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Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets

Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China
Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China
Department of Agro-meteorology and Geo-informatics, Magbosi Land, Water and Environment Research Centre (MLWERC), Sierra Leone Agricultural Research Institute (SLARI), Tower Hill, Freetown PMB 1313, Sierra Leone
Department of Land Management, Zhejiang University, Hangzhou 310058, China
Shanghai Climate Center, Shanghai Meteorological Bureau, Shanghai 200031, China
Author to whom correspondence should be addressed.
Academic Editors: Jan Dempewolf, Jyotheshwar Nagol, Min Feng, James Campbell and Prasad S. Thenkabail
Remote Sens. 2017, 9(3), 257;
Received: 25 November 2016 / Accepted: 6 March 2017 / Published: 10 March 2017
PDF [11379 KB, uploaded 11 March 2017]


Sentinel-1A and Landsat 8 images have been combined in this study to map rice fields in urban Shanghai, southeast China, during the 2015 growing season. Rice grown in paddies in this area is characterized by wide inter-field variability in addition to being fragmented by other landuses. Improving rice classification accuracy requires the use of multi-source and multi-temporal high resolution data for operational purposes. In this regard, we first exploited the temporal backscatter of rice fields and background land-cover types at the vertical transmitted and vertical received (VV) and vertical transmitted and horizontal received (VH) polarizations of Sentinel-1A. We observed that the temporal backscatter of rice increased sharply at the early stages of growth, as opposed to the relatively uniform temporal backscatter of the other land-cover classes. However, the increase in rice backscatter is more sustained at the VH polarization, and two-class separability measures further indicated the superiority of VH over VV in discriminating rice fields. We have therefore combined the temporal VH images of Sentinel-1A with the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI) derived from a single-date cloud-free Landsat 8 image. The integration of these optical indices with temporal backscatter eliminated all commission errors in the Rice class and increased overall accuracy by 5.3%, demonstrating the complimentary role of optical indices to microwave data in mapping rice fields in subtropical and urban landscapes such as Shanghai. View Full-Text
Keywords: Sentinel-1A; Landsat 8 OLI; subtropics; urban agriculture; smallholder; paddy rice Sentinel-1A; Landsat 8 OLI; subtropics; urban agriculture; smallholder; paddy rice

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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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Mansaray, L.R.; Huang, W.; Zhang, D.; Huang, J.; Li, J. Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets. Remote Sens. 2017, 9, 257.

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