Rmote sensing of leaf water content for winter wheat using grey relational analysis (GRA), stepwise regression method (SRM) and partial least squares (PLS)

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Leaf water content (LWC) is an important parameter for evaluating crop health and predicting crop yield. The objective of this study was to compare two methods for the precision of estimating 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, using data from 2008 was utilized to analyze the grey relationships between LWC and the selected typical water vegetation indices (WVIs) to determined 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-PLS based on the sensitive WVIs was selected by GRA between WVIs and LWC, and then the method with the highest determination coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) 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 anthesis for winter wheat with 0.63 and 0.46. To validate two model estimation accuracy by using 2009 data, we compared actual value with predicted value by using PLS and SRM-PLS and RMSEs were 2.6 % and 3.12 %, 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.