ESA CCI SM MEDIUM RESOLUTION (0.1°) Long-term Climate Data Record of Surface Soil Moisture from merged multi-satellite observations

Evaluation Report - Medium Spatial Resolution (ER MSR) D4.9 Version 1.0. Zenodo. doi.org/10.5281/zenodo.22284687

Abstract

The ESA CCI Soil Moisture medium-resolution (MR) science product provides soil moisture at a finer spatial resolution of 0.1° x 0.1°. The MR product delivers COMBINED SSM data for the time period 2002-2024. The production of the MR product follows the same methodology as the main CCI product, with adaptations introduced to account for the higher resolution.

A key objective of the MR product is to better capture mesoscale SSM patterns, because the variability at these scales plays an important role in triggering convective storms through land–atmosphere interactions (Chug, 2023). Particularly, SSM contrasts over distances of 10 to 40 km have a strong influence on the initiation of mesoscale convective systems (MCSs) in the Sahel (Taylor, 2011), where such systems account for most of the annual rainfall (Mathon, 2002).

The main CCI product at 0.25° resolution has proven to be too coarse for resolving the land surface features to convective system initiations.

Summary

Daily global estimates of volumetric surface soil moisture from 2002-2024 at 0.1° resolution Based on a selection of active (ASCAT) and passive sensors (AMSR-E, AMSR-2, GMI, FengYun, SMOS and SMAP). 0.1° resolution obtained using a nearest neighbour resampling (passive) or a two-dimensional Hamming window approach (active) For more information see: ATBD (link coming soon..) 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 directory DOWNLOAD_DIR=~/Downloads base_url="researchdata.tuwien.at/records/k32ss-1kh79/files" # Loop through years 2002 to 2024 and download & extract data for year in {2002..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 2002-2024 period at 0.1° (~10 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-MR-COMBINED-YYYYMMDD000000-fv9.2.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: float, datetime encoded as "number of days since 1970-01-01 00:00:00 UTC" and 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 (0.1 degree). sm_uncertainty : (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations.

Derived using triple collocation analysis. dn_flag : (int) Indicator for satellite orbit(s) used in the retrieval (day/nighttime). 1=day, 2=night, 3=both flag : (int) Indicator for data quality / missing data indicator. For more details, see netcdf attributes. freqbandID : (int) Indicator for frequency band(s) used in the retrieval. For more details, see netcdf attributes. mode : (int) Indicator for satellite orbit(s) used in the retrieval (ascending, descending) sensor : (int) Indicator for satellite sensor(s) used in the retrieval.

For more details, see netcdf attributes. t0 : (float) Representative time stamp, based on overpass times of all merged satellites. Additional information for each variable are given in the netCDF attributes. 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 Duchemin, D., Prajapati, R., Rodriguez-Fernandez, N. J., & D.Kovács, D. (2026).

Evaluation Report - Medium Spatial Resolution (ER MSR) D4.9 Version 1.0. Zenodo. doi.org/10.5281/zenodo.22284687 D.Kovács, D., Moldenhauer, C., Preimesberger, W., Dorigo, W., & Johanna, L. (2025). Algorithm Theoretical Basis Document (ATBD) – Medium Resolution (MR) D2.8 Version 1.0.

Zenodo. doi.org/10.5281/zenodo.22284561 Chug, D., Dominguez, F., Taylor, C.M., Klein, C. and Nesbitt, S.W., 2023. Dry-to-wet soil gradients enhance convection and rainfall over subtropical South America. Journal of Hydrometeorology, 24(9), pp.1563-1581.

Taylor, C.M., Gounou, A., Guichard, F., Harris, P.P., Ellis, R.J., Couvreux, F. and De Kauwe, M., 2011. Frequency of Sahelian storm initiation enhanced over mesoscale soil-moisture patterns. Nature Geoscience, 4(7), pp.430-433.

Mathon, V., Laurent, H. and Lebel, T., 2002. Mesoscale convective system rainfall in the Sahel. Journal of applied meteorology, 41(11), pp.1081-1092.