ABSTRACT
Considering the great relationships between shortwave infrared (SWIR) and leaf area index (LAI), innovative indices based on water vegetation indices and visible-infrared vegetation indices were presented. In the present work, PROSAIL model was used to study the saturation sensitivity of new vegetation indices to LAI. The estimate models about LAI of winter wheat were built on the basis of the experiment data in 2009 acting as train sample and their precisions were evaluated and tested on the basis of the experiment data in 2008. Ten visible-infrared vegetation indices and five water vegetation indices were used to construct new indices. The result showed that newly developed indices have significant relationships with LAI by numerical simulations and in-situ measurements. In particular, by implementing modified standardized LAI Determining Index (sLAIDI *), all new indices were neither sensitive to water variations nor affected by saturation at high LAI levels. The evaluation models could improve prediction accuracy and have well reliability for LAI retrieval. The result indicated that visible-infrared vegetation indices combined with water index have greater advantage for LAI estimation.
Subject(s)
Spectrophotometry, Infrared , Triticum , Models, Theoretical , Plant Leaves , Reproducibility of Results , WaterABSTRACT
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.
