Last modified on Dec 04, 2025 17:33

Contributors:  Wolfgang Preimesberger (WP, TU Wien), Johanna Lems (JL, TU Wien), Wouter Dorigo (WD, TU Wien), Alena Dostalova (AD, EODC), Richard Kidd (RK, EODC)

Issued by: EODC/Alena Dostalova

Date: 07/11/2025

Ref: C3S2_313c_EODC_WP1-DDP-SSM-v1_202506_PUGS; C3S2_313c_EODC_WP1-DDP-RZSM-v1_202506_PUGS; C3S2_313c_EODC_WP1-DDP-FT-v1_202506_PUGS

Official reference number service contract: 2024/C3S2_313c_EODC/SC1

History of modifications

List of datasets covered by this document

Acronyms

General definitions  

Active (soil moisture) retrieval: the process of modelling soil moisture from radar (scatterometer and synthetic aperture radar) measurements. The measurand of active microwave remote sensing systems is called “backscatter”.

Backscatter is the measurand of “active” microwave remote sensing systems (radar). As the energy pulses emitted by the radar hit the surface, a scattering effect occurs and part of the energy is reflected back. The received energy is called “backscatter”, with rough surfaces producing stronger signals than smooth surfaces. It comprises reflections from the soil surface layer (“surface scatter”), vegetation (“volume scatter”) and interactions of the two. Under very dry soil conditions, structural features in deeper soil layers can act as volume scatterers (“subsurface scattering”).

Breakthrough requirement: An Essential Climate Variable (ECV) requirement level set by Global Climate Observing System (GCOS) which “[…] if achieved, would result in a significant improvement for the targeted application […] at which specified uses within climate monitoring become possible. It may be appropriate to have different breakthrough values for different uses.” (GCOS-245) 

Bias: “Bias is defined as an estimate of the systematic measurement error” GCOS-200 (GCOS, 2016)

Brightness Temperature is the measurand of “passive“ microwave remote sensing system (radiometers). Brightness temperature (in degree Kelvin) is a function of kinetic temperature and emissivity. Wet soils have a higher emissivity than dry soils and therefore a higher brightness temperature. Passive soil moisture retrieval uses this difference between kinetic temperature and brightness temperature, to model the amount of water available in the soil of the observed area, while taking into account factors such as the water held by vegetation.

Dekad: the period or interval of 10 days

Error: “The term error refers to the deviation of a single measurement (estimate) from the true value of the quantity being measured (estimated), which is always unknown” (Gruber et al., 2020)

Freeze/Thaw: refers to the state of soil moisture during a year. Water in the upper soil layer transitions within the soil between its liquid and solid (ice) states due to changes in temperature. When soil temperatures drop below freezing, liquid soil water turns into ice,  altering the soil's dielectric properties, which is a key factor in microwave remote sensing of soil conditions. Conversely, as temperatures rise above freezing, the ice in the soil melts, reintroducing liquid water and activating various ecological and hydrological processes. The freeze/thaw cycle significantly influences energy, water, and mass exchanges between the land surface and the atmosphere, affecting surface runoff, plant growth, soil respiration, and other crucial environmental factors. In remote sensing, detecting these freeze/thaw states is important for accurately estimating soil moisture and understanding surface conditions. Frozen soil can be detected using changes in brightness temperature and backscatter observed by microwave sensors

Level 2 pre-processed (L2P): this is a designation of satellite data processing level. “Level 2” means geophysical variables derived from Level 1 source data on the same grid (typically the satellite swath projection). “Pre-processed” means ancillary data and metadata added following GHRSST Data Specification.

Level 3 /uncollated/collated/super-collated (L3U/L3C/L3S): this is a designation of satellite data processing level. “Level 3” indicates that the satellite data is a geophysical quantity (retrieval) that has been averaged where data are available to a regular grid in time and space. “Uncollated” means L2 data granules have been remapped to a regular latitude/longitude grid without combining observations from multiple source files. L3U files will typically be “sparse” corresponding to a single satellite orbit. “Collated” means observations from multiple images/orbits from a single instrument combined into a space-time grid. A typical L3C file may contain all the observations from a single instrument in a 24-hour period. “Super-collated” indicates that (for those periods where more than one satellite data stream delivering the geophysical quantity has been available) the data from more than one satellite have been gridded together into a single grid-cell estimate, where relevant.

Passive (soil moisture) retrieval: the process of modelling soil moisture from radiometer measurements. The measurand of passive microwave remote sensing is called “brightness temperature”. The retrieval model in the context of Copernicus Climate Change Service (C3S) soil moisture is generally the Land Parameter Retrieval Model (LPRM).

Radiometer: Spaceborne radiometers are satellite-carried sensors that measure energy in the microwave domain emitted by the Earth. The amount of radiation emitted by an object in the microwave domain (~1-20 GHz). The observed quantity is called “brightness temperature” and depends on kinetic temperature of an object and its emissivity. Due to the high emissivity of water compared to dry matter, radiometer measurements of Earth’s surface contain information in the water content in the observed area.

Root-Zone: The (variable) layer of soil where plant roots grow and absorb water and nutrients. It typically extends from the soil surface to the maximum depth that roots can reach, and its characteristics—such as moisture, structure, and composition—are crucial for plant health and ecosystem functioning.

Root Zone Soil Moisture: Water content in the 0–1 m soil layer where most plant roots extract water, expressed as volumetric soil moisture (m³m-³).

Scatterometer: Spaceborne scatterometers are satellite-carried sensors that use microwave radars to measure the reflection or scattering effect produced by scanning a large area on the surface of the Earth. The initially submitted pulses of energy are reflected by the Earth’s surface depending on its geometrical and geophysical properties in the target area. The received energy is called “backscatter”. Soil moisture retrieval relies on the fact that wet soils have a higher reflectivity (and therefore backscatter) than dry soils due to the high dielectric constant of liquid water compared to dry matter.

