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Table of Contents

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History of modifications

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Version	Date	Description of modification	Chapters / Sections
i0.1	16/12/2024	Document modified from the draft ATBD – update of Figure 5, Tables 5, 6 and 7 and FY satellite table (Section 1.1.9).	All
i1.0	18/12/2024	Internal review and document finalization	All
i1.1	17/01/2024	External review and document finalization	All

List of datasets covered by this document

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Deliverable ID	Product title	Product type (CDR, ICDR)	C3S version number	Product ID
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Passive) Daily	CDR	v5.0	v202312
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Passive) Dekadal	CDR	v5.0	v202312
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Passive) Monthly	CDR	v5.0	v202312
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Active) Daily	CDR	v5.0	v202312
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Active) Dekadal	CDR	v5.0	v202312
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Active) Monthly	CDR	v5.0	v202312
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Combined) Daily	CDR	v5.0	v202312
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Combined) Dekadal	CDR	v5.0	v202312
WP2-FDDP-SM-CDR-v5	Surface Soil Moisture (Combined) Monthly	CDR	v5.0	v202312

Acronyms

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Acronym	Definition
AMI	Active Microwave Instrument
AMI-WS	Active Microwave Instrument Wide Swath
AMSR-E	Advanced Microwave Scanning Radiometer-Earth Observing System
AMSR2	Advanced Microwave Scanning Radiometer 2
AMSU	Advanced Microwave Sounding Unit
ASAR	Advanced Synthetic Aperture Radar
ASCAT	Advanced Scatterometer (Metop)
ATBD	Algorithm Theoretical Baseline Document
CAS	Chinese Academy of Sciences
CCI	Climate Change Initiative
CDF	Cumulative Distribution Function
CDR	Climate Data Record
CDS	Climate Data Store
CEOP	Coordinated Energy and Water Cycle Observations Project
CMORPH	Morphing Method of the Climate Prediction Centre
CPC	Climate Prediction Centre
C3S	Copernicus Climate Change Service
DARD	Data Access Requirement Document
DGG	Discrete Global Grid
DMSP	Defense Meteorological Satellite Program
DTED	Digital Terrain Elevation Model
EASE	Equal-Area Scalable Earth
ECV	Essential Climate Variable
ENVISAT	Environmental Satellite
EO	Earth Observation
ERA-Interim	ECMWF ReAnalysis Interim
ERA-40	ECMWF ReAnalysis 40
ERA5-Land	ECMWF ReAnalysis 5 enhanced for Land variables
ERS	European Remote Sensing Satellite (ESA)
ESA	European Space Agency
EUMETSAT	European Organisation for the Exploitation of Meteorological Satellites
FAO	Food and Agriculture Organisation
FTP	File Transfer Protocol
GCOM	Global Change Observation Mission (JAXA)
GIMMS	Global Inventory Modelling and Mapping Studies
GLDAS	Global Land Data Assimilation System
GLWD	Global Lakes and Wetlands Database (GSPC/University of Kassel)
GPCC	Global Precipitation Climatology Centre
GPCP	Global Precipitation Climatology Project
GPM	Global Precipitation Measurement
GRACE	Gravity Recovery And Climate Experiment
GSWP	Global Soil Wetness Project
GTOPO30	Global 30 Arc-Second Elevation
HSP	Homogeneous (sensor) Sub-Periods
ICDR	Interim Climate Data Record
ICTB	Inter-Calibrated Brightness Temperature dataset
IIASA	International Institute for Applied Systems Analysis
ISMN	International Soil Moisture Network
ISRIC	International Soil Reference and Information Centre
ISS-CAS	Institute of Soil Sciences of the Chinese Academy of Sciences
ITRDB	International Tree-Ring Data Bank
JAXA	Dokuritsu-gyosei-hojin Uchu Koku Kenkyu Kaihatsu Kiko, (Japan Aerospace Exploration Agency)
JPL	Jet Propulsion Laboratory (NASA)
JRC	Joint Research Centre
LPRM	Land Parameter Retrieval Model
METOP	Meteorological Operational Satellite (EUMETSAT)
MIRAS	Microwave Imaging Radiometer with Aperture Synthesis (MIRAS)
MPDI	Microwave Polarization Difference Index
NASA	National Aeronautics and Space Administration
NDVI	Normalized Difference Vegetation Index
NESDIS	NOAA's National Environmental Satellite, Data, and Information Service
NIMA	National Imagery and Mapping Agency
NOAA	National Oceanic and Atmospheric Administration
NRT	Near Real Time
NSIDC	National Snow and Ice Data Center (radlab)
NWS	National Weather Service (NOAA)
QCM	Quantile Category Matching
RFI	Radio Frequency Interference
RMSE	Root Mean Square Error
SAR	Synthetic Aperture Radar
SCAT	Scatterometer
SM	Soil Moisture
SMAP	Soil Moisture Active and Passive mission
SMMR	Scanning Multichannel Microwave Radiometer
SMOS	Soil Moisture and Ocean Salinity (ESA)
SNR	Signal-to-Noise Ratio
SOW	Statement of Work
SSF	Surface State Flag
SSM	Surface Soil Moisture
SSM/I	Special Sensor Microwave Imager
TCA	Triple Collocation Analysis
TDR	Time Domain Reflectometry
TMI	TRMM Microwave Imager
TRMM	Tropical Rainfall Measuring Mission
TWS	Terrestrial Water Storage
USGS	United States Geological Survey
VIC	Variable Infiltration Capacity
VOD	Vegetation Optical Depth
VUA	Vrije Universiteit Amsterdam
WACMOS	Water Cycle Multimission Observation Strategy
WARP	Water Retrieval Package
WindSat	WindSat Radiometer

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”.

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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

The Algorithm Theoretical Basis Document (ATBD) provides a detailed description of the algorithms that are used within the C3S Soil Moisture (SM) production system to produce the soil moisture Climate Data Record (CDR) and Interim Climate Data Records (ICDR). This document relates to C3S SM product versions v202312 for CDR and ICDR products.

Executive summary

The C3S SM production system is based on the algorithms initially developed within European Space Agency’s (ESA) Climate Change Initiative (CCI) Soil Moisture Project [D1] and has been updated and re-engineered to enable the provision of soil moisture retrievals on a near-real-time basis. In this context “near real time” refers to the provision of soil moisture information with a minimum delay of 10 days and a maximum of 23 days after sensing. This ATBD consists of a description of the input and auxiliary data used in the soil moisture retrievals, a description of the algorithms behind the soil moisture retrievals for both active and passive microwave observations, a description of the merging process and finally an overview of the output data.

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Chapter 3 gives an in-depth description of the algorithm used to retrieve soil moisture from passive microwave observations and points to relevant resources regarding active microwave retrievals from external operational sources. It contains a description of the main principles and methodologies in the LPRM, information on the parametrization for the most common frequency bands and the retrieval of soil moisture from daytime observations. An overview over currently known limitations of the model under certain conditions is given. The chapter also described the merging strategy applied in C3S soil moisture, i.e. how the retrievals from different satellite sensors are combined into a consistent merged soil moisture database, and how new data is repeatedly appended to this record. Chapter 4 briefly describes the output fields of the final soil moisture product.

Product Change Log

Table 1 provides an overview of the differences between different versions of the product up-to, and including, the current version v202312 (CDR v5.0). For a full list of all records provided in this version, see Table "List of datasets covered by this document".

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Version	Product Changes
v202312	Artificial breaks in the COMBINED product are now removed using the Quantile Category Matching method described in Preimesberger et al. (2021). Uncertainty values are now produced on a seasonal basis, meaning that the provided uncertainties now not only depend on the merged satellite sensors but also reflect their seasonal uncertainties better than before.
v202212	The PASSIVE and COMBINED product now include SM data from FengYun 3B/C/D and GPM, which are derived using v7 of the Land Parameter Retrieval Model (LPRM). TMI is extended to 2015, and scatterometer derived SM from ASCAT-C using the Water Retrieval Package (WARP) is included for the first time. This version also includes day-time observations for all passive data, a new approach to estimate intra-annual changes in error estimates for single sensors, updated flagging of frozen soils in active and passive data, cross flagging of frozen soils between active and passive data, and the first optional flag (i.e. soil moisture values are provided when this flag is active; users can decide whether to use them or not) in the product: for grid cells dynamically classified as "bare ground".
v202012	The PASSIVE and COMBINED product now include SM data from SMAP brightness temperature measurements derived through the LPRM v6 retrieval model. Intercalibration of AMSRE and AMSR2 observations, that lead to a negative break in PASSIVE SM in the past has been corrected. The CDF matching method has been updated. LPRM v6 is now used to derive SM for all decommissioned passive sensor products.
v201912	Product algorithm same as v201812. Product extended to 2019-12-31.
v201812	Product algorithm updated and now based on ESA CCI SM v4.4 rather than v4.3 applied to version v201806. Product extended to 2018-12-31.
v201806	The combined product is now generated by merging all active and passive L2 products directly, rather than merging the generated active and passive products. Spatial gaps in TC-based SNR estimates now filled using a polynomial SNR_VOD regression. sm_uncertainties now available globally for all sensors except SMMR. A p-value based mask used to exclude unreliable input data sets in the combined product has been modified and it is also applied to the passive product. Masking of unreliable retrievals is undertaken prior to merging.
v201801	Updated CDR includes SMOS data from the end of 2016 onwards. SMOS is also included in the ICDRs produced from January 2018 onwards. CDR produced until 2017-12-31; ICDR produced from 2018-01-01.
v201706	First release of the dataset. CDR produced until 2017-06-30; ICDR produced from 2017-07-01.

