A national knowledge-based crop recognition in mediterranean environment
International Journal of Applied Earth Observation and Geoinformation
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
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The main purpose of this study is to present a methodology for mapping and monitoring temporal signatures of Mediterranean crops over several years in irrigated areas, and to study their inter-annual dynamics. These goals were achieved by remote sensing using 36 Landsat images from 2002 to 2005. Four crop maps, one for each year, with six agricultural categories and a thematic accuracy of 93%, 95%, 96% and 94% were obtained using a hybrid classifier. A mean of nine images produced these highly accurate results, but the absence of one image in the growth period of 2002 resulted in lower accuracies, particularly in fruit trees (85% of user accuracy). This highlights the importance of a multi-temporal approach based on a relatively large number of images. After the classification results were validated, two parameters were used to characterize the dynamics of the four crops (rice, maize, alfalfa and fruit trees): greenness, extracted from the Normalized Difference Vegetation Index (NDVI), and wetness, calculated from the Tasselled Cap Wetness (TCW) Index. In order to differentiate the wetness origin of crops, an analysis of local daily precipitation (which could cause significant anomalies in the TCW coefficients) and water stored in the Susqueda reservoir (which may result in farmers making important management decisions when water is limited) was conducted during this four-year period. After applying statistical analysis, the results showed that, of the four crops analysed, rice, alfalfa and fruit trees had more stable dynamics than maize, which was planted later in case of water deficit at the beginning of the irrigation campaign (in 2002) and earlier when the deficit occurred later (in 2005).