Last modified on Nov 06, 2023 16:25

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

Issued by: EODC/Alena Dostalova

Date: 24/08/2022

Ref: C3S2_312a_Lot4.WP2-FDDP-SM-v1_202212_SM_PUGS-v4_i1.1

Official reference number service contract: 2021/C3S2_312a_Lot4_EODC/SC1

History of modifications

List of datasets covered by this document

Related documents

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”).

Bias: “Bias is defined as an estimate of the systematic measurement error” GCOS-200 [D9]

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)

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.

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.

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.

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) [D10]. “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) [D9]

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)

Scope of the document

This Product User Guide and Specification (PUGS) relates to the Copernicus Climate Change Service (C3S) 312b Lot4 Soil Moisture (SM) Climate Data Record (CDR) v202212 and Intermediate Climate Data Record (ICDR) products. It describes the Climate Data Records (CDRs) in a manner that is understood by the product user with focus on the:

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

Executive summary

The CDR and the ICDR are soil moisture Climate Data Records (CDRs) based on the European Space Agency (ESA) Climate Change Initiative (CCI) Soil Moisture data product version 7. The C3S Lot4 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 three data products: The ACTIVE and the PASSIVE products are created by fusing scatterometer and radiometer soil moisture data, respectively; the COMBINED product is a blended product based on the former two products.

All products provide datasets featuring Daily, Dekadal (10-day) mean, and Monthly mean 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 product description including the target requirements and information about the data usage. Chapter 2 provides information about the data access. In Chapter 3, structure, file format and NetCDF attributes of the ACTIVE, PASSIVE and COMBINED products are described and ancillary datasets are listed. NetCDF data file variables and attributes are further detailed in Chapter 4. Finally, Chapter 5 provides an overview of the Earth Observation and modelled data used to create the C3S products.

For more information, the theoretical and algorithmic base of the CDRs is described in [D1], and the products and the applied algorithms are extensively discussed in Dorigo et al. (2017), and in Gruber et al. (2019).

1. Climate Data Records: CDR and ICDR

The ECMWF C3S 312b Lot4 Soil Moisture provides two types of data record: the CDR, and the ICDR. The CDR and ICDR consist of three surface soil moisture data sets: ACTIVE, PASSIVE AND COMBINED.

The ACTIVE and the PASSIVE product are created by using scatterometer, and radiometer soil moisture products, respectively; the COMBINED product is a blended product based on the former two data sets. For each data set the Daily, the Dekadal (10-days) mean, and the Monthly mean are available as NetCDF-4 classic format [D5] and comprise global merged surface soil moisture images at a 0.25 degree spatial resolution.

The theoretical and algorithmic basis of the product are described in [D1]. 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 [D2] and in 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 Essential Climate Variable (ECV) 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/).

1.1. CDR

A detailed description of the algorithm for the product generation is provided in [D1]. The underlying algorithm is based on that used in the generation of the ESA CCI SM v07.1 product. In addition, detailed provenance traceability information can be found in the metadata of the product (Section 3.3).

A new major version of the CDR is released each year and usually 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

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

1.2. ICDR

The Intermediate Climate Data Record 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 23) are processed to extend the ICDR products.

1.3. Product description

Both, the CDR and the ICDR comprise the ACTIVE, PASSIVE, and the COMBINED surface soil moisture data products. For each of these data sets, the Daily, the Dekadal mean, and the Monthly mean are available as global images stored in NetCDF4-classic files following the CF1.8 convention [D5]. The Dekadal files feature a 10-day mean of a month, 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. While the Monthly mean represents the soil moisture mean of each month, the Daily files are created directly through the merging of microwave soil moisture data from multiple satellite instruments (see Table 1). The Dekadal and Monthly means are calculated from averaging these Daily files.

The soil moisture attributes of the Daily files are: day/night flag, satellite orbit mode, instrument operating frequency, sensor, original observation timestamp, soil moisture observation status flag, and soil moisture uncertainty. 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).