Surface Soil Moisture: The water content in the surface layer. There is no common definition of the surface layer, but it is generally assumed to range between 0.02-0.05 m. It is given in volumetric soil moisture (m³m-³) or percentage of saturation (%).

Signal-to-Noise Ratio (SNR): The SNR is a measure for the random error variance (noise) in a signal relative to the strength of the desired signal itself. It is usually expressed on a logarithmic scale in decibels [dB]. A positive soil moisture SNR indicates a well distinguishable representation of soil water content over time. Negative SNR values suggest that the soil moisture signal is overshadowed due to sensor inaccuracy (noise), signal interference (vegetation) or other deteriorative factors, and therefore not reliable.

Stability: “The change in bias over time” (GCOS-245)(GCOS, 2022) . “Stability may be thought of as the extent to which the uncertainty of measurement remains constant with time. […] ‘Stability’ refer[s] to the maximum acceptable change in systematic error, usually per decade.” (GCOS-200) (GCOS, 2016)

Uncertainty: “Satellite soil moisture retrievals […] usually contain considerable systematic errors which, especially for model calibration and refinement, provide better insight when estimated separate from random errors. Therefore, we use the term bias to refer to systematic errors only and the term uncertainty to refer to random errors only, specifically to their standard deviation (or variance)” (Gruber et al., 2020)

Executive summary

This document is the Product User Guide and Specification (PUGS) for products Copernicus Climate Change Service (C3S) Satellite Surface Soil Moisture (SSM), Freeze/Thaw (F/T) and Root Zone Soil Moisture (RZSM), version v202505, produced by TU Wien, EODC, and Planet Labs from a large set of active and passive microwave remote sensing instruments. It describes the v202505 Climate Data Record (CDR) and Interim Climate Data Record (ICDR) products in a manner that is understandable by the product user with focus on the:

• Geophysical data product content
• Known limitations of the products
• Practical Usage Considerations
• Product grid and geographic projection
• Ancillary data used
• Structure and format of the product
• Data file variables and attributes

The C3S Soil Moisture v202505 data records are based on the European Space Agency (ESA) Climate Change Initiative (CCI) Soil Moisture data product version 9. The C3S Soil Moisture products are available at the European Centre for Medium Range Weather Forecasting (ECMWF) C3S Climate Data Store (CDS). Both the CDR and the ICDR comprise five data products:

• Three surface soil moisture (SSM) products (~0-5 cm depth): The ACTIVE and the PASSIVE products are created by fusing scatterometer and radiometer soil moisture data, respectively; the COMBINED product merges all input sensors from the former two products into a single record. All products are distributed separately as daily, 10-daily ("dekadal") and monthly averages.
• A root-zone soil moisture (RZSM) product (0-1 m depth) which is based on the COMBINED surface product (daily, 10-daily and monthly).
• A freeze/thaw (F/T) classification product based on scatterometer and radiometer input data (daily only).

All products are provided as netCDF4 images at global scale. The data sets span a time period from November 1978 onwards. While the update policy of CDR is subject to certain criteria, the ICDR represents a consistent extension of the CDR. The generation of the ICDR uses the same algorithms and parameters, which are used to create the CDR. The incremental update of the ICDR takes place every 10 days. The CDR has an annual update cycle and either undergoes an evolution update in response to new merging algorithms, parameters, or new input data sets, or a maintenance update in response to processor maintenance.

Chapter 1 of this document provides a product description including the target requirements and information about the data usage. Chapter 2 provides information about the data access and user support. Additionally, Annex A provides an overview of the input Earth Observation and modelled data used to create the C3S SM products.

For more information, we refer to the Algorithm Theoretical Basis Document (ATBD) of the CDRs (Preimesberger et al., 2025a). The underlying CCI SM algorithm and data products are extensively discussed in Dorigo et al. (2017), and Gruber et al. (2019).

Product Descriptions 

Common information

The ECMWF C3S Satellite Soil Moisture Service provides two types of data record: Climate Data Records (CDRs), and Interim Climate Data Records (ICDR). CDRs and ICDRs consist of five soil moisture data sets: 3 surface soil moisture products (ACTIVE, PASSIVE, COMBINED), a root-zone soil moisture product (RZSM) and a freeze/thaw product (F/T). Each dataset contains multiple variables related to soil moisture.

The ACTIVE and the PASSIVE product are created by using scatterometer, and radiometer soil moisture products, respectively. The COMBINED and F/T product is a blended product based on both sensor types. As RZSM is based on the COMBINED product, it is therefore also based on both active and passive sensors. For the surface and root-zone soil moisture data sets, the Daily, the Dekadal (10-days) mean (starting from the 1^st to the 10^th, from 11^th to the 20^th, and from 21^st to the last day of a month), and the Monthly mean are provided. The Daily files are created directly through the merging of microwave soil moisture data from multiple satellite instruments, and the Dekadal and Monthly means are calculated from averaging these Daily files. For F/T only daily classification results are provided (see Table 1). All datasets are available in NetCDF-4 format (Eaton et al., 2020) and comprise global gridded (raster) images at a 0.25 degree regular spatial (grid) resolution.

The sensors used for each period and product are best described by Figure 1:

Figure 1: Temporal coverage of input products used to construct the ACTIVE (blue), PASSIVE (red), COMBINED (green), RZSM (yellow), and F/T (purple) CDR/ICDR. Sensors highlighted with cross on the right side of the figure were not used for the F/T retrieval. The periods of unique sensor combinations are referred to as 'merging periods'. The letters next to the sensor names indicate frequency bands the sensor operates in. 

