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Temporal stability of northern forest biophysical retrievals using SIR-C/X-SAR

Smmary form only given, as follows. A semi-empirical approach for retrieval of forest biophysical properties (basal area, height, woody stem biomass and crown biomass) has been previously demonstrated for forests in Northern Michigan using SIR-C/X-SAR data. This technique uses a multi-step process that incorporates (1) a land-cover classification to specify forest structural type, (2) estimates of SAR-derived basal area, height and canopy biomass from class-specific empirical algorithms using multifrequency and polarimetric SIR-C/XSBR data (3) in conjunction with readily available ancillary information to convert volume to dry mass in the analysis of SIR-C/X-SAR data. This paper examines the temporal stability of the retrieval process. At issue is the sensitivity of the algorithms to scene dynamics such as ground-cover conditions (i.e., snow-cover and soil moisture) and phenological control of plant water status. The authors examine four SIR-C/XSAR scenes all acquired at approximately 30/spl deg/ angle of incidence over the Raco Supersite in Northern Michigan. These SAR scenes represent four distinct conditions with respect to dielectric attributes at the test site. Two scenes from SRL-1 in April 1994, represent early spring tree physiology with thawed tree stems and sap movement in some species and without deciduous leaf-cover; the scenes are differentiated by the thickness and wetness conditions of the snowpack (dry and wet). Two additional scenes, from SRL-2 in October 1994, represent early senescence of deciduous species, and are differentiated by near-surface soil moisture conditions (i.e., before and after heavy precipitation). The vegetation is not frozen during any of the SAR observations. The results show that the date-to-date differences in the algorithm coefficients are related to dielectric effects of the surface (soil and snow-cover). The net effects are shown to be strongest for areas of low biomass conditions, wherein SAR sensitivity of surface properties is strongest due to limited extinction by the overlying vegetation.