Remote 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.

遥感在全球土地利用和土地覆盖 (LULC) 制图过程中发挥着重要作用。结合星载光学和微波数据对于在云层频繁覆盖的区域（例如热带地区）进行准确分类至关重要。在这项研究中，我们研究了当 SAR 数据与光学数据一起被纳入分类过程时可能的改进。2019 年，我们使用 MSI/Sentinel-2 和 SAR/Sentinel-1 提供了巴西罗赖马州的 LULC 测绘。该州位于热带地区，全年云层覆盖频繁。云量变得很大，尤其是在作物生长的 5 月至 8 月期间。本研究考虑了 29 个场景，涉及基于光学和 SAR 的特征以及数据采集时间的组合。我们的结果表明，单独使用的光学或 SAR 数据不足以提供准确的 LULC 映射。使用光学图像的多时相地表反射率和植被指数 (VI) 指标以及不同偏振下的后向散射系数值及其比率获得了总体精度 (OA) 方面的最佳结果，OA 为 86.41 ± 1.74% . 分析用于分类的三个时期的数据（1 月至 4 月、5 月至 8 月和 9 月至 12 月）使我们能够确定区分特定类别的最佳时期。在将我们的 LULC 图与 MapBiomas 项目中派生的 LULC 产品进行比较时，我们观察到我们的方法在绘制一年生和多年生作物以及水类图方面表现更好。我们的方法为罗赖马州提供了更准确的 LULC，