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Remote Sens. 2016, 8(6), 479; doi:10.3390/rs8060479

An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India

University of Chinese Academy of Sciences, Beijing 100049, China
Division for Digital Agriculture, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, West Beichen Road, Chaoyang District, Beijing 100101, China
Author to whom correspondence should be addressed.
Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 29 March 2016 / Revised: 30 May 2016 / Accepted: 31 May 2016 / Published: 7 June 2016
View Full-Text   |   Download PDF [5601 KB, uploaded 7 June 2016]   |  


Rice is the staple food for half of the world’s population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial resolution multispectral data to accurately classify the rice. Phenology was used to capture the seasonal dynamics of the crops, while multispectral data provided the spatial variation patterns. Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China’s Environmental Satellite (HJ-1A/B) data. Classification results were evaluated by a confusion matrix using 100 sample points. The overall accuracy of the resulting map of rice area generated by both spectral and phenology is 93%. The results indicate that the use of phenology improved the overall classification accuracy from 2%–4%. The comparison between the estimated rice areas and the State’s statistics shows underestimated values with a percentage difference of −34.53%. The results highlight the potential of the combined use of crop phenology and multispectral satellite data for accurate rice classification in a large area. View Full-Text
Keywords: object-based image analysis; phenology; data fusion; paddy rice; classification object-based image analysis; phenology; data fusion; paddy rice; classification

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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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Singha, M.; Wu, B.; Zhang, M. An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India. Remote Sens. 2016, 8, 479.

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