The detailed specifications including the geophysical parameters used in the CDR products are described in Chapter 3.

Table 1: CDR / ICDR products and data sets: The mean data sets are calculated from the Daily files, which represent the daily observation derived by merging soil moisture data from multiple microwave sensors.

1.3.1. ACTIVE Product

The ACTIVE product is the output of merging scatterometer-based soil moisture data, which are derived from Active Microwave Instrument - WindScat (AMI-WS) and ASCAT (Metop-A, Metop-B, Metop-C). Please see Table 23 for detailed information of the active microwave instruments. The ACTIVE CDR product spans the time period from 1991-08-05 to 2022-12-31, and the ACTIVE ICDR product is available from 2023-01-01 onwards. Table 2 shows the used sensors in the corresponding periods:

Table 2: SNR blending period for the ACTIVE CDR and ICDR products

1.3.2. PASSIVE Product

The PASSIVE product merges data from 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 includes soil moisture data from 1978-11-01 to 2022-12-31, whereas the PASSIVE ICDR represents its extension from 2023-01-01 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:

Table 3: SNR blending period for the PASSIVE CDR and ICDR products.

*The [SSM/I, TMI, SSM/I] period is latitudinally divided into [90S, 37S] and [90N, 37N] for SSM/I, and the region in between for TMI.

1.3.3. 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 2022-12-31. The COMBINED ICDR extends the CDR from 2023-01-01 onwards. The merging periods and the used sensors are listed in Table 4:

Table 4: SNR blending period for the COMBINED CDR and ICDR products.

*The [SSM/I, TMI, SSM/I] period is latitudinally divided into [90S, 37S] and [90N, 37N] for SSM/I, and the region in between for TMI.

1.4. Product Target requirements

Table 5 assembles the C3S ECV Soil Moisture product target requirements adopted from the Global Climate Observing System (GCOS)-200 target requirements (GCOS, 2016) and shows to what extent these requirements are currently met by the latest C3S 312 Lot 4 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) [D7], Product Quality Assurance Document (PQAD) [D3] (methodology to assess) and Product Quality Assessment Report (PQAR) [D4] (assessment).

Table 5: 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 [D7].

1.5. Data usage information

1.5.1. Known Limitations for Passive product

The known limitations in deriving soil moisture from passive microwave observations are provided in detail in the Algorithm Theoretical Basis Document (ATBD) [D1]. 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 passive microwave observations in general.

1.5.1.1. Vegetation

Vegetation affects the microwave emission, and under a sufficiently dense canopy the emitted soil radiation will become completely masked by the overlaying vegetation.

Please see section 3.2.1 of [D1]

1.5.1.2. Frozen surfaces and snow

Under frozen surface conditions the dielectric properties of the water changes dramatically

Please see section 3.2.2 of [D1]

1.5.1.3. Water bodies

Water bodies within the satellite footprint can strongly affect the observed brightness temperature due to the high dielectric properties of water.

Please see section 3.2.3 of [D1].

1.5.1.4. Rainfall

Rainstorms during the satellite overpass affect the brightness temperature observation

Please see section 3.2.5 of [D1]

1.5.1.5. Radio Frequency interference

Natural emission in several low frequency bands are affected by artificial sources, so called Radio Frequency Interference (RFI).

Please see section 3.2.6 of [D1]

1.5.2. Known Limitations for Active product

The known limitations in deriving soil moisture from active microwave observations are provided in detail in Chapter 7 of the ESA CCI ATBD [D6]. 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.

1.5.2.1. 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.

1.5.2.2. 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 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.

1.5.2.3. 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.

1.5.2.4. Positive SM trend in ASCAT

ASCAT SM shows an assumed unnatural wetting trend in some areas, especially in RFI affected regions (densely populated areas) and regions with significant changes in landcover/vegetation over time (Hahn et al., 2023). ASCAT SM is the main input for the C3S SM ACTIVE product. Therefore the issue is also found there.

1.5.3. 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 Asged Questions (FAQ).