Instruments

The CDR/ICDR each comprise of three products, being termed "active", "passive" and "combined", with each product being provided at three different temporal resolutions (daily, dekadal and monthly). A suite of data from various sensors are used in generating each product depending upon the temporal period and latitude of the retrieval. A schematic overview of the instruments used in the generation of the CDR/ICDR is provided in Figure 1, with information about the sensor characteristics being provided in Sections 1.1 and 1.2.

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Figure 1: Temporal coverage of input products used to construct the CDR/ICDR: ACTIVE (blue), PASSIVE (red), COMBINED (red and blue) products.

Passive Microwave Systems

Satellite missions from different space agency during the last 40+ years were equipped with microwave radiometers to measure electromagnetic radiation emitted by Earth at different frequencies in the microwave domain. A selection of frequency bands (L-, C-, X-, and Ku-band) allows the retrieval of soil moisture information from these measurements using the Land Parameter Retrieval Model (LPRM; described in more detail in Chapter 3.1). The model is applied to all hereafter listed passive sensors as part of the production of C3S SM. An overview over the data providers, data properties, used sensors and their main characteristics are given in the following chapters.

Nimbus-7 SMMR (NASA)

Originating System	Scanning Multichannel Microwave Radiometer on board Nimbus 7
Data class	Earth observation
Sensor Type and key technical characteristics	Microwave Radiometer, 5 frequencies data in swath format (level 1b) 6.6, 10.7, 18 and 37 GHz in both horizontal and vertical polarization, 21 GHz in vertical polarization Inc. angle: 50.3 degrees Spatial sampling: 25x25 km Spatial resolution: 148x95 km (6.6 GHz), 91x59 km (10.7 GHz), 55x41 km (18 GHz), 46x30 km (21 GHz), 27x18 km (37 GHz) Temporal resolution, ~6 days, sun-synchronous observation with (day-time (12 hr) and night-time (24 hr) equator crossings. Data Citation: Njoku (1996)
Data Availability and Coverage	October 1978 – August 1987, 180°W 90°S – 180°E 90°N A summary of the data can be found on https://nsidc.org/data/nsidc-0036/versions/1 Fu et al. (1988)
Source Data Name and Product Technical Specifications	SMMR Level 1b Technical Specification Njoku (1996) https://nsidc.org/data/nsidc-0036/versions/1+ Fu et al. (1988)
Data Quantity	Total volume is 70 GB (compressed)
Data Quality and Reliability	Instrument specification: Radiometric sensitivity: 0.7 K (6.6 GHz), 0.8 K (10.7 GHz), 0.9 K (18 GHz), 1 K (21 GHz), 1.4 K (37 GHz) Validation reports Njoku et al. (1995) Gloersen et al. (1984)
Ordering and delivery mechanism	Ordering via NSIDC Data Centre http://nsidc.org Delivery is possible via FTP over Internet
Access conditions and pricing	Freely accessible
Issues	In 1986 there is a high frequency of bad antenna counts, especially during and for some time after the Special Operations Period (April – October 1986). Interpolating radiometric samples within scans and between adjacent scans tends to smear the effect of these "bad" antenna counts, which are most noticeable in browse image maps of 6.6 GHz horizontal polarization data.

DMSP-SSM/I (NESDIS NOAA)

Originating System	the Special Sensor Microwave Imager (SSM/I) of the Defense Meteorological Satellite Program (DMSP)
Data class	Earth observation
Sensor Type and key technical characteristics	Version 6 20+ year inter-calibrated microwave Radiometer observations from 6 different satellites (F8, F10, F11, F13, F14, F15) F8, F11, F13 are currently included in C3S SM. Other sensors are so far not used due to unstable orbits which can affect the soil moisture retrieval. 19.4, 37 and 85.5 GHz in both horizontal and vertical polarization, 22.3 GHz in vertical polarization Inc. angle: ~53 degrees Spatial sampling: 25x25 km Spatial resolution: 69x43 km (19.4 GHz), 50x40 km (22.2 GHz), 28x25 km (37 GHz), 15x13 km (85.5 GHz) Temporal resolution, ~daily, sun-synchronous observation with various observation time depending on satellite platform the ascending equator crossings for the different satellites are F8 6h15, F10 19h42, F11 17h00, F13 17h42, F14 20h21, F15 21h31 Data Citation: Wentz et al. (2007)
Data Availability and Coverage	F08 (Jun 87 – Aug 1991), F10 (Dec 1990 – Nov 1997), F11 (Nov 1991 – Dec 2000), F13 (Mar 1995 – Now), F14 (May 1997 – Aug 2008), F15 (Dec 1999, Now), 180°W 90°S – 180°E 90°N A summary of the data can be found on Wentz (1993)
Source Data Name and Product Technical Specifications	Version 6 20+ years SSMI brightness temperatures Technical Specification Wentz (1991) Wentz (1993) Wentz et al. (2007)
Data Quantity	100 GB/year
Data Quality and Reliability	Instrument specification: Radiometric sensitivity (noise): 0.4 K (19.4 GHz), 0.7 K (22.2 GHz), 0.4 K (37 GHz), 0.7 K (85.5 GHz) Validation reports Hillburn and Shie (2011)
Ordering and delivery mechanism	Ordering via SSMI Data Centre [No longer available online] Delivery is possible via Order Hard disk
Access conditions and pricing	Freely accessible
Issues	The data used here is based on a series of different satellites.

TRMM-TMI (NASA/JAXA)

Originating System	Tropical Rainfall Measurement Mission Microwave Imager (TRMM-TMI)
Data class	Earth observation
Sensor Type and key technical characteristics	5 frequency Radiometer 10.7, 19.4, 37, and 85.5 GHz in both horizontal and vertical polarization. 21.3 GHz in vertical polarization Inc. angle: 53.4 degrees Spatial sampling: 25x25 km Spatial resolution: 63.2x36.8 km (10.7 GHz), 30.4x18.4 km (19.4 GHz), 27x18.4 km (21.3 GHz), 16x9.2 km (37 GHz), 7.2x4.6km (85.5 GHz) Temporal resolution, ~3 hourly; a non-sun-synchronous orbit to measure with large daily variation quantitatively. The inclination angle of the orbit is 35 degrees, and the satellite orbits the Earth every 90 minutes, 16 times per day Variable observation times Data Citation: Specification document for TRMM products
Data Availability and Coverage	November 1997 – April 2015, 180°W 38°S – 180°E 38°N A summary of the data can be found on https://pps.gsfc.nasa.gov/Documents/ICSVol3.pdf
Source Data Name and Product Technical Specifications	Level 1 b calibrated brightness temperatures TRMM TMI Technical Specification can be found in the TRMM Data Users Handbook https://www.eorc.jaxa.jp/TRMM/document/text/handbook_e.pdf
Data Quantity	10 GB per year
Data Quality and Reliability	Instrument specification: Radiometric sensitivity (noise): about 1 K for all frequencies Validation reports Validation completed by the TRMM validation Office: https://gpm.nasa.gov/resources/trmm-publications
Ordering and delivery mechanism	Ordering via NASA GES DISC Data Centre https://www.earthdata.nasa.gov/eosdis/daacs/gesdisc
Access conditions and pricing	Freely accessible
Issues	The satellite observations slightly changed after a boost in August 2001 and there is a pre-boost dataset (before 7-8-2001) and a post boost dataset (after 24-8-2001). TRMM satellite orbit declining March/April 2015.

AQUA-AMSR-E

Originating System	The Advanced Microwave Scanning Radiometer onboard the AQUA satellite
Data class	Earth observation
Sensor Type and key technical characteristics	Multi frequency Scanning Radiometer 6.9, 10.7, 18.7, 23.8, 36.5 and 89 GHz in both horizontal and vertical polarization. Inc. angle: ~55 degrees Spatial sampling: 10x10 km Spatial resolution: 75x43 km (6.9 GHz), 51x29 km (10.7 GHz), 26x16 km (18.7 and 23.8 GHz), 14x8 (36.5 GHz), 6x4 km (89 GHz) Temporal resolution, ~daily, sun-synchronous observations the ascending equator crossings at 13:30 hr Data Citation: Ashcroft, Peter and Frank Wentz. (2000).
Data Availability and Coverage	June 2002 – October 2011, 180°W 89.24°S – 180°E 89.24°N A summary of the data can be found on Ashcroft and Wentz (2000)
Source Data Name and Product Technical Specifications	AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures Technical Specification Ashcroft and Wentz (2000)
Data Quantity	1 TB per year
Data Quality and Reliability	Instrument specification: Radiometric sensitivity (noise): 0.3 K (6.9 GHz), 0.6 K (10.7, 18.7, 23.8, and 36.5 GHz), 1.1 K (89 GHz) Validation reports Bennartz and Michelson (2012)
Ordering and delivery mechanism	Ordering via NASA GES DISC or NSIDC data center: e.g. https://disc.gsfc.nasa.gov/mirador-guide
Access conditions and pricing	Freely accessible
Issues	Versions older than V07 had significant geolocation problems (a few km off).