The underlying level 2/3 satellite soil moisture merging algorithm is based on that used in the generation of the ESA CCI SM v9 product. In addition, detailed provenance traceability information can be found in the netCDF metadata of the product (Section 1.8.2). The theoretical and algorithmic basis of the products are described in Preimesberger et al. (2025a). The Signal to Noise Ratio (SNR) merging algorithm is described in Gruber et al. (2017). An overview of all known errors of the soil moisture datasets is provided in Ikonen et al. (2016), Preimesberger et al. (2025b) and Dorigo et al. (2017). Since this suite of products provided by this C3S service are based upon the scientific products developed in ESA’s Climate Change Initiative (CCI) Soil Moisture project, further background and reference documentation can be found on the CCI Soil Moisture project web site https://climate.esa.int/en/projects/soil-moisture/ (last access: 2025-08-25).

For the surface soil moisture products (COMBINED, ACTIVE, PASSIVE) the following related variables are provided with the soil moisture values in each netCDF file: 

• Day/Night Flag
• Quality Flag
• Observation Frequency Band Identification
• Satellite Orbit Mode
• Sensor
• Soil Moisture Uncertainty

For the root-zone soil moisture product the following related variables are provided with the soil moisture values:

• RZSM Uncertainty of Layer 1 (0-10 cm depth)
• RZSM Uncertainty of Layer 2 (10-40 cm depth)
• RZSM Uncertainty of Layer 3 (40-100 cm depth)

For the Freeze/Thaw product the following related variables are provided with the freeze/thaw classification:

• Day/Night Flag
• Frozen Soils Sensor Agreement Index
• Satellite Mode
• Sensor
• Available Sensor Count
• Sensor Count Frozen

For the Dekadal and the Monthly mean the frequency band, the used sensor, and the number of observations are attributed to the soil moisture entity (see NetCDF data file variables and attributes in Section 1.8.2). Some variables, such as the uncertainty estimates, are only provided for the daily but not for the temporally aggregated products.

Climate Data Records: CDR and ICDR

A new major version of the CDR is normally released each year and comprise one or more of the following points

1. Merging algorithm updates
2. Processing parameter updates
3. Addition of new sensors using an existing algorithm
4. Change in input products / retrieval algorithm
5. Extensions of the CDR record length

A minor release (patch) of individual files is made in case of product errors requiring processor maintenance or upgrade, to supersede erroneous files.

The ICDR is a consistent extension of the CDR. The ICDR products are generated every 10 days (for the penultimate dekad; resulting in a delay of 10-20 days to present day) and extend the CDR of the same version as the ICDR. The same algorithm and software processor are used for generating the ICDR products. The scaling and merging parameters, derived as part of the CDR generation, are reused. New near real time observation data from the Advanced Scatterometer (ASCAT)-B/C and Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP) & Global Precipitation Mission (GPM) sensors (also see Table 29) are processed to extend the ICDR products.

Geophysical parameters

The homogenised and merged soil moisture products have global coverage and a spatial resolution of 0.25°. The Daily data set has a temporal sampling of 1 day, the Dekadal mean represents a 10-day average of the Daily data, and the Monthly mean performs the averaging of the Daily files for each month. The reference time is set at 0:00 Coordinated Universal Time (UTC) for all products. The soil moisture data for the PASSIVE the COMBINED and the RZSM product are provided in volumetric units [m³m^-3], while the ACTIVE soil moisture data are expressed in percentage of saturation [%]. The Freeze Thaw is a classification product (-1= missing data, 0=not frozen, 1=frozen), without unit. 

Product Grid and Projection

The grid is a 0.25° x 0.25° longitude-latitude global array of points, based on the World Geodetic System 1984 (WGS 84) reference system. Its dimension is 1440 x 720, where the first dimension, X (longitude), is incremental from West (-180°) to East (180°), and the second dimension, Y (latitude) is incremental from South (-90°) to North (90°). Grid edges are at multiple of quarter-degree values (e.g. 90.00, 89.75, 89.50, 89.25, …), and the grid centers are exactly between the two grid edges:

First point center = (–89.875°S, –179.875°W) = Grid Point Index = 0
Second point center = (–89.875°S, –179.625°W) = Grid Point Index = 1
…
1441st point center = (–89.625°S, –179.875°W) = Grid Point Index = 1440
…
Last point center = (89.875°N, 179.875°E) = Grid Point Index = 1036799

In total, there are 1440 x 720 = 1036800 grid points, where 244243 points are land points. The land mask has been derived from the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG v2.2.2) (Wessel and Smith, 1996). Only water bodies with an area of more than 600 km² were considered in the definition of the land mask.

Figure 2 shows the land points which are used for each product described in this document. In addition, a static Tropical forest mask – derived from the 2002 to 2011 mean AMSR-E Vegetation Optical Depth (VOD) – has been applied to the soil moisture product images. Surface and root-zone soil moisture (and uncertainties) values are set to NaN in these (rainforest) regions as for most sensors no reliable soil moisture retrieval is possible. Grid cells classified as "rainforest" are shown in dark green along the equator in Figure 2, compare mean AMSR-E VOD in Figure 3 on which the classifcaiton is based. 

Figure 2: Land mask used for the merged product (Antarctica is excluded). The 0.25° grid starts indexing from "lower left" to the "upper right". Note that not all grid points are available for all sensors, e.g. ASCAT retrievals are available between Latitude degrees 80° N and 60° S. Dark green represents the masked tropical rain forest areas.

Ancillary data

The process of generating the C3S satellite soil moisture products requires the usage of various ancillary data sets. These ancillary datasets are described in the following subsections.

