilized to analyze the grey relationships between LWC and the selected typical WVIs were used to determine the sensitivity of different WVIs to LWC. Secondly, the two methods of estimating LWC in winter wheat were compared, one was to directly use PLS and the other was to combine SRM and PLS, and then the method with the highest determination coefficient (R2) and lowest root mean square error (RMSE) was selected to estimate LWC in winter wheat. The results showed that the relationships between the first five WVI and LWC were stable by using GRA, and then LWC was estimated by using PLS and SRM-PLS at the whole stages with the R2 and RMSEs being 0.605 and 0.575, 4.75% and 7.35%, respectively. The results indicated that the estimation accuracy of LWC could be improved by using GRA firstly and then by using PLS and SRM-PLS.