融合可见光-近红外与短波红外特征的新型植被指数估算冬小麦LAI

考虑到短波红外特征与叶面积指数(LAI)有很好的关联，将短波红外特征的典型水分指数与基于可见光-近红外特征的植被指数相融合，尝试构建新的植被指数估算作物LAI。通过PROSAIL辐射传输模型分析新植被指数对LAI饱和响应的特征;利用2009年和2008年北京地区冬小麦实测光谱数据进行LAI估算建模与验证。结果表明：所选择的10个典型可见光-近红外植被指数分别与5个水分植被指数相结合构建的新指数，都能够有效提高与LAI的相关性，特别是在融合了含有短波红外特征的sLAIDI*指数后，新指数显著提高了对LAI响应的饱和点，而对植被水分变化不敏感，LAI估算精度得到改善。研究表明：将短波红外特征引入到可见光-近红外植被指数中，构建的新植被指数对冬小麦LAI估算具有明显的优势。

New Vegetation Index Fusing Visible-Infrared and Shortwave Infrared Spectral Feature for Winter Wheat LAI Retrieval

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.