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Open AccessFeature PaperArticle

European Rice Cropland Mapping with Sentinel-1 Data: The Mediterranean Region Case Study

Department of Geodesy and Geoinformation, Vienna University of Technology, Gusshausstrasse 27-29, A-1040 Vienna, Austria
Department of Photogrammetry and Remote Sensing, Hanoi University of Mining and Geology, Hanoi 10000, Vietnam
Author to whom correspondence should be addressed.
Academic Editors: Arjen Y. Hoekstra, Frédéric Frappart and Luc Bourrel
Water 2017, 9(6), 392;
Received: 7 February 2017 / Revised: 5 May 2017 / Accepted: 26 May 2017 / Published: 1 June 2017
(This article belongs to the Special Issue The Use of Remote Sensing in Hydrology)
Rice farming is one of the most important activities in the agriculture sector, producing staple food for the majority of the world's growing population. Accurate and up-to-date assessment of the spatial distribution of rice cultivated area is a key information requirement of all stakeholders including policy makers, rice farmers and consumers. Timely assessment with high precision is, e.g., crucial for water resource management, market prices control and during humanitarian food crisis. Recently, two Sentinel-1 (S-1) satellites carrying a C-band Synthetic Aperture Radar (SAR) sensor were launched by the European Space Agency (ESA) within the homework of the Copernicus program. The advanced data acquisition capabilities of S-1 provide a unique opportunity to monitor different land cover types at high spatial (20 m) and temporal (twice-weekly to biweekly) resolution. The objective of this research is to evaluate the applicability of an existing phenology-based classification method for continental-scale rice cropland mapping using S-1 backscatter time series. In this study, the S-1 images were collected during the rice growing season of 2015 covering eight selected European test sites situated in six Mediterranean countries. Due to the better rice classification capabilities of SAR cross-polarized measurement as compared to co-polarized data, S-1 cross-polarized (VH) data were used. Phenological parameters derived from the S-1 VH backscatter time series were used as an input to a knowledge-based decision-rule classifier in order to classify the input data into rice and non-rice areas. The classification results were evaluated using multiple regions of interest (ROIs) drawn from high-resolution optical remote sensing (SPOT 5) data and the European CORINE land cover (CLC 2012) product. An overall accuracy of more than 70% for all eight study sites was achieved. The S-1 based classification maps reveal much more details compared to the rice field class contained in the CLC 2012 product. These findings demonstrate the potential and feasibility of using S-1 VH data to develop an operational rice crop monitoring framework at the continental scale. View Full-Text
Keywords: rice mapping; Sentinel-1 A; SAR time series; remote sensing rice mapping; Sentinel-1 A; SAR time series; remote sensing
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Nguyen, D.B.; Wagner, W. European Rice Cropland Mapping with Sentinel-1 Data: The Mediterranean Region Case Study. Water 2017, 9, 392.

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