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Remote Sens. 2019, 11(7), 887; https://doi.org/10.3390/rs11070887

Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

1
IRSTEA, TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier CEDEX 5, France
2
CESBIO (CNRS/UPS/IRD/CNES), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
3
UMR 1114 EMMAH, INRA, University of Avignon, 84914 Avignon, France
4
CIHEAM-IAMM, UMR-System, 34090 Montpellier, France
*
Author to whom correspondence should be addressed.
Received: 14 January 2019 / Revised: 28 March 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
(This article belongs to the Special Issue Remote Sensing: 10th Anniversary)
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Abstract

This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas. View Full-Text
Keywords: rice; SAR; Sentinel-1; random forest; decision tree; classification rice; SAR; Sentinel-1; random forest; decision tree; classification
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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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Bazzi, H.; Baghdadi, N.; El Hajj, M.; Zribi, M.; Minh, D.H.T.; Ndikumana, E.; Courault, D.; Belhouchette, H. Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France. Remote Sens. 2019, 11, 887.

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