RADOLAN_API: An Hourly Soil Moisture Data Set Based on Weather Radar, Soil Properties and Reanalysis Temperature Data

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.

Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma Part II: New Multi-Sensor Soil Moisture (MSSM) Product

“…Since all three (LSW-MSSM, EW-MSSM, and SMAP L4) are model-generated, the point-to-state interpolated RK-SM product [15] was selected as a reference benchmark due to the robustness of the multi-physics regression method that accounts for the main drivers of the soil moisture variable. By including soil textural and precipitation into a regression kriging framework, RK-SM seems to provide previously undetectable mesoscale soil moisture dynamics in Oklahoma and has been recognized as a benchmark product by several studies since 2019 [51][52][53]. Overall, and consistently across land cover types, soil textures, and climates, the LSW-MSSM shows both the highest spatially integrated correlation but the lowest RMSE and near-zero bias values with respect to the RK-SM benchmark at the daily scale during the study period.…”