The Impact of Sentinel-1-Corrected Fractal Roughness on Soil Moisture Retrievals
Fractals are widely recognized as one of the best geometric models to depict soil roughness on various scales from tillage to micro-topography smaller than radar wavelength. However, most fractal approaches require an additional geometric description of experimental sites to be analysed by existing...
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MDPI AG
2024-02-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/8/3/137 |
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| author | Ju Hyoung Lee Hyun-Cheol Kim |
| author_facet | Ju Hyoung Lee Hyun-Cheol Kim |
| author_sort | Ju Hyoung Lee |
| collection | DOAJ |
| description | Fractals are widely recognized as one of the best geometric models to depict soil roughness on various scales from tillage to micro-topography smaller than radar wavelength. However, most fractal approaches require an additional geometric description of experimental sites to be analysed by existing radiative transfer models. For example, fractal dimension or spectral parameter is often related to root-mean-square (RMS) height to be characterized as the microwave surface. However, field measurements hardly represent multi-scale roughness. In this study, we rescaled Power Spectral Density with Synthetic Aperture Radar (SAR)-inverted rms height, and estimated non-stationary fractal roughness to accommodate multi-scale roughness into a radiative transfer model structure. As a result, soil moisture was retrieved over the Yanco site in Australia. Local validation shows that the Integral Equation Model (IEM) poorly simulated backscatters using inverted roughness as compared to fractal roughness even in anisotropic conditions. This is considered due to a violation of time-invariance assumption used for inversion. Spatial analysis also shows that multi-scale fractal roughness better illustrated the hydrologically reasonable backscattering partitioning, as compared to inverted roughness. Fractal roughness showed a greater contribution of roughness to backscattering in dry conditions. Differences between IEM backscattering and measurement were lower, even when the isotropic assumption of the fractal model was violated. In wet conditions, the contribution of soil moisture to backscattering was shown more clearly by fractal roughness. These results suggest that the multi-scale fractal roughness can be better adapted to the IEM even in anisotropic conditions than the inversion to assume time-invariance of roughness. |
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| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-04-24T18:15:41Z |
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| spelling | doaj.art-5a7b67c9c48e47659495b773290f8e1d2024-03-27T13:42:02ZengMDPI AGFractal and Fractional2504-31102024-02-018313710.3390/fractalfract8030137The Impact of Sentinel-1-Corrected Fractal Roughness on Soil Moisture RetrievalsJu Hyoung Lee0Hyun-Cheol Kim1Department of Geography, Environment and Geomatics, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, CanadaCenter of RS & GIS, KOPRI 26, Songdomirae-ro, Yeonsu-gu, Incheon 21990, Republic of KoreaFractals are widely recognized as one of the best geometric models to depict soil roughness on various scales from tillage to micro-topography smaller than radar wavelength. However, most fractal approaches require an additional geometric description of experimental sites to be analysed by existing radiative transfer models. For example, fractal dimension or spectral parameter is often related to root-mean-square (RMS) height to be characterized as the microwave surface. However, field measurements hardly represent multi-scale roughness. In this study, we rescaled Power Spectral Density with Synthetic Aperture Radar (SAR)-inverted rms height, and estimated non-stationary fractal roughness to accommodate multi-scale roughness into a radiative transfer model structure. As a result, soil moisture was retrieved over the Yanco site in Australia. Local validation shows that the Integral Equation Model (IEM) poorly simulated backscatters using inverted roughness as compared to fractal roughness even in anisotropic conditions. This is considered due to a violation of time-invariance assumption used for inversion. Spatial analysis also shows that multi-scale fractal roughness better illustrated the hydrologically reasonable backscattering partitioning, as compared to inverted roughness. Fractal roughness showed a greater contribution of roughness to backscattering in dry conditions. Differences between IEM backscattering and measurement were lower, even when the isotropic assumption of the fractal model was violated. In wet conditions, the contribution of soil moisture to backscattering was shown more clearly by fractal roughness. These results suggest that the multi-scale fractal roughness can be better adapted to the IEM even in anisotropic conditions than the inversion to assume time-invariance of roughness.https://www.mdpi.com/2504-3110/8/3/137soil roughnesssoil moisturefractal methodpower-law spectrumsynthetic aperture radar (SAR) |
| spellingShingle | Ju Hyoung Lee Hyun-Cheol Kim The Impact of Sentinel-1-Corrected Fractal Roughness on Soil Moisture Retrievals Fractal and Fractional soil roughness soil moisture fractal method power-law spectrum synthetic aperture radar (SAR) |
| title | The Impact of Sentinel-1-Corrected Fractal Roughness on Soil Moisture Retrievals |
| title_full | The Impact of Sentinel-1-Corrected Fractal Roughness on Soil Moisture Retrievals |
| title_fullStr | The Impact of Sentinel-1-Corrected Fractal Roughness on Soil Moisture Retrievals |
| title_full_unstemmed | The Impact of Sentinel-1-Corrected Fractal Roughness on Soil Moisture Retrievals |
| title_short | The Impact of Sentinel-1-Corrected Fractal Roughness on Soil Moisture Retrievals |
| title_sort | impact of sentinel 1 corrected fractal roughness on soil moisture retrievals |
| topic | soil roughness soil moisture fractal method power-law spectrum synthetic aperture radar (SAR) |
| url | https://www.mdpi.com/2504-3110/8/3/137 |
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