From early 2004, Lake Maracaibo (northwest Venezuela) experienced an unprecedented invasion of duckweed Lemna obscura. Recurrent blooms of the plant in the past 2 years illustrate the need for an automatic monitoring method to follow the plant cover with time and to plan contingency measures. We present an approach that allows the cover of the duckweed to be quantified through the classification of MODIS 250 m RGB composite images available from the internet. The method improves the accuracy of the results of the Support Vector Machine (SVM) algorithm for classification by including a bootstrap step during the training phase. Using only 200 pixels for training (<0.05% of the total), the bootstrapped SVM method allows a better identification of the duckweed class, reducing the number of false negatives by half and improving the KHAT statistic by almost 40% in comparison to the standard SVM method. This method has proved to be a reliable solution in cases where rapid responses are needed and only medium-resolution, free satellite imagery is available.