Contributors: R. de Jeu (vandersat/planet labs), R. van der Schalie (vandersat/planet labs), Christoph Paulik (vandersat/planet labs), W. Dorigo (tuwien), T. Scanlon (tuwien), P. Stradiotti (tuwien), W. Preimesberger (tuwien), R. Kidd (EODC), C. Reimer (EODC), A. Dostalova (EODC)
Issued by: EODC/Richard Kidd
Date: 16/08/2023
Ref: C3S2_312a_Lot4.WP2-FDDP-SM-v1_202212_SM_ATBD-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 (WMO, 2016)
Brightness Temperature is the measurand of “passive“ microwave remote sensing system (radiometers). Brightness temperature (in degree Kelvin) is a function of kinetic temperature and emissivity. Wet soils have a higher emissivity than dry soils and therefore a higher brightness temperature. Passive soil moisture retrieval uses this difference between kinetic temperature and brightness temperature, to model the amount of water available in the soil of the observed area, while taking into account factors such as the water held by vegetation.
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.
Polarisation: The polarization of microwaves refers to the orientation of the electric field vector of the transmitted beam with respect to the horizontal direction. the beam is said to be "V" polarized when the electric field vector oscillates along a direction perpendicular to the horizontal direction and "H" polarized when the electric field vector oscillates along a direction parallel to the horizontal direction.
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.
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 v202212 for CDR and ICDR products.
Executive summary
The C3S SM production system is based on the algorithms initially developed within the European Space Agency’s (ESA) Climate Change Initiative Soil Moisture Project (Phases 1 and 2) [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 a minimum 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.
More specifically this document relates to the C3S Soil Moisture production system used to generate product versions v202212 and is based on the algorithm developed for ESA CCI SM v07.1.
Chapter 1 provides an overview of the satellite instruments that are used for the production of the soil moisture data, which is a combination of both passive and active microwave sensors dating back to 1978. The satellites are selected based on the availability of compatible Level 3 products, respectively the applicability of the Land Parameter Retrieval Model (LPRM) in the case of microwave radiometers and their suitability for soil moisture retrieval in general (i.e. operating in preferably X, C or L-band frequencies).
Chapter 2 presents an overview of all the auxiliary data that is used during the processing of the soil moisture dataset, for which a distinction is made between input and the validation data. These datasets are partly used in the LPRM soil moisture retrieval for passive sensors and partly used in the subsequent merging of the individual active and passive products. The role of the in-situ data is to provide as good as possible reference observations to validate the final C3S products and rate their general performance. The validation process is described in more detail in the “Product Quality Assurance Report” (PQAR) [D4].
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 of 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 v202212 (CDRv4.0). See List of datasets covered by this document.
Table 1: Changes in the product between versions.
Version | Product Changes |
---|---|
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.
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 |
|
Data Availability and Coverage | October 1978 – August 1987, 180°W 90°S – 180°E 90°N
|
Source Data Name and Product Technical Specifications | SMMR Level 1b
|
Data Quantity | Total volume is 70 GB (compressed) |
Data Quality and Reliability | Instrument specification:
Validation reports
|
Ordering and delivery mechanism | |
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 |
|
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
|
Source Data Name and Product Technical Specifications | Version 6 20+ years SSMI brightness temperatures
|
Data Quantity | 100 GB/year |
Data Quality and Reliability | Instrument specification:
Validation reports
|
Ordering and delivery mechanism | Ordering via SSMI Data Centre [No longer available online] Delivery is possible via
|
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 |
|
Data Availability and Coverage | November 1997 – April 2015, 180°W 38°S – 180°E 38°N |
Source Data Name and Product Technical Specifications | Level 1 b calibrated brightness temperatures TRMM TMI |
Data Quantity | 10 GB per year |
Data Quality and Reliability | Instrument specification:
Validation reports
|
Ordering and delivery mechanism | Ordering via NASA GES DISC Data Centre |
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 |
|
Data Availability and Coverage | June 2002 – October 2011, 180°W 89.24°S – 180°E 89.24°N
|
Source Data Name and Product Technical Specifications | AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures
|
Data Quantity | 1 TB per year |
Data Quality and Reliability | Instrument specification:
Validation reports
|
Ordering and delivery mechanism | Ordering via NASA GES DISC or NSIDC data center: |
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 |
|
Data Availability and Coverage | January 2003 – present, 180°W 90°S – 180°E 90°N
|
Source Data Name and Product Technical Specifications | WindSat Brightness Temperatures
|
Data Quantity | ~1 TB per year |
Data Quality and Reliability | Instrument specification:
Validation reports
|
Ordering and delivery mechanism | Ordering via Naval Research laboratory |
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 |
|
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 Quantity | ~42 GB per year |
Data Quality and Reliability | Instrument specification:
|
Ordering and delivery mechanism | Please see |
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 |
|
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)
|
Data Quantity | ~5 Tb per year |
Data Quality and Reliability | Instrument specification:
|
Ordering and delivery mechanism | Ordering via ESA at https://earth.esa.int/eogateway/missions/smos/data
|
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 |
|
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))
|
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
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) |
Source Data Name and Product Technical Specifications | FengYun-3 swath based brightness temperature data from Ka- and X-band |
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) |
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
|
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)
|
Data Quantity | 165.0 MB per file (Brightness Temperature level) ~2.5 GB / year (L3 LPRM derived soil moisture data) |
Data Quality and Reliability |
|
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 |
|
Data Availability and Coverage | IFREMER |
Source Data Name and Product Technical Specifications | IFREMER
ESA Rolling Archive
|
Data Quantity | ~32 GB |
Data Quality and Reliability | Instrument specification
|
Ordering and delivery mechanism | IFREMER
|
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 |
|
Data Availability and Coverage | 2007/01/01 – 2021/11/15., 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)H101 SSM ASCAT-A NRT O12.5 (H SAF)
|
Data Quantity | ~100 GB/year for L2 soil moisture 25 km resolution |
Data Quality and Reliability | Instrument specification
Validation reports
|
Ordering and delivery mechanism | Ordering via EUMETSAT Data Centre
|
Access conditions and pricing | EUMETSAT data policy |
Issues | N/A |
ASCAT Metop-B (EUMETSAT)
Originating System | Advanced Scatterometer (ASCAT) onboard Metop-B |
Data class | Earth observation |
Sensor Type and key technical characteristics |
|
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)
|
Data Quantity | ~100 GB/year for L2 soil moisture 25 km resolution |
Data Quality and Reliability | Instrument specification
Validation reports
|
Ordering and delivery mechanism | Ordering via EUMETSAT Data Centre
|
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. 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 |
|
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)
ASCAT Soil Moisture Product Handbook |
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 (Section 2.1) and for validation activities (Section 2.2).
