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This study presents a novel classification model, the NDVI, DOP, Texture Classification Model (NDTCM), for both regional and global scales, utilizing a synergistic approach that combines the Normalized Difference Vegetation Index (NDVI) from optical data with the Degree of Polarization (DOP) and its associated texture from Synthetic Aperture Radar (SAR) data. This integration effectively enhances Land Use and Land Cover (LULC) mapping. Specifically, MODIS&#x002F;Landsat images for NDVI extraction and dual polarization Phased-Array L-band Synthetic Aperture Radar (PALSAR) data for DOP are employed in this study. This integration enables the NDTCM to effectively classify land cover into five categories: forest, shrubland, urban, cultivated land, and bare surface. Applied to Mediterranean land cover mapping, the NDTCM achieved high accuracy, with rates of 93.3&#x0025; for forests, 57.5&#x0025; for shrublands, 64.4&#x0025; for urban areas, 76.8&#x0025; for cultivated lands, and 92.8&#x0025; for bare surfaces. Compared to global land-cover models like GlobCover, the NDTCM showed superior performance in forest and shrubland classification, exceeding GlobCover&#x0027;s accuracy of 84.3&#x0025; for forests and 35.4&#x0025; for shrublands, in this study case. The contribution of each data source to the classification results was significant. NDVI data were instrumental in identifying vegetative cover. The DOP and texture information enriched the model&#x0027;s capability to discern land cover types by providing insights into the physical structure and heterogeneity of the surfaces, critical for distinguishing between different land covers such as forest and shrubland. This comprehensive integration demonstrates the NDTCM&#x0027;s potential as a robust framework for future advancements in LULC mapping and environmental studies.

Regional and Global-Scale LULC mapping by Synergetic Integration of NDVI from Optical data and Degree of Polarization from SAR data