Coriolis WindSat (Naval Research Laboratory)

Originating System	WindSat Radiometer onboard the Coriolis satellite
Data class	Earth observation
Sensor Type and key technical characteristics	Multi frequency Scanning Radiometer 6.8, 10.7, 18.7, 23.8, and 37 GHz in both horizontal and vertical polarization. Inc. angle varies: 53.5 degrees for 6.8 GHz, 49.9 degrees for 10.7 GHz, 55.3 degrees for 18.7 GHz and 53.0 degrees for 23.8 and 37.0 GHz Spatial sampling: 25x25 km Spatial resolution: 60x40 km (6.9 GHz), 25x38 km (10.7 GHz), 26x16 km (18.7 and 23.8 GHz), 8x13 km (37 GHz) Temporal resolution, ~daily, sun-synchronous observations the ascending equator crossings at 18:00 hr Data Citation: Gaiser et al. (2004)
Data Availability and Coverage	January 2003 – present, 180°W 90°S – 180°E 90°N A summary of the data can be found on Gaiser et al. (2004) and WindSat Data Products Users' Manual (2006)
Source Data Name and Product Technical Specifications	WindSat Brightness Temperatures Technical Specification WindSat Data Products Users' Manual (2006)
Data Quantity	~1 TB per year
Data Quality and Reliability	Instrument specification: Radiometric sensitivity (noise): 0.75 K for all frequencies Validation reports Gaiser et al. (2004) Parinussa et al. (2012)
Ordering and delivery mechanism	Ordering via Naval Research laboratory Please contact windsat@nrl.navy.mil Delivery is possible via Hard disk
Access conditions and pricing	Access to brightness temperature data is restricted by data provider and only possible upon request.
Issues	Access to data from February 2003 to July 2012 was given but is not public.

GCOM-W1 AMSR-2 (JAXA)

Originating System	The second Advanced Microwave Scanning Radiometer onboard the GCOM-W1 satellite
Data class	Earth observation
Sensor Type and key technical characteristics	Multi frequency Scanning Radiometer 6.9/7.3, 10.7, 18.7, 23.8, 36.5 and 89 GHz in both horizontal and vertical polarization. Inc. angle: ~55 degrees Spatial sampling: 10x10 km 1450 km swath width Spatial resolution: 62x43 km (6.9 GHz/7.3 GHz), 42x24 km (10.7 GHz), 22x14 km (18.7 GHz), 26x15 km (23.8 GHz), 12x7 (36.5 GHz), 5x3 km (89 GHz) Temporal resolution, ~daily, sun-synchronous observations the ascending equator crossings at 13:30 hr
Data Availability and Coverage	Launch 2012, 180°W 89.24°S – 180°E 89.24°N
Source Data Name and Product Technical Specifications	LPRM/AMSR2/GCOM-W1 L2 Surface Soil Moisture, Ancillary Params, and QC Data Resolution Latitude Resolution: 31-46 km Longitude Resolution: 31-46 km Temporal Resolution: 15 orbits per day
Data Quantity	~42 GB per year
Data Quality and Reliability	Instrument specification: Radiometric sensitivity (noise): ~0.1 K
Ordering and delivery mechanism	Please see https://hydro1.gesdisc.eosdis.nasa.gov/data/WAOB/LPRM_AMSR2_SOILM2.001/
Access conditions and pricing	Freely accessible
Issues	N/A

SMOS (ESA)

Originating System	Soil Moisture and Ocean Salinity Mission
Data class	Earth observation
Sensor Type and key technical characteristics	Passive Microwave 2D interferometer L-band (21 cm, 1.4 GHz) Level 1 C multi-angular brightness temperatures Full polarization Spatial resolution 35 km at centre of field of view Angular range 0-55 degrees 3-day revisit at Equator (sun-synchronous, 6 AM ascending) Data citation: Kerr et al. (2010)
Data Availability and Coverage	November 2009 – Now, 180°W 90°S – 180°E 90°N
Source Data Name and Product Technical Specifications	SMOS Level 3 brightness temperatures (MIR_CDF3TA & MIR_CDF3TD datasets for ascending and descending overpass) SMOS auxiliary data with ECMWF forecast information (AUX_CDFECA and AUX_CDFECD datasets for ascending and descending overpass) Technical Specification L3 SM Algorithm Theoretical Based Document (ATBD) (2013) CATDS Level 3 data product description – Soil Moisture and Brightness Temperature (2014)
Data Quantity	~5 Tb per year
Data Quality and Reliability	Instrument specification: Radiometric sensitivity: 2.5 – 4 K
Ordering and delivery mechanism	Ordering via ESA at https://earth.esa.int/eogateway/missions/smos/data Delivery is possible via FTP / HTTP over Internet
Access conditions and pricing	Freely accessible
Issues	Data is continuously reprocessed

SMAP (NASA)

Originating System	Soil Moisture Active Passive Mission
Data class	Earth observation
Sensor Type and key technical characteristics	L-band Radiometer (1.41 GHz): Polarization: V, H, U, Resolution: 40 km, Relative accuracy: 1.5 K Polarizations: H, V, 3^rd & 4^th Stokes Relative accuracy (36 km grid): 1.3 K L-band Radar (1.26 GHz): Data transmission stopped on Jul 7^th 2015 685-km altitude, near-polar, sun-synchronous 6am/6pm, 8-day exact repeat (2-3 days revisit time), frozen orbit Antenna swath width: 1000 km Data citation: O'Neill et al. (2020a)
Data Availability and Coverage	March 2015 – cont., N: 85.044, S: -85.044, E: 180, W: -180
Source Data Name and Product Technical Specifications	SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture (SPL3SMP, ascending and descending overpass) (O'Neill et al. (2020a)) Technical Specification L3 Passive SM Product Specification Document v7 (Chan et al., 2020) Algorithm Theoretical Basis Document L2 & L3 Radar/Radiometer Soil Moisture (Active/Passive) Data Products (Entekhabi et al. (2014), O'Neill et al. (2020b))
Data Quantity	10GB/year (Level 3, daily data)
Data Quality and Reliability	Calibration and Validation for the L2/3_SM_P Version 7 and L2/3_SM_P_E Version 4 Data Products (O'Neill et al. (2020c))
Ordering and delivery mechanism	Download via NSIDC at https://nsidc.org/data/SPL3SMP
Access conditions and pricing	Freely accessible
Issues	Data is continuously reprocessed

FengYun 3B/C/D (CMA/NSMC)

Originating System	FengYun 3 Series
Data class	Earth observation
Sensor Type and key technical characteristics	MWRI Micro-Wave Radiation Imager Frequency bands relevant for soil moisture retrieval 10.65 GHz (180 MHz band width) X-band, H&V polarization 18.7 GHz (200 MHz band width) Ku-band, H&V polarization Sun-synchronous orbit, Altitude: 836 km Design life 3 years with a goal of 4 years
Data Availability and Coverage	FY3B: ECT 14:45 asc; available 2011-2021 (decommissioned) FY3C: ECT 08:20 desc; available 2013-2020-02 (satellite operational but MWRI data delivery stopped) FY3D: ECT 13:29 asc; operational since 2019
Source Data Name and Product Technical Specifications	FengYun-3 swath based brightness temperature data from Ka- and X-band More information: Fengyun-3 Series Meteorological Satellite Data Archiving and Service System (Xu et al., 2016)
Data Quantity	~3 GB per year (Level 3 LPRM derived soil moisture, asc. & des. orbit data, raw sensor / orbit data is multiple GB per day)
Data Quality and Reliability	The FengYun-3 Microwave Radiation Imager On-Orbit Verification (Yang et al., 2011) Environmental Data Records from FengYun-3B Microwave Radiation Imager (Yang et al., 2012)
Ordering and delivery mechanism	Ordering through data portal (limited to 100 GB per day)
Access conditions and pricing	Access is via registration and made available for use within the projects via the dragon 5 cooperation https://dragon5.esa.int/
Issues	Documentation is largely missing / not publicly accessible, no programmatic Near real time (NRT) data access possible at the moment, hence no inclusion in C3S SM ICDRs.

