Abstract
Image classification has become one of the most common applications in remote sensing yielding to the creation of a variety of operational thematic maps at multiple spatio-temporal scales. The information contained in these maps summarizes key characteristics related with the physical environment and provides fundamental information of the Earth for vegetation monitoring or land use status over time. However, high spatial resolution land cover maps are usually only produced for specific small regions or in an image tile. We present a general methodology to obtain a high spatial resolution land cover maps using Landsat spectral information, the powerful Google Earth Engine platform, and operational coarse classification schemes such as the MODIS (MOD12) land cover. After the experimental analysis for different regions, we conclude that the method allows to successfully learn the MODIS Plant Functional Type classification scheme at 500 m pixel resolution which greatly improves the level of spatial detail when the machine learning model is applied to Landsat pixel resolution (30 m) reflectance data.
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