Assessment of Sentinel-2 spatial and temporal coverage based on the scene classification layer
This work provides a method for evaluating data quality in remote sensing applications, which is incremental but useful for researchers and practitioners using Sentinel-2 data.
The paper tackled the problem of assessing clean optical coverage in Sentinel-2 data using the scene classification layer, showing that regions with low spatial and temporal coverage lead to worse classification results in ML models, with correlations demonstrated in the AI4EO challenge.
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.