Input data
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 |
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° |
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 |
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. |
Access conditions and pricing | 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 the dedicated page on the Decommissioning of ECMWF Public Datasets service1. |
Issues | N/A |
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: |
Data Availability and Coverage | Global (except Antarctica) |
Source Data Name and Product Technical Specifications | |
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 |
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/) |
Access conditions and pricing | Free |
Issues | N/A |
Modelled Data
Global Land Data Assimilation System (GLDAS) V2.0
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° |
Data Availability and Coverage | 1948 – 2010 for 1°x1° |
Source Data Name and Product Technical Specifications | Global Land Data Assimilation System: |
Data Quantity | 3-hourly data |
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
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) |
Data class | Water and energy budget components, forcing data |
Sensor Type and key technical characteristics | Spatial resolution: 0.25° |
Data Availability and Coverage | 2000 – present for 0.25°x0.25° |
Source Data Name and Product Technical Specifications | Global Land Data Assimilation System: |
Data Quantity | 3-hourly data |
Data Quality and Reliability | 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 available |
Issues | None identified |
ERA Interim
Originating System | ERA-Interim uses mostly the sets of observations acquired for ERA-40, supplemented by data for later years from ECMWF's operational archive. |
Data class | Gridded analyses, modelled data |
Sensor Type and key technical characteristics | 3-hourly surface parameters, describing weather as well as ocean-wave and land-surface conditions, and 6-hourly 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. |
Data Availability and Coverage | 1979 – 2019, 180°W 90°S – 180°E 90°N |
Source Data Name and Product Technical Specifications | ERA Interim |
Data Quantity | Depends upon number of variables that are required from the dataset |
Data Quality and Reliability | Please see Dee et al. (2011) |
Ordering and delivery mechanism | 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 the dedicated page on the Decommissioning of ECMWF Public Datasets service1. |
Access conditions and pricing | Free for research users. See ECWMF for Terms and conditions for commercial usage. |
Issues | N/A |
ERA-Interim/Land
Originating System | ERA Interim/Land applies a recent version of the HTESSEL land-surface model using atmospheric forcing from ERA-Interim, with precipitation adjustments based on GPCP v2.1. |
Data class | Gridded analyses, modelled data |
Sensor Type and key technical characteristics | 6-hourly surface parameters providing information on global integrated and coherent water resources |
Data Availability and Coverage | 1979 – 2010, 180°W 90°S – 180°E 90°N |
Source Data Name and Product Technical Specifications | ERA Interim/Land |
Data Quantity | Depends upon number of variables that are required from the dataset |
Data Quality and Reliability | See Balsamo et al. (2010) |
Ordering and delivery mechanism | 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 the dedicated page on the Decommissioning of ECMWF Public Datasets service1 |
Access conditions and pricing | Free for research users. See ECWMF for Terms and conditions for commercial usage. |
Issues | N/A |
Validation data
In situ data from the International Soil Moisture Network (ISMN)
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 from 1952 (oldest) to present. |
Data class | Ground-based in-situ measurements data |
Sensor Type | The sensor types most commonly used have changed over the time period of the ISMN:
|
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 provide data in near real time. See Dorigo et al. (2011) |
Source Data Name and Product Technical Specifications | The source is open as the network´s data sets are provided from different countries worldwide operated by different universities and national and regional organizations. |
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.
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) for more information. |
Ordering and | 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:
|
Algorithms
Passive Microwave Algorithm
This chapter is largely based upon the “Algorithm Theoretical Baseline Document (ATBD) D2.1 Supporting Version 07.1” [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.
A result of this physical parameterisation is that any differences in frequency and incidence angle that exist among different satellite platforms are accounted for within the framework of the radiative transfer model based on global constant parameters (de Jeu et al., 2014). This important aspect makes LPRM suitable for the development of a long-term consistent soil moisture products which can be used in the ESA CCI SM products.
The different processing steps of LPRM are described in detail in the next section, while Figure 2 presents a flowchart of the entire methodology.
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 (Tb) as measured by a space borne radiometer can be described as:
Where Γa and Γv are the atmosphere and vegetation transmissivity respectively, Tb_s is the surface brightness temperature, er is the rough surface emissivity, Tb_extra, the extra-terrestrial brightness temperature and the Tb_u and Tb_d are the upwelling and downwelling atmospheric brightness temperatures. The subscript p denotes either horizontal (H) or vertical (V) polarization. The vegetation/atmosphere transmissivity is further defined in terms of the optical depth, τv/a, and satellite incidence angle, u, such that
The upwelling brightness temperature from the atmosphere is estimated as (Bevis et al. 1992):
Were Ta is the atmospheric temperature. In LPRM the downwelling Temperature (Td) is assumed to be equal to the upwelling temperature (Tu) and the extraterrestrial temperature is set to 2.7 K (Ulaby et al. 1982). The radiation from a land surface (Tb_s,p) is described according to a simple radiative transfer (Mo et al. 1982):
Where Ts and Tv are the thermodynamic temperatures of the soil and the vegetation, ω is the single scattering albedo. LPRM uses the model of Wang and Choudhury (1981) to describe the rough surface emissivity as:
rs is the surface reflectivity and p1 and p2 are opposite polarization (horizontal (H) or vertical (V)). The surface reflectivity is calculated from the Fresnel equations:
Where rs,H is the horizontal polarized reflectivity, and rs,V is the vertical polarized reflectivity and ε the complex dielectric constant of the soil surface (ε = ε' + ε"i)._ The dielectric constant is an electrical property of matter and is a measure of the response of a medium to an applied electric field. The dielectric constant is a complex number, containing a real (ε') and imaginary (ε") part. The real part determines the propagation characteristics of the energy as it passes upward through the soil, while the imaginary part determines the energy losses (Schmugge et al. 1986). There is a large contrast in dielectric constant between water and dry soil, and several dielectric mixing models have been developed to describe the relationship between soil moisture and dielectric constant (Dobson et al. 1985; Mironov et al. 2004; Peplinski et al. 1995; Wang and Schmugge 1980). Owe and Van de Griend (1998) compared the Dobson and Wang and Schmugge model and they concluded that the Wang and Schmugge model had better agreement with the laboratory dielectric constant measurements. Consequently, LPRM uses the Wang and Schmugge model, which requires information on the soil porosity (P) and wilting point (WP), observation frequency (F), TS, and θ.
A special characteristic of LPRM is the internal analytical approach to solve for the vegetation optical depth, τv (Meesters et al. 2005). This unique feature reduces the required vegetation parameters to one, the single scattering albedo. LPRM makes use of the Microwave Polarization Difference Index (MPDI) to calculate τv. The MPDI is defined as:
When one assumes that τ and ω have minimal polarization dependency at satellite scales, then the vegetation optical depth can be described as:
Where
And
By using all these equations in combination with the dielectric mixing model, soil moisture could be solved in a forward model together with a parameterization of the following parameters: atmosphere, soil and vegetation temperature (Ta, Ts, Tc), the optical depth of the atmosphere (τa), the roughness parameters Q and h, soil wilting point (WP) and porosity (P), and the single scattering albedo (ω).
The temperatures were estimated using Ka-band (37 GHz) observations according to the method of Holmes et al. (2009).
For the day-time (ascending) observations the following equation is used:
and for the night-time (descending):
However, since the current L-band missions do not observe the Earth at the Ka-band frequency, they still require modelled TS from land surface models as an input. Van der Schalie et al. (2021) have developed a method to replace the land surface model with data from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. However, this is not yet applied to L-band data used in C3S SM.
The soil P and WP were derived from the FAO soil texture map available from the Harmonised World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009), while all the other parameters were given a fixed value. Table 2 summarizes the values used for the different frequencies.
Table 2: Values of the different parameters used in LPRM for the different frequencies
Parameter | Frequency | |||
---|---|---|---|---|
L-band (~1.4 GHz) | C-band (~6.9 GHz) | X-band (~10.8 GHz) | Ku-band (~19 GHz) | |
τa | 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 |
AV | 0.7 | 0.3 | 0.3 | n/a |
BV | 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).
Due to more dynamic VOD behavior as compared to L-band, the VOD based vegetation correction has too much impact on the soil moisture signal from C-, X- and Ku-band. Therefore, we have studied ways to remove this effect from LPRM for the higher frequencies. Together with the renewed parameterization, the roughness has now been simplified to the soil moisture (SM) and Porosity (P) dependent:
with h = 0 when SM > P.
Prior to the optimization of the parameters, the temperature relation with Ka-band has been revisited for use in LPRMv7. Local slope and intercept have been calculated between Ka-band and the average of the ERA5-Land land surface temperature (0 cm) and ERA5-Land layer 1 soil temperature (0-7cm), see Figure 3. This dataset is known to have a good quality and the expected seasonal behavior.
Figure 3. Slope (SLP), Intercept (ITC) and Correlation (R) of Ka-band against ERA5-Land temperature. Note that the interpolation over the tropical region in Africa has since been improved.
On top of this new local temperature relation, the dT is used in the optimization to correct the effective temperature from the microwave emission, assuming that the seasonal dynamics are similar to that of ERA5-Land. During testing, a variable slope has also been considered, but this gave a negligible effect on the final results. The optimization process for both parameters (simultaneously) searches for the best correlation against SMAP L-band LPRM and limits the results to datasets that had at least 90% valid retrievals between 0.01 and 0.75 m3m-3. Resulting parameterization can be found in Figure 4, with an example of the final quality: the standard deviation and the 10/90 percentiles, in Figure 5.
Figure 4. The single scattering albedo (⍵) and local bias correction of the land surface temperature (dT), results from the optimization for C- (6.9 GHZ), X- (10.7 GHZ) and Ku-band (18.7 GHZ).