GPM (NASA, JAXA)

Originating System	GPM Microwave Imager (GMI) onboard the GPM core observatory. Satellite Evolution of TMI on TRMM
Data class	Earth observation
Sensor Type and key technical characteristics	GPM Microwave Imager, Multi-purpose imager, with emphasis on precipitation Instrument description: https://arthurhou.pps.eosdis.nasa.gov/gpminstruments.html Scanning Technique: Conical: 53° zenith angle; useful swath: 850 km - Scan rate: 32 scan/min = 13.4 km/scan Sun-synchronous orbit that covers the Earth from 65°S to 65°N, altitude: 407 kilometers Available frequency bands relevant for soil moisture retrieval 18.7 GHz (200 MHz band width) Ku-band, H&V polarization (Instantaneous field of view: 6.0x13.4 km) 10.65 GHz (100 MHz band width) X-band, H&V polarization (Instantaneous field of view: 19x32 km)
Data Availability and Coverage	GPM Core Observatory launched on February 27th, 2014, data is available from 2014-03-04 to presentNear-global coverage in 2 days; high latitudes (> 70°) not covered.
Source Data Name and Product Technical Specifications	Raw data: GPM GMI XCAL Common Calibrated Brightness Temperatures L1BASE 1.5 hours 13 km V07 (GPM_BASEGPMGMI_XCAL) DOI: 10.5067/GPM/GMI/BASE-XCAL/07, GPM Science Team (2022) File specification document: https://gpmweb2https.pps.eosdis.nasa.gov/pub/GPMfilespec/filespec.GPM.pdf Contact: gsfc-dl-help-disc@mail.nasa.gov
Data Quantity	165.0 MB per file (Brightness Temperature level)~2.5 GB / year (L3 LPRM derived soil moisture data)
Data Quality and Reliability	GPM spacecraft anomalous events: https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/gpm_anomalous.html GPM microwave imager key performance and calibration results (Newell et al., 2014)
Ordering and delivery mechanism	Access through GES DISC portal (HTTP) at https://disc.gsfc.nasa.gov/
Access conditions and pricing	Freely accessible
Issues	Data is continuously reprocessed

Active Microwave Systems

Soil moisture retrieval from radar scatterometers was first done for ESA's ERS satellites (Wagner 1998; Wagner et al., 1999b). Since then, the Water Retrieval Package (WARP) was developed based on the TU-Wien method to derive soil moisture from backscatter measurements of Eumetsat's MetOp ASCAT sensors (H SAF, 2018), which is now distributed by H SAF as an operational product in near-real-time and used in the generation of C3S soil moisture. Soil moisture products from synthetic aperture radar measurements (such as Sentinel-1) are gaining popularity due to their higher spatial resolution and therefore potential new applications. They are, however, not yet part of the C3S records.

ERS AMI (ESA)

Originating System	Active Microwave Instrument (AMI) Wind Scatterometer (WS) on-board ERS-1 and ERS-2
Data class	Earth Observation data
Sensor Type and key technical characteristics	Scatterometer with 3 fan-beam antennae Frequency 5.3 GHz ± 52kHz (C-Band) Polarization: linear vertical (VV) Inc. angles: 18 – 47 (mid), 25 – 59 (fore/aft) Spatial resolution: ≥ 45 km Swath width: ≥ 500 km Swath stand-off: 200 km to right of sub-sat. Track
Data Availability and Coverage	IFREMER 1991/08/05 – 2001/01/17, 180°W 82°S – 180°E 82°N ESA Rolling Archive 2003/08/13 – 2010/01/28, 180°W 82°S – 180°E 82°N
Source Data Name and Product Technical Specifications	IFREMER ERS.WSC.WNF (IFREMER product WNF Format) Technical Specification: Spatial Resolution: 50x50 km, Spatial Sampling: 25x25 km C2-MUT-W-01-IF ESA Rolling Archive ERS.WSC.UWI (fast delivery product BUFR Format) Technical Specification: Spatial Resolution: 50x50 km, Spatial Sampling: 25x25 km https://directory.eoportal.org/web/eoportal/satellite-missions/e/ers-1
Data Quantity	~32 GB
Data Quality and Reliability	Instrument specification Radiometric stability: 0.46 dB Localisation accuracy: 5 km
Ordering and delivery mechanism	IFREMER Ordering via CERSAT webpage http://cersat.ifremer.fr, currently no information about delivery mechanism. (CD) ESA Rolling Archive: Ordering via ESA Earthnet Online https://earth.esa.int Delivery is possible via FTP
Access conditions and pricing	Freely accessible
Issues	Due to the loss of gyroscopes onboard of ERS-2 in January 2001, data from 2001/01/17 to 2003/08/13 was initially lost but partly restored later on.

ASCAT Metop-A (EUMETSAT)

Originating System	Advanced Scatterometer (ASCAT) onboard Metop-A
Data class	Earth observation
Sensor Type and key technical characteristics	Scatterometer with 6 fan-beam antennae 5.255 GHz (C-band), λ 5.70 cm (VV Polarization) Inc. angles: 25 – 53.5 (mid), 33.7-64.5 (fore/aft) Spatial sampling: 25x25 / 12.5x12.5 km Spatial resolution: 50x50 / 37x25 km
Data Availability and Coverage	2007/01/01 – 2021/11/15., 180°W 90°S – 180°E 90°N A summary of data gaps can be found in EUM/OPS/DOC/09/2481 EUM/OPS/REP/09/3033
Source Data Name and Product Technical Specifications	ASCAT Soil Moisture at 12.5 km Swath Grid (EUMETSAT)H101 SSM ASCAT-A NRT O12.5 (H SAF) Technical Specification EUM/OPS-EPS/MAN/04/0028 EUM/OPS-EPS/SPE/07/0343 ASCAT Soil Moisture Product Handbook
Data Quantity	~100 GB/year for L2 soil moisture 25 km resolution
Data Quality and Reliability	Instrument specification Radiometric stability: 0.46 dB Localisation accuracy: 4.46x4.46 km Validation reports EUM/OPS/DOC/12/3849
Ordering and delivery mechanism	Ordering via EUMETSAT Data Centre https://eoportal.eumetsat.int/ or http://hsaf.meteoam.it/ Delivery is possible via HTTP over Internet (EUMETSAT) FTP over Internet (H SAF) EUMETCast
Access conditions and pricing	EUMETSAT data policy
Issues	ASCAT-A was decommissioned as of 15 November 2021

ASCAT Metop-B (EUMETSAT)

Originating System	Advanced Scatterometer (ASCAT) onboard Metop-B
Data class	Earth observation
Sensor Type and key technical characteristics	Scatterometer with 6 fan-beam antennae 5.255 GHz (C-band), λ 5.70 cm (VV Polarization) Inc. angles: 25 – 53.5 (mid), 33.7-64.5 (fore/aft) Spatial sampling: 25x25 / 12.5x12.5 km Spatial resolution: 50x50 / 37x25 km
Data Availability and Coverage	2012/11/06 – cont., 180°W 90°S – 180°E 90°N
Source Data Name and Product Technical Specifications	ASCAT Soil Moisture at 12.5 km Swath Grid (EUMETSAT)SSM ASCAT-B NRT O12.5 H16 (H SAF) Technical Specification EUM/OPS-EPS/MAN/04/0028 EUM/OPS-EPS/SPE/07/0343 ASCAT Soil Moisture Product Handbook
Data Quantity	~100 GB/year for L2 soil moisture 25 km resolution
Data Quality and Reliability	Instrument specification Radiometric stability: 0.46 dB Localisation accuracy: 4.46x4.46 km Validation reports EUM/OPS/DOC/12/3849
Ordering and delivery mechanism	Ordering via EUMETSAT Data Centre https://eoportal.eumetsat.int/ or http://hsaf.meteoam.it/ Delivery is possible via HTTP over Internet (EUMETSAT) FTP over Internet (H SAF) EUMETCast
Access conditions and pricing	EUMETSAT data policy
Issues	Intercalibration between MetOp-B and MetOp-A NRT data is only available after June 2015 because of which MetOp-B can only be used after this date in C3S SM. A backward processing of MetOp-B may be performed once intercalibrated data become available from H-SAF/EUMETSAT.