1.5.3.1. 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 chapter 3.2). 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.

1.5.3.2. 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.

1.5.3.3. 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 14).

• 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 number 1 in the NetCDF file.
• 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).

1.5.3.4. 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.

1.5.3.5. Data characteristics

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

Figure 1: Temporal coverage of input products used to construct the  ACTIVE, (blue) PASSIVE, (red) COMBINED (red and blue) CDR/ICDR. The periods of unique sensor combinations are referred to as 'merging periods'.

1.5.3.6. 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).

1.5.3.7. 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.

1.6. Product Change Log

Table 6 provides an overview of the differences between different versions of the product up-to, and including, the current version:

Table 6: Changes in the product between versions.

2. Data access information

2.1. Climate Data Store

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.

2.2. C3S Soil Moisture data

C3S satellite soil moisture CDRs and ICDRs are available via the CDS. The DOI for all satellite soil moisture versions is DOI: 10.24381/cds.d7782f18.

2.3. User Support

A dedicated service desk has been set up, 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 soil moisture dataset must be submitted through the service desk where appropriate agents will deal with it.
There is a forum (https://confluence.ecmwf.int/display/CUSF/forum) where users can browse issues or a knowledge base (https://confluence.ecmwf.int//display/CKB) where customers can also submit direct enquiries. 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.
The C3S Lot4 service provides dedicated level 2 user support to the CUS Jira Ticketing Service

3. Specifications for CDR and ICDR

3.1. Geophysical parameters

The ACTIVE product is the output of merging scatterometer-based soil moisture data, which were derived from AMI-WS and ASCAT (Metop-A, Metop-B, Metop-C). The PASSIVE product merges data from SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FengYun-3B, FengYun-3C, FengYun-3D and GPM. The COMBINED product merges all ACTIVE and the PASSIVE input products. The merging algorithm described in [D1] is an evolution of the algorithm described in Dorigo et al. (2017); Liu et al. (2012); Liu et al. (2011); Wagner et al. (2012)), which was used in all previous product versions. The introduced algorithm is described in detail in Gruber et al., 2019. The homogenised and merged products present surface soil moisture with a global coverage and a spatial resolution of 0.25°. The Daily data set has a temporal resolution 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 and the COMBINED product are provided in volumetric units [m³m^-3], while the ACTIVE soil moisture data are expressed in percentage of saturation [%].

3.1.1. 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). Lakes and rivers with areas less than 600 km² were not considered in the calculation of the land points.

Figure 2 shows the land points which are used for each product described in this document.

Figure 2: Land mask used for the merged product. 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° and –60°.

The Tropical forest mask – derived from the mean AMSR-E Vegetation Optical Depth (VOD) (Figure 3) for 2002 to 2011 – has been applied to the soil moisture product images. The soil moisture and the soil moisture uncertainty values are set to NaN in these rainforest regions.

Figure 3: Tropical forest Mask used applied to the product images. 1 (green) represents rainforest regions.

3.2. 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.

3.2.1. 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 [D1].

3.2.2. ASCAT Advisory Flag

The following two ASCAT advisory flags (Scipal, 2005) are used to mask out regions of frozen soils, or snow covered soils:

• Probability of snow covered land

Derived from historic analysis of SSM/I snow cover data (averaged over the 9 years 1996-2004) and gives the probability for the occurrence of snow for any day of the year.

• Probability of frozen land

Derived from historic analysis of modelled climate data (7 years 1995-2001 of ECMWF ReAnalysis 40 (ERA-40) soil temperature) and gives the probability for the frozen soil/canopy conditions for each day of the year.

3.2.3. 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 4). The provided global values represent the averaged VOD from 2002 to 2011.

Figure 4: AMSR-E (from LPRM) average vegetation optical depth derived for the period 2002-2011 in the 6.9 GHz band.

3.2.4. 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 5).

Figure 5: Topographic complexity from the USGS 30-second Global Elevation Data (GTOPO30).