Figure 5. The 10/90 percentiles (P10/P90) and standard deviation (STDEV) of the LPRMv7 soil moisture retrievals from C- (6.9 GHZ), X- (10.7 GHZ) and Ku-band (18.7 GHZ). Note that Greenland is normally not run and therefore shows artefacts that are not in the final product.
Day-time Retrievals
Due to the fact that 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:
- Linearly interpolated dataset for day-time and night-time to allow sufficient overlap.
- Calculate the median ratio between day-time and night-time over a 3-week (±10 day) window. The median was chosen over the mean to reduce the impact of strong individual events between overpasses.
- Apply this ratio to correct the day-time data to night-time.
- Apply LPRM to the dataset using the exact same parameterization as for night-time retrievals
The resulting skill of the day-time retrievals from AMSR2 using LPRMv7.0 can be seen in Figure 6. The day-time values are still reaching good (r > 0.6) correlations over much of the same regions where the night-time performs well against SMAP Level 4 SM. Because the theoretical issue of non-existing thermal equilibrium for midday observations remains present within the vegetation and soil surface, an increase in overall noise within the day-time datasets does lead to an average decrease in correlation, therefore, when available, the night-time retrievals still have a preference.
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), X- (10.7 GHz), and Ku-band (18.7 GHz) 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.
Figure 7 gives an example of the relationship between the analytical error estimate in soil moisture as described in the previous section and vegetation optical depth. This figure shows larger error values in the retrieved soil moisture product for higher frequencies at similar vegetation optical depth values. For example, for a specific agricultural crop (VOD=0.5), the error estimate for the soil moisture retrieval in the C-band is around 0.07 m3·m−3; in the X-band, this is around 0.11 m3·m−3, and in the Ku-band, this is around 0.16 m3·m−3. All relevant frequency bands show an increasing error with increasing vegetation optical depth. This is consistent with theoretical predictions, which indicate that, as the vegetation biomass increases, the observed soil emission decreases, and therefore, the soil moisture information contained in the microwave signal decreases (Owe et al., 2001).
In addition, retrievals from the higher frequency observations (i.e., X- and Ku-bands) show adverse influence by a much thinner vegetation cover. Soil Moisture retrievals with a soil moisture error estimate beyond 0.2 m3m-3 are considered to be unreliable and are masked out.
Figure 7.Error of soil moisture as related to the vegetation optical depth for 3 different frequency bands (from Parinussa et al., 2011).
For the L-band based retrievals from SMOS, the vegetation influence is less as compared to the C-, X- and Ku-band retrievals, which can be seen from the Rvalue and Triple Collocation Analysis (TCA) results in Figure 8 (top). In Figure 8 (bottom), the SMOS LPRM and AMSR-E LPRM (based on C-band) are included and show more stable results over dense vegetation, i.e. Normalized Difference Vegetation Index (NDVI) values of over 0.45.
Figure 8. Triple collocation analysis (TCA: top) and Rvalue 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).
Figure 9. (b) Barren ground and related desert regions are clearly visible when using the first step of the snow/frozen flag without the correction for low physical temperatures. (a) Final decision tree for the barren soil flagging.
The dynamic behavior of barren ground conditions is captured well, as displayed in Figure 10.
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.
The new methodology that is now used for RFI detection uses the estimation of the standard error between two different frequencies. It uses both the correlation coefficient between two observations and the individual standard deviation to determine the standard error in Kelvin. A threshold value of 3 Kelvin is used to detect RFI. This method does not produce false positives in extreme environments and is more sensitive to weak RFI signals in relation to the traditional methods (e.g. Li et al., 2004).
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 Rvalue 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 Rvalue 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.
Figure 11. (top) comparison of Rvalue with the old and new day-time land surface temperature binned over NDVI, (bottom) the difference in Rvalue compared to the old temperature parameterisation in [%].
This explorative work showed the high impact of temperature on the quality of the LPRM retrievals. Secondly, there are issues with (seasonal) bias in the effective temperature derived from Ka-band using a linear regression method, which are caused by the seasonal changes in soil moisture, vegetation cover and atmospheric composition. The day-time retrieval has been finally integrated with LPRM as of version 7.0, according to the methodology detailed in Section 3.1.4.
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:
- ERS-1 AMI surface soil moisture products have been generated at TU Wien (2013) utilizing change detection method described in Wagner et al. 1999b.
- ERS-2 AMI surface soil moisture data sets stem from reprocessing activities which have been carried out within ESA's SCIRoCCo project (Crapolicchio et al., 2016). The ERS-2 data set used in all ESA CCI SM versions is the ERS.SSM.H.TS 25 km soil moisture time series product (ESA, 2017).
- Metop ASCAT surface soil moisture data sets stem from the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF, https://hsaf.meteoam.it/).
A detailed overview of the retrieval methodology employed in the generation of the Surface Soil Moisture products from the ERS-1 AMI instrument is provided in Wagner et al. 1999b, for the Surface Soil Moisture products from the ERS-2 AMI instrument please refer to Crapolicchio et al. 2004, and for the ASCAT derived products an overview of the retrieval methodology can be found in H-SAF 2018c.
Merging Strategy
This chapter is largely based upon the “Algorithm Theoretical Baseline Document (ATBD) D2.1 Supporting Version 07.1” [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:
- Scatterometer-based soil moisture products
- Sensors: ERS-1/2 and Metop-A/B/C ASCAT
- Retrieval method: TU Wien change detection method (Wagner et al. 1999b; H-SAF 2018c)
- Time span: 1991 – now
- Radiometer-based soil moisture products
- Sensors: SMMR, SSM/I, TRMM, AMSR-E, AMSR2, WindSat, SMOS, SMAP, FengYun 3B/C/D, GPM
- Retrieval method: VUA-NASA LPRM v6 and v7 model (Owe et al. 2008; van der Schalie et al. 2016)
- Time span: 1978 – now
- Modelled 0 – 10 cm soil moisture from the Noah land surface model of the Global Land Data Assimilation System version 2.0 and v2.1 (GLDAS; Rodell et al., 2004).
- Time span: 1948 – 2010 and 2000 - Present (0.25-degree resolution)
The homogenized and merged product presents surface soil moisture with a global coverage and a spatial resolution of 0.25°. The time period spans the entire period covered by the individual sensors, i.e. 1978 – now, while measurements are provided at a 1-day sampling.
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):
- Spatial resampling and temporal resampling (including flagging and cross-flagging of observations)
- Rescaling passive and active level 2 observations into radiometer and scatterometer climatologies (for the PASSIVE and ACTIVE product), and separately rescaling all level 2 observations into a common model-based climatology (for the COMBINED product)
- Triple collocation analysis (TCA)-based error characterisation of all rescaled level 2 products
- Polynomial regression between VOD and error estimates to fill spatial gaps where errors could not be reliably retrieved i.e., where TCA is deemed unreliable
- Merging rescaled passive and active time series into the PASSIVE, ACTIVE, and COMBINED products, respectively.
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 join process is performed on datasets of each line and on datasets separated by comma within the rectangular process symbol. *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.
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.
Spatial Resampling
The final merged products are provided on a regular grid with a spatial resolution of 0.25° in both latitude and longitude extension. This is a trade-off between the higher resolution scatterometer data and the generally coarser passive microwave observations without leading to any under-sampling. The same resolution is often adopted by land surface models.
For the LPRM passive data, nearest neighbor resampling is performed on the radiometer input data sets to bring them into the common regular grid. Following this resampling technique, each grid point in the reference (regular grid) data set is assigned to the value of the closest grid point in the input dataset. In general, the nearest neighbor resampling algorithm can be applied to data set with regular degree grid.
For the active microwave data sets, where equidistant grid points are defined by the geo-reference location of the observation, the hamming window function is used to resample the input data to a 0.25° regular grid. The search radius is a function of latitude of the observation location, as the distance between two regular grid points reduces as the location tends towards the poles. In contrast, the active microwave data set uses the discrete global grid (DGG), where the distance between every two points is the same. This main difference between the DGG (active) and the targeted regular degree grid is rectified by using a hamming window with search radius dependent on the latitude for the spatial resampling of the active microwave data.
