Please find the description of the applied algorithm in Remote Sensing: Ramsauer, T.; Weiß, T.; Löw, A.; Marzahn, P. RADOLAN_API: An Hourly Soil Moisture Data Set Based on Weather Radar, Soil Properties and Reanalysis Temperature Data. Remote Sens. 2021, 13, 1712. https://doi.org/10.3390/rs13091712 Abstract Soil moisture is a key variable in the terrestrial water and energy system. This study presents an hourly index that provides soil moisture estimates on a high spatial and temporal resolution (1 km × 1 km). The long established Antecedent Precipitation Index (API) is extended with soil characteristic and temperature dependent loss functions. The Soilgrids and ERA5 data sets are used to provide the controlling variables. Precipitation as main driver is provided by the German weather radar data set RADOLAN. Empiric variables in the equations are fitted in a optimization effort using 23 in-situ soil moisture measurement stations from the Terrestial Environmental Observatories (TERENO) and a separately conducted field campaign. The volumetric soil moisture estimation results show error values of 3.45 Vol% mean ubRMSD between RADOLAN_API and station data with a high temporal accordance especially of soil moisture upsurge. Further potential of the improved API algorithm is shown with a per-station calibration of applied empirical variables. In addition, the RADOLAN_API data set was spatially compared to the ESA CCI soil moisture product where it altogether demonstrates good agreement. The resulting data set is provided as open access data. Keywords: soil moisture; high resolution; weather radar; hourly; API; soil properties; Soilgrids; TERENO; ESA CCI SM; RADOLAN Data set info: Dimensions: 692 × 1188 × 43,824 (latitude, longitude, time) Spatial Resolution: 1 km × 1 km Spatial Coverage: Territory of Germany Temporal Coverage: 01.01.2015–31.12.2019 Data sets that contribute to RADOLAN_API: DWD's RADOLAN RW: weather radar data: ECMWF's ERA5 t2m: temperature data: Soilgrids: soil characteristics:

{"references": ["Ramsauer, T.; Wei\u00df, T.; L\u00f6w, A.; Marzahn, P. RADOLAN_API: An Hourly Soil Moisture Data Set Based on Weather Radar, Soil Properties and Reanalysis Temperature Data. Remote Sens. 2021, 13, 1712. https://doi.org/10.3390/rs13091712", "Winterrath, T.; Brendel, T.; Jungh\u00e4nel, T.; Klameth, A.; Lengfeld, K.; Walawender, E.; Weigl, E.; Hafer, M.; Becker, A. An overview of the new radar-based precipitation climatology of the Deutscher Wetterdienst \u2013 data, methods, products 2019. doi:10.3929/ETHZ-B-000347607.", "Hengl, T.; de Jesus, J.M.; Heuvelink, G.B.M.; Gonzalez, M.R.; Kilibarda, M.; Blagoti\u0107, A.; Shangguan, W.; Wright, M.N.; Geng, X.; Bauer-Marschallinger, B.; Guevara, M.A.; Vargas, R.; MacMillan, R.A.; Batjes, N.H.; Leenaars, J.G.B.; Ribeiro, E.; Wheeler, I.; Mantel, S.; Kempen, B. SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 2017, 12, e0169748. doi:10.1371/journal.pone.0169748.", "Copernicus Climate Change Service (C3S). ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), 2017. doi:10.24381/cds.adbb2d47."]}