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Remote Sens. 2014, 6(7), 6472-6499; doi:10.3390/rs6076472

Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa

1
Department of Remote Sensing, University of Wurezburg, Oswald-Külpe-Weg 86, 97074 Wuerzburg, Germany
2
Institute for Geography and Geology, University of Wuerzburg, 97074 Am Hubland, Germany
3
Competency Center, West African Science Service Center on Climate Change and Adapted Land Use, Ouagadougou BP 9507, Burkina Faso
*
Author to whom correspondence should be addressed.
Received: 14 April 2014 / Revised: 7 July 2014 / Accepted: 8 July 2014 / Published: 15 July 2014
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Abstract

Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10%–15% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75% are achievable. View Full-Text
Keywords: crop mapping; agriculture; West Africa; RapidEye; TerraSAR-X; random forest crop mapping; agriculture; West Africa; RapidEye; TerraSAR-X; random forest
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Forkuor, G.; Conrad, C.; Thiel, M.; Ullmann, T.; Zoungrana, E. Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa. Remote Sens. 2014, 6, 6472-6499.

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