Rice is the most important food crop in Taiwan. During recent decades, rice production in Taiwan has sharply declined because of industrialization and urbanization. Monitoring the areas of rice cultivation thus becomes important due to the official initiatives to ensure food supply. This study aims to develop a remote sensing classification approach for mapping double-cropped irrigated rice fields in Taiwan using time-series SPOT (Satellite Pour l’Observation de la Terre) data. Three study sites with different farming conditions in Taitung, Chiayi, and Taoyuan counties were chosen to test the new classification method. Data processing steps include: 1. filtering time-series SPOT-based normalized difference vegetation index (NDVI) using empirical mode decomposition (EMD) and wavelet transform, 2. classifying double-cropped irrigated rice fields using statistical methods (i.e., correlation analysis and sign-test statistics), and 3. assessing classification accuracy. The comparisons between the classification maps and ground-truth maps in 2005 indicated that classification using the EMD-based filtered NDVI time-series data yielded more accurate results than did the wavelet transform-based filtered data.