DOI: 10.48436/j5jzf-y7q97
title: Study data for "Accounting for seasonal retrieval errors in the merging of multi-sensor satellite soil moisture products"

description: This data repository contains the accompanying data for the study by Stradiotti et al. (2025). Developed as part of the ESA Climate Change Initiative (CCI) Soil Moisture project. Project website: https://climate.esa.int/en/projects/soil-moisture/

Journal Article (Open Access)

This dataset was created as part of the following study, which contains a description of the algorithm and validation results.



Stradiotti, P., Gruber, A., Preimesberger, W., & Dorigo, W. (2025). Accounting for seasonal retrieval errors in the merging of multi-sensor satellite soil moisture products. Science of Remote Sensing, 12, 100242. https://doi.org/10.1016/j.srs.2025.100242


Summary

This repository contains the final, merged soil moisture and uncertainty values from Stradiotti et al. (2025), derived using a novel uncertainty quantification and merging scheme. In the accompanying study, we present a method to quantify the seasonal component of satellite soil moisture observations, based on Triple Collocation Analysis. Data from three independent satellite missions are used (from ASCAT, AMSR2, and SMAP). We observe consistent intra-annual variations in measurement uncertainties across all products (primarily caused by dynamics on the land surface such as seasonal vegetation changes), which affect the quality of the received signals. We then use these estimates to merge data from the three missions into a single consistent record, following the approach described by Dorigo et al. (2017). The new (seasonal) uncertainty estimates are propagated through the merging scheme, to enhance the uncertainty characterization of the final merged product provided here. 

Evaluation against in situ data suggests that the estimated uncertainties of the new product are more representative of their true seasonal behaviour, compared to the previously used static approach. Based on these findings, we conclude that using a seasonal TCA approach can provide a more realistic characterization of dataset uncertainty, in particular its temporal variation. However, improvements in the merged soil moisture values are constrained, primarily due to correlated uncertainties among the sensors.

Technical details

The dataset provides global daily gridded soil moisture estimates for the 2012-2023 period at 0.25° (~25 km) 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). All file names follow the naming convention:



L3S-SSMS-MERGED-SOILMOISTURE-YYYYMMDD000000-fv0.1.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 contains the daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).  Based on (merged) observations from ASCAT, AMSR2 and SMAP using the new merging scheme described in our study.

sm_uncertainty: (float) The Soil Moisture Uncertainty variable contains the uncertainty estimates (random error) for the ‘sm’ field. Based on the uncertainty estimation and propagation scheme described in our study. 

dnflag: (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.


Software to open netCDF files

After extracting the .nc files from the downloaded zip archived, they can read by any software that supports Climate and Forecast (CF) standard conform netCDF files, such as:



Xarray (python)

netCDF4 (python)

esa_cci_sm (python)

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

GIS and netCDF tools such as CDO, NCO, QGIS, ArCGIS.

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


Funding

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/