02597naa a2200265 a 450000100080000000500110000800800410001902200150006002400570007510000230013224501040015526000090025952017960026865300260206465300180209065300180210865300200212670000150214670000180216170000190217970000160219870000140221470000220222877300810225021431512022-05-17       2022    bl uuuu     u00u1 u    #d  a0924-2716/7 ahttps://doi.org/10.1016/j.isprsjprs.2022.04.0252DOI1 aPRUDENTE, V. H. R.  aMultisensor approach to land use and land cover mapping in Brazilian Amazon.h[electronic resource]  c2022  aRemote sensing has an important role in the Land Use and Land Cover (LULC) mapping process worldwide. Combining spaceborne optical and microwave data is essential for accurate classification in areas with frequent cloud cover, such as tropical regions. In this study, we investigate the possible improvements, when SAR data is incorporated into the classification process along with optical data. We used MSI/Sentinel-2 and SAR/Sentinel-1 to provide LULC mapping in the Roraima State, Brazil, in 2019. This State is located in a tropical area, where the cloud cover is frequent over the year. Cloud cover becomes substantial, especially during the May-August period when crops are grown. Twenty-nine scenarios involving a combination of optical- and SAR-based features, as well as times of data acquisition, were considered in this study. Our results showed that optical or SAR data used individually are not enough to provide accurate LULC mapping. The best results in terms of overall accuracy (OA) were achieved using metrics of multi-temporal surface reflectance and vegetation index (VI) for optical imagery, and values of backscatter coefficient in different polarizations and their ratios yielding an OA of 86.41 ± 1.74%. Analysis of three periods of data (January to April, May to August, and September to December) used for classification allowed us to identify the optimal period for distinguishing specific classes. When comparing our LULC map with a LULC product derived within the MapBiomas project we observed that our method performed better to map annual and perennial crops and water classes. Our methodology provides a more accurate LULC for the Roraima State, and the proposed technique can be applied to benefit other regions that are affected by persistent cloud cover.  aMultilayer Perceptron  aRandom Forest  aRoraima state  aSentinel images1 aSKAKUN, S.1 aOLDONI, L. V.1 aXAUD, H. A. M.1 aXAUD, M. R.1 aADAMI, M.1 aSANCHES, I. D. A.  tISPRS Journal of Photogrammetry and Remote Sensinggv. 189, p. 95-109, 2022.