期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B7
页码：108-113
出版社：Copernicus Publications
摘要：Leaf area index (LAI) is a key canopy descriptor that is used to determine foliage cover, and predict photosynthesis and evapotranspiration in order to assess crop yield. Its estimation from remote sensing data has been the focus of many investigations in recent years. In this context, we have used ground measured reflectances to study the potential of spectral indices for LAI prediction using remotely sensed data. LAI measurements and corresponding ground spectra were collected over four years (2000, 2001, 2002 and 2003) for three crop types (corn, beans, and peas) in a study area at Saint-Jean-sur-Richelieu, near Montreal (Quebec, Canada). Hence, a set of vegetation indices were assessed in terms of their linearity with LAI variation, as well as their prediction ability for a range of crops types. Predictive equations have been developed from ground measured data, and then applied to airborne CASI hyperspectral images acquired over agricultural fields of corn, wheat, and soybean grown during summer 2001 (former greenbelt farm of Agriculture and Agri-Food Canada, Ottawa). The results demonstrated that while indices like NDVI suffer from saturation at medium and high LAI values others like MSAVI2 and MTVI2 result in significantly improved performances. Evaluation of predictions revealed excellent agreement with field measurements: values of CASI-estimated LAI were very similar to the measured ones
关键词：Hyperspectral; Precision agriculture; Modelling; Algorithm development; Spectral indices; LAI