Table 3: Major characteristics of passive microwave instruments used C3S SM
Passive microwave products | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
SMMR | SSM/I | TMI | AMSR-E | AMSR2 | Windsat | MIRAS | SMAP | GMI | MWRI | MWRI | MWRI | |
Platform | Nimbus 7 | DMSP | TRMM | Aqua | GCOM-W1 | Coriolis | SMOS | SMAP | GPM | FY-3B | FY-3C | FY-3D |
Product | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) | LPRM (VanderSat) |
Product Source | NASA, Tape derived | NASA EarthData, XCAL calibrated with GPM | NASA EarthData, XCAL calibrated with GPM | JAXA, G-portal | JAXA, G-portal | Bespoke order | CATDS | NASA EarthData | NASA EarthData, XCAL calibrated with GPM | nsmc | nsmc | nsmc |
Algorithm Product version | LPRM v7.0 (2) | LPRM v7.0 (2) | LPRM v7.0 (2) | LPRM v7.0 (2) | LPRM v7.0 (2) | LPRM v7.0 (2) | LPRM v06.2 (2, 3) | LPRM v06.2 (2, 3) | LPRM v7.0 (2) | LPRM v7.0 (2) | LPRM v7.0 (2) | LPRM v7.0 (2) |
Time period used | 11/1978–8/1987 | 09/1987– 12/2007 | 01/1998– 12/2013 | 07/2002– 10/2011 | 05/2012– Present | 10/2007–7/2012 | 01/2010–Present | 04/2015-Present | 03/2014 – Present | 06/2011 – 08/2019 | 09/2013 – 02/2020 | 07/2012 – 08/2019 |
Channel used for soil moisture | 6.6 GHz | 19.3 GHz | 10.7 GHz | 6.9/10.7 GHz | 6.925/10.65 GHz | 6.8/10.7 GHz | 1.4 GHz | 1.4GHz | 10.7 GHz | 10.7 GHz | 10.7 GHz | 10.7 GHz |
Original spatial resolution(1) (km2) | 150×150 | 69 × 43 | 59 × 36 | 76 × 44 | 35 x 62 | 25 x 35 | 40 km | 38 x 49 | 19x32 | 51 x 85 | 51 x 85 | 51 x 85 |
Spatial coverage | Global | Global | N40o to S40o | Global | Global | Global | Global | Global | N70o to S70o | Global | Global | Global |
Swath width (km) | 780 | 1400 | 780/897 after boost in Aug 2001 | 1445 | 1450 | 1025 | 600 | 1000 | 931 | 1400 | 1400 | 1400 |
Equatorial crossing time | Descending: | Descending: | Varies (non polar-orbiting) | Descending: | Descending 01:31 | Descending 6:03 | Ascending 6:00 | Descending 06:00 | Varies (non polar-orbiting) | Descending: | Descending: | Descending: |
Unit | m3m-3 | m3m-3 | m3m-3 | m3m-3 | m3m-3 | m3m-3 | m3m-3 | m3m-3 | m3m-3 | m3m-3 | m3m-3 | m3m-3 |
(1) For passive microwave instruments, this stands for the footprint spatial resolution.
(2) LPRM v6.1 references: van der Schalie et al. (2016, 2017, 2018), van der Vliet (2020)
(3) LPRM v6.2 consists of a temporal extension of LPRMv6.1, including day-time retrievals
Table 4: Major characteristics of active microwave instruments and model products used in C3S SM
Active microwave products | Model product | ||||||
---|---|---|---|---|---|---|---|
AMI-WS | AMS-WS | ASCAT | ASCAT | ASCAT | GLDAS-2-Noah | GLDAS-2-Noah | |
Platform | ERS1/2 | ERS2 | Metop-A | Metop-B | Metop-C | — | — |
Product | SSM Product (TU WIEN, 2013) | SSM Product (Crapolicchio et al., 2016) | H 119 (H-SAF 2018a and 2018b) | H 16 (H-SAF 2018a and 2018b) | H 104 (H-SAF 2018a and 2018b) | — | — |
Algorithm Product version | TU WIEN Change Detection (2) | TU WIEN Change Detection (2) | TU WIEN Change Detection (2) | V2.0 | V2.1 | ||
Time period used | 7/1991– 12/2006 | 5/1997– 2/2007 | 1/2007– | 07/2015–Present | 11/2018–Present | 1/1948– 12/2010 | 1/2000– 12/2022 |
Channel used for soil moisture | 5.3 GHz | 5.3 GHz | 5.3 GHz | 5.3 GHz | 5.3 GHz | — | — |
Original spatial resolution(1) (km2) | 50 × 50 | 25 x 25 | 25 × 25 | 25 × 25 | 25 × 25 | 25 × 25 | 25 × 25 |
Spatial coverage | Global | Global | Global | Global | Global | Global | Global |
Swath width (km) | 500 | 500 | 1100 (550×2) | 1100 (550×2) | 1100 (550×2) | — | — |
Equatorial crossing time | Descending: | Descending 10:30 | Descending: | Descending: | Descending: | — | — |
Unit | Degree of saturation (%) | Degree of saturation (%) | Degree of saturation (%) | Degree of saturation (%) | Degree of saturation (%) | kg m-2 | kg m-2 |
(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.
The temporal resampling strategy firstly searches for the valid observation that is closest to the reference time. In the case that there are only invalid observations, which are flagged other than "0" (zero), within this time frame, the closest measurement among these invalid observations is selected. In the event that there are no measurements available at all within a time frame, no value is assigned to that day. This strategy results in data gaps when no observations within ±12 hours from the reference time are available.
Starting from v07.1, 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, in v07.1 the temporal resampling 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.
The ASCAT and ERS products include a Surface State Flag (SSF) which effectively encodes information about whether or not the surface is frozen or snow-covered. In the ESA CCI SM / C3S SM product, those soil moisture values where Bit 1 (indicating frozen conditions) in the binary representation of the SSF bitflag is active are used to flag the observation as frozen. The ASCAT and ERS products do not provide information on high VOD.
The LPRMv6.1 (and following) products provide a FLAGS field which provides information on high VOD, frozen conditions, and the performance of the LPRM algorithm. The thresholds above which VOD is considered 'high' are set based on the saturation point in the VOD signal for each sensor and band. This is the point at which the VOD value is considered to equal 100% vegetation signal. Secondly, the frozen/snow flag was applied using the new approach by Van der Vliet et al. (2020), which derives the frozen/snow conditions from Ku-, K- and Ka-band observations. In addition to this, from LPRMv7 onwards a flag for barren grounds and desert areas is included (see section 3.2.3). This is implemented as an advisory flag as of C3S SM v202212. Including it as a critical flag would have a big impact on the soil moisture availability in desert-prone regions. Therefore, more validation is needed to establish whether the optional usage of the barren ground flag or a more conservative version thereof (as a critical flag) is suitable in C3S SM.
New is the implementation of cross-flagging 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.
Scaling is performed using cumulative distribution function (CDF) matching which is a well-established method for calibrating datasets with deviating climatologies (Drusch et al. 2005; Liu et al. 2007; Liu et al. 2011; Reichle et al. 2004, Moesinger et al. 2020). CDF-matching is applied for each grid point individually and based on piece-wise linear matching. This variation of the CDF-matching technique proved to be robust also for shorter time periods (Liu et al. 2011). The matching is shown by means of an example for a grid point centred at 41.375oN, 5.375oW. Figure 14 shows for this location the time series of soil moisture estimates from GLDAS-Noah, AMSR-E and ASCAT, respectively. CDF-matching for this time series is performed in the following way:
- For the time-collocated data points, CDFs (Figure 15) are computed. In the passive product AMSR2 is scaled using the parameters derived from the last 3 years of AMSRE and first 3 years of AMSR2. SMAP is then scaled to the scaled AMSR2.
- If more than 400 time-collocated data points exist, for each CDF curve the 0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95 and 100 percentiles are identified. Else evenly spaced percentile bins are generated such that each of them contains at least 20 observations.
Use the n percentiles of the CDF curves to define n-1 segments. The CDF curves of these circled values are shown in Figure 15 (a), (b) and (c).
$$slope_i = \frac{pref_{i+1} - pref_i}{psrc_{i+1} - psrc_i} \quad Eqn. 16$$ $$intercept_i = pref_i - (psrc_i \ast slope_i) \quad Eqn. 17$$where 𝑖=1…12, is the number of the segments, and 𝑝𝑟𝑒𝑓 is the percentile of the GLDAS-Noah data (reference), and 𝑝𝑠𝑟𝑐 is the percentile of either AMSR-E or ASCAT data (source) respectively.