Global Land Data Assimilation System (GLDAS)

The PASSIVE and ACTIVE products represent volumetric soil moisture (m³m^-3) and degree of saturation (%), respectively. To combine these data, both products need to be adjusted to a common reference which can be achieved using a reference dataset. The reference dataset requires global coverage with a spatial resolution and temporal interval that are comparable to both of the microwave products (i.e., approximately 25 km resolution and daily interval), a long time record, and reasonable surface soil moisture estimates for all land cover types (i.e., representative soil layer is not deeper than 10 cm).

The GLDAS-Noah v2.1 Land Surface Model L4 3 Hourly 0.25 x 0.25 degree soil moisture model data satisfies these requirements and is employed as the reference dataset. Both (the PASSIVE and ACTIVE) products were rescaled against the GLDAS-Noah data using the cumulative distribution function (CDF) matching technique. The methodology behind the use of this data set is provided in (Preimesberger et al., 2025a).

Gldas Noah can be downloaded from https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/ (last access: 22 October 2025).

ASCAT Advisory Flag

The following two H SAF ASCAT SSM flags are used to mask out regions of frozen soils, insensitive retrievals, or subsurface scattering, respectively:

• surface_soil_moisture_sensitivity < 1 [dB]
• subsurface_scattering_probability > 10 [%]
• surface_flag == 2 

Under the selected conditions, ASCAT SSM is not used in any C3S SM product. 

ASCAT Soil Moisture products are available for download at https://hsaf.meteoam.it/Products/ProductsList?type=soil_moisture (last access: 22 October 2025).

Average Vegetation Optical Depth from AMSR-E

Vegetation optical depth (VOD) estimated from AMSR-E with the VUA-NASA LPRM (Vrije Universiteit Amsterdam - National Aeronautics and Space Administration Land Parameter Retrieval model) method are provided to give an indication of vegetation density (Figure 3). The provided global values represent the averaged VOD from 2002 to 2011. AMSR-E data for different processing levels can be downloaded from https://nsidc.org/data/amsre/data (last access: 22 October 2025).

Figure 3: AMSR-E (from LPRM) average vegetation optical depth derived for the period 2002-2011 in the 6.9 GHz band.

Topographic Complexity

The topographic complexity (Normalized standard deviation of topography) is derived from the United States Geological Survey (USGS) 30-second Global Elevation Data (GTOPO30) (USGS, 1996). This can be used to help understand the potential distortion of backscatter in mountainous regions (i.e. calibration errors due to the deviation of the surface from the assumed ellipsoid and the rough terrain, the influence of permanent snow and ice cover, a reduced sensitivity due to forest and rock cover and highly variable surface conditions). The topographic complexity flag is derived from GTOPO30 data. For each cell of the Discrete Global Grid (DGG), the standard deviation of elevation is calculated, and the result is normalised to values between 0 and 100 % (Figure 4).

USGS GTOPO30 data are available at https://doi.org/10.5066/F7DF6PQS (last access: 22 October 2025).

Figure 4: Topographic complexity from the USGS 30-second Global Elevation Data (GTOPO30).

Wetland fraction

The open water fraction is defined as fraction coverage of areas with inundation potential. The inundation potential has been derived from the Global Lakes and Wetlands Database (GLWD) level 3 product, which includes several wetland and inundation types (Lehner et al., 2004). The wetland fraction is calculated for the DGG and the conversion from DGG to the 0.25 degree grid is based on the nearest-neighbour search algorithm (Figure 5).

GLWD Level 3 data are available at https://databasin.org/datasets/a029e83d54864d23b588f67bcb8eb5b7/ (last access: 22 October 2025).

Figure 5: Wetland fraction derived from the Global Lakes and Wetlands Database (GLWD).

ACTIVE Product

The ACTIVE product is the output of merging scatterometer-based soil moisture data, which are derived from the Active Microwave Instrument - WindScat (AMI-WS) and ASCAT (Metop-A, Metop-B, Metop-C). Please see Table 29 for detailed information of the active microwave instruments. The ACTIVE CDR product v202505 spans the time period from 1991-08-05 to 2024-12-31, and the ACTIVE ICDR product is available from 2025-01-01 onwards. Table 2 shows the used sensors in the corresponding periods:

PASSIVE Product

The PASSIVE product merges data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), WindSat, AMSR2, SMOS, Feng-Yun (FY)-3B/C/D, GPM and SMAP. The PASSIVE CDR v202505 includes soil moisture data from 1978-11-01 to 2024-12-31, whereas the PASSIVE ICDR represents its extension from 2025-01-01 onwards. Data from the ascending and descending orbits of all passive sensors is used in the CDR. Only the descending (ascending for SMOS) orbit data (night time) are used in the ICDR. The merging periods and the used sensors are listed in Table 3:

COMBINED Product

The COMBINED CDR is generated by merging the ACTIVE and the PASSIVE products, therefore the time span for this product ranges from 1978-11-01 to 2024-12-31. The COMBINED ICDR extends the CDR from 2025-01-01 onwards. The merging periods and the used sensors are listed in Table 4.

RZSM Product

The RZSM product uses the merged COMBINED product as input and is therefore based on the same sensors as listed in Table 4.

Freeze/Thaw product

The F/T product also uses surface state indicators from both active and passive sensors. However, L-band sensors (SMAP and SMOS) are not used here as they don't carry instruments that measure microwave radiation in the frequency domain required to assess surface temperature. ASCAT (A, B, and C) sensors are also excluded, as the current HSAF SSM products (as of H121 published in 2025) do no longer provide native (backscatter-based) surface state flags on frozen soils (Naeimi et al., 2012). Instead, the current version relies on ECMWF forecast temperature data, rather than backscatter measurements, to classify the surface state. As C3S satellite SM products aim to remain independent of (C3S) reanalysis products, ASCAT is currently not part of the F/T product. All other sensors are listed in Table 5.

