Since the launch of the Sentinel-2 (S2) satellites, many ML models have used the data for diverse applications. The scene classification layer (SCL) inside the S2 product provides rich information for training, such as filtering images with high cloud coverage. However, there is more potential in this. We propose a technique to assess the clean optical coverage of a region, expressed by a SITS and calculated with the S2-based SCL data. With a manual threshold and specific labels in the SCL, the proposed technique assigns a percentage of spatial and temporal coverage across the time series and a high/low assessment. By evaluating the AI4EO challenge for Enhanced Agriculture, we show that the assessment is correlated to the predictive results of ML models. The classification results in a region with low spatial and temporal coverage is worse than in a region with high coverage. Finally, we applied the technique across all continents of the global dataset LandCoverNet.

翻译：自Sentinel-2（S2）卫星发射以来，许多机器学习模型已将其数据用于多样化应用。S2产品中的场景分类层（SCL）为训练提供了丰富信息，例如可筛选高云量图像。然而，其潜力远不止于此。我们提出一种评估区域洁净光学覆盖度的技术，该技术通过时间序列影像表达，并基于S2的SCL数据计算得出。通过人工设定阈值并结合SCL中的特定类别标签，所提技术能够为整个时间序列分配时空覆盖百分比，并进行高/低等级评估。通过对AI4EO增强农业挑战赛的评估，我们证明该评估结果与机器学习模型的预测性能具有相关性。在时空覆盖度较低区域的分类结果，明显差于高覆盖度区域。最后，我们将该技术应用于全球数据集LandCoverNet的所有大陆区域。