Accurate information about the location, extent, and type of Land Cover (LC) is essential for various applications. The only recent available country-wide LC map of Iran was generated in 2016 by the Iranian Space Agency (ISA) using Moderate Resolution Imaging Spectroradiometer (MODIS) images with a considerably low accuracy. Therefore, the production of an up-to-date and accurate Iran-wide LC map using the most recent remote sensing, machine learning, and big data processing algorithms is required. Moreover, it is important to develop an efficient method for automatic LC generation for various time periods without the need to collect additional ground truth data from this immense country. Therefore, this study was conducted to fulfill two objectives. First, an improved Iranian LC map with 13 LC classes and a spatial resolution of 10 m was produced using multi-temporal synergy of Sentinel-1 and Sentinel-2 satellite datasets applied to an object-based Random forest (RF) algorithm. For this purpose, 2,869 Sentinel-1 and 11,994 Sentinel-2 scenes acquired in 2017 were processed and classified within the Google Earth Engine (GEE) cloud computing platform allowing big geospatial data analysis. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final Iran-wide LC map for 2017 was 95.6% and 0.95, respectively, indicating the considerable potential of the proposed big data processing method. Second, an efficient automatic method was developed based on Sentinel-2 images to migrate ground truth samples from a reference year to automatically generate an LC map for any target year. The OA and KC for the LC map produced for the target year 2019 were 91.35% and 0.91, respectively, demonstrating the efficiency of the proposed method for automatic LC mapping. Based on the obtained accuracies, this method can potentially be applied to other regions of interest for LC mapping without the need for ground truth data from the target year.

有关土地覆被（LC）的位置，范围和类型的准确信息对于各种应用至关重要。伊朗航天局（ISA）于2016年使用平均分辨率成像分光辐射计（MODIS）图像以非常低的准确度绘制了伊朗唯一可用的全国性LC图。因此，需要使用最新的遥感，机器学习和大数据处理算法来制作最新，准确的全伊朗LC图。此外，重要的是要开发一种有效的方法，以在各个时间段自动生成LC，而无需从这个庞大的国家收集额外的地面真实数据。因此，进行该研究以实现两个目标。第一，使用应用于基于对象的随机森林（RF）算法的Sentinel-1和Sentinel-2卫星数据集的多时间协同作用，生成了具有13个LC类和10 m空间分辨率的改进的伊朗LC地图。为此，在Google Earth Engine（GEE）云计算平台中对2017年获得的2869个Sentinel-1和11994个Sentinel-2场景进行了处理和分类，从而可以进行大的地理空间数据分析。最终的全伊朗LC图的2017年总体准确性（OA）和Kappa系数（KC）分别为95.6％和0.95，表明拟议的大数据处理方法具有巨大潜力。其次，基于Sentinel-2图像开发了一种有效的自动方法，可以从参考年迁移地面真相样本，以自动生成任何目标年份的LC图。针对2019年度目标制作的LC映射的OA和KC分别为91.35％和0.91，证明了所提出的自动LC映射方法的效率。基于获得的精度，此方法可以潜在地应用于其他感兴趣的区域进行LC映射，而无需获取目标年份的地面真实数据。