3.2.5. 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. 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 6).

Figure 6: Wetland fraction derived from the Global Lakes and Wetlands Database (GLWD).

3.3. Structure and file format

3.3.1. Data file 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 1.8. 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:

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

<Variable>
Active product: SSMS (surface soil moisture degree of saturation absolute); Passive and Combined product: SSMV (surface soil moisture volumetric absolute).

<Dataset>
ACTIVE; PASSIVE; COMBINED

<Interval>
DAILY; DEKADAL; 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>
Type of Climate Data Record: CDR; ICDR
v<Version>
Major.Minor.Run e.g. v202212.0.0
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.

3.4. NetCDF global attributes for the ACTIVE products

Table 7: Global NetCDF Attributes for the ACTIVE Daily product

3.5. NetCDF global attributes for the PASSIVE products

Table 8: Global NetCDF Attributes for the PASSIVE Daily product

Global Attribute Name	Content
title	C3S Surface Soil Moisture merged PASSIVE Product
institution	EODC (AUT); TU Wien (AUT); VanderSat B.V. Noordwijk (NL)
contact	C3S_SM_Science@eodc.eu
source	LPRMv07/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv07/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv07/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv07/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv07/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv07/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv06/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 & RE04; LPRMv06/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/MWRI/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/MWRI/FengYun-3C L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/MWRI/FengYun-3D L3 Surface Soil Moisture, Ancillary Params, and quality flags; GPM: LPRMv7/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags
history	<date and time auditing trail of modifications to the original data> - file produced
references	https://climate.copernicus.eu; Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M., Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014; Liu, Y.Y., Parinussa, R.M., Dorigo, W.A., De Jeu, R.A.M., Wagner, W., van Dijk, A.I.J.M., McCabe, M.F., & Evans, J.P. (2011): Developing an improved soil moisture dataset by blending passive and active microwave satellite based retrievals. Hydrology and Earth System Sciences, 15, 425-436; Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012): Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012. XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia; Gruber, A., Dorigo, W. Crow, W., & Wagner, W. (2017). Triple collocation-based merging of satellite soil moisture retrievals. IEEE Transactions on Geoscience and Remote Sensing, 1-13. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y.,Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I. Smolander, T., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.07.001.
tracking_id	<xxxxxxxx-yyyy-zzzz-nnnn-mmmmmmmmmmmm> a UUID value
conventions	CF-1.8
product_version	v202212
summary	The data set was produced with funding from the Copernicus Climate Change Service.
keywords	Soil Moisture/Water Content
id	<filename>
naming_authority	EODC
keywords_vocabulary	NASA Global Change Master Directory (GCMD) Science Keywords
cdm_data_type	Grid
comment	These data were produced as part of the Copernicus Climate Change Service. Service Contract No 2021/C3S2_312a_Lot4_EODC/SC1
date_created	<file creation date>
creator_name	Earth Observation Data Center (EODC)
creator_url	https://eodc.eu
creator_email	C3S_SM_Science@eodc.eu
project	Copernicus Climate Change Service.
geospatial_lat_min	-90.0
geospatial_lat_max	90.0
geospatial_lon_min	-180.0
geospatial_lon_max	180.0
geospatial_vertical_min	0.0
geospatial_vertical_max	0.0
time_coverage_start	<date time start>
time_coverage_end	<date time end>
time_coverage_duration	<Daily> P1D; <Dekadal mean>: P10D|P8D|P9D|P10D|P11D; <Monthly mean> : P1M
time_coverage_resolution	P1D
standard_name_vocabulary	NetCDF Climate and Forecast (CF) Metadata Convention
license	Copernicus Data License
platform	Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP, FY-3B, FY-3C, FY-3D, GPM
sensor	SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer, MWRI, MWRI, MWRI, GMI
spatial_resolution	25km
geospatial_lat_units	degrees_north
geospatial_lon_units	degrees_east
geospatial_lon_resolution	0.25 degree
geospatial_lat_resolution	0.25 degree