- An exception is the first and the last segment. Instead of using the first and last percentile for interpolation, the slope is derived using least squares regression. This is more robust to outliers.
- The n percentile values from the AMSR-E and ASCAT CDF curves are plotted against those of Noah (Figure 15 (d) and (e)) and scaling linear equations (e.g., slope and intercept) between two consecutive percentiles are computed.
The obtained linear equations are used to scale all observations of the target data set (i.e., also the time steps that do not have a corresponding observation in the reference data set) to the climatology of the reference data set (Figure 15 (f)).
$$sm_r = slope_i \ast sm + intercept_i \quad Eqn. 18$$where smr is the rescaled soil moisture and sm is the original soil moisture value. slopei and intercepti are chosen depending on the sm value and its corresponding i-percentile. The AMSR-E and ASCAT values outside of the range of CDF curves can also be properly rescaled, using the linear equation of the closest value.
Figure 14: Time series of soil moisture estimates from (a) Noah, (b) AMSR-E and (c) ASCAT for a grid cell (centred at 41.375° N, 5.375° W) in 2007. Circles represent days when Noah, AMSR-E and ASCAT all have valid estimates. (Figure taken from Liu et al. 2011)
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 v07.1 of CCI SM, 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 such effect 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).
For the ACTIVE product, the limited overlap between AMI-WS ERS1/2 and Metop-A ASCAT in time (i.e., a few months) rules out the global adjustment method based on the information of their overlapping period. However, as retrievals from Metop-A ASCAT and AMI-WS capture similar seasonal cycles (Liu et al., 2011), we assume that their dynamic ranges are identical and, therefore, can use non-overlapping observations for the rescaling (i.e. the entire time period for each sensor).
From evaluation of previous versions to v07.1, it was noticed that the soil moisture (and backscatter) signal from Metop-B ASCAT is characterized by a positive bias on a global level. This has a detrimental effect particularly on the soil moisture trends from the ACTIVE product. To correct for this, Metop-B ASCAT is scaled to the reference of Metop-A ASCAT leading to a homogenization of the data record, as shown in Figure 16.
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.
For the PASSIVE product, all datasets with the exception of SMAP are rescaled to AMSR-E. Where sufficient overlap is available, this is utilised; for all other cases (except AMSR2), the entire time period of AMSR-E and the sensor being scaled is utilised. For AMSR2, data in the last three years of AMSR-E and the first three years of ASMR2 are used, i.e. 2008-10-04 to 2015-07-01. SMAP is rescaled to AMSR2 which has already been rescaled to AMSR-E.
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 3.4.3.8).
Triple collocation analysis is a statistical tool that allows estimating the individual random error variances of three data sets without assuming any of them acting as supposedly accurate reference (Gruber et al. 2016a, 2016b). This method requires the errors of the three data sets to be uncorrelated, therefore triplets always comprise of i) an active data set, (ii) a passive data set, and (iii) the GLDAS-Noah land surface model, which are commonly assumed to fulfil this requirement (Dorigo et al. 2010). Error variance estimates are obtained as:
where σε2 denotes the error variance; σ2 and σ denote the variances and covariances of the data sets; and the superscripts denote the active (a), the passive (p), and the modelled (m) data sets, respectively. For a detailed derivation see Gruber et al. (2016b). Note that these error estimates represent the average random error variance of the entire considered time period, which is commonly assumed to be stationary. Furthermore, the soil moisture uncertainties of the three products (ACTIVE, PASSIVE, and COMBINED) are determined by the above equations, hence the uncertainty estimates provided with the product apply to an entire merging period and do not vary with every timestamp.
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).
In these areas, error estimates are derived by deriving a Signal-to-Noise Ratio (SNR)-VOD regression model per land cover class and using this to determine the SNR based on the VOD at each location where SNR could not be retrieved:
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
Table 5: Used passive sensors in the PASSIVE product
Time Period | Passive Sensors (mode: ascending (a) or descending (d)) |
---|---|
01/11/1978 – 31/07/1987 | SMMR (a/d) |
01/09/1987 – 31/12/1997 | SSM/I (a/d) |
01/01/1998 – 18/06/2002 | SSM/I (a/d), TMI (a/d) |
19/07/2002 – 30/09/2007 | AMSR-E (a/d), TMI (a/d) |
01/10/2007 – 14/01/2010 | AMSR-E (a/d), WindSat (a/d), TMI (a/d) |
15/01/2010 – 31/55/2011 | AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d) |
31/05/2011 – 04/10/2011 | AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d), FY-3B (a/d) |
05/10/2011 – 30/06/2012 | WindSat (a/d), SMOS (a/d), TMI (a/d), FY-3B (a/d) |
01/07/2012 – 28/09/2013 | SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d) |
29/09/2013 – 28/02/2014 | SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d), FY-3C (a/d) |
01/03/2014-30 - 09-2014 | SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d), FY-3C (a/d), GPM (a/d) |
01/10/2014 – 30/03/2015 | SMOS (a/d), AMSR2 (a/d), FY-3B (a/d), FY-3C (a/d), GPM (a/d) |
31/03/2015 – 31/12/2018 | SMOS (a/d), AMSR2 (a/d), FY-3B (a/d), FY-3C (a/d), GPM (a/d), SMAP (a/d) |
01/01/2019 – 19/08/2019 | SMOS (a/d), AMSR2 (a/d), FY-3B (a/d), FY-3C (a/d), FY-3D (a/d), GPM (a/d), SMAP (a/d) |
20/08/2019 – Present | SMOS (a/d), AMSR2 (a/d), FY-3C (a/d), FY-3D (a/d), GPM (a/d), SMAP (a/d) |
Table 6: Used active sensors in the ACTIVE product
Time Periods | Active Sensors |
---|---|
05/08/1991 – 19/05/1997 | ERS1/2 (AMI-WS) |
20/05/1997 – 17/02/2003 | ERS2 (AMI-WS) |
18/02/2003 – 31/12/2006 | ERS1/2 (AMI-WS) |
01/01/2007 – 20/07/2015 | Metop-A ASCAT |
20/07/2015 – 07/11/2018 | Metop-A ASCAT, Metop-B ASCAT |
08/11/2018 – 15/11/2021 | Metop-A ASCAT, Metop-B ASCAT, Metop-C ASCAT |
16/11/2021 – Present | Metop-B ASCAT, Metop-C ASCAT |
Table 7: Used sensors in individual time periods.
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/d), TMI (a/d) |
31/05/2011 – 04/10/2011 | Metop-A ASCAT | AMSR-E (a/d), WindSat (a/d), SMOS (a/d), TMI (a/d). FY-3B (a/d) |
05/10/2011 – 30/06/2012 | Metop-A ASCAT | WindSat (a/d), SMOS (a/d), TMI (a/d), FY-3B (a/d) |
01/07/2012 – 28/09/2013 | Metop-A ASCAT | SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d) |
29/09/2013 – 28/02/2014 | Metop-A ASCAT | SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d), FY-3C (a/d) |
01/03/2014-30 - 09-2014 | Metop-A ASCAT | SMOS (a/d), AMSR2 (a/d), TMI (a/d), FY-3B (a/d), FY-3C (a/d), GPM (a/d) |
01/10/2014 – 20/07/2015 | Metop-A ASCAT | SMOS (a/d), AMSR2 (a/d), FY-3B (a/d), FY-3C (a/d), GPM (a/d) |
20/07/2015– 08/11/2018 | Metop-A ASCAT, Metop-B ASCAT | SMOS (a/d), AMSR2 (a/d), FY-3B (a/d), FY-3C (a/d), GPM (a/d) |
09/11/2018 – 31/12/2018 | Metop-A ASCAT, Metop-B ASCAT, Metop-C ASCAT | SMOS (a/d), AMSR2 (a/d), FY-3B (a/d), FY-3C (a/d), GPM (a/d), SMAP (a/d) |
01/01/2019 – 19/08/2019 | Metop-A ASCAT, Metop-B ASCAT, Metop-C ASCAT | SMOS (a/d), AMSR2 (a/d), FY-3B (a/d), FY-3C (a/d), FY-3D (a/d), GPM (a/d), SMAP (a/d) |
20/08/2015/11/2021/2021 | Metop-A ASCAT, Metop-B ASCAT, Metop-C ASCAT | SMOS (a/d), AMSR2 (a/d), FY-3C (a/d), FY-3D (a/d), GPM (a/d), SMAP (a/d) |
16/11/2021– Present | Metop-B ASCAT, Metop-C ASCAT | SMOS (a/d), AMSR2 (a/d), FY-3C (a/d), FY-3D (a/d), GPM (a/d), SMAP (a/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:
Where Θm denotes the merged soil moisture product; Θi are the soil moisture products that are being merged, and wi are the merging weights.
