基于热点植被指数的冬小麦叶面积指数估算

针对传统植被指数方法中利用单一方向的光谱特性估测LAI容易出现饱和现象和冠层结构信息不足的缺陷,以二向反射特性的归一化植被指数(NHVI)为基础,将表征叶片空间分布模式的热暗点指数(HDS)引入土壤调整型植被指数(SAVI),增强型植被指数(EVI)中,构建具有二向反射特性的土壤调整型热点植被指数(SAHVI)和增强型热点植被指数(EHVI)。同时使用红光,近红外,蓝光和绿光波段计算HDS,选择对LAI敏感性较高的HDS参与构建新型植被指数,并利用试验测量的小麦冠层二向反射率数据和叶面积指数,研究新型植被指数与LAI的线性关系。结果表明:基于蓝光和红光波段计算的HDS参与构建的EHVI、SAHVI与LAI的线性相关程度要优于EVI、SAVI,且较NHVI有进一步提高,能有效缓解LAI估算中植被指数饱和现象。

Estimation of winter wheat LAI using hotspot-signature vegetation indices

Leaf area index (LAI) is often retrieved from mono-angle remote sensing, the main weaknesses of which are the saturation limits at intermediate values of LAI and lacking structure information. Given the above deficiencies, two new vegetation indices, Soil Adjusted Hotspot-signature Vegetation Index (SAHVI) and Enhanced Hotspot-signature Vegetation Index (EHVI), were proposed for a better quantitative estimation of LAI. To obtain the new indices, we adjusted at-nadir Enhanced Vegetation Index (EVI) and Soil Adujsted Vegetation Index (SAVI) to incorporate Hot-Dark Spot (HDS) index respectively that represents spatial distribution pattern of leaves. Next, the red, near-infrared, blue and green bands were exploited to calculate the respective HDS indices. Four HDS indices were compared for correlation with increasing LAI and those relatively more sensitive to LAI variability were then selected to construct SAHVI and EHVI. At last, the linear relationships between the new indices and LAI were investigated based on in-situ measurements of bi-directional reflectance and LAI from winter wheat. It was found stronger correlations between SAHVI, EHVI, NHVI and LAI than between EVI, SAVI, NDVI and LAI. Better resistance to saturation limits were both observed for SAHVI and EHVI.