Page History

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Bias: “Bias is defined as an estimate of the systematic measurement error” GCOS-200 (WMO, 2016; RD.1)

Brightness Temperature is the measurand of “passive“ microwave remote sensing systems (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.

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Stability: “Stability may be thought of as the extent to which the uncertainty of measurement remains constant with time. […] “Stability” refer[s] to the maximum acceptable change in systematic error, usually per decade.” GCOS-200 (WMO, 2016; RD.1)

Target requirement: ideal requirement which would result in a considerable improvement for the target application.

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The ACTIVE products rely on data from the Active Microwave Instrument (AMI) on European Remote Sensing Satellite (ERS) -1/2 and the Advanced SCATterometer (ASCAT) onboard the Meteorological Operational Satellites (MetOp-A, MetOp-B). Metop-A was decommissioned as of 15 November 2021. Metop-B is therefore the only operational active sensor in all ICDRs after this date. However, starting with CDR/ICDR v202212 (to be published in spring 2023), MetOp-C will be included in all future data records. Temporal coverage and sensors specifications are shown in Figure 1 and Table 3.

The PASSIVE products rely on satellite microwave radiometers, and 8 sensors are currently integrated in the CDR (Scanning Multichannel Microwave Radiometer: SMMR, Special Sensor Microwave Imager: SSM/I, Tropical Rainfall Measuring Mission Microwave Imager: TMI, Advanced Microwave Scanning Radiometer-Earth Observing System: AMSR-E, Windsat, Soil Moisture and Ocean Salinity: SMOS, Advanced Microwave Scanning Radiometer 2: AMSR2, Soil Moisture Active and Passive mission: SMAP). SSMR, SSM/I, TMI and AMSR-E were operational in different time periods between 1978 and 2012 are now decommissioned (compare Figure 1 and Table 4). AMSR2, SMOS and SMAP are operational and their retrievals form the input to the passive microwave near-real-time ICDR processing. The same retrieval model – the Land Parameter Retrieval Model (Owe et al., 2008) – is used to derive soil moisture from observations of all passive sensors.

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Whilst the current soil moisture products are already compliant with C3S target requirements – up-to-date for Soil Moisture (SM) on the GCOS website¹ and described in detail in the GCOS Implementation Plan (WMO, 2016; RD.1) – and in many cases even go beyond, there is still a requirement to further develop the retrieval methodology based on user requirements including the needs of the community expressed in the ESA Climate Change Initiative (CCI). The future development covers algorithm improvements and satellite datasets that have already been evaluated, with many of these ongoing research activities and developments being undertaken within various phases of the ESA CCI and CCI+ programmes.

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Both the CDR and ICDR consist of three surface soil moisture datasets derived from operational satellite instruments: the ACTIVE product is derived from scatterometer / backscatter measurements, the PASSIVE product is derived from radiometer / brightness temperature measurements and the COMBINED product, in which ACTIVE AND PASSIVE products are merged. The sensors used in the generation of the three C3S soil moisture products are shown in Figure 1.

Figure 1: Sensor time periods used in the generation of the C3S ACTIVE (blue sensors), PASSIVE (red sensors) and COMBINED (all sensors) soil moisture product. FY-3B/C/D, GPM and ASCAT-C are only included starting with (I)CDR v202212. Note that for some satellite missions not the full available time range is used.

Each product is provided at three temporal resolutions: Daily, Dekadal (10-days) mean, and Monthly mean. Those are available in NetCDF-4 classic format, using CF 1.7² conventions (Hassell et al., 2017), and comprise global merged surface soil moisture images at a 0.25 degree spatial resolution. In total, there are 18 products available, as listed in Table 1.

The Daily files are created directly through the merging of microwave soil moisture data retrieved from operational satellite instruments. The Dekadal and Monthly means are calculated for each grid cell from these Daily files by averaging all available observations in a dekad or month. However, no threshold for minimum number of observations is applied, which means that the dekadal/monthly average can in some extreme cases be based on a single day. The Dekadal averages consider the daily data starting from the 1st to the 10th, from 11th to the 20th, and from 21st to the last day of each month, while the Monthly mean represents the soil moisture mean of all daily observations within each month.

A detailed description of the product generation is provided in the Algorithm Theoretical Basis Document (ATBD) [RD.4] with further information on the product given in the Product User Guide and Specification (PUGS) [RD.3]. The underlying algorithm is based on that used in the generation of the ESA CCI v05.2 product, which is described in relevant documents (Dorigo et al. (2017), Gruber et al. (2017), ATBD CCI Soil Moisture [RD.7], Liu et al. (2012)). In addition, detailed provenance traceability information can be found in the metadata of the product.

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The aim of C3S Soil Moisture is to provide data that meets the accuracy requirements set by GCOS-200 [RD.1]³, while staying in line with community requirements on data coverage, format, provision system and metadata.

The community requirements (with a focus on climate model development) are collected by the European Space Agency (ESA) Climate Change Initiative (CCI) Climate Modelling User Group (CMUG) and documented in the “Climate Community Requirements Document” [RD.9]. CMUG has identified through a survey among expert users that soil moisture data is required by 9 out of 9 generic climate applications, highlighting its importance for the climate modelling community. The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup provides the “Validation Good Practice Protocol” [RD.10] (Montzka et al., 2020), which is a set of guidelines for data production and evaluation. CEOS also judges the maturity of soil moisture validation activities (assessing the fulfillment requirements) to be very high (stage 3 of 4), meaning that uncertainties in the data are quantified, community-agreed validation practices are defined, and reference data are available. Gruber et al. (2020) defined a best-practice protocol for satellite soil moisture validation and error assessments in satellite soil moisture retrievals.

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A set of standard requirements have been defined for the C3S soil moisture products based on the above described documents. All requirements and their origin are summarised in Table 2 and will be reviewed and updated for new versions of C3S Soil Moisture.

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Gap Analysis

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Description of past, current and future satellite coverage

Figure 1 shows spatial-temporal coverage that is used for the construction of the CDR and ICDR for the C3S Soil Moisture products. An extensive description of these instruments and the data specifications can be found in the C3S ATBD [RD.4]. This gives an indication of the continuously changing availability of sensors over time as used in the production of the soil moisture data records. C3S ATBD [RD.4] also explains how this variability is taken into account and how this affects the quality of the final product.

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Active microwave observations used in the production of C3S soil moisture data products (see Table 3) are based on intercalibrated backscatter measurements from the Active Microwave Instrument (