摘要
Information of paddy rice distribution is essential for food production and methane emission calculation. Phenology-based algorithms have been uti- lized in the mapping of paddy rice fields by identifying the unique flooding and seedling transplanting phases using multi-temporal moderate resolution （500m to 1 km） images. In this study, we developed simple algorithms to identify paddy rice at a fine resolution at the regional scale using multi-temporal Landsat imagery. Sixteen Landsat images from 2010-2012 were used to generate the 30 m paddy rice map in the Sanjiang Plain, northeast China-one of the major paddy rice cultivation regions in China. Three vegetation indices, Normalized Difference Vegetation Index （NDVI）, Enhanced Vegetation Index （EVI）, and Land Surface Water Index （LSWI）, were used to identify rice fields during the flooding/transplanting and ripening phases. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively. The Landsat-based paddy rice map was an improvement over the paddy rice layer on the National Land Cover Dataset, which was generated through visual interpretation and digitalization on the fine-resolution images. The agricultural census data substantially underreported paddy rice area, raising serious concern about its use for studies on food security.
Information of paddy rice distribution is essential for food production and methane emission calculation. Phenology-based algorithms have been uti- lized in the mapping of paddy rice fields by identifying the unique flooding and seedling transplanting phases using multi-temporal moderate resolution （500m to 1 km） images. In this study, we developed simple algorithms to identify paddy rice at a fine resolution at the regional scale using multi-temporal Landsat imagery. Sixteen Landsat images from 2010-2012 were used to generate the 30 m paddy rice map in the Sanjiang Plain, northeast China-one of the major paddy rice cultivation regions in China. Three vegetation indices, Normalized Difference Vegetation Index （NDVI）, Enhanced Vegetation Index （EVI）, and Land Surface Water Index （LSWI）, were used to identify rice fields during the flooding/transplanting and ripening phases. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively. The Landsat-based paddy rice map was an improvement over the paddy rice layer on the National Land Cover Dataset, which was generated through visual interpretation and digitalization on the fine-resolution images. The agricultural census data substantially underreported paddy rice area, raising serious concern about its use for studies on food security.
基金
This study was supported by the NASA Land Use and Land Cover Change Program （NNX09AC39G, NNXlIAJ35G）, the US National Science Foundation EPSCoR program （NSF- 0919466）, and the National Institutes of Health （1R01AI101028-01A1）. The WorldView-2 high-resolution images were provided by NASA under the terms of the National Geospatial-Intelligence Agency＇s （NGA） Nextview License Agree-ment. We thank the reviewers for their insightful comments on earlier versions of the manuscript. We would also like to thank Drs. Kaishan Song, Jia Du, and Zhenghong Miao for providing the GPS data, field photos and agricultural census statistic data, and Sarah Xiao for the English and grammar corrections.
