Leandro Leal Parente
Senior Researcher at OpengeoHub
Landsat is the longest running program to provide space-based data for Earth’s land surface. Based on nine satellites, the program has been monitoring the planet since 1972, consistently providing multi spectral images for several applications. Due to technology differences among the satellites / image sensors, the reflectance values may have significant variations across the entire time-series. Data gaps, due cloud cover, and stripe artifacts, caused by the Scan Line Corrector failure (Landsat 7), add an additional level of complexity for the users interested in perform long-term time series analysis and machine learning on this data. Considering these challenges and the potential of usability of the Landsat imagery, here we presented a workflow to produce analysis-ready and cloud-optimized (ARCO) global mosaics including: 1) data harmonization, 2) cloud and artifact screening, 3) temporal aggregation, 4) gapfilling, and 5) mosaicking. Relying on de-facto standards (Cloud-Optimized GeoTIFF - COG and SpatioTemporal Asset Catalog - STAC), the Landsat global ARCO mosaics have potential to boost the access of Landsat data and contribute with the monitoring of land use conversion, food production, biodiversity, climate change and land productivity.