Overview of Product Target requirements

Table 6 assembles the C3S ECV Soil Moisture product target requirements adopted from the Global Climate Observing System (GCOS)-245 target requirements (NASA, 2021) and shows to what extent these requirements are currently met by the latest C3S Satellite SM products. As one can see, the CDR and ICDR products currently provided by the system are compliant with C3S target requirements and in many cases even go beyond. Further details on product accuracy and stability are provided in Target Requirements and Gap Analysis Document (TRGAD) (Kidd et al., 2024) and the Product Quality Assessment Report (PQAR) (Preimesberger et al., 2025b).

Table 6: Summary of C3S ECV Soil Moisture requirements, the specification of the current satellite soil moisture products, and the target requirements proposed by the consortium. Adapted from TRGAD (Kidd et al., 2024).

Data usage information

Data format and file naming

The file format used for storing the data is NetCDF-4 classic. All NetCDF files follow the NetCDF Climate and Forecast (CF) Metadata Conventions version 1.9. The NetCDF soil moisture data files are stored in folders for each year with one file per day. The following file naming convention is applied:

Surface SM Products (ACTIVE, PASSIVE, COMBINED):

     C3S-SOILMOISTURE-L3S-<Variable>-<Dataset>-<Interval>-<Reference_date>-<CDR>-v<Version>.nc

Root-Zone Product (RZSM):

     C3S-RZSM-L3S-RZSMV-<Interval>-<Reference_date>-<CDR>-v<Version>.nc

Freeze/Thaw Product (F/T):

     C3S-SOILMOISTURE-L3S-FT-DAILY-<Reference_date>-<CDR>-v<Version>.nc

Template components:

• <Variable>: "SSMS" or "SSMV" 
  • Surface Soil Moisture degree of Saturation (active sensors) or Surface Soil Moisture Volumetric (passive sensors and COMBINED).
• <Dataset>: "ACTIVE" or "PASSIVE" or "COMBINED"
• <Interval>: "DAILY" or "DEKADAL" or "MONTHLY"
• <Reference_date>
  • YYYYMMDDhhmmss – Reference date and time of the file in UTC. Each daily file contains data from this reference time +- 12 hours. For monthly and dekadal files this reference time is the start of the period. E.g. for the dekadal data the dates can only be YYYYMM01000000, YYYYMM11000000, or YYYYMM21000000. The reference date for the monthly data is always YYYYMM01000000.
• <CDR>: "CDR" or "ICDR"; Type of Climate Data Record
• <Version>: Major.Minor.Run e.g. 202505.0.0
  • The Major number usually represents the year (YYYY) and month (MM) of date. The initial value for Minor is zero and will increment when updating the file. If there is a need – e.g. because of technical issues – to replace a file which already has been made public, the Run number of the replacement file shifts to the next increment. The initial Run number is zero.

File contents

Common variables

Lon (All Products)

Table 7: Attribute Table for Variable lon

 Lat (All Products)

Table 8: Attribute Table for Variable lat

Time (All products)

The reference timestamp of the day is saved in the “time” variable. The data values for the reference time are stored as number of “days since 1970-01-01 00:00:00 UTC.”

Table 9: Attribute Table for Variable time (reference time)

For the monthly and dekadal products, "time" refers to the start date of the representative period (e.g. the first day of the month for which the mean was computet).

Surface Soil Moisture products (ACTIVE, PASSIVE, COMBINED)

dnflag (Daily)

The Day or Night Flag specifies, whether the observation(s) occurred at local day (1) or night (2) time. A value of 3 indicates that the data is a result of merging satellite microwave data observed during day as well as during night time. In cases where the information cannot be determined the value is set to 0 (zero).

Table 10: Attribute Table for Variable dnflag, only available in the Daily files

flag (Daily)

Flag values are stored as signed bytes, and the default value (NaN) is 127. By reading the flag for the surface soil moisture data, the user gets information for that grid point. No activated bits, i.e. a “0” (zero) informs the user that the sm value for that grid point has been checked, but there was no inconsistency found. Bit 0 (2^0) and combinations where this bit is active denote, that the soil for that location is covered with snow or the temperature is below zero; Bit 1 (2^1) and combinations where this bit is active indicate that the observed location is covered by dense vegetation; Bit 2 (2^2) and combinations where this bit is active is activated indicates undefined other cases, e.g. no convergence in the model, thus no valid soil moisture estimates; Bit 3 (2^3) and combinations where this bit is active denote days that are masked because not all data sets have valid observations and those which do are deemed unreliable when used alone; Bit 4 (2^4) and combinations  where this bit is active denote locations where the weight of measurements is too low; Bit 5 (2^5) and combinations where this bit is active denote locations where barren grounds dominate the scene. Please see Table 11 for the meaning of all other flag values.

Table 11: Attribute Table for Variable flag, only available in the Daily files

freqbandID (Daily, Dekadal, Monthly)

The surface soil moisture data has its sources from multiple and different satellite sensors, which operate in various frequencies. The freqbandID values are representing the operating frequencies and comprise the combination of different frequency bands. Table 12 lists these combinations:

Table 12: Attribute Table for Variable freqbandID

mode (Daily)

The NetCDF variable mode stores the information of the sensor’s orbit direction. Ascending direction are denoted as 1, and descending orbit as 2. In cases where the orbit direction cannot be determined, the NaN value 0 (zero) is used. A value of 3 means that the merged data comprises both ascending and descending satellite modes.

Table 13: Attribute Table for Variable mode

nobs (Dekadal, Monthly)

The NetCDF variable nobs stores an integer which is the number of valid observations which have been used to compute the dekadal or monthly mean.

