Time-series of Landsat-based spectral indices for continental Europe for 2000--2022 to support soil health monitoring

Abstract As the importance of soil health in supporting European ecosystems and agriculture becomes increasingly critical, effective monitoring is essential for its protection and restoration. This paper describes production and quality assessment of a data cube (17TB in size) derived from Landsat Analysis Ready Data version 2 (ARD V2) dataset and tailored for soil health monitoring, featuring multiple spectral indices, long time span, high resolution, and analysis readiness. The focus was on indices of importance for monitoring soil health: 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 set is available with a resolution of 30~m, with bimonthly (i.e. one image available per two months), annual, and long-term analysis throughout continental Europe, including Ukraine, the UK, and Turkey, covering 2000 to 2022. The results of the quality assessment, including both visual examination and plausibility check with ground survey data, show that these indices can effectively capture environmental processes and offer insight into soil health through aspects such as vegetation, crop status, tillage practices, and soil exposure directly or serve as covariates in digital soil mapping. In particular, the BSF shows a strong negative correlation of -0.73 with crop coverage data from 2006 to 2017, suggesting an effective detection of soil exposure. The minNDTI shows a moderate positive correlation of 0.57 with the Eurostat tillage practices survey data, indicating that it provides valuable information on tillage intensity, although not definitively. This data set is analysis-ready and cloud-optimized, making it suitable for applications in soil property mapping and the development of comprehensive soil health assessment approaches throughout Europe.