Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region

Abstract

The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI–SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI–SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI–SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.