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Agriculture is one of the most important sectors in the economy of Southeast Asia countries, especially Thailand and Vietnam. These\udtwo countries have been the largest rice suppliers in the world and played a critical role in global food security. Yearly rice crop\udmonitoring to provide policymakers with information on rice growing areas is thus important to timely devise plans to ensure food\udsecurity. This study aimed to develop an approach for regional mapping of cropping patterns from time-series MODIS data. Data were\udprocessed through three steps: (1) noise filtering of time-series MODIS NDVI data with wavelet transform, (2) image classification of\udcropping patterns using artificial neural networks (ANNs), and (3) classification accuracy assessment using ground reference data. The\udresults by a comparison between classification map and ground reference data indicated the overall accuracy of 80.3% and Kappa\udcoefficient of 0.76

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

MAPPING MAJOR CROPPING PATTERNS IN SOUTHEAST ASIA FROM MODIS DATA USING WAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORKS