A biogeophysical approach for automated SWIR unmixing of soils and vegetation

Arid and semiarid ecosystems endure strong spatial and temporal variation o f climate and land use that results in uniquely dynamic vegetation phenolog y, cover, and leaf area characteristics Previous remote sensing efforts hav e not fully captured the spatial heterogeneity of vegetation properties req uired for functional analyses of these ecosystems, or have done so only wit h manually intensive algorithms of spectral mixture analysis that have limi ted operational use. Those limitations motivated the development of an auto mated spectral unmixing approach based on, a comprehensive analysis of vege tation and soil spectral variability resulting from biogeophysical variatio n in arid and semiarid regions. A field spectroscopic database of bare soil s, green canopies, and litter canopies was compiled for 17 arid and semiari d sites in North and South America, representing a wide array of plant grow th forms and species, vegetation conditions, and soil mineralogical-hydrolo gical properties. Spectral reflectance of dominant cover types (green veget ation, litter, and bare soil) varied widely within and between sites, but t he reflectance derivatives in the shortwave-infrared (SWIR2: 2,100-2,400 nm ) were similar within and separable between each cover type. Using this res ult, art automated SWIR2 spectral unmixing algorithm was developed that inc ludes a Monte Carlo approach for estimating errors in derived subpixel cove r fractions resulting from endmember variability. The algorithm was applied to SWIR2 spectral data collected by the Airborne Visible and infrared Imag ing Spectrometer instrument over the Sevilleta and Jornada Long-Term Ecolog ical Re-search sites. Subsequent comparisons to field data and geographical information system (GIS) maps were deemed successful. The SWIR2 region of the reflected solar spectrum provides a robust means to estimate the extent of bare soil and vegetation covers in arid and semiarid regions. The compu tationally efficient method developed here could be extended globally using SWIR2 spectrometer data to be collected from platforms such as the NASA Ea rth Observing-1 satellite. (C) Elsevier Science Inc., 2000.