DOI: 10.48436/hcm6n-t4m35
title: ESA CCI SM GAPFILLED Long-term Climate Data Record of Surface Soil Moisture from merged multi-satellite observations

description: This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 4 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/

This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Dataset Paper (Open Access)

A description of this dataset, including the methodology and validation results, is available at:

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, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, 2025. 

Known issues



Please note the following issue in this version of the dataset


The location order of the mask of potentially frozen regions ("frozenmask") for the year 2024 can be wrong (as can clearly be seen when visualizing the "frozenmask" field for a random day in 2024). As a result, the wrong interpolation algorithm (DCT-PLS vs linear interpolation) may be used for the misclassified regions in the gap-filling process (linear interpolation is used for frozen conditions). This affects only regions where in a 5x5 degree region both frozen and unfrozen soil conditions appeared. Spatial subsets, where all locations are frozen or non-frozen, are not affected. Other years (before 2024) are not affected. Please contact us if you require more information.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v9) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary



Gap-filled global estimates of volumetric surface soil moisture from 1979-2024 at 0.25° sampling

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

Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.

More information: See Preimesberger et al. (2025) and ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)


Programmatic (Bulk) 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 ~/Download on Linux or macOS systems (~50 GB).





#!/bin/bash# Set download directoryDOWNLOAD_DIR=~/Downloadsbase_url="https://researchdata.tuwien.at/records/c0wbr-xf278/files"# Loop through years 1979 to 2024 and download & extract datafor year in {1979..2024}; 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 1979-2024 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:



ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.2.nc


Data Variables

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



sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).

sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.

sm_original: Original measurements (with observation gaps) before gap-filling.

sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.

gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.

frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.


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

Version Changelog

Changes in v9.2 (previous version was v09.1r1):



Now based on the v9.2 COMBINED product

Years 1979 to 1990, and 2024 are now included as well

Algorithm is the same as described in Preimesberger et al. (2025)

sm_anomaly field is replaced with sm_original


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

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, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, 2025. 

Related Records

This record and all related records are part of the ESA CCI Soil Moisture science data records community.