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A number of classification techniques to generate land cover maps from satellite imagery have been proposed but supervised classification with manual selection and delineation of training samples (TSs) continues to be the preferred technique. The current practices of field visits and manual delineation of TSs by visual recognition are highly demanding on both resources and time, with limited utility. With an increase in the number of Earth Observation Satellite (EOS) platforms and the enormous data that they generate, there is a need to process the data quickly and efficiently for creating global science products. Towards this goal, an attempt has been made in this article to develop a method for the automatic extraction of the TSs from the time series of Moderate Resolution Imaging Spectroradiometer (MODIS – 250 m) vegetation index (VI), which can then be used for supervised classification to create a land cover map with any classification technique on relevant remotely sensed data. The TSs contained 1.27%, 0.09% and 1.18% of the total pixels for the forest, crop and water classes of the study region. Validation with Advanced Wide Field Sensor (AWiFS – 56 m)-derived national land use/land cover (LULC) map of India shows a complete agreement with the location of what can be considered as pure class pixels. The article also demonstrates and compares the utility of these TSs with an expert choice of TSs on MODIS time-series data using k-nearest neighbour, and support vector machine (SVM) classifiers and on a single-scene Linear Imaging Self-Scanning Sensor-3 (LISS-3 – 24 m) imagery using maximum likelihood (ML) classifier.