ASCAT Metop-C (EUMETSAT)

Originating System	Advanced Scatterometer (ASCAT) onboard Metop-C
Data class	Earth Observations
Sensor Type and key technical characteristics	Scatterometer with 6 fan-beam antennae 5.255 GHz (C-band), λ 5.70 cm (VV Polarization) Inc. angles: 25 – 53.5 (mid), 33.7-64.5 (fore/aft) Spatial sampling: 25x25 / 12.5x12.5 km Resolution: 25-34 km x 25-34 km Cycle: ≈3 minutes Timeliness: 2 hours
Data Availability and Coverage	2018/11 – cont., 180°W 90°S – 180°E 90°N (global)
Source Data Name and Product Technical Specifications	ASCAT Soil Moisture at 12.5 km Swath Grid (EUMETSAT)SSM ASCAT-C-NRT O12.5 (H104) (H SAF) Technical Specification EUM/OPS-EPS/MAN/04/0028 EUM/OPS-EPS/SPE/07/0343 ASCAT Soil Moisture Product Handbook Website: https://hsaf.meteoam.it/Products/Detail?prod=H104 Formats: Values in grid points of specified coordinates in the orbital projection (BUFR, EPS Native, NetCDF) Soil moisture content in the surface layer (0.5-2 cm)Unit: degree of saturation (0 – 100 %)
Data Quantity	~100 GB/year for L2 soil moisture 25 km resolution
Data Quality and Reliability	Accuracy: threshold S/N 3 dB, target S/N 3 dB, optimal S/N 6 dB
Ordering and delivery mechanism	Dissemination: FTP, EUMETCast (type NRT)
Access conditions and pricing	EUMETSAT data policy
Issues	N/A

Input and auxiliary data

In the following an overview of the input and auxiliary data is provided. A division is made between data which is used as input for data production (Sections 2.1 and 2.2) and for validation activities (Section 2.3).

Advisory flags

The following section describes datasets that are used to generate the advisory flags provided as additional information to the soil moisture products. The advisory flags are not provided as part of the soil moisture datasets but only as datasets to support interpretation and analysis of the soil moisture products.

SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent

Originating System	Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS)
Data class	Earth observation
Sensor Type and key technical characteristics	The SSM/I is a seven-channel, four-frequency, orthogonally polarized, passive microwave radiometric system that measures atmospheric, ocean and terrain microwave brightness temperatures at 19.35, 22.2, 37.0, and 85.5 GHz.
Data Availability and Coverage	1995/05/04 – cont., 180°W 90°S – 180°E 90°N
Source Data Name and Product Technical Specifications	SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent
Data Quantity	~800 MB/year
Data Quality and Reliability
Ordering and delivery mechanism	Data are available via FTP over Internet https://nsidc.org/data/nise/versions/5
Access conditions and pricing	Freely accessible
Issues	N/A

ERA-40

Originating System	ERA-40 is a re-analysis of meteorological observations produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observations used in ERA-40 were accumulated from many sources.
Data class	Gridded analyses, modelled data
Sensor Type and key technical characteristics	Spatial resolution: 0.5°/2.5° Temporal coverage: Daily (6 hourly) More information can be found in: Uppala et al. (2005)
Data Availability and Coverage	SEP 1957 – AUG 2002, 180°W 90°S – 180°E 90°N
Source Data Name and Product Technical Specifications	ERA-40 Project Report Series Uppala et al. (2005)
Data Quantity	~800 MB/year
Data Quality and Reliability	ERA 40 Performance
Ordering and delivery mechanism	Detailed information of how to order data can be obtained from the ECMWF Data Services. https://apps.ecmwf.int/datasets/ ¹
Access conditions and pricing	Freely available after registration
Issues	N/A

Info

icon	false

¹ The ECMWF Public Datasets service is being decommissioned and access to most datasets closed on June 1st, 2023. In a final step later this year, access to the remaining multi-model datasets S2S and TIGGE will be migrated to a different system. For more information and alternative access, please visit our dedicated page on the Decommissioning of ECMWF Public Datasets service: Decommissioning of ECMWF Public Datasets Service (URL resource validated 18^th December 2023)

Global Lakes and Wetlands Database (GLWD)

Originating System	See Table 1 in Lehner and Döll (2004)
Data class	GIS database
Sensor Type and key technical characteristics	Organisation in three levels: Level 1 comprises the shoreline polygons of the 3067 largest lakes (surface area 50 km²) and 654 largest reservoirs (storage capacity 0.5 km³) worldwide and offers extensive attribute data. Level 2 contains the shoreline polygons of approx. 250,000 smaller lakes, reservoirs and rivers (surface area 0.1 km²), excluding all water bodies of level 1 Level 3 represents lakes, reservoirs, rivers, and different wetland types in the form of a global raster map at 30-second resolution, including all water bodies of levels 1 and 2.
Data Availability and Coverage	Global (except Antarctica)
Source Data Name and Product Technical Specifications	GLWD https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database
Data Quantity	~50 MB
Data Quality and Reliability	More information is provided in Lehner, B. & Döll, P., 2004.
Ordering and delivery mechanism	Download from FTP https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database
Access conditions and pricing	GLWD is available for non-commercial scientific, conservation, and educational purposes.
Issues	N/A

GTOPO30

Originating System	GTOPO30 is based on data derived from 8 sources of elevation information, including vector and raster data sets
Data class	GIS database
Sensor Type and key technical characteristics	Digital Terrain Elevation Data (DTED) is a raster topographic data base with a horizontal grid spacing of 3-arc seconds (approximately 90 meters) produced by the National Imagery and Mapping Agency (NIMA) (formerly the Defense Mapping Agency).
Data Availability and Coverage	Global
Source Data Name and Product Technical Specifications
Data Quantity	~3 GB
Data Quality and Reliability	±650 m (Vertical)
Ordering and delivery mechanism	USGS Earth Explorer (https://earthexplorer.usgs.gov/) Global 30 Arc-Second Elevation (GTOPO30) Digital Object Identifier (DOI) number: /10.5066/F7DF6PQS
Access conditions and pricing	Free
Issues	N/A

Merging Framework

The following data sets are used for merging L3 satellite soil moisture products into the harmonised C3S SM records.

Global Land Data Assimilation System (GLDAS) V2.0

GLDAS Noah is used in the Triple Collocation Analysis (TCA) step to estimate random uncertainties of the satellite products before merging. GLDAS Noah also acts as the scaling reference for the COMBINED product and is used there to harmonize all sensor time series via Cumulative Distribution Function (CDF) matching.

Originating System	The forcing data set combines multiple data sets for the period of January 1, 1979 to present
Data class	Water and energy budget components, forcing data
Sensor Type and key technical characteristics	Spatial resolution: 0.25° and 1.0° Temporal resolution: 3-hourly or monthly
Data Availability and Coverage	1948 – 2010 for 1°x1° 1948 – 2000 for 0.25°x0.25° 180°W 60°S – 180°E 90°N
Source Data Name and Product Technical Specifications	Global Land Data Assimilation System: Variable: soil moisture (0-0.1, 0.1-0.4, 0.4-1.0, 1.0-2.0 m) Variable: surface temperature Variable: soil temperature (0-0.1, 0.1-0.4, 0.4-1.0, 1.0-2.0 m) A detailed description of GLDAS is given at https://ldas.gsfc.nasa.gov/gldas/specifications
Data Quantity	3-hourly data ~2.6 GB/year 1°x1° ~42 GB/year 0.25°x0.25° Monthly data ~9.6 MB/year 1°x1° ~153 MB/year 0.25°x0.25°
Data Quality and Reliability	Please see Rodell et al. (2004); "The Global Land Data Assimilation System".
Ordering and delivery mechanism	Data are available via https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS
Access conditions and pricing	Freely accessible
Issues	N/A

Global Land Data Assimilation System (GLDAS) V2.1

GLDAS Noah is used in the TCA step to estimate random uncertainties of the satellite products before merging. GLDAS Noah also acts as the scaling reference for the COMBINED product and is used there to harmonize all sensor time series via CDF matching.

Originating System	The forcing data set combines multiple data sets for the period of January 1, 2000 to present. (see Rodell et al. (2004)), Beaudoing and Rodell (2016) and https://ldas.gsfc.nasa.gov/gldas/ for details) Version 2.1 is a replacement for version 1.0 of GLDAS.
Data class	Water and energy budget components, forcing data
Sensor Type and key technical characteristics	Spatial resolution: 0.25° Temporal resolution: 3-hourly or monthly
Data Availability and Coverage	2000 – present for 0.25°x0.25° 180°W 60°S – 180°E 90°N
Source Data Name and Product Technical Specifications	Global Land Data Assimilation System: Variable: soil moisture (0-0.1, 0.1-0.4, 0.4-1.0, 1.0-2.0 m) Variable: surface temperature Variable: soil temperature (0-0.1, 0.1-0.4, 0.4-1.0, 1.0-2.0 m) A detailed description of GLDAS is given in Rui et al. (2018)
Data Quantity	3-hourly data ~55 GB/year 0.25°x0.25° Monthly data ~153 MB/year 0.25°x0.25°
Data Quality and Reliability	Rodell et al. (2004). The Global Land Data Assimilation System. Beaudoing and Rodell (2016). GLDAS Noah Land Surface Model L4 3 hourly 0.25 x 0.25 degree V2.1 https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/README_GLDAS2.pdf https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_3H_V2.1/summary
Ordering and delivery mechanism	Data are available via https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS
Access conditions and pricing	Freely available
Issues	None identified

ERA 5 / ERA5-Land

ECMWF ReAnalysis 5 (ERA5) and ERA5-Land variables are used as part of the soil moisture retrieval process (surface temperature) and act as the reference for break correction (surface soil moisture) in the production of the COMBINED product.

