ESA CCI SM RZSM Long-term Climate Record of Root-Zone Soil Moisture from merged multi-satellite observations

Context and methodology This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB" ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). It contains information on the Root-Zone Soil Moisture (RZSM) content at different depth layers as derived from Surface SM satellite observations of the ESA CCI SM products. The RZSM estimates and relative uncertainties are derived using the method of Pasik et al. (2023) forced with observations of the ESA CCI SM Combined product (Dorigo et al., 2017; Gruber et al., 2019; Preimesberger et al., 2021). Technical details The dataset provides global daily estimates for the 1978-2023 period at 0.25° (~25 km) horizontal resolution. The compressed downloadable rzsm_v09.1_1978_2023.tar.gz file is structured in sub-directories each including all files for a specific year. Each netCDF file contains the data of a specific day (DD), month (MM), and year (YYYY) in a 2-dimensional (longitude, latitude) grid system. The file name has the following convention: ESA_CCI_RZSM-YYYYMMDD000000-fv0.9.1.nc The RZSM data reflects the estimates calibrated for 4 depth layers: rzsm1: 0-10 cm rzsm2: 10-40 cm rzsm3: 40-100 cm rzsm4: 0-100 cm A package is available in python for reading the data as daily images and converting these images to time series and reading them. The source code for our python package and installation instructions are available here: https://github.com/TUW-GEO/esa_cci_sm The package can be installed via pip using "pip install esa_cci_sm" The documentation for this package is available here: https://esa-cci-sm.readthedocs.io/en/latest/ The "parameter" argument (e.g., https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39) can be specified to any of the layer variables (rzsm1, rzsm2, ...) Any software that can handle CF conform data should be able to import the raw netCDF files (e.g. CDO, NCO, QGIS, ArCGIS, Matlab, R, ...). You can also use the GUI software Panoply to view each file. Reference Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations, Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, 2023 Additional citations Dorigo, W.A.,