DOI: 10.48436/8dda4-xne96
title: ESA CCI SM PASSIVE Daily Gap-filled Root-Zone Soil Moisture from merged multi-satellite observations

description: This dataset provides global daily estimates of Root-Zone Soil Moisture (RZSM) content at 0.25° spatial grid resolution, derived from gap-filled merged satellite observations of 14 passive satellites sensors operating in the microwave domain of the electromagnetic spectrum. Data is provided from January 1991 to December 2023.

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").  Project website: https://climate.esa.int/en/projects/soil-moisture/. Operational implementation is supported by the Copernicus Climate Change Service implemented by ECMWF through C3S2 312a/313c.

Studies using this dataset [preprint]

This dataset is used by Hirschi et al. (2025) to assess recent summer drought trends in Switzerland.

Hirschi, M., Michel, D., Schumacher, D. L., Preimesberger, W., and Seneviratne, S. I.: Recent summer soil moisture drying in Switzerland based on measurements from the SwissSMEX network, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-416, in review, 2025. 

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations from various microwave satellite remote sensing sensors (Dorigo et al., 2017, 2024; Gruber et al., 2019). This version of the dataset uses the PASSIVE record as input, which contains only observations from passive (radiometer) measurements (scaling reference AMSR-E). The surface observations are gap-filled using a univariate interpolation algorithm (Preimesberger et al., 2025). The gap-filled passive observations serve as input for an exponential filter based method to assess soil moisture in different layers of the root-zone of soil (0-200 cm) following the approach by Pasik et al. (2023). The final gap-free root-zone soil moisture estimates based on passive surface input data are provided here at 4 separate depth layers (0-10, 10-40, 40-100, 100-200 cm) over the period 1991-2023.

Summary



Gap-free root-zone soil moisture estimates from 1991-2023 at 0.25° spatial sampling from passive measurements

Fields of application include: climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, agriculture and meteorology

More information: See Dorigo et al. (2017, 2024) and Gruber et al. (2019) for a description of the satellite base product and uncertainty estimates, Preimesberger et al. (2025) for the gap-filling, and Pasik et al. (2023) for the root-zone soil moisture and uncertainty propagation algorithm.


Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Downloads on Linux or macOS systems.





#!/bin/bash# Set download directoryDOWNLOAD_DIR=~/Downloadsbase_url="https://researchdata.tuwien.ac.at/records/8dda4-xne96/files"# Loop through years 1991 to 2023 and download & extract datafor year in {1991..2023}; do    echo "Downloading $year.zip..."    wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"    unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR    rm "$DOWNLOAD_DIR/$year.zip"done



Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:



ESA_CCI_PASSIVERZSM-YYYYMMDD000000-fv09.1.nc


Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:



rzsm_1: (float) Root Zone Soil Moisture at 0-10 cm. Given in volumetric units [m3/m3].

rzsm_2: (float) Root Zone Soil Moisture at 10-40 cm. Given in volumetric units [m3/m3].

rzsm_3: (float) Root Zone Soil Moisture at 40-100 cm. Given in volumetric units [m3/m3].

rzsm_4: (float) Root Zone Soil Moisture at 100-200. Given in volumetric units [m3/m3].

uncertainty_1: (float) Root Zone Soil Moisture uncertainty at 0-10 cm from propagated surface uncertainties [m3/m3].

uncertainty_2: (float) Root Zone Soil Moisture uncertainty at 10-40 cm from propagated surface uncertainties [m3/m3].

uncertainty_3: (float) Root Zone Soil Moisture uncertainty at 40-100 cm from propagated surface uncertainties [m3/m3].

uncertainty_4: (float) Root Zone Soil Moisture uncertainty at 100-200 cm from propagated surface uncertainties [m3/m3].


Additional information for each variable is given in the netCDF attributes.

Version Changelog



v9.1



Initial version based on PASSIVE input data from ESA CCI SM v09.1 as used by Hirschi et al. (2025).




Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:



Xarray (python)

netCDF4 (python)

esa_cci_sm (python)

Similar tools exists for other programming languages (Matlab, R, etc.)

Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS

You can also use the GUI software Panoply to view the contents of each file


References



Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment, 203, 185-215, 10.1016/j.rse.2017.07.001, 2017

Dorigo, W., Stradiotti, P., Preimesberger, W., Kidd, R., van der Schalie, R., Frederikse, T., Rodriguez-Fernandez, N., & Baghdadi, N. (2024). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 09.0. Zenodo. https://doi.org/10.5281/zenodo.13860922

Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.

Hirschi, M., Michel, D., Schumacher, D. L., Preimesberger, W., Seneviratne, S. I.: Recent summer soil moisture drying in Switzerland based on the SwissSMEX network, 2025 (paper submitted)

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

Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.


Related Records

Please see the ESA CCI Soil Moisture science data records community for more records based on ESA CCI SM.