Scientific and Technical Aerospace Reports Volume 38 July 28, 2000

20000064563 California Univ., Inst. for Computational Earth System Science, Santa Barbara, CA USA
Automated Subpixel Snow Parameter Mapping with AVIRIS Data
Painter, Thomas H., California Univ., USA; Roberts, Dar A., California Univ., USA; Dozier, Jeff, Jet Propulsion Lab., California
Inst. of Tech., USA; Green, Robert O., Jet Propulsion Lab., California Inst. of Tech., USA; Summaries of the Seventh JPL Airborne
Earth Science Workshop January 12-16, 1998; Dec. 19, 1998; Volume 1, pp. 301-307; In English; See also 20000064520
Contract(s)/Grant(s): NAGw-5185; No Copyright; Avail: CASI; A02, Hardcopy; A04, Microfiche
We describe an automated algorithm (MEMSCAG) for mapping subpixel snow covered area (SCA) and snow grain size with
AVIRIS data. The algorithm is based on the multiple endmember approach to spectral mixture analysis in which the spectral endmembers
and the number of endmembers can vary on a pixel-by-pixel basis. This approach accounts for surface cover heterogeneity
within a scene. The mixture analysis runs on endmembers from a spectral library of snow, vegetation, rock, soil, and lake ice
spectra. Snow endmembers of varying grain size were produced with a radiative transfer model. All non-snow endmembers were
collected with a portable field spectrometer. Mapping is performed through sequential 2-endmember, 3-endmember, and 4- endmember
mixture model runs, each subject to constraints on RMS, residuals, fractions and priority. Grain size is determined by
the grain size of the snow endmember used in the optimal mixture model. We apply MEMSCAG to AVIRIS data collected over
Mammoth Mountain, CA and the northern site of the BOREAS in Manitoba, Canada. MEMSCAG produces appropriate snow
covered area estimates in all regions. A preliminary comparison of grain size estimates from MEMSCAG with field measurements
demonstrates high accuracy.
Author
Computer Aided Mapping; Thematic Mapping; Pixels; Snow Cover; Spectral Reflectance; Spectrum Analysis; Remote Sensing
20000064564 California Univ., Dept. of Land, Air, and Water Resources, Davis, CA USA
Remote Sensing of Soils in the Santa Monica Mountains: Hierarchical Foreground and Background Analysis
Palacios-Orueta, Alicia, California Univ., USA; Pinzon, Jorge E., California Univ., USA; Ustin, Susan L., California Univ., USA;
Ustin, Susan L., California Univ., USA; Summaries of the Seventh JPL Airborne Earth Science Workshop January 12-16, 1998;
Dec. 19, 1998; Volume 1, pp. 309-318; In English; See also 20000064520; No Copyright; Avail: CASI; A02, Hardcopy; A04,
Microfiche
The extreme spatial and temporal variability of surface processes makes soil properties extremely variable and therefore, difficult
to measure. Since soil and ecosystem processes occur at different scales, it is necessary to work at sufficiently large spatial
resolution and coverage for generalizations to be made. Organic matter is a soil property closely related to soil quality, not only
as an indicator of soil erosion and degradation, but also as a regulating factor of processes such as nutrient availability, water holding
capacity, and permeability. Because values of organic content are highly variable and react very quickly to external changes,
decomposition rates show high spatial variability. The spatial distribution of organic matter content can be an indicator of the rate
of decomposition and other processes happening on the soil surface, such as differences in deposition and erosion rates or microclimate
factors. Imaging spectrometry offers a potential way to map certain soil properties that are relevant to surficial processes
at the landscape scale. In the last few years the analysis of hyperspectral data and image processing techniques have improved
to the point that they offer the potential for direct analysis of soil properties. Several multispectral sensors have already been used
for discrimination between soils. Specifically, there are several studies where organic matter has been analyzed in terms of its
reflectance properties. Hyperspectral data, specifically Advanced Visible Infrared Imaging Spectrometer (AVIRIS) data has been
shown to be useful for improved discrimination of minerals. Also, several other studies have dealt with soil identification and
discrimination directly or indirectly using AVIRIS data. A significant problem for soil analysis is the presence of vegetation in
most pixels. Because the signatures of soils and vegetation are so different, the lesser variability contained within the soil component
is not significant enough for soil discrimination when vegetation is also present in the pixel. HFBA (Hierarchical Foreground
and Background) is a new steerable analytic technique where the Foreground and Background Analysis (FBA) equation is applied
at several levels in a hierarchical way, thus, the variability contained in the data set is confined at each step, making it possible
to extract subtle absorption features. For this model FBA was modified to project the spectra into a property-specific axis of continuous
variation. to examine the application of this method to extract and improve detection of soil properties using an imaging
spectrometer, we applied it to map the spatial distribution of organic matter content from samples in two watersheds in the Santa
Monica Mountains Recreation Area. In earlier work, Palacios-Orueta and Ustin (1988a) found that soils from each valley could
be discriminated based on organic matter and iron content and that these could be spectrally estimated with reasonable accuracy
in soil samples. The purpose of this work was to test the performance of HFBA (Hierarchical Foreground and Background Analysis)
applied to AVIRIS data for the discrimination of these soils and soil properties.
Derived from text
Infrared Imagery; Organic Materials; Remote Sensing; Soil Sampling; Spatial Resolution; Surface Properties; Spectrum Analysis;
Mountains; Image Processing; Spectroscopic Analysis
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