Time series of Landsat-based bimonthly 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 Türkiye), derived from the Landsat analysis-ready dataset version 2 (ARD V2) produced by Global Land Analysis and Discovery (GLAD) team and covering the period from 2000 to 2022, is described. The data cube consists of 17 TB of data at a 30 m 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 to provide a comprehensive feature space for environmental modeling and mapping. The quality of the produced time series was assessed by (1) assessing the accuracy of gap-filled bimonthly Landsat data with artificially created gaps; (2) visual examination for artifacts and inconsistencies; (3) plausibility checks with ground survey data; and (4) predictive modeling tests, examples with soil organic carbon (SOC) and land cover (LC) classification. The time series reconstruction demonstrates high accuracy, with a root mean squared error (RMSE) smaller than 0.05, and R2 higher than 0.6, across all bands. The visual examination indicates that the product is complete and consistent, except for winter periods in northern latitudes and high-altitude areas, where high cloud and snow density introduce significant gaps and hence many artifacts remain. The plausibility check further shows that the indices logically and statistically capture the processes. The BSF index showed a strong negative correlation (−0.73) with crop coverage data, while the minNDTI index had a moderate positive correlation (0.57) with the Eurostat tillage practice survey data. 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 60 723 LUCAS observations: long-term characteristics (tier 4) were particularly valuable for predictive mapping of SOC and LC, coming out on 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 at https://doi.org/10.5281/zenodo.10776891 (Tian et al., 2024) under a CC-BY license and will be continuously updated.