Originating System	ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5.
Data class	Gridded analyses, modelled data
Sensor Type and key technical characteristics	1-hourly surface parameters, describing weather as well as ocean-wave and land-surface conditions, upper-air parameters covering the troposphere and stratosphere, as well as vertical integrals of atmospheric fluxes, monthly averages for many of the parameters, and other derived fields. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis.
Data Availability and Coverage	1950 – present., 180°W 90°S – 180°E 90°N
Product Technical Specifications	ERA5: https://doi.org/10.24381/cds.adbb2d47 ERA5-Land: https://doi.org/10.24381/cds.e2161bac
Data Quantity	Depends upon number of variables that are required from the dataset
Data Quality and Reliability	See Hersbach et al. (2020) and Muñoz-Sabater et al. (2021)
Ordering and delivery mechanism	ERA5 and ERA5-Land data can be downloaded from the Copernicus Climate Data Store (CDS). Available at https://cds.climate.copernicus.eu
Access conditions and pricing	Free for research users. See CDS for Terms and conditions: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf .
Issues	N/A

Validation data

In situ data from the International Soil Moisture Network (ISMN)

ISMN in situ observations are the primary reference to assess the quality of the satellite based C3S soil moisture products.

Originating System	In situ soil moisture measurements provided mainly from universities and regional and national organizations. Depending on the sites, there are different original data sets generated in 1952 (oldest) or to present.
Data class	Ground-based in-situ measurements data
Sensor Type and key technical characteristics	The sensor types most commonly used has changed over the time period of the ISMN: TDR (Time Domain Reflectometry) ML2x-Theta Probe (Impedance dielectric sensor) Hydra Probe Analogue/Digital (Coaxial Impedance dielectric sensor: measurements of soil moisture, soil electrical conductivity and soil temperature) EnviroSCAN (Capacitance sensor: measure soil moisture at different depths with the same profile probe) Cosmic-ray Probe (Cosmic rays collide with soil and generate a big amount of fast neutrons, from which those escaped back into the air are counted by a detector. The observed count rate is related to water content because of hydrogen is the most effective element to absorb fast neutrons)
Data Availability and Coverage	Some of the networks began their measurements in 1952, and nowadays there are around 20 networks proving data, five of which are now providing data in near real time. See Dorigo et al. (2011) The coverage varies from Alaska and Finland in northern hemisphere to Africa, Brazil and even Antarctica in southern hemisphere.
Source Data Name and Product Technical Specifications	The source is open due to the networks data sets are provided from different countries worldwide operated by different universities and national and regional organizations. Depending on the site and the sensor used, the data set provided will be with different number of variables measured. All measure soil moisture and in addition some of them measure also soil temperature, snow water equivalent, snow depth, precipitation and/or air temperature. The original data sets received are in ASCII (.txt, .dat), but also in EXCEL (.xls) formats. The ISMN output format is in ASCII (.txt).
Data Quantity	The temporal resolution of the data sets is very diverse depending on the site: 20 min, 30 min, hourly, 6-hourly, 12-hourly, daily and weekly. As of February 2020: Database volume: 61 networks, 2609 stations. ~150GB data volume. Download of all data sets takes ~ 16 hours. Multiple concurrent downloads significantly increase time per data set.
Data Quality and Reliability	Each site is responsible of the quality of its data. Nevertheless, after processing the original data a Quality Control is performed with a data flagging system. See Dorigo et al. (2011) and Dorigo et al. (2021) for more information.
Ordering and delivery mechanism	Via ISMN data viewer providing a compressed (.zip) file at https://ismn.earth
Access conditions and pricing	Free with previous registration and for scientific use only. Neither onward distribution nor commercial use is permitted.
Issues	Data Formats: Two standardised formats are supported for ISMN data: Variables stored in separate files (CEOP formatted) Variables stored in separate files (Header+values)

ERA5-Land

ERA5-Land (see Chapter 2.2.3) is the main globally available, gap-free reference source for evaluating C3S SM. While ERA5 is also used in the production of the data set (during break correction; Chapter 3.3.7), it was found that no features from ERA5 are introduced to the satellite data in that step. ERA5-Land is therefore considered to be independent and is therefore used as a reference for validation.

Algorithms

Passive Microwave Algorithm

This chapter is largely based upon the “Algorithm Theoretical Baseline Document (ATBD) D2.1 Supporting Version 08.1” [D1] document produced under ESA Climate Change Initiative Soil Moisture project.

Principles of the Land Parameter Retrieval Model

Brightness temperatures can be derived from passive microwave sensors with different radiometric characteristics. The observed brightness temperatures are converted to soil moisture values with the Land Parameter Retrieval Model (LPRM; van der Schalie et al., 2017). This model is based on a microwave radiative transfer model that links soil moisture to the observed brightness temperatures. A unique aspect of LPRM is the simultaneous retrieval of vegetation optical depth (VOD) in combination with soil moisture and surface temperature.

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Figure 2: Flowchart of the main processes of the Land Parameter Retrieval Model (LPRM). Soil moisture is solved when the observed brightness temperature equals the modelled brightness temperature as derived by the radiative transfer.

Methodology

The thermal radiation in the microwave region is emitted by all natural surfaces and is a function of both the land surface and the atmosphere. According to LPRM the observed brightness temperature (T_b) as measured by a space borne radiometer can be described as:

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Parameter	Frequency
Parameter	L-band (~1.4 GHz)	C-band (~6.9 GHz)	X-band (~10.8 GHz)	Ku-band (~19 GHz)
τ_a	0	0.01	0.01	0.05
ω	0.12	0.075	0.075	0.06
h1 (h for Ku-band)	1.1 to 1.3	1.2	1.2	0.13
Q	0	0.115	0.127	0.14
A_V	0.7	0.3	0.3	n/a
B_V	2	2	2	n/a

C-, X- and Ku-band Model Parametrization

Soil moisture retrieval using higher frequencies are known to have issues in the more extreme climates, e.g. tropical regions, deserts and boreal forests. A single global parameterization function is used for L-band but leads to questionable behavior for some of the higher frequencies. For example, unnaturally high/low soil moisture values over certain land cover classes or non-valid model retrievals. Starting from LPRMv7 onwards, a variable parameterization is used. This counts for two parameters, dT (a local bias correction of the land surface temperature) and ⍵ (the single scattering albedo).

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Figure 5: The 10/90 percentiles (P10/P90) and standard deviation (STDEV) of the LPRMv7 soil moisture retrievals from C- (6.9 GHZ, top row), X- (10.7 GHZ, middle row) and Ku-band (18.7 GHZ, bottom row). Note that Greenland is normally not run and therefore shows artefacts that are not in the final product.

Day-time Retrievals

Since the temperature distribution within the vegetation and soil is not in equilibrium during the 1:30 am observation period, and the situation varies throughout the seasons, a standard approach of optimizing LPRM left too many regions without a functional parameterization. Therefore, an alternative was found that corrects the day-time brightness temperatures to night-time values and is applied to all frequencies including Ka-band for the temperature retrieval. This was done using the following steps:

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Figure 6: Visualized correlation of night-time (descending: Desc.) and day-time (ascending: Asc.) observations against SMAP Level 4 SM, for C- (6.9 GHZ, top row), X- (10.7 GHZ, middle row) and Ku-band (18.7 GHZ, bottom row) from AMSR2, using LPRMv7.0.

Known Limitations

The known limitations in deriving soil moisture from passive microwave observations are listed and described in detail in this section. These issues do not only apply to the current C3S CDR and ICDR soil moisture dataset releases but also to soil moisture retrieval from passive 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 overlying vegetation. The simultaneously derived vegetation optical depth can be used to detect areas with excessive vegetation, of which the boundary varies with observation frequency.

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Figure 8: Triple collocation analysis (TCA: top) and R-value results (bottom) for several soil moisture datasets, including SMOS LPRM and AMSR-E LPRM, for changing vegetation density (NDVI). Based on Van der Schalie et al. (2018).

Frozen surfaces and snow

Under frozen surface conditions the dielectric properties of the water changes dramatically. As snow cover, ice, and frozen conditions were demonstrated to have a big impact on data quality and availability within the current Passive product, a uniform satellite driven flagging strategy was designed by Van der Vliet et al. (2020). Prior to this, all pixels where the surface temperature is observed to be at or below 274.15 K are assigned with an appropriate frozen data flag, this was determined using the method of Holmes et al. (2009). However, as this methodology is insufficiently accurate in detecting the transition to snow and frozen conditions, the new methodology was introduced by Van der Vliet et al. (2020), which uses three frequencies (Ku-, K- and Ka-band) to properly flag these conditions.