3.6. NetCDF global attributes for the COMBINED products

Table 9: Global NetCDF Attributes for the COMBINED Daily product

Global Attribute Name	Content
title	C3S Surface Soil Moisture COMBINED active+passive Product
institution	EODC (AUT); TU Wien (AUT); VanderSat B.V. Noordwijk (NL)
contact	C3S_SM_Science@eodc.eu
source	WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; ASCSMR02/ASCAT/MetOp-A SSM Swath Grid 12.5 km sampling; ASCSMR02/ASCAT/MetOp-B SSM Swath Grid 12.5 km sampling; ASCSMR02/ASCAT/MetOp-C SSM Swath Grid 12.5 km sampling; LPRMv07/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv07/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv07/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv07/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv07/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv07/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv06/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 & RE04; LPRMv06/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/MWRI/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/MWRI/FengYun-3C L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/MWRI/FengYun-3D L3 Surface Soil Moisture, Ancillary Params, and quality flags; GPM: LPRMv7/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags
history	<date and time auditing trail of modifications to the original data> - file produced
references	https://climate.copernicus.eu; Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M., Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014; Liu, Y.Y., Parinussa, R.M., Dorigo, W.A., De Jeu, R.A.M., Wagner, W., van Dijk, A.I.J.M., McCabe, M.F., & Evans, J.P. (2011): Developing an improved soil moisture dataset by blending passive and active microwave satellite based retrievals. Hydrology and Earth System Sciences, 15, 425-436; Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012): Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012. XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia; Gruber, A., Dorigo, W. Crow, W., & Wagner, W. (2017). Triple collocation-based merging of satellite soil moisture retrievals. IEEE Transactions on Geoscience and Remote Sensing, 1-13. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y.,Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I. Smolander, T., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.07.001.
tracking_id	<xxxxxxxx-yyyy-zzzz-nnnn-mmmmmmmmmmmm> a UUID value
conventions	CF-1.8
product_version	v202212
summary	The data set was produced with funding from the Copernicus Climate Change Service.
keywords	Soil Moisture/Water Content
id	<filename>
naming_authority	EODC
keywords_vocabulary	NASA Global Change Master Directory (GCMD) Science Keywords
cdm_data_type	Grid
comment	These data were produced as part of the Copernicus Climate Change Service. Service Contract No 2021/C3S2_312a_Lot4_EODC/SC1
date_created	<file creation date>
creator_name	Earth Observation Data Center (EODC)
creator_url	https://eodc.eu
creator_email	C3S_SM_Science@eodc.eu
project	Copernicus Climate Change Service.
geospatial_lat_min	-90.0
geospatial_lat_max	90.0
geospatial_lon_min	-180.0
geospatial_lon_max	180.0
geospatial_vertical_min	0.0
geospatial_vertical_max	0.0
time_coverage_start	<date time start>
time_coverage_end	<date time end>
time_coverage_duration	<Daily> P1D; <Dekadal mean>: P10D|P8D|P9D|P10D|P11D; <Monthly mean> : P1M
time_coverage_resolution	P1D
standard_name_vocabulary	NetCDF Climate and Forecast (CF) Metadata Convention
license	Copernicus Data License
platform	Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP; ERS-1, ERS-2, METOP-A, METOP-B, METOP-C, FY-3B, FY-3C, FY-3D, GPM
sensor	SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer; AMI-WS, ASCAT-A, ASCAT-B, ASCAT-C, MWRI, MWRI, MWRI, GMI
spatial_resolution	25km
geospatial_lat_units	degrees_north
geospatial_lon_units	degrees_east
geospatial_lon_resolution	0.25 degree
geospatial_lat_resolution	0.25 degree

4. NetCDF data file variables and attributes

Lon (Daily, Dekadal, Monthly)

Table 10: Attribute Table for Variable lon

 Lat (Daily, Dekadal, Monthly)

Table 11: Attribute Table for Variable lat

Time (Daily, Dekadal, Monthly)

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 12: Attribute Table for Variable time (reference time)

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 13: 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 activet 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 14 for the meaning of all other flag values.

