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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) content derived from satellite observations in the microwave domain. Abstract The MODELFREE product of the ESA CCI SM v9.1 science data suite provides - similar to the COMBINED product - global, harmonized daily satellite soil moisture measurements from both radar and radiometer observations. This product contains soil moisture estimates at 0.25-degree spatial resolution, and covers the period from 2002-2023. Soil moisture is derived from observations of 13 different active and passive satellites operating across various frequency bands (K, C, X, and L-band). Unlike the COMBINED product, for which soil moisture fields from the GLDAS Noah model dataset are used to harmonize individual satellite sensor measurements, the MODELFREE product utilizes a satellite-only scaling reference dataset. This reference incorporates gap-filled soil moisture derived from AMSR-E (2002-2010) and from intercalibrated SMAP/SMOS brightness temperature data (2010-2023). The merging algorithm employed is consistent with that of the v9.1 COMBINED product. The new scaling reference leads to significantly different absolute soil moisture values, especially in latitudes above 60 °N. Data from the SMMR, SSMI and ERS missions are not included in this product. This product is in its early development stage and should be used with caution, as it may contain incomplete or unvalidated data. Summary First version of a model-independent version of the ESA CCI SM COMBINED product 2002-2023, global, 0.25 deg. resolution GLDAS Noah (model) is replaced with a purely satellite-based scaling reference Different absolute value range compared to the COMBINED product is expected due to the different scaling reference used Known issues A temporal inconsistency is observed between the AMSR-E and SMOS period (at 01-2010). This can affect long-term trends in the data In the period from 01-2002 to 06-2002 no data are available above 37 °N and below 37 °S respectively (all measurements in this period are from the TRMM Microwave Imager) Technical Details The dataset provides global daily estimates for the 2002-2023 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_MODELFREE-YYYYMMDD000000-fv09.1.nc 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 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 is 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 R. Madelon et al., “Toward the Removal of Model Dependency in Soil Moisture Climate Data Records by Using an L-Band Scaling Reference," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 831-848, 2022, doi: 10.1109/JSTARS.2021.3137008. Related Records The following records are all part of the Soil Moisture Climate Data Records from satellites community 1 ESA CCI SM RZSM Root-Zone Soil Moisture Record 10.48436/v8cwj-jk556 2 ESA CCI SM GAPFILLED Surface Soil Moisture Record 10.48436/hcm6n-t4m35

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) content derived from satellite observations in the microwave domain. Abstract ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) 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. Summary Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 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 More information: ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023) Technical details The dataset provides global daily estimates for the 1991-2023 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-fv09.1.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 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 satellite observations, on which the interpolation is based (this variable is experimental and will change in future versions of the record). gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (0), and where the interpolated value is used (1) in the 'sm' field. Additional information for each variable is 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 Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869 Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020 Related Records The following records are all part of the Soil Moisture Climate Data Records from satellites community 1 ESA CCI SM MODELFREE Surface Soil Moisture Record 10.48436/v8cwj-jk556 2 ESA CCI SM RZSM Root-Zone Soil Moisture Record 10.48436/hcm6n-t4m35

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) content derived from satellite observations in the microwave domain. Abstract ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) 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 1991-2023 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: ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023) Technical details The dataset provides global daily estimates for the 1991-2023 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-fv09.1r1.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_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`) sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also available when 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 (0), and where the interpolated value is used (1) 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 Compared to the previous version (v9.1 - see 'related works'), this version (v9.1r1) uses a novel uncertainty estimation scheme as described in Preimesberger et al. (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 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., The ESA CCI Soil Moisture GAPFILLED product: A gap-free global long-term satellite soil moisture climate data record with uncertainty estimates, 2024 (submitted) Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869 Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020 Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004. Related Records The following records are all part of the Soil Moisture Climate Data Records from satellites community 1 ESA CCI SM MODELFREE Surface Soil Moisture Record 10.48436/v8cwj-jk556 2 ESA CCI SM RZSM Root-Zone Soil Moisture Record 10.48436/hcm6n-t4m35