Detecting Rice Crop Phenology from Time-series Modis Data

Information on rice crop phenology is important for crop management. This study aimed to detect rice crop phenology in the Vietnamese Mekong Delta using time-series MODIS data in 2007. Data processing steps included: (1) constructing time-series MODIS NDVI data, (2) filtering noise from the time-series data using empirical mode decomposition (EMD) and wavelet transform, (3) detecting rice crop phenology (sowing, heading, and harvesting dates) using local maxima algorithm, and (4) verifying the results using field survey data. The results indicated that EMD produced more accurate results in rice crop phenology detection than did wavelet transform. Comparisons between the estimated sowing and harvesting dates achieved by EMD and the field survey data indicated the root mean squared error (RMSE) values of 7.5 and 8.2 days, while those by wavelet transform were 21.3 and 21.6 days, respectively. The error of the estimated sowing date was generally smaller than that of the harvesting date. This discrepancy was due to the fact that the timing for rice harvesting was partly dependent on the weather conditions, especially for the second crop in the rainy season. This study demonstrated the merit of using EMD for rice crop phenology detection. The information of rice crop phenology produced from this study would be further used for studies of rice crop mapping and monitoring.