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

Malcolm Davidson (Corresponding Author), Thuy Le Toan, Francesco Mattia, G. Satalino, Terhikki Manninen, Maurice Borgeaud

Research output: Contribution to journalArticleScientificpeer-review

202 Citations (Scopus)

Abstract

The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation 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, the authors 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.
Original languageEnglish
Pages (from-to)630 - 640
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume38
Issue number2
DOIs
Publication statusPublished - 2000
MoE publication typeA1 Journal article-refereed

Fingerprint

agricultural soil
roughness
Remote sensing
Radar
Surface roughness
radar
remote sensing
Soils
surface roughness
tillage
profiler
autocorrelation
Surface scattering
synthetic aperture radar
soil moisture
laser
Soil moisture
scattering
topography
Autocorrelation

Keywords

  • remote sensing

Cite this

Davidson, Malcolm ; Le Toan, Thuy ; Mattia, Francesco ; Satalino, G. ; Manninen, Terhikki ; Borgeaud, Maurice. / On the characterization of agricultural soil roughness for radar remote sensing studies. In: IEEE Transactions on Geoscience and Remote Sensing. 2000 ; Vol. 38, No. 2. pp. 630 - 640.
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title = "On the characterization of agricultural soil roughness for radar remote sensing studies",
abstract = "The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation 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, the authors 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.",
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Davidson, M, Le Toan, T, Mattia, F, Satalino, G, Manninen, T & Borgeaud, M 2000, 'On the characterization of agricultural soil roughness for radar remote sensing studies', IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 2, pp. 630 - 640. https://doi.org/10.1109/36.841993

On the characterization of agricultural soil roughness for radar remote sensing studies. / Davidson, Malcolm (Corresponding Author); Le Toan, Thuy; Mattia, Francesco; Satalino, G.; Manninen, Terhikki; Borgeaud, Maurice.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 2, 2000, p. 630 - 640.

Research output: Contribution to journalArticleScientificpeer-review

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T1 - On the characterization of agricultural soil roughness for radar remote sensing studies

AU - Davidson, Malcolm

AU - Le Toan, Thuy

AU - Mattia, Francesco

AU - Satalino, G.

AU - Manninen, Terhikki

AU - Borgeaud, Maurice

N1 - Project code: A6SU00646

PY - 2000

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N2 - The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation 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, the authors 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.

AB - The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation 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, the authors 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.

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