Table 14: Attribute Table for Variable nobs

sensor (Daily, Dekadal, Monthly)

The values for sensor are stored as signed integer, with NaN as 0 (zero). These values indicate the satellite sensors which have been used for a specific grid point. Valid values range from 1 to 131072. Table 15 lists all available sensor combinations.

Table 15: Attribute Table for Variable sensor

sm (Daily, Dekadal, Monthly)

The “sm” parameter holds the surface soil moisture estimates that are generated by blending passive and active microwave soil moisture retrievals as a weighted average with the weights being proportional to the SNR of the data sets. SNRs are estimated using triple collocation (TC) analysis (Gruber et al., 2017). The data are provided in percentage of saturation [%] units for the ACTIVE product, and volumetric [m³m^-3] units for the PASSIVE and COMBINED products. Figure 6 shows a plotted example of the "sm" variable.

Table 16: Attribute Table for Variable sm for the PASSIVE and COMBINED products

Figure 6: Visualisation of the NetCDF data variable “sm” for July 1^st  2024 (from the daily COMBINED CDR product).

sm_uncertainty (Daily, Dekadal, Monthly)

The merging of soil moisture data from different sensors requires a harmonization of the data. The data need to be brought into a common climatology by running them through several scaling procedures performing the CDF matching technique. The provided “sm_uncertainty” parameter represents the error standard deviation of the data sets (in the respective climatology of the data set), estimated through TC analysis, which are used to calculate the relative weighting of the data sets. In periods where TC cannot be applied, or in cases where the TC-based error standard deviation estimates do not converge, sm_uncertainty is set to NaN. The unit of sm_uncertainty for the ACTIVE product is percentage of saturation [%]. For the PASSIVE and the COMBINED product the unit is volumetric soil moisture [m³m^-3]. On days where only measurements of one single data set are available, sm_uncertainty represents their error standard deviation as obtained from TC analysis. On days where two or more data sets are merged, sm_uncertainties represents the estimated error standard deviation of the merged soil moisture measurements, obtained by propagating the TC-based error standard deviation estimates of the contributing data sets through the merging algorithm using a standard error propagation scheme. sm_uncertainty values exceeding the maximum value of 100 (ACTIVE) or 1 (PASSIVE and COMBINED) are set to the maximum value respectively. Table 18 lists the availability of the soil moisture uncertainty information for each product. Figure 7 plots the uncertainty for day 2024-07-01 of the daily CDR COMBINED product.

Table 17: Attribute Table for Variable sm_uncertainty

Table 18: sm_uncertainty data provided in the Daily data sets

Figure 7: Visualisation of the NetCDF data variable “sm_uncertainty” for July 1^st  2024 (from the daily COMBINED CDR product).

t0 (Daily)

The original observation timestamp is stored within the NetCDF variable t0 (t-zero). Time values coming from two different sensors are averaged. Values of -9999.0 are used as NaN values. t0 data values are stored as number of "days since 1970-01-01 00:00:00 UTC."

NetCDF global attributes

Tables 20 to 22 list the NetCDF global attributes for the ACTIVE, PASSIVE and COMBINED products.

Root-zone Soil Moisture product

rzsm (Daily, Dekadal, Monthly)

• rzsm_1 (Daily, Dekadal, Monthly)
  This variable contains the root-zone soil moisture estimates derived from the COMBINED product via the infiltration model described by Pasik et al. (2023). The model infilitration speed was calibrated with in situ measurements for three different depth layers. Layer 1 is representative of the first 10 cm of soil (in m³m^-3). Corresponding uncertainty estimates are provided in the "uncertainty_1" variable.
• rzsm_2 (Daily, Dekadal, Monthly)
  Same as rzsm_1 but for the second root-zone layer at 10-40 cm depth (in m³m^-3). Corresponding uncertainty estimates are provided in the "uncertainty_2" variable in the daily product only.
• rzsm_3 (Daily, Dekadal, Monthly)
  Same as rzsm_1 but for the third root-zone layer at 40-100 cm depth (in m³m^-3). Corresponding uncertainty estimates are provided in the "uncertainty_3" variable in the daily product only.
• rzsm_1m (Daily, Dekadal, Monthly)
  The weighted average of layers 1-3. Weights are chosen according to the layer width. The third layer therefore contributes most to this estimate (in m³m^-3). Note that no uncertainty estimates are provided for this layer.

Figure 8: Visualisation of the NetCDF data variable "rzsm_1m" for day 2024-04-01 from the daily RZSM CDR (v202505). Gray areas indicate no / masked data.

uncertainty (Daily)

• uncertainty_1
  Contains uncertainty estimates for the root-zone soil moisture values of Layer 1 (0-10 cm) according to Pasik et al. (2023). Given in m³m^-3. This field is only available for the daily product.
• uncertainty_2
  Same as uncertainty_1 but for Layer 2 (10-40 cm).
• uncertainty_3
  Same as uncertainty_1 but for Layer 3 (40-100 cm).

For additional information we refer to the attributes given in the netCDF files directly.

NetCDF global attributes

Table 25 lists the NetCDF global attributes for the RZSM product.

Freeze/Thaw product (Daily)

For more details on each variable, we refer to the netCDF attributes given in the data files directly.

ft

Soil moisture freeze-thaw state (binary) 0=not frozen, 1=frozen (Figure 9).

Figure 9: Visualisation of the NetCDF data variable "ft" for day 2016-03-01 from the F/T CDR (from v202505). Gray areas indicate no data.

Other Variables

• dnflag
  Same as for the surface soil moisture product
• ft_agreement
  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 from multiple sensors contradict.
• mode
  Same as for the surface soil moisture products
• sensor
  Same as for the surface soil moisture products
• sensor_count
  Absolute number of measuring sensors / overpasses that were considered in the "ft" and "ft_agreement" fields.
• sensor_count_frozen
  Absolute number of measuring sensors / overpasses that were considered and detected frozen soils.