Barren Grounds and Desert Areas

Very dry conditions above deserts and other barren areas lead to subsurface scattering phenomena and complicate the process of defining a correct land surface temperature due to the increased sensing depth. To account for errors in very dry soils affecting the passive retrieval, flagging of these conditions is applied. Figure 9(b) illustrates how barren grounds can likely be flagged in a similar manner as the snow/frozen conditions (Van der Vliet et al., 2020). Based on MODIS Landcover data a first classification was made in LPRMv7 that was stepwise improved by considering the spatial patterns. The related decision tree can be found in Figure 9(a).

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Figure 10: Example of resulting flags for four different months in the year.

Water bodies

Water bodies within the satellite footprint can strongly affect the observed brightness temperature due to the high dielectric properties of water. Especially when the size of a water body changes over time, they can dominate the signal. LPRM uses a 5 % water body threshold based on MODIS observations and pixels with more than 5 % surface water are masked (Owe et al., 2008).

Rainfall

Rainstorms during the satellite overpass can strongly affect the brightness temperature observations, and therefore should be flagged in LPRM. Ongoing investigations are done to define a proper filtering mechanism derived from the passive microwave observations themselves. Currently, only strong events are removed due to its effect on the retrieved temperature from Ka-band, which then drops below 274.15K.

Radio Frequency interference

Natural emission in several low frequency bands is affected by artificial sources, so called Radio Frequency Interference (RFI). As a diagnostic for possible errors, an RFI index is calculated according to De Nijs et al. (2015). Most passive microwave sensors that are used for soil moisture retrieval observe in several frequencies. This allows LPRM to switch to higher frequencies in areas affected by RFI.

...

As the currently integrated SMOS mission does not have multiple frequencies to apply this method, here we base the filtering on the RFI probability information that is supplied by in the SMOS Level 3 data. SMAP, by using different channels around 1.4GHz, already has an internal mitigation of RFI that removes almost all occurrence of RFI, therefore no extra filtering is needed for use with the C3S SM.

Updated temperature input from Ka-band observations

The land surface temperature plays a unique role in solving the radiative transfer model and therefore directly influences the quality of the soil moisture retrievals. The current linear regression to link Ka-band measurements to the effective soil temperature has been adjusted and optimized by Parinussa et al. (2016) for day-time observations (Figure 11). This is done using an optimisation procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called R_value metric. In this optimisation, different biases were locally applied to the existing linear regression and final results have been used to create an updated global linear regression. The focus of this study was to improve the skill to capture the temporal dynamics of the soil moisture. After the updated linear regression for the land surface temperature, the R_value increased on average with 16.5% and the triple collocation analysis showed an average reduction in Root Mean Square Error (RMSE) of 15.3%, showing an improved skill in day-time retrievals from LPRM.

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In order to remove model dependency for the L-band soil moisture retrievals, we have collocated Ka-band observations from other satellites to SMAP and SMOS. This way, temperature input to the L-band missions is taken from actual satellite observations. This also allows to apply the new filtering methodology developed by Van der Vliet et al. (2020).

Active Microwave Algorithm

The active microwave soil moisture products utilized in the generation of the C3S soil moisture datasets are obtained from external operational sources as follows:

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Info

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² https://hsaf.meteoam.it/ (URL resource last accessed 18^th December 2023)

Merging Strategy

This chapter is largely based upon the “Algorithm Theoretical Baseline Document (ATBD) D2.1 Supporting Version 08.1” [D1] document produced under ESA Climate Change Initiative Soil Moisture project.

Principle of the merging process

The generation of the long-term soil moisture data set involves three steps (as detailed in Figure 12): (1) merging the original passive microwave soil moisture products into one product, (2) merging the original active microwave soil moisture products into one product, and (3) merging all original active and passive microwave soil moisture products into one. The input datasets considered for generating the merged soil moisture product are:

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Figure 12: Overview of the three-step blending approach from original products to the final blended active & passive microwave soil moisture product (Adapted from Liu et al. 2012)

Overview of processing steps

The level 2 surface soil moisture products derived from the active and passive remotely sensed data undergo a number of processing steps in the merging procedure (see Figure 13 for an overview):

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Figure 13: Overview of the processing steps in the C3S product generation: The merging of two or more data sets is done by weighted averaging and involves overlapping time periods, whereas the process of joining data sets only concatenates two or more data sets between the predefined time periods. *The [SSM/I, TMI] period is specified not only by the temporal, but also by the spatial latitudinal coverage.

Methodology

In this section, the algorithms of the scaling and merging approach are described. Notice that several algorithms, e.g. rescaling, are used in various steps of the process, but will be described only once.

Spatial Resampling

The sensors used for the different merged products have different technical specifications (Table 3, Table 4). Obvious are the differences in spatial resolution and crossing times. Both elements need to be brought into a common reference before the actual merging can take place.

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(1) For active microwave instruments, this stands for the footprint spatial resolution.
(2) TU Wien change detection algorithm references for AMI-WS: Wagner et al. (1999)

Temporal Resampling

The temporal sampling of the merged product is 1 day. The reference time for the merged dataset is set at 0:00 UTC. For each day starting from the time frame center at 0:00 UTC observations within ±12 hours are considered.

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Starting from v202212, day-time observations are available from the passive sensors (LPRMv7). While it is planned in future dataset versions to achieve a sub-daily temporal resolution, the temporal resampling at the current stage applies indistinctly to day- and night-time observations.

Flagging

During the temporal resampling stage, flagging is applied to the datasets where relevant information is available. The key flags set during this process are 'frozen', 'high VOD' and 'Other' and these flags are propagated through the entire processing chain to the final product.

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A cross-flagging approach is implemented for frozen soils. This means that any frozen flags provided in any of the datasets are effectively transferred to all of the datasets. It works by reading in all of the flag data for all of the datasets, determining if the frozen flag is set in any of them and if it is, applying it to all relevant observations in all of the sensors.

Rescaling

Due to different observation frequencies, observation principles, and retrieval techniques, the contributing soil moisture datasets are available in different observation spaces. Therefore, before merging can take place at either level, the datasets need to be rescaled into a common climatology. All soil moisture observations of each product are rescaled to the climatology of a different reference, namely AMSRE, ASCAT or GLDAS for the passive, active or combined product respectively.

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Figure 15: Example illustrating how the cumulative distribution function (CDF) matching approach was implemented to rescale original AMSR-E and ASCAT against Noah soil moisture product in this study. (a, b, c) CDF curves of AMSR-E, Noah and ASCAT soil moisture estimates for the grid cell shown in Fig. 2. (d) Linear regression lines of AMSR-E against Noah for 12 segments. (e) Same as (d), but for ASCAT and Noah. (f) CDF curves of Noah (black), rescaled AMSR-E (blue) and rescaled ASCAT (red) soil moisture products. (Figure taken from Liu et al. 2011)

Intra-annual bias correction

From version v202212 on, the source and reference samples used in the scaling procedure are first divided into 366 subsets corresponding to the data points belonging to each day of the year. The scaling parameters are then calculated and applied separately to each subset, effectively providing a seasonality and thus accounting for non-stationary biases between the different sensors (or between a sensor and the reference model). These biases are generated by the different effect that the seasonally variable environmental conditions exert on the retrieval. One of such effects is for instance the vegetation state which impacts differently the scatterometric and radiometric datasets.

As the time series are subset into smaller samples, it can occur that the calculation of scaling parameters relies on too few observations, leading to overfitting. To avoid this, the usual threshold of a minimum of 20 points is used, below which the parameters are not calculated. In this case, the global time series parameters are used, allowing to prevent data loss.

Rescaling of Active Datasets

Different sensor specifications between ERS1/2 and ERS2 (e.g. spatial resolution) need to be compensated for using scaling. The CDF curves for ERS2 are calculated based on the overlap with ERS1/2. Rescaling ERS2 against ERS1/2 and then joining them generates the AMI-WS active data set, which is subsequently scaled to the Metop-A ASCAT data (ACTIVE product) or the GLDASv2.1 data (COMBINED product) (see Figure 13).

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Figure 16: Comparison of global and hemispheric averages of soil moisture from ASCAT before (left) and after rescaling of Metop-B ASCAT on Metop-A ASCAT.

Rescaling of Passive Datasets

The seasonal cycle associated with the SSM/I dataset is deemed to be unreliable and therefore, for all CCI products, the SSM/I seasonal cycle is replaced with that from AMSR-E. The high frequency variations (anomalies) associated with SSM/I are scaled to those from AMSR-E prior to recombining the decomposed signal. An example of the SSM/I decomposition and rescaling is shown in Figure 17.

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Figure 17: Example illustrating how (a) the TMI was rescaled against AMSR-E, (b-e) the SSM/I anomalies were rescaled against AMSRE-E anomalies, reconstructed and merged with rescaled TMI and AMSR-E, and (f) the SMMR was rescaled and merged with the others. The grid cell is centred at 13.875°N, 5.875°W (Image courtesy Liu et al., 2012).

Rescaling in the COMBINED product

For generating the combined product, all passive and active level 2 data sets are rescaled against GLDASv2.1, with the exception of ERS1/2, ASCAT and SSMI which are discussed above.

