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Super-resolution mapping (SRM) is a recently developed research task in the field of remotely sensed information processing. It provides the ability to obtain land-cover maps at a finer scale using relatively low-resolution images. Existing algorithms based on indicator geostatistics and downscaling cokriging offer an SRM approach using spatial structure models derived from real data. In this article, a novel SRM method is developed based on a sequentially produced with local indicator variogram (SLIV) SRM model. In the SLIV method, indicator variograms extracted from target-resolution classification are produced from a representative local area as opposed to using the entire image. This simplifies the application of the method since limited target-resolution reference data are required. Our investigation on three diverse case studies shows that the local window (approximately 2% of the entire study area) selection process offers comparable accuracy results to those using globally derived spatial structures, indicating our methodology to be a promising practice. Furthermore, comparison of the proposed method with random realizations indicates an improvement of 7–12% in terms of overall accuracy and 15–18% in terms of the kappa coefficient. The evaluation focused on a 270–30 m pixel size reconstruction as a potential popular application, for example moving from Moderate Resolution Imaging Spectroradiometer (MODIS) to Landsat-type resolutions.
