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| Titel |
Inversion of the Hapke model with diurnal multispectral reflectance data for assessing soil surface roughness |
| VerfasserIn |
Jesús Rodríguez González, Pablo J. Zarco-Tejada, José Alfonso Gómez |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250052435
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| Zusammenfassung |
| Soil surface roughness (SSR) affects a variety of surface processes on bare terrestrial surfaces
and soils under agricultural use, such as water and wind erosion. Therefore, erosion models
like the RUSLE, WEPP, Kineros or STREAM include quantitative measures of soil surface
roughness as input parameters. Generally, non-contact methods are preferred to
measure SSR, such as using laser profiling instruments or scanners, digital close range
stereo-photogrammetry and terrestrial laser scanning or LIDAR systems. However, the area
that can be reasonably covered by these methods is usually well below several square
meters, and they lack the potential to assess SSR over lager areas at the plot or field
scales.
Numerous investigations on soil reflectance in the optical wavelength region showed that
the observed bidirectional soil reflectance anisotropy is caused by shadow casting and mutual
shadowing between soil particles and soil aggregates, facets or clods and topographic features
– in other words soil roughness. Hapke’s semi-physical BRDF model (Hapke, 1993), a
standard BRDF model for Planetary Remote Sensing applications, accounts for the
influence of SSR through a shadowing function and its roughness parameter θ. The
application of the full model, i.e. including its shadowing function, has been suggested
only recently to assess surface roughness of terrestrial desert surfaces (Wu et al.,
2009). The roughness parameter θ as a quantitative descriptor of physical SSR of
terrestrial soils has the potential to map SSR on a pixel-by-pixel basis at suitable spatial
scales.
We report here on the results obtained for the inversion of the full Hapke model under
simplified conditions using diurnal reflectance data gathered over an experimental field
(100x40m). The relationship between the model’s roughness parameter θ obtained through
inversion and different quantitative roughness indices extracted from field measured digital
elevation models (DEMs) was then studied using simple linear correlation and regression
analysis.
Different roughness levels were obtained applying five conventional tillage methods on
different subplots of the experimental field. A laser-scanning instrument was used to
obtain three representative digital elevation models (DEMs) for each treatment with
a grid resolution of 7.2x7.2mm over an area of 0.9x0.9m. Four main roughness
indices (standard deviation, the tortuosity indices TA and TB, mean surface slope)
were calculated for each DEM as quantitative descriptors of SSR. For each DEM
location, reflectance data at wavelengths 550, 670 and 800nm at four times of the day
were extracted from very high spatial resolution reflectance imagery (12.5cm). The
imagery was acquired with the narrow-band multispectral ADC sensor (Tetracam Inc.,
USA) onboard an unmanned aerial vehicle (UAV) platform and then corrected to
at-ground reflectance after geometric, radiometric sensor calibration, and atmospheric
correction.
The inversion of the Hapke model was validated with a RMSE of 0.03 in reflectance
units. The comparison between the model’s roughness parameter θ and the physical
roughness indices showed determination coefficients (r2) above 0.70. These results indicate
that the suggested approach using the Hapke model could be useful for providing a
quantitative measure of SSR at very high spatial resolutions, providing SSR maps by physical
model inversion.
Wu, Y., Gong, P., Liu, Q. and Chappell, A. (2009): Retrieving photometric properties of
desert surfaces in China using the Hapke model and MISR data. Remote Sensing of
Environment, 113, 1, pp. 213-223.
Hape, B. (1993): Theory of reflectance and emittance spectroscopy. (Cambridge
University Press). |
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