Per definition, the optimal weights for a weighted average are determined by the error variances of the input data sets and write as follows:
where j denote the index of the respective data sets; i is the data set for which the weight is being calculated; and N is the total number of data sets which are being averaged. The required error variances are calculated using Eqn. 19. Notice that error covariance terms are neglected as they cannot be estimated reliably.
It should be mentioned that the above definition of the weights based on error variances assumes all data sets to be in the same data space. However, data sets usually vary in their signal variability due to algorithmic differences, varying signal frequencies, etc. Therefore, conceptually, it is more appropriate to define relative weights in terms of the data sets SNR properties rather than of their error variance (Gruber et al., 2017). Nevertheless, the actual merging requires a harmonization of the data sets into a common data space, which in the case of the CCI SM data set is done using the CDF matching approach described in Section 3.4.3.5 . Therefore, the calculation of the weights using Eqn. 22 suffices, keeping in mind that they represent the rescaled error variances of rescaled data sets.
Notice that soil moisture estimates of the various sensors are not available every day, hence there are certain dates during the overlapping periods on which not all data sets provide a valid estimate to calculate the weighted average. In such cases, the weights are re-distributed amongst the remaining data sets, again based on their relative SNR properties.
However, this re-distribution of weights could significantly worsen data quality on these days because of the increasing contribution of measurements which initially would have had a low weight due to their (relatively) low SNR. Therefore, soil moisture estimates in the merged product on days where not all data sets provide valid estimates are set to NaN values (Not a Number), if the sum of the initial weight of the remaining data sets is lower than 1/(2N) where N is the total number of data sets that are potentially available for the corresponding merging period. This threshold has been derived empirically to provide a good trade-off between temporal measurement density and average data quality.
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.
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 18 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.
Figure 18: Overview of the 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].
Table 8: Overview of output data fields
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.
Attema, E., & Ulaby, F. (1978). Vegetation modeled as water cloud. Radio Science, 13, 357-364
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., Dee, D., Dutra, E., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale, T., and Vitart, F. (2015), “ERA-Interim/Land: a global land surface reanalysis dataset”, Hydrol. Earth Syst. Sci., vol. 19, pp. 389-407, doi: 10.5194/hess-19-389-2015.
Balsamo, G., S. Boussetta, P. Lopez, L. Ferranti (2010), Evaluation of ERA-Interim and ERA-Interim-GPCP-rescaled precipitation over the U.S.A., ERA Report Series, n. 5, pp10 [pdf].
Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer, S., Scipal, K., Bonekamp, H., Figa, J., & Anderson, C. (2007). Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT). Geophy. Res. Lett, 34, L20401
Beaudoing, H. and Rodell M., NASA/GSFC/HSL (2016), GLDAS Noah Land Surface Model L4 3 hourly 0.25 x 0.25 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/E7TYRXPJKWOQ
Bennartz, R., and Michelson, D.B. (2012), Validation of AMSR-E and AMSU/HSB level 1 brightness temperatures and level 2 cloud and precipitation parameters, NAG5--12579, Meteorological and Hydrological Institute, Report, March 2004
Bevington, P.R., & Robinson, D.K. (2002). Data reduction and error analysis for the physical sciences. (3th ed.). Boston: McGraw-Hill Science/Engineering/Math
Bevis, M., Businger, S., Herring, T., Rocken, C., Anthes, R., & Ware, R. (1992). GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System. Journal of Geophysical Research, 97, null-15801
CATDS SMOS L3 soil moisture retrieval processor Algorithm Theoretical Baseline Document (ATBD) (2013), https://labo.obs-mip.fr/wp-content-labo/uploads/sites/18/2013/08/ATBD_L3_rev2_draft.pdf
CATDS LEVEL 3 DATA PRODUCT DESCRIPTION Soil Moisture and Brightness Temperature (2014), https://www.cen.uni-hamburg.de/en/icdc/data/land/docs-land/so-tn-cb-ca-0001-3a.pdf (URL resource last accessed 14th August 2023)
Chan, S., Dunbar, R. S. (2020), Soil Moisture Active Passive (SMAP) Mission Level 3 Passive Soil Moisture Product Specification Document Version 7.0. online: https://nsidc.org/sites/default/files/psd_spl3smp_v7.pdf (URL resource last accessed 14th August 2023)
Crapolicchio, R., A. Bigazzi, G. De Chiara, X. Neyt, A. Stoffelen, M. Belmonte, W. Wagner, C. Reimer (2016) The scatterometer instrument competence centre (SCIRoCCo): Project's activities and first achievements, Proceedings European Space Agency Living Planet Symposium 2016, 9-13 May 2016, Prague, Czech Republic, 9-13.
Crow, W.T., Wagner, W., & Naeimi, V. (2010). The impact of radar incidence angle on soil moisture retrieval skill. IEEE Geoscience and Remote Sensing Letters, 7, 501-505
De Jeu R.A.M, T.R.H. Holmes, R. M. Parinussa, M Owe (2014), A spatially coherent global soil moisture product with improved temporal resolution, Journal of Hydrology 516, 284-296.
De Nijs, A.H., Parinussa, R.M., De Jeu, R.A.M., Schellekens, J. and Holmes, T.R. (2015), “A methodology to determine radio-frequency interference in AMSR2 observations”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 9, pp. 5148-5159.
Dee, D.P. et al., 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), pp.553–597
Dobson, M.C., Ulaby, F.T., Hallikainen, M.T., & Elrayes, M.A. (1985). Microwave dielectric behavior of wet soil. Part II: Dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, 23, 35-46
Dorigo, W.A., et al. (2011) "The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements", Hydrology and Earth System Sciences 15 (5), pp. 1675-1698
Dorigo, W.A., Scipal, K., Parinussa, R.M., Liu, Y.Y., Wagner, W., De Jeu, R.A.M., and Naeimi, V. (2010), “Error characterisation of global active and passive microwave soil moisture datasets”, Hydrology and Earth System Sciences, vol. 14, pp. 2605 – 2616, doi: 10.5194/hess-14-2605-2010.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., et al. (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 (URL resource last accessed 14th August 2023)
Drusch, M., Wood, E., & Gao, H. (2005). Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture. Geophysical Research Letters, 32, L15403
Entekhabi, D., Das, N., Njoku, E.G.,Yueh, S., Johnson, J., Shi, J.(2014) Algorithm Theoretical Basis Document L2 & L3 Radar/Radiometer Soil Moisture, L2 & L3 Radar/Radiometer Soil Moisture,
ESA (2017) ERS-2 SCATTEROMETER Surface Soil Moisture Time Series in High Resolution - ERS.SSM.H.TS (25 km Time-Series product), SCI-MAN-16-0047-v02 https://earth.esa.int/eogateway/documents/20142/37627/scirocco-pum-ts.pdf (URL resource last accessed 14th August 2023)
FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009. Harmonized World Soil Database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria
Fu, C. C., D. Han, S. T. Kim, and P. Gloersen (1988). User's guide for the Nimbus- 7 Scanning Multichannel Microwave Radiometer (SMMR) CELL-ALL tape. NASA Reference Publication #1210, National Aeronautics and Space Administration, Washington, D.C.
Fung, A.K. (1994). Microwave scattering and emission models and their applications. Boston: Artech House
Gaiser, P.W., K. M. St. Germain, E. M. Twarog, G. A. Poe, W. Purdy, D. Richardson, W. Grossman, W. L. Jones, D. Spencer, G. Golba, J. Cleveland, L. Choy, R. M. Bevilacqua and P. S. Chang (2004), "The WindSat spaceborne polarimetric microwave radiometer: Sensor description and early orbit performance," IEEE Trans. Geosci. Remote Sens., vol. 42, 2347-2360, http://www.emc.ncep.noaa.gov/mmb/data_processing/satellite_ingest.doc/File_Format.doc/windsatdocjan06-1.pdf. (URL resource last accessed 14th August 2023)
Gloersen, P., D. J. Cavalieri, A. T. C. Chang, T. T. Wilheit, W. J. Campbell, O. M. Johannessen, K. B. Katsaros, K. F. Kunzi, D. B. Ross, D. Staelin, E. P. L. Windsor, F. T. Barath, P. Gudmansen, E. Langham, and R. Ramseier (1984). A summary of results from the first Nimbus-7 SMMR observations. J. Geophys. Res. 89, 5335-5344.
