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| Titel |
Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks |
| VerfasserIn |
N. Baghdadi, R. Cresson, M. Hajj, R. Ludwig, I. Jeunesse |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 6 ; Nr. 16, no. 6 (2012-06-04), S.1607-1621 |
| Datensatznummer |
250013323
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| Publikation (Nr.) |
 copernicus.org/hess-16-1607-2012.pdf |
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| Zusammenfassung |
| The purpose of this study was to develop an approach to estimate soil
surface parameters from C-band polarimetric SAR data in the case of bare
agricultural soils. An inversion technique based on multi-layer perceptron
(MLP) neural networks was introduced. The neural networks were trained and
validated on a noisy simulated dataset generated from the Integral Equation
Model (IEM) on a wide range of surface roughness and soil moisture, as it is
encountered in agricultural contexts for bare soils. The performances of
neural networks in retrieving soil moisture and surface roughness were
tested for several inversion cases using or not using a-priori knowledge on
soil parameters. The inversion approach was then validated using
RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge
on the soil moisture (dry to wet soils or very wet soils) improves the soil
moisture estimates, whereas the precision on the surface roughness estimation
remains unchanged. Moreover, the use of polarimetric parameters α1 and anisotropy were used to improve the soil parameters estimates.
These parameters provide to neural networks the probable ranges of soil
moisture (lower or higher than 0.30 cm3 cm−3) and surface roughness
(root mean square surface height lower or higher than 1.0 cm). Soil moisture
can be retrieved correctly from C-band SAR data by using the neural networks
technique. Soil moisture errors were estimated at about 0.098 cm3 cm−3 without a-priori information on soil parameters and 0.065 cm3 cm−3
(RMSE) applying a-priori information on the soil moisture.
The retrieval of surface roughness is possible only for low and medium
values (lower than 2 cm). Results show that the precision on the soil
roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm,
the precision on the soil roughness is better with an RMSE about 0.5 cm. The
use of polarimetric parameters improves only slightly the soil
parameters estimates. |
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