Time-series of Landsat-based bi-monthly and annual spectral indices for continental Europe for 2000–2022

Abstract. The production and evaluation of the Analysis Ready and Cloud Optimized (ARCO) data cube for continental Europe (including Ukraine, the UK, and Turkey), derived from the Landsat Analysis Ready Data version 2 (ARD V2) produced by Global Land Analysis and Discovery team (GLAD) and covering the period from 2000 to 2022 is described. The data cube consists of 17TB of data at a 30–meter resolution and includes bimonthly, annual, and long-term spectral indices on various thematic topics, including: surface reflectance bands, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Normalized Difference Snow Index (NDSI), Normalized Difference Water Index (NDWI), Normalized Difference Tillage Index (NDTI), minimum Normalized Difference Tillage Index (minNDTI), Bare Soil Fraction (BSF), Number of Seasons (NOS), and Crop Duration Ratio (CDR). The data cube was developed with the intention of providing a comprehensive feature space for environmental modeling and soil, vegetation, and land cover mapping. To evaluate its effectiveness for this purpose, the quality of the produced time series was assessed by: (1) visual examination for artifacts and inconsistencies, (2) plausibility checks with ground survey data, and (3) predictive modeling tests, examples with soil organic carbon (SOC) and land cover (LC) classification. The results of visual examination indicate that the gap-filled product is complete and consistent, except for winter periods in northern latitudes and high-altitude areas where high cloud and snow density make gap-filling complex, and hence many artifacts remain. The plausibility results further show that the indices effectively help differentiate landscapes and crop types: the BSF index showed a strong negative correlation (-0.73) with crop coverage data, effectively detecting soil exposure. The minNDTI index had a moderate positive correlation (0.57) with the Eurostat tillage practices survey data, indicating valuable information on the intensity of the tillage. The detailed temporal resolution and long-term characteristics provided by different tiers of predictors in this data cube proved to be important for both soil organic carbon regression and LC classification experiments based on the 60,723 LUCAS observations: long-term characteristics (tier 4) were particularly valuable for predictive mapping of SOC and LC coming on the top of variable importance assessment. Crop-specific indices (NOS and CDR) provided limited value for the tested applications, possibly due to noise or insufficient quantification methods. The data cube is made available under a CC-BY license and will be continuously updated.