LAND-COVER CLASSIFICATION AND ESTIMATION OF TERRAIN ATTRIBUTES USING SYNTHETIC-APERTURE RADAR

This paper presents progress toward a geophysical and biophysical information processor for synthetic aperture radar (SAR). This processor operates in a sequential fashion to first classify terrain according to structural attributes and then apply class-specific retrievals for geophysical and biophysical properties. Structural and electrical attributes control the radar backscattering from terrain. Experimental data and theoretical results illustrate the sensitivity of synthetic aperture radar to structural properties, such as surface roughness and canopy architecture, to soil moisture and to the aboveground biomass of vegetation. and its moisture status. Accurate land-cover classification is of great value in many types of regional- to global-scale modeling, and is also an essential precursor to many techniques for extracting geophysical and biophysical information from SAR data. The sensitivity of SAR to the structural features of terrain leads to landcover classification into simple and easily interpreted structural classes. Knowledge-based, hierarchical classifiers require no a priori information or statistical understanding of a local scene, and are found to yield overall accuracy in excess of 90%. Classification using existing data fron the orbital ERS-1 and JERS-1 SARs yield unambiguous land-cover categorizations at greater accuracy and resolution than that afforded by an unsupervised classification of Normalized Difference Vegetation Index as derived from multitemporal AVHRR data. Level I of the SAR terrain classifier differentiates three structural classes; surfaces, short vegetation, and tall vegetation. These classes can be quantized, averaged over the appropriate grid scab and used directly as roughness inputs to general circulation models. Level II of the classifier differentiates vegetation classes on the basis of growth form and leaf type. This level of structural classification is essential in order to improve the performance of semiempirical approaches for retrieving near-surface soil moisture and aboveground biomass.