Table 14: 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 15 lists these combinations:

Table 15: 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 16: 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 17: 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 18 lists all available sensor combinations.

Table 18: Attribute Table for Variable sensor

sm (Daily, Dekadal, Monthly)

The “sm” parameter holds the surface soil moisture estimates 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 7 shows a plotted example of the sm variable.

Table 19: Attribute Table for Variable sm for the PASSIVE and COMBINED products

Figure 7: Visualisation of the NetCDF data variable “sm” for day 2017-06-21 from the COMBINED CDR product (from v201801).

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 dataset), 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 21 lists the availability of the soil moisture uncertainty information for each product. Figure 8 plots the uncertainty for day 2017-06-21 of the CDR COMBINED product.

Table 20: Attribute Table for Variable sm_uncertainty

Table 21: sm_uncertainty data provided in the Daily data sets

Figure 8: Visualisation of the NetCDF data variable "sm_uncertainty" for day 2017-06-21 from the COMBINED CDR (from v201801).

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."

Table 22: Attribute Table for Variable t0

5. Overview of EO and modelled data used to create the C3S products

Table 23: Major characteristics of passive and active microwave instruments and model products

References

CRAPOLICHIO, R., BIGAZZI, A., DE CHIARA, G., NEYT, X., STOFFELEN, A., BELMONTE, M., WAGNER, W. & REIMER, C. 2016. The scatterometer instrument competence centre (SCIRoCCo): Project's activities and first achievements, 

DORIGO, W., WAGNER, W., ALBERGEL, C., ALBRECHT, F., BALSAMO, G., BROCCA, L., CHUNG, D., ERTL, M., FORKEL, M., GRUBER, A., HAAS, E., HAMER, P. D., HIRSCHI, M., IKONEN, J., DE JEU, R., KIDD, R., LAHOZ, W., LIU, Y. Y., MIRALLES, D., MISTELBAUER, T., NICOLAI-SHAW, N., PARINUSSA, R., PRATOLA, C., REIMER, C., VAN DER SCHALIE, R., SENEVIRATNE, S. I., SMOLANDER, T. & LECOMTE, P. 2017. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment.

GCOS (GLOBAL CLIMATE OBSERVING SYSTEM), 2016, THE GLOBAL OBSERVING SYSTEM FOR CLIMATE: IMPLEMENTATION NEEDS, GCOS-200, https://library.wmo.int/doc_num.php?explnum_id=3417 

GRUBER, A., DORIGO, W., CROW, W. & WAGNER, W. 2017. Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions of Geoscience and Remote Sensing.

GRUBER, A., SCANLON, T., VAN DER SCHALIE, R., WAGNER, W., and DORIGO, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.

GRUBER, A., DE LANNOY, G., ALBERGEL, C., AL-YAARI, A., BROCCA, L., CALVET, J. C., COLLIANDER, A., COSH, M., CROW, W., DORIGO, W., DRAPER, C., HIRSCHI, M., KERR, Y., KONINGS, A., LAHOZ, W., MCCOLL, K., MONTZKA, C., MUÑOZ-SABATER, J., PENG, J., REICHLE, R., RICHAUME, P., RÜDIGER, C., SCANLON, T., VAN DER SCHALIE, R., WIGNERON, J. P. & WAGNER, W. 2020. Validation practices for satellite soil moisture retrievals: What are (the) errors? Remote Sensing of Environment, 244, 111806. 

