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Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

1
IRSTEA, TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier CEDEX 5, France
2
CESBIO (CNRS/UPS/IRD/CNES/INRA), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
3
CIHEAM-IAMM, UMR-System, 34090 Montpellier, France
4
isardSAT, Parc Tecnològic Barcelona Activa, Carrer de Marie Curie, 8, 08042 Barcelona, Catalunya, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1836; https://doi.org/10.3390/rs11151836
Received: 1 July 2019 / Revised: 2 August 2019 / Accepted: 4 August 2019 / Published: 6 August 2019
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Abstract

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data. View Full-Text
Keywords: irrigation; Sentinel-1; principal components; wavelet transformation; random forest; deep learning; convolutional neural network; Spain irrigation; Sentinel-1; principal components; wavelet transformation; random forest; deep learning; convolutional neural network; Spain
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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.; Ienco, D.; El Hajj, M.; Zribi, M.; Belhouchette, H.; Escorihuela, M.J.; Demarez, V. Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain. Remote Sens. 2019, 11, 1836.

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