http://www.cnr.it/ontology/cnr/individuo/prodotto/ID262693

On the characterization of agricultural soil roughness for radar remote sensing studies (Articolo in rivista)

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On the characterization of agricultural soil roughness for radar remote sensing studies (Articolo in rivista) (literal)

Anno

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Alternative label

Davidson, MWJ and Le Toan, T and Mattia, F and Satalino, G and Manninen, T and Borgeaud, M (2000)
On the characterization of agricultural soil roughness for radar remote sensing studies
in IEEE transactions on geoscience and remote sensing; IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
(literal)

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Davidson, MWJ and Le Toan, T and Mattia, F and Satalino, G and Manninen, T and Borgeaud, M (literal)

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Pagina fine

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2nd International Workshop on Retrieval of Bio and Geo Physical Parameters from SAR Data for Land Applications, EUROPEAN RES & TECHNOL CTR, NOORDWIJK, NETHERLANDS, OCT 21-23, 1998 (literal)

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Rivista

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Note

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Ctr Etudes Biosphere, CESBIO, Toulouse, France. Ist Tecnol Informat Spaziali, CNR, Matera, Italy. Inst Signal & Image Proc, IESI, CNR, Bari, Italy. VTT Automat, Remote Sensing, Espoo, Finland. ESA, Estec, Noordwijk, Netherlands. (literal)

Titolo

On the characterization of agricultural soil roughness for radar remote sensing studies (literal)

Abstract

The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO; and IEM) are the roof: mean square (RMS) height s, and auto-correlation length l, However, soil moisture retrieval studies based on these models have yielded inconsistent results, not so much because of the failure of the models themselves, but because of the complexity of natural surfaces and the difficulty in estimating appropriate input roughness parameters. In this paper, we address the issue of soil roughness characterization in the case of agricultural fields having different tillage (roughness) states by making use of an extensive multisite database of surface profiles collected using a novel laser profiler capable of recording profiles up to 25 m long. Using this dataset, the range of RMS height and correlation values associated with each agricultural roughness state is estimated, and the dependence of these estimates on profile length is investigated. The results show that at spatial scales equivalent to those of the SAR resolution cell, agricultural surface roughness characteristics are well described by the superposition of a single scale process related to the tillage state with a multiscale random fractal process related to field topography. (literal)

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