Estimation of leaf area index over agricultural areas from polarimetric SAR images

Leaf area index (LAI) is an important quantity governing the physical and biological processes of plant canopies. The conventional definition of LAI with respect to radiation interception is the total one-sided green leaf area per unit ground surface area. The interest in monitoring the global vegetation condition has stimulated the measurement of this parameter over various vegetation types. Historically, optical remote sensing instruments have been used to estimate LAI by deriving the normalized difference vegetation index (NDVI), which is the ratio of the difference and the sum of the near-infrared and red radiation. Because the active microwave remote sensing of vegetation is sensitive to the canopy water content and geometry has also been suggested as a potential technique for measuring LAI. The authors examine the relationship of the radar backscatter data and LAI for various agricultural crops through modeling and measurements. The JPL AIRSAR system aboard the NASA DC-8 aircraft has been used to collect data over an agricultural area in the region of Davis, California. The main crops in this area consist of wheat, alfalfa, sugarbeet, tomato, and corn. The vegetation structural parameters, LAI (with LAI-2000), wet and dry biomass, soil surface roughness, and soil moisture have been collected during the AIRSAR overpass. A discrete backscatter model for vegetation canopies based on the distorted Born approximation (DBA) has been used to show the relationship of the polarimetric backscattering coefficient with LAI at L-band and C-band. By using the parameters derived from the in situ measurements, the radar backscatter data have been simulated. The effect of the crop type in the backscatter data is generalized in order to develop a semi-empirical model to estimate the LAI from SAR images.