Error characterization

Errors in the individual active and passive products are characterized by means of triple collocation analysis. These errors are used both for estimating the merging parameters and for characterizing the errors of the merged product (see section 3.3.6).

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As expected, the error structure and the relative performance between the two sensors is reflecting the seasonal vegetation patterns (Dorigo et al., 2010). The relative weights of the sensors change accordingly throughout the year, as given by Eqn. 21-22.h3.

Error gap-filling

TCA does not provide reliable error estimates in all regions, mainly if there is no significant correlation between all members of the triplet, which often happens for example in high-latitude areas or in desert areas. TCA error estimates are therefore disregarded where the Pearson correlation between any of the data sets is deemed insignificant (i.e., p-value < 0.05).

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Where the subscript denotes the spatial location; and the parameters ai are derived from a global polynomial regression between VOD and TCA based error estimates at locations where they are considered reliable (i.e., all data sets are significantly correlated). For TMI and WINDSAT, third order polynomials (N=3) are used and for all other sensors second order polynomials (N=2) are used, which was empirically found to provide the best regression results.

Merging

The merging procedure consists of (1) merging the original passive microwave product into the PASSIVE product, (2) merging the original active microwave products into the ACTIVE product, and (3) merging the original active and passive microwave products into the COMBINED product. The merging periods are shown in Figure 1 and listed in Table 5, Table 6, Table 7.

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Time Periods	Active Sensors	Passive Sensors
01/11/1978 – 31/07/1987	N/A	SMMR (a/d)
01/09/1987 – 05/08/1991	N/A	SSM/I (a/d)
05/08/1991 – 31/12/1997	AMI-WS	SSM/I (a/d)
01/01/1998 – 18/06/2002	AMI-WS	SSM/I (a/d). TMI (a/d
19/07/2002 – 31/12/2006	AMI-WS	AMSR-E (a/d), TMI (a/d)
01/01/2007 – 30/09/2007	Metop-A ASCAT	AMSR-E (a/d), TMI (a/d)
01/10/2007 – 14/01/2010	Metop-A ASCAT	AMSR-E (a/d), WindSat (a/d) , TMI (a/d)
15/01/2010 – 31/55/2011	Metop-A ASCAT	AMSR-E (a/d), WindSat (a/d), SMOS (a), TMI (a/d)
31/05/2011 – 04/10/2011	Metop-A ASCAT	AMSR-E (a/d), WindSat (a/d), SMOS (a), TMI (a/d). FY-3B (a/d)
05/10/2011 – 30/06/2012	Metop-A ASCAT	WindSat (a/d), SMOS (a), TMI (a/d), FY-3B (a/d)
01/07/2012 – 28/09/2013	Metop-A ASCAT	SMOS (a), AMSR2 (d), TMI (a/d), FY-3B (a/d)
29/09/2013 – 28/02/2014	Metop-A ASCAT	SMOS (a), AMSR2 (d), TMI (a/d), FY-3B (a/d), FY-3C (a/d)
01/03/2014-30 - 09-2014	Metop-A ASCAT	SMOS (a), AMSR2 (d), TMI (a/d), FY-3B (a/d), FY-3C (a/d), GPM (d)
01/10/2014 – 20/07/2015	Metop-A ASCAT	SMOS (a), AMSR2 (d), FY-3B (a/d), FY-3C (a/d), GPM (d)
20/07/2015– 08/11/2018	Metop-A ASCAT, Metop-B ASCAT	SMOS (a), AMSR2 (d), FY-3B (a/d), FY-3C (a/d), GPM (d)
09/11/2018 – 31/12/2018	Metop-A ASCAT, Metop-B ASCAT, Metop-C ASCAT	SMOS (a), AMSR2 (d), FY-3B (a/d), FY-3C (a/d), GPM (d), SMAP (d)
01/01/2019 – 19/08/2019	Metop-A ASCAT, Metop-B ASCAT, Metop-C ASCAT	SMOS (a), AMSR2 (d), FY-3B (a/d), FY-3C (a/d), FY-3D (a/d), GPM (d), SMAP (d)
20/08/2019 – 04/02/2020	Metop-A ASCAT, Metop-B ASCAT, Metop-C ASCAT	SMOS (a), AMSR2 (d), FY-3C (a/d), FY-3D (a/d), GPM (d), SMAP (d)
04/02/2020 – 15/07/2021	Metop-A ASCAT, Metop-B ASCAT, Metop-C ASCAT	SMOS (a), AMSR2 (d), FY-3D (a/d), GPM (d), SMAP (d)
16/11/2021– Present	Metop-B ASCAT, Metop-C ASCAT	SMOS* (a), AMSR2* (d), FY-3D (a/d), GPM* (d), SMAP* (d)

Weight estimation

The merging is performed by means of a weighted average which takes into account the error properties of the individual data sets that are being merged:

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Similar to the generation of the PASSIVE product, relative weights at each time step are derived from the TCA- or VOD-regression based error estimates for each individual sensor. Depending on how many sensors are available within a particular period, a (1/2N) threshold for the minimum weight of a particular sensor was applied if not all sensors provide a soil moisture estimate at that day.

Break detection and correction

In C3S v202312, break detection and correction methods are introduced to the COMBINED product. These methods use reanalysis soil moisture as a reference to detect inhomogeneities in the merged satellite records.

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Figure 21: Longest homogenous period in ESA CCI SM v04.4 (COMBINED) before adjustment (top) and after adjustment (bottom) using the QCM method. Taken from (Preimesberger et al., 2021).

Near real time (NRT) mode of the processor

The processing steps described in the previous sections work on the complete time series of the datasets. This is not feasible during NRT production of the products. Figure 22 gives an overview of the NRT processing chain. The parameters for CDF scaling and the characterised errors are precomputed during generation of the CDR and then used directly in the ICDR processing. This speeds up the processing and results in a consistent time series based on stable merging parameters. At the moment it is only possible to use night-time retrievals from passive sensors in the ICDR generation.

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Figure 22: Overview of NRT processing chain

Output data

Table 8 lists the output fields generated by the processing system and available to data users. Detailed information about each field and the file format are provided in the Product User Guide and Specification (PUGS) [D3].

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Field name	Description
dnflag	Day – night flag. Information about observation times used in the soil moisture product
flag	Ancillary flag containing information about the reason of data unavailability
freqbandID	Identifies the frequency band combination used for soil moisture retrieval
mode	Specifies the orbit direction of the observation
sensor	Information about the sensors used in the product
sm	The soil moisture value
sm_uncertainty	Uncertainty of the soil moisture value
t0	The original observation time stamp of the observation

References

Ashcroft, P. and Wentz, F. (2000). Algorithm Theoretical Basis Document: AMSR Level-2A Algorithm, Revised 03 November. Santa Rosa, California USA: Remote Sensing Systems. https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd-amsr-level2A.pdf (URL resource last accessed 18th December 2023)

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_{This document has been produced in the context of the Copernicus Climate Change Service (C3S).}

_{The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.}

_{The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.}

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History of modifications

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Acronyms

General definitions

Scope of the document

Executive summary

Product Change Log

Instruments

Passive Microwave Systems

Nimbus-7 SMMR (NASA)

DMSP-SSM/I (NESDIS NOAA)

TRMM-TMI (NASA/JAXA)

AQUA-AMSR-E

Coriolis WindSat (Naval Research Laboratory)

GCOM-W1 AMSR-2 (JAXA)

SMOS (ESA)

SMAP (NASA)

FengYun 3B/C/D (CMA/NSMC)

GPM (NASA, JAXA)

Active Microwave Systems

ERS AMI (ESA)

ASCAT Metop-A (EUMETSAT)

ASCAT Metop-B (EUMETSAT)

ASCAT Metop-C (EUMETSAT)

Input and auxiliary data

Advisory flags

SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent

ERA-40

Global Lakes and Wetlands Database (GLWD)

GTOPO30

Merging Framework

Global Land Data Assimilation System (GLDAS) V2.0

Global Land Data Assimilation System (GLDAS) V2.1

ERA 5 / ERA5-Land

Validation data

In situ data from the International Soil Moisture Network (ISMN)

ERA5-Land

Algorithms

Passive Microwave Algorithm

Principles of the Land Parameter Retrieval Model

Methodology

C-, X- and Ku-band Model Parametrization

Day-time Retrievals

Known Limitations

Vegetation

Frozen surfaces and snow

Barren Grounds and Desert Areas

Water bodies

Rainfall

Radio Frequency interference

Updated temperature input from Ka-band observations

Active Microwave Algorithm

Merging Strategy

Principle of the merging process

Overview of processing steps

Methodology

Spatial Resampling

Temporal Resampling

Flagging

Rescaling

Intra-annual bias correction

Rescaling of Active Datasets

Rescaling of Passive Datasets

Rescaling in the COMBINED product

Error characterization

Error gap-filling

Merging

Weight estimation

Break detection and correction

Near real time (NRT) mode of the processor

Output data

References

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