GPM Science Team (2022), GPM GMI XCAL Common Calibrated Brightness Temperatures L1BASE 1.5 hours 13 km V07, Greenbelt, MD, USA, NASA Goddard Earth Science Data and Information Services Center (GES DISC), 10.5067/GPM/GMI/BASE-XCAL/07
Gruber, A., Dorigo, W., Crow, W., and Wagner, W. (2017), “Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals”, IEEE trans. geosc. rem. sens., DOI: 10.1109/TGRS.2017.2734070
Gruber, A., Su, C.H., Crow, W.T., Zwieback, S., Dorigo, W.A., & Wagner, W. (2016a). Estimating error cross-correlations in soil moisture data sets using extended collocation analysis. Journal of Geophysical Research: Atmospheres, 121(3), 1208-1219.
Gruber, A., Su, C.H., Zwieback, S., Crow, W.T., Wagner, W., & Dorigo, W. (2016b). Recent advances in (soil moisture) triple collocation analysis. International Journal of Applied Earth Observation and Geoinformation, 45, 200-211.
Gruber, A., De Lannoy, G., Albergel, C., lL-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., Munoz-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.
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 (URL resource last accessed 14th August 2023)
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:0108?query=H114&s=advanced (URL resource last accessed 14th August 2023)
H SAF (2018c) Algorithm Theoretical Baseline Document (ATBD), Metop ASCAT Soil Moisture Data Records v0.7 (H113) https://hsaf.meteoam.it/CaseStudy/GetDocumentUserDocument?fileName=ASCAT_SSM_CDR_ATBD_v0.7.pdf&tipo=ATBD (URL resource last accessed 14th August 2023)
Hillburn KA, and CL Shie, (2011) Decadal trends and variability in special sensor microwave imager (SSM/I) Brightness Temperatures and Earth Incidencs angle, NASA RSS Technical Report 092811
Holmes, T.R.H., De Jeu, R.A.M., Owe, M., & Dolman, A.J. (2009). Land surface temperature from Ka band (37 GHz) passive microwave observations. Journal of Geophysical Research -Atmospheres, 114
Hsieh, C.-Y., Fung, A.K., Nesti, G., Sieber, A.J., & Coppo, P. (1997). A further study of the IEM surface scattering model. IEEE Transaction on Geoscience and Remote Sensing, 35, 901-909
Kerr, Y.H., Waldteufel, P., Wigneron, J.P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M.J., Font, J., Reul, N., Gruhier, C., Juglea, S.E., Drinkwater, M.R., Hahne, A., Martin-Neira, M., and Mecklenburg, S. (2010), “The SMOS mission: New tool for monitoring key elements of the global water cycle”, Proceedings of the IEEE, vol. 98, no. 5, doi: 10.1109/JPROC.2010.2043043.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15, 259-263
Lehner, B. & Döll, P., 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296(1-4), pp.1–22
Li, L., Njoku, E.G., Im, E., Chang, P.S., and St. German, K. (2004), “A preliminary survey of radio-frequency interference over the U.S. in Aqua AMSR-E data”, IEEE Trans. Geosc., vol. 42, no. 2, pp. 380 – 390, doi: 10.1109/TGRS.2003.817195.
Liu, Y., de Jeu, R.A.M., van Dijk, A.I.J.M., & Owe, M. (2007). TRMM-TMI satellite observed soil moisture and vegetation density (1998-2005) show strong connection with El Nino in eastern Australia. Geophysical Research Letters, 34, Art. No. L15401
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 andactive microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436
Meesters, A., De Jeu, R.A.M., & Owe, M. (2005). Analytical derivation of the vegetation optical depth from the microwave polarization difference index. IEEE Geoscience and Remote Sensing Letters, 2, 121-123
Mo, T., Choudhury, B.J., Schmugge, T.J., Wang, J.R., & Jackson, T.J. (1982). A model for microwave emission from vegetation-covered fields. Journal of Geophysical Research-Oceans and Atmospheres, 87, 1229-1237
Mironov, V.L., Dobson, M.C., Kaupp, V.H., Komarov, S.A., & Kleshchenko, V.N. (2004). Generalized refractive mixing dielectric model for moist soils. IEEE Transactions on Geoscience and Remote Sensing, 42, 773-785
Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://essd.copernicus.org/articles/12/177/2020/ (URL resource last accessed 14th August 2023), 2020.
Naeimi, V., Bartalis, Z., & Wagner, W. (2008). ASCAT soil moisture: Data quality and consistency with the ERS heritage. Journal Of Hydrometeorology, submitted
Newell, D., Draper, D.,Figgins, D., Berdanier, B., Kubitschek, M., Holshouser, D., Sexton, A. , Krimchansky, S., Wentz, F., Meissner, T., "GPM microwave imager key performance and calibration results," 2014 IEEE Geoscience and Remote Sensing Symposium, 2014, pp. 3754-3757, doi: 10.1109/IGARSS.2014.6947300.
Njoku, E. (1996). Nimbus-7 SMMR Pathfinder brightness temperatures. Boulder, CO: National Snow and Ice Data Center
Njoku, E. G., B. Rague, And K. Flemming (1995): Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR): Brightness Temperature Data (SMMR Level 1B Pathfinder). JPL Publication, Jet Propulsion Laboratory, Pasadena, CA. 'User's Guide for the Scanning Multichannel Microwave Radiometer Instrument First Year Antenna Temperature Data Set', Systems & Applied Sciences Corporation, August 1982, NASA Contract NAS5-27393. 'Nimbus-7 SMMR Pathfinder Brightness Temperatures', NSIDC Dataset Guide Documentation.
O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell. 2020a. SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 7. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center.
O'Neill, P. E., R. Bindlish, S. Chan, J. Chaubell, E. Njoku, and T. Jackson. 2020b. SMAP Algorithm Theoretical Basis Document: Level 2 & 3 Soil Moisture (Passive) Data Products, Revision F, August 31, 2020, SMAP Project, JPL D-66480, Jet Propulsion Laboratory, Pasadena, CA. (see https://nsidc.org/sites/nsidc.org/files/technical-references/L2_SM_P_AR_R17_Aug2020_clean_final.pdf (URL resource last accessed 14th August 2023))
O’Neill, P. E., S. Chan, R. Bindlish, M. Chaubell, A. Colliander, F. Chen, S. Dunbar, T. Jackson, J. Peng, M. Cosh, T. Bongiovanni, J. Walker, X. Wu, A. Berg, H. McNairn, M. Thibeault, J. Martínez-Fernández, Á. González-Zamora, E. Lopez-Baeza, K. Jensen, M. Seyfried, D. Bosch, P. Starks, C. Holifield Collins, J. Prueger, Z. Su, R. van der Velde, J. Asanuma, M. Palecki, E. Small, M. Zreda, J. Calvet, W. Crow, Y. Kerr, S. Yueh, and D. Entekhabi. 2020c. Calibration and Validation for the L2/3_SM_P Version 7 and L2/3_SM_P_E Version 4 Data Products, SMAP Project, JPL D-56297, Jet Propulsion Laboratory, Pasadena, CA. (see https://nsidc.org/sites/nsidc.org/files/technical-references/L2_SM_P_AR_R17_Aug2020_clean_final.pdf (URL resource last accessed 14th August 2023)).
