Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat

Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405-835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI). To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient (R²) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e.g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVI-like in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results.