Alternative approaches for estimating leaf area index (LAI) from remotely sensed satellite and aircraft imagery

Abstract

Plant foliage density expressed as leaf area index (LAI) is an important parameter that is widely used in many ecological, meteorological and agronomic models. LAI retrieval using optical remote sensing usually requires the collection of surface calibration values or the use of image information to invert radiative transfer models. A comparison of LAI retrieval methods was conducted that included both empirical methods requiring ground-based LAI calibration measurements and image-based methods using remotely sensed data and literature-reported parameter values. The empirical approaches included ordinary least squares regression with the Normalized Difference Vegetation Index (NDVI) and the Gitelson green index (GI) spectral vegetation indices (SVI) and a geostatistical approach that uses ground-based LAI measurements and image-derived kriging parameters to predict LAI. The image-based procedures included the scaled SVI approach, which uses NDVI to estimate fraction of vegetation cover, and a hybrid approach that uses a neural network and a radiative transfer model to retrieve LAI. Comparable results were obtained with the empirical SVI methods and the scaled SVI method. The geostatistical approach produced LAI patterns similar to interpolated ground-based LAI measurements. The results demonstrated that although reasonable LAI estimates are possible using optical remote sensing data without in situ calibration measurements, refinements to the analytical steps of the various approaches are warranted.