Owe M., R. de Jeu and J. Walker, "A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index," in IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 8, pp. 1643-1654, Aug. 2001. doi: 10.1109/36.942542
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
Owe, M., & Van de Griend, A.A. (1998). Comparison of soil moisture penetration depths for several bare soils at two microwave frequencies and implications for remote sensing. Water Resour. Res., 34, 2319-2327
Parinussa, R, TRH Holmes and RAM de Jeu (2012), Soil moisture retrievals from the WindSat spaceborne polarimetric microwave radiometer. IEEE Trans. Geosci. Remote Sens., DOI: 10.1109/TGRS.2011.2174643
Parinussa, R.M., Meesters, A., Liu, Y.Y., Dorigo, W., Wagner, W., & de Jeu, R.A.M. (2011). Error Estimates for Near-Real-Time Satellite Soil Moisture as Derived From the Land Parameter Retrieval Model. IEEE Geoscience and Remote Sensing Letters, 8, 779-783
Parinussa, R. M., R. H. Thomas, N. W. Holmes, A. D. Wouter, A. M. Richard, and R. A. de Jeu. (2014). “A Preliminary Study toward Consistent Soil Moisture from AMSR2.” Journal of Hydrometeorology 16 (2): 932–947. doi:10.1175/JHM-D-13-0200.1
Parinussa RM, De Jeu RAM, Van der Schalie R, Crow WT, Lei F, Holmes TRH. A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input. Climate. 2016; 4(4):50. https://doi.org/10.3390/cli4040050 (URL resource last accessed 14th August 2023)
Peplinski, N.R., Ulaby, F.T., & Dobson, M.C. (1995). Dielectric properties of soils in the 0.3-1.3 GHz range. IEEE Transactions on Geoscience and Remote Sensing, 33, 803-807
Reichle, R.H., Koster, R.D., Dong, J., & Berg, A.A. (2004). Global Soil Moisture from Satellite Observation, Land Surface Models, and Ground Data: Implications for Data Assimilation. Journal of Hydrometeorology, 5, 430-442
Rodell, M. et al., 2004. The Global Land Data Assimilation System. Bulletin of the American Meteorological Society, 85, pp.381–394.
Rui, H., Beaudoing, H., Loeser, C. and Li, B. (2018). README Document for NASA GLDAS Version 2 Data Products. [online] Hydro1.gesdisc.eosdis.nasa.gov. Available at: https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS/README_GLDAS2.pdf. URL accessed 04-10-2018.
Schmugge, T.J., O'Neill, P.E., & Wang, J.R. (1986). Passive microwave soil moisture research. IEEE Transaction on Geoscience and Remote Sensing, GE-24, 12-22
Schneeberger, K., Schwank, M., Stamm, C., de Rosnay, P., Matzler, C., & Fluhler, H. (2004). Topsoil structure influencing soil water retrieval by microwave radiometry. Vadose Zone Journal, 3, 1169-1179
Seto, S.; Takahashi, N.; Iguchi, T. Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land Using Statistical Information for Brightness Temperatures under No-Rain Conditions. J. Appl. Meteorol. 2005, 44, 1243–1259.
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.
Ulaby, F.T., Moore, B., & Fung, A.K. (1981). Microwave Remote Sensing - Active and Passive, Vol. I: Fundamentals of Radiometry. Addison-Weslay, 56 pp
Ulaby, F.T., Moore, B., & Fung, A.K. (1982). Microwave Remote Sensing - Active and Passive, Vol. II: Radar Remote Sensing and Surface Scattering and Emission Theory. Norwood: Artech
Uppala, S.M. et al., 2005. The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society, 131(612), pp.2961–3012.
Van der Schalie, R., De Jeu, R.A.M., Kerr, Y.H., Wigneron, J.-P., Rodriguez-Fernandez, N.J., Al-Yaari, A., Parinussa, R.M., Mecklenburg, S., and Drusch, M. (2017), “The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E”, Remote Sensing of Environment, vol. 189, doi: http://dx.doi.org/10.1016/j.rse.2016.11.026 (URL resource last accessed 14th August 2023).
Van der Schalie, R., Kerr, Y.H., Wigneron, J.P., Rodriguez-Fernandez, N.J., Al-Yaari, and De Jeu, R.A.M. (2016), “Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model”, Int. J. Appl. Earth Observ. Geoinf., doi: http://dx.doi.org/10.1016/j.jag.2015.08.005 (URL resource last accessed 14th August 2023).
Van der Schalie, R., Parinussa, R.M., De Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodriguez-Fernandez, N.J., Al-Yaari, A., Mecklenburg, S., and Drusch, M. (2018), “The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors”, Remote Sensing, vol. 10 (1), no. 107, doi: https://doi.org/10.3390/rs10010107 (URL resource last accessed 14th August 2023).
Van der Schalie R, van der Vliet M, Rodríguez-Fernández N, Dorigo WA, Scanlon T, Preimesberger W, Madelon R, de Jeu RAM. L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records. Remote Sensing. 2021; 13(13):2480. https://doi.org/10.3390/rs13132480 (URL resource last accessed 14th August 2023)
Van der Vliet, Mendy, Robin van der Schalie, Nemesio Rodriguez-Fernandez, Andreas Colliander, Richard de Jeu, Wolfgang Preimesberger, Tracy Scanlon, and Wouter Dorigo (2020). Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, no. 20: 3439.
Wagner, W. (1998). Soil Moisture Retrieval from ERS Scatterometer Data. In, Institute for Photogrammetry and Remote Sensing. Vienna: Technical University of Vienna
Wagner, W., Lemoine, G., Borgeaud, M., & Rott, H. (1999a). A Study of Vegetation Cover Effects on ERS Scatterometer Data. IEEE Transactions on Geoscience and Remote Sensing, 37,938-948
Wagner, W., Lemoine, G., & Rott, H. (1999b). A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data. Remote Sensing of Environment, 70, 191-207
Wagner, W., Noll, J., Borgeaud, M., & Rott, H. (1999c). Monitoring soil Moisture over the Canadian Prairies with the ERS Scatterometer. IEEE Trans. Geosci. Rem. Sens., 37, 206-216
Wagner, W., & Scipal, K. (2000). Large-Scale Soil Moisture Mapping in western Africa using the
ERS Scatterometer. IEEE Trans. Geosci. Rem. Sens., 38, 1777-1782
Wang, J.R., & Choudhury, B.J. (1981). Remote sensing of soil moisture content over bare field at 1.4 GHz frequency. Journal of Geophysical Research-Oceans and Atmospheres, 86, 5277-5282
Wang, J.R., & Schmugge, T.J. (1980). An empirical model for the complex dielectric permittivity of soils as a function of water content. IEEE Transactions on Geoscience and Remote Sensing,18, 288-295
Wentz, F. J. (1991), User's Manual: SSM/I Antenna Temperature Tapes (Revision 1), report number 120191, Remote Sensing Systems, Santa Rosa, CA, 73 pp. (available online: https://images.remss.com/papers/rsstech/1991_120191_Wentz_SSMI_TA_manual_rev1.pdf (URL resource last accessed 14th August 2023))
Wentz, F. J. (1993), User's Manual: SSM/I Antenna Temperature Tapes (Revision 2), report number 120193, Remote Sensing Systems, Santa Rosa, CA, 36 pp. (available online: https://images.remss.com/papers/rsstech/1993_120193_Wentz_SSMI_TA_manual_rev2.pdf (URL resource last accessed 14th August 2023))
Wentz, F. J., L. Ricciardulli, K. A. Hilburn, and C. A. Mears (2007), How much more rain will global warming bring?, Science, 317, 233-235.
WindSat Data Products Users’ Manual, Sensor & Environmental Data Records, Version 3.0, January 2006, D- 29825, JPL.
WMO (2016). The Global Observing System for Climate (GCOS): Implementation Needs GCOS-200 Available online: https://library.wmo.int/doc_num.php?explnum_id=3417 (URL resource last accessed 14th August 2023).
Xu, Z., Qian, J., Xian, D., Qi, Y. "Fengyun-3 Series Meteorological Satellite Data Archiving and Service System," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, pp. 5477-5480, doi: 10.1109/IGARSS.2016.7730427.
Yang, H., Zou, X., Li, X.,You, R., "Environmental Data Records From FengYun-3B Microwave Radiation Imager," in IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 12, pp. 4986-4993, Dec. 2012, doi: 10.1109/TGRS.2012.2197003.
Yang, H., Weng, F., Lv, L., Lu, N., Liu, G., Bai, M. Qian, Q., He, J., Xu, H., "The FengYun-3 Microwave Radiation Imager On-Orbit Verification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4552-4560, Nov. 2011, doi: 10.1109/TGRS.2011.2148200.