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Water 2018, 10(2), 131; https://doi.org/10.3390/w10020131

Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils

1
Territoires, Environnement, Télédétection et Information Spatiale (TETIS), Institut National de Recherche en Sciences et Technologies Pour l’Environnement et l’Agriculture (Irstea), University of Montpellier, 500 rue François Breton, 34093 Montpellier CEDEX 5, France
2
Centre d’Etudes Spatiales de la BIOsphère (CESBIO), Centre National de la Recherche Scientifique (CNRS), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
3
Écologie Fonctionnelle et Ecotoxicologie des Agroécosystèmes (ECOSYS), AgroParisTech, Institut National de la Recherche Agronomique (INRA), Université Paris-Saclay, 78850 Thiverval-Grignon, France
*
Author to whom correspondence should be addressed.
Received: 13 December 2017 / Revised: 24 January 2018 / Accepted: 29 January 2018 / Published: 31 January 2018
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology)
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Abstract

The purpose of this study is to analyze the potential of Sentinel-1 C-band SAR data in VV polarization for estimating the surface roughness (Hrms) over bare agricultural soils. An inversion technique based on Multi-Layer Perceptron neural networks is used. It involves two steps. First, a neural network (NN) is used for estimating the soil moisture without taking into account the soil roughness. Then, a second neural network is used for retrieving the soil roughness when using as an input to the network the soil moisture that was estimated by the first network. The neural networks are trained and validated using simulated datasets generated from the radar backscattering model IEM (Integral Equation Model) with the range of soil moisture and surface roughness encountered in agricultural environments. The inversion approach is then validated using Sentinel-1 images collected over two agricultural study sites, one in France and one in Tunisia. Results show that the use of C-band in VV polarization for estimating the soil roughness does not allow a reliable estimate of the soil roughness. From the synthetic dataset, the achievable accuracy of the Hrms estimates is about 0.94 cm when using the soil moisture estimated by the NN built with a priori information on the moisture volumetric content “mv” (accuracy of mv is about 6 vol. %). In addition, an overestimation of Hrms for low Hrms-values and an underestimation of Hrms for Hrms higher than 2 cm are observed. From a real dataset, results show that the accuracy of the estimates of Hrms in using the mv estimated over a wide area (few km2) is similar to that in using the mv estimated at the plot scale (RMSE about 0.80 cm). View Full-Text
Keywords: Sentinel-1; SAR; C-band; soil; roughness; moisture; Integral Equation Model; neural network Sentinel-1; SAR; C-band; soil; roughness; moisture; Integral Equation Model; neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Baghdadi, N.; El Hajj, M.; Choker, M.; Zribi, M.; Bazzi, H.; Vaudour, E.; Gilliot, J.-M.; Ebengo, D.M. Potential of Sentinel-1 Images for Estimating the Soil Roughness over Bare Agricultural Soils. Water 2018, 10, 131.

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