HAHN, S., WAGNER, W., ALVES, O., MUGUDA SANJEEVAMURTHY, P., VREUGDENHIL, M., & MELZER, T. (2023). Metop ASCAT soil moisture trends: Mitigating the effects of long-term land cover changes. In EGU General Assembly 2023. EGU General Assembly 2023, Wien, Austria. https://doi.org/10.5194/egusphere-egu23-16205

H SAF (2018a). ASCAT Surface Soil Moisture CDR2017 time series 12.5 km sampling – Metop (H113), EUMETSAT SAF on Support to Operational Hydrology and Water Management. http://dx.doi.org/10.15770/EUM_SAF_H_0005

H SAF (2018b). ASCAT Surface Soil Moisture CDR2017-EXT time series 12.5 km sampling – Metop (H114), EUMETSAT SAF on Support to Operational Hydrology and Water Management. https://navigator.eumetsat.int/product/EO:EUM:DAT:METOP:H114

H SAF (2018c) Algorithm Theoretical Baseline Document (ATBD), Metop ASCAT Soil Moisture Data Records v0.7 (H113) http://hsaf.meteoam.it/documents/ATDD/ASCAT_SSM_CDR_ATBD_v0.7.pdf

LIU, Y. Y., DORIGO, W. A., PARINUSSA, R. M., DE JEU, R. A. M., WAGNER, W., MCCABE, M. F., EVANS, J. P. & VAN DIJK, A. I. J. M. 2012. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment, 123, 280-297.

LIU, Y. Y., PARINUSSA, R. M., DORIGO, W. A., DE JEU, R. A. M., WAGNER, W., VAN DIJK, A., MCCABE, F. M. & EVANS, J. P. 2011. Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436.

OWE, M., DE JEU, R. & HOLMES, T. 2008. Multisensor historical climatology of satellite-derived global land surface moisture. Journal of Geophysical Research-Earth Surface, 113, F01002.

PARINUSSA, R. M., HOLMES, T. R. H., WANDERS, N., DORIGO, W. & DE JEU, R. A. M. 2015. A preliminary study towards consistent soil moisture from AMSR2. Journal of Hydrometeorology, 16, 932–947.

SCIPAL, K., NAEIMI, V., HASENAUER, S. 2005. Definition of Quality Flags. ASCAT Soil Moisture Report Series. Vienna: Vienna University of Technology.

SZCZYPTA, C., CALVET, J.-C., MAIGNAN, F., DORIGO, W., BARET, F. & CIAIS, P. 2014. Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts Geosci. Model Dev., 7.

TU WIEN (2013). ERS AMI-WS (ESCAT) Surface Soil Moisture Product generated from E1/2-SZ-WNF/UWI-00 dataset. Department of Geodesy and Geoinformation, TU Wien, 2013.

USGS 1996. Global 30-Arc-Second Elevation Data Set (GTOPO30). U.S. Geological Survey.

VAN DER SCHALIE, R., KERR, Y.H., WIGNERON, J.P., RODRIGUEZ-FERNANDEZ, N.J., AL-YAARI, and DE JEU, R.A.M. (2015)), "Global SMOS Soil Moisture Retrievals from The the Land Parameter Retrieval Model", Int. J. Appl. Earth Observ. Geoinf., doi: http://dx.doi.org/10.1016/j.jag.2015.08.005

WAGNER, W., DORIGO, W., DE JEU, R., FERNANDEZ, D., BENVENISTE, J., HAAS, E. & ERTL, M. 2012. Fusion of Active and Passive Microwave Observations To Create an Essential Climate Variable Data Record on Soil Moisture. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, I-7, 315--321.

WAGNER, W., LEMOINE, G., & ROTT, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70, 191-207

WAGNER, W., LINDORFER, R., MELZER, T., HAHN, S., BAUER-MARSCHALLINGER, B., MORRISON, K., CALVET, J-C., HOBBS, S., QUAST, R., GREIMEISTER-PFEIL, I., VREUGDENHIL, M. (2022). Widespread occurrence of anomalous C-band backscatter signals in arid environments caused by subsurface scattering. Remote Sensing of Environment, 276, 113025. doi:10.1016/j.rse.2022.113025

WESSEL, P. & SMITH, W. H. F. 1996. A global, self‐consistent, hierarchical, high‐resolution shoreline database. J. Geophys. Res., 10, 8741-8743.

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