Classification of rice cropping systems by empirical mode decomposition and linear mixture model for time-series MODIS 250 m NDVI data in the Mekong Delta, Vietnam

Abstract

Estimating the area of rice planting is vital for production prediction. This study utilizes time-series MODIS NDVI data from 2002 to 2007 to discriminate rice cropping systems in the Mekong Delta (MD), Vietnam. Data are processed using Empirical Mode Decomposition (EMD) and the Linear Mixture Model (LMM). Various spatial and non-spatial data are also collected for accuracy validation. The results indicate that EMD acts as a well-fitted filter for noise reduction of the time-series NDVI data. The classification results derived from the LMM for 2002 showed an overall classification accuracy of 71.6% and a Kappa coefficient of 0.6. The provincial level area estimates were strongly correlated with the rice statistics. An examination of the change in cropping patterns between 2002 and 2007 showed that 29.0% of the triple irrigated-rice cropping systems had been changed to double irrigated-rice cropping systems and that 12.0% and 9.0% of the double irrigated and rainfed-rice cropping systems, respectively, had been changed to triple rice cropping systems. These changes were verified by visual comparisons with Landsat images.