• Energy Research

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979-2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04m3m-3, respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007-2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates.

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/ 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/

Abstract Editor’s note: For easy download the posted pdf of the State of the Climate for 2017 is a low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.