Paper Info
Title
An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India.
Abstract
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.
Year
DOI
Venue
2016
10.3390/rs8060479
REMOTE SENSING
Keywords
Field
DocType
object-based image analysis,phenology,data fusion,paddy rice,classification
Population,Confusion matrix,Crop,Remote sensing,Multispectral image,Normalized Difference Vegetation Index,Spatial variability,Staple food,Geology,Phenology
Journal
Volume
Issue
Citations 
8
6
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Mrinal Singha120.73
Wu Bingfang216539.66
Miao Zhang3387.75