NetCDF global attributes

Table 27 lists the NetCDF global attributes for the F/T product.

 Examples of known climate applications and best practices

A tutorial on how to use the data can be found here. The tutorial covers how to:

• download C3S Satellite Soil Moisture data from the Copernicus Climate Data Store (CDS)
• read data stacks in python using xarray
• perform simple analyses of soil moisture anomalies over selected study areas.

The data is used in the following state of the climate reports:

European State of the Climate

The data has contributed to the soil moisture section of the European State of the Climate report of 2024. Monthly, Seasonal and Annual Soil Moisture anomalies are mapped out.  In 2024, surface soil moisture was drier than average for Europe as a whole. There was a clear east-west contrast, with eastern Europe experiencing drier-than-average conditions and western Europe being wetter than average (Figure 10).

All produced soil moisture figures can be found here, and the full 2024 report is available here.

Figure 10. Annual surface soil moisture anomalies (%) in 2024, showing positive (green) and negative (brown) anomalies, expressed as a percentage of the annual average for the 1991–2020 reference period. Data: C3S Satellite SM.

BAMS

The data contributed to section 11 of the BAMS State of the Climate in 2024. In 2024, global soil moisture conditions were wetter than the 1991–2020 average (Figure 11), with notable regional contrasts (Figure 12). As can be seen in Figure 11 the Northern Hemisphere experienced wetter-than-normal conditions, while the Southern Hemisphere remained drier than average. 

Figure 11. (top) Time series of global (black), Northern Hemisphere (purple), and Southern Hemisphere (orange) monthly surface soil moisture anomalies (m3 m−3) for the period 1991–2024 (1991–2020 base period), and (bottom) the valid observations as a percentage (%) of total global land surface. Data are masked where no retrieval is possible or where the quality is not assured and flagged, for example due to dense vegetation, frozen soil, permanent ice cover, or radio frequency interference. (Source: Copernicus Climate Change Service [C3S] Soil Moisture.)

Figure 12. Copernicus Climate Change Service (C3S) average surface soil moisture anomalies (m3 m−3). Data are masked where no retrieval is possible or where the quality is not assured and flagged, for example due to dense vegetation, frozen soil, or radio frequency interference

Known Limitations

The known limitations can be split into those specific to deriving soil moisture from passive microwave observations, those specific to deriving soil moisture from active microwave observations and practical user considerations for the C3S Soil Moisture products in general.

Known Limitations for Passive product

The known limitations in deriving soil moisture from passive microwave observations are provided in detail in the ATBD (Preimesberger et al., 2025a) Chapter 3.1.6 ("Known Limitations"). It should be noted that these issues do not only apply to the current CDR/ICDR data set release, but also to soil moisture retrieval from microwave observations in general.

Vegetation

Vegetation affects the microwave emission, and under a sufficiently dense canopy the emitted soil radiation will become completely masked by the overlaying vegetation. Areas affected by dense vegetation are masked out permanently in C3S SM (i.e., tropical rainforests). Other, seasonally dynamic regions, are masked in some input products (mainly Ku- and X-band) but available from others (C- and L-band).  

Frozen surfaces and snow

Under frozen surface conditions the dielectric properties of the water changes dramatically. Therefore, C3S SM aims to mask all retrievals from frozen soils and provides a separate flag for these conditions. In addition, the Freeze/Thaw product provides the same (binary) information.  

Water bodies

Water bodies within the satellite footprint can strongly affect the observed brightness temperature due to the high dielectric properties of water. The impact of open water is either mitigated by the retrieval algorithms, or affected regions are masked out in C3S SM. 

Rainfall

Rainstorms during the satellite overpass affect the brightness temperature observation. The signal characteristics are similar to those observed for barren grounds. At the moment there is no differentiation in the quality flags. 

Radio Frequency interference

Natural emission in several low frequency bands are affected by artificial sources, so called Radio Frequency Interference (RFI). Areas affected by RFI are flagged in C3S SM. However, the passive product in countries such as Turkey or Ukraine is still known to be affected by insufficiently captured RFI interference.

Known Limitations for Active product

The known limitations in deriving soil moisture from active microwave observations are provided in Dorigo et al. (2017). It should be noted that these issues do not only apply to the current CDR/ICDR data set release, but also to soil moisture retrieval from active microwave observations in general.

Subsurface scattering effects in deserts

Radar backscatter can increase under very dry soil conditions due to the presence of near-surface rocks, which can subsequently lead to an (erroneous) increases in soil moisture in some retrieval models (Wagner et al., 2022). This mainly affects the ACTIVE product of C3S, but also COMBINED in some regions.

Intercalibration of ERS and ASCAT

The generation of the European Remote Sensing Satellite (ERS) and ASCAT products is still based on their individual time series. The merged ERS + ASCAT dataset could significantly profit from an appropriate Level 1 intercalibration. Besides improving the quality of the individual measurements this would improve the robustness of the calculation of the dry and wet references.

Data gaps

Similar as for the passive products, merging ERS and ASCAT into a merged dataset is based on a strict separation in time. Gaps in ASCAT time series can be potentially filled with ERS observations, although the spatial and temporal overlap between both sensors is limited.

Practical Usage Considerations

Some Practical Usage Considerations are provided in the following section. These considerations result from direct user feedback on the use of the ESA CCI SM product during the period 2011 to 2017 and form the core of the ESA CCI SM product Frequently Asked Questions (FAQ).

