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/

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

The operational (ACTIVE, PASSIVE, COMBINED) ESA CCI SM products are available at https://catalogue.ceda.ac.uk/uuid/c256fcfeef24460ca6eb14bf0fe09572/ 

Abstract

Understanding whether the soil surface is frozen or thawed is crucial for interpreting satellite-based soil moisture measurements and for many Earth system applications. The physical state of water in the soil strongly affects its dielectric properties, which in turn determine how satellites sense moisture content. Current ESA CCI Soil Moisture products exclude data when the surface is likely frozen, as reliable retrievals are not possible under such conditions. Yet, the freeze/thaw state itself carries valuable environmental information: it reflects the changing energy and water exchange between land and atmosphere, shapes seasonal hydrological cycles, and influences agriculture, ecosystems, and climate feedbacks across much of the Northern Hemisphere.

This dataset provides global estimates of the soil moisture freeze/thaw state for the period from 11-1978 to 12-2024 derived from PASSIVE (radiometer) satellite observations within the ESA CCI Soil Moisture framework. These radiometers, operating in the K-band frequency range, are sensitive to surface temperature, enabling the detection of frozen versus thawed conditions at daily temporal and ~25 km spatial sampling. Data from L-band missions (e.g., SMAP, SMOS) are not included, resulting in a total number of 12 satellites.

The classification algorithm, described in Van der Vliet et al. (2020), was originally developed to flag frozen conditions in soil moisture retrievals and has since evolved into a dedicated data product. It applies a decision-tree approach using multi-frequency satellite measurements to classify the surface state for each sensor. Individual classifications are then merged into a single spatiotemporal record using a conservative unanimity rule—if any contributing satellite detects a frozen surface, the merged product is classified as “frozen.”

While this approach ensures reliability, it may lead to some over-flagging, which could be refined in future versions. The current product achieves an estimated accuracy of 75% against in situ surface temperature observations and 92% compared to ERA5 reanalysis data.

Summary

Daily binary (true/false) freeze/thaw surface soil moisture state classification dataset (~25 km spatial sampling) for the period November 1978 to December 2024.

Based on a satellite brightness temperature (K-band) classification algorithm (Van der Vliet et al., 2020) from 12 satellite radiometers.

A pixel is classified as "frozen" if it was classified accordingly for at least one satellite. This can lead to potential over-flagging in the current version.

Approximately 75% agreement with in situ surface temperature measurements (Dorigo et al., 2021) and 92% with ERA5-Land reanalysis temperature fields (Muñoz-Sabater et al., 2021)

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.

!/bin/bash# Set download directoryDOWNLOAD_DIR=~/Downloadsbase_url="https://researchdata.tuwien.at/records/m3g2x-a6958/files"# Loop through years 1978 to 2024 and download & extract datafor year in {1978..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

Filename template

The dataset provides global daily estimates for the 1978-2024 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) and month (MM) of that year in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name follows the convention:

ESACCI-SOILMOISTURE-L3S-FT-YYYYMMDD000000-fv09.2.nc

Data Variables

Each netCDF file contains 3 coordinate variables

lon: longitude (WGS84), [-180,180] degree W/E

lat: latitude (WGS84), [-90,90] degree N/S

time: datetime, encoded as "number of days since 1970-01-01 00:00:00 UTC"

and the following data variables

ft: (int) Soil moisture freeze-thaw state binary indicator (0=not frozen, 1=frozen, -1=missing data)

ft_agreement (float): Classification agreement between available sensors. 1 means that the frozen/unfrozen classification was the same for all merged sensors. The number decreases as the classification results between available satellites contradict.

sensor_count (int): Total number of merged sensors/overpasses

sensor_count_frozen (int): Total number of measuring sensors/overpasses that detected frozen soils

mode: (int) Indicator for satellite orbit(s) used in the retrieval (1=ascending, 2=descending, 3=both, 0=missing data)

sensor: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes.

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

Version Changelog

Changes in v9.2 (first released version):

This version uses the classification algorithm described by Van der Vliet et al. (2020) applied to 12 sensors and a unanimous merging approach. Covers the period from 11-1978 to 12-2024.

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 exist 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., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.

van der Vliet, M.; van der Schalie, R.; Rodriguez-Fernandez, N.; Colliander, A.; de Jeu, R.; Preimesberger, W.; Scanlon, T.; Dorigo, W. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sens. 2020, 12, 3439. https://doi.org/10.3390/rs12203439

Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. 

Related Records

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