Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region

The amount and spatial and temporal dynamics of vegetation are important in formation in environmental studies and agricultural practices. There has be en a great deal of interest in estimating vegetation parameters and their s patial and temporal extent using remotely sensed imagery. There are primari ly two approaches to estimating vegetation parameters such as leaf area ind ex (LAI). The first one is associated with computation of spectral vegetati on indices (SVI) from radiometric measurements. This approach uses an empir ical or modeled LAI-SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this emp irical 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 sur face types. The second approach involves using bidirectional reflectance di stribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI wing 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 d ifficulty of obtaining the required input parameters by the model. In this study, we present ct strategy that combines BRDF models and conventional LA I-SVI approaches to circumvent these limitations. The proposed strategy run s implemented in three sequential steps. In the first step, a BRDF model wa s inverted with a limited number of dam points or pixels to produce a train ing data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control pro cedure 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 trai n 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 d istribution. This approach was applied to Landsat TM imagery acquired in th e semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experim ental sites near Niamy, Niger. The results were compared with limited groun d-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid region s. This study was not intended to show accuracy improvement of LAI estimati on from remotely sensed data. Rather, it provides an alternative that is si mple and requires little knowledge of study target and few ground measureme nts. (C) Elsevier Science Inc., 2000.