Climate trends in general and relative dynamics

Before merging the ACTIVE and PASSIVE products into a COMBINED product, we first scale both data sets into the dynamic range of the Global Land Data Assimilation System (GLDAS)-Noah surface soil moisture fields (dataset described in Section 1.1.4.1). We perform this processing step to obtain a final product in absolute volumetric units [m³/m³]. Even though the original dynamics of the remote sensing observations are preserved, this step imposes the absolute values and dynamic range (min-max) of the GLDAS-Noah product on the combined product. As a consequence, the COMBINED product cannot be considered an independent dataset representing absolute true soil moisture. Hence, the statistical comparison metrics like root-mean-square-difference and bias based on our combined dataset are scientifically not meaningful. However, the product can be used as a reference for computing correlation statistics or the unbiased root-mean-square-difference. A model-free version of the COMBINED product, where GLDAS Noah is replace with a scaling reference from L-band satellite measurements, is currently in development by ESA CCI SM.

Temporal availability

In the time period 1978 – 1987, the product is only based on the SMMR radiometer. SMMR had a 24 hr on-off cycle to save power, but this was sometimes changed. For example, in 1986 there is a period with daily observations (they switched the 24 hr on-off cycle off). So, the observation density changes over time. In addition, SMMR observes the Earth surface at 12:00 and 24:00 local solar time, which sometimes leads to a shift of one day for the night-time observations.

Spatial availability

For areas with dense vegetation (tropical, boreal forests), strong topography (mountains), ice cover (Greenland, Antarctica, Himalayas), a large fractional coverage of water, or extreme desert areas we are not able to make meaningful soil moisture retrievals. Hence, we mask them (see Table 11).

Especially images of the first years from 1978 onwards show data stripes. This is a typical characteristic in the observation through satellite microwave instruments. Microwave images from the earth's surface are taken while the satellite is orbiting the earth in fixed paths. These paths represent the data stripes on the images. If we move forward in time, the spatial data availability is getting higher and higher, and the data stripes are getting closer and closer. This is due to the fact that not only the number of available input data sources (satellites) is growing, but also the technology of satellites instruments is getting better and better.

Some image files do not provide any soil moisture data at all. All values are NaN. We call these images "blank" or "empty" days. Because of many reasons, e.g. technical failures, there is no data available for that day. Especially the SMMR and the AMI-WS (ERS1/2) instruments are known for their data outages causing these blank days. Other instruments also have short time periods with no data availability. In most cases these empty periods are replaced or filled with data from the remaining microwave sensor(s). So blank days are most likely experienced on days where only one sensor is used as input source, which then fails to deliver data for that time.

When the soil is frozen or covered with snow, we are not able to make a meaningful soil moisture retrieval. Such observations are masked and indicated with flag values where bit 0 (least significant) in the binary conversion of the number is activated.

Based on the sensitivity to vegetation density, we decided for each pixel whether to use either the scatterometer or the radiometer retrievals, or to use a weighted average of the available observations from different sensors. This merging scheme may lead to data gaps in the following situations:

• No observation is available (sensors fail). This is for example the case between 2001 and 2006 in Western Europe, parts of Siberia, parts of North and South America, due to failure of the onboard storage capacity of ERS-2.
• Changes in observation wavelength (frequency) may lead to increased sensitivity to vegetation. Hence, larger areas need to be masked. This is for example visible for the period after 1987 where based on the SSM/I Ku-band observations, the extent of masked areas increases with respect to the preceding SMMR period (C-Band).

Data inconsistencies

For AMI-WS and ASCAT soil moisture values may show jumps where ascending and descending swaths overlap with each other, e.g. in the higher northern latitudes. This is a natural phenomenon related to the differences in overpass time (up to 24h). Potentially different soil moisture values may result from precipitation or evaporation taking place between the two observation time steps. We therefore recommend using the original observation time (t0) and not the nominal overpass time if you want to make a direct comparison e.g. with in-situ observations.

Data usage in models

In theory, the COMBINED product combines the best of the active and passive products, so we consider it as most suitable for model verification. Only for the mountain ranges in southern Turkey the merged dataset is known to be inferior to the PASSIVE product, see also: Szczypta et al. (2014).

Converting volumetric soil moisture in soil wetness content

Eq. (1) shows how to convert volumetric soil moisture (SM_vol in m³m^-3) into degree of saturation (SM_sat in %).

Where ϕ_vol is the soil porosity in m³m^-3 which can be obtained from soil porosity maps such as the one provided for CCI SM though the CEDA data archive (Dorigo et al., 2024; last access: 2025-08-25).

Product Change Log

Table 28 provides an overview of the differences between different versions of the product up-to, and including, the current version:

Table 28: Changes in the product between versions (latest vesion at the top).

Data access information 

Climate Data Store (CDS)

The Copernicus Climate Change Service provides data storage infrastructure and make ECV data products available through the CDS. The store provides not only consistent estimates of ECVs, but also climate indicators, and other relevant information about the past, present, and future evolution of the coupled climate system, on global, continental, and regional scales. It supports users with data dissemination and visualisation tools¹.

C3S Soil Moisture data

C3S satellite soil moisture CDRs and ICDRs are available via the CDS. The shared DOI for all satellite soil moisture versions is DOI: 10.24381/cds.d7782f18 and should be cited by all studies and applications that use any of the data products.

User Support

A dedicated support portal (https://confluence.ecmwf.int/site/support) has been set up by the Copernicus User Support (CUS) team, which provides support to users of the Copernicus Atmosphere Monitoring Service (CAMS) and C3S services at ECMWF. All enquiries about the TWSA dataset can be submitted through the service desk where appropriate agents will deal with it. Once submitted, the user may add comments or further information to the issue, including responding to questions / requests for additional information from the support team.

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Annex A 

Table 29: Major characteristics of passive and active microwave instruments and model products

Related articles

• Page:
  Soil Moisture v202505: Product Quality Assessment Report (PQAR)
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  Soil Moisture v202505: Product User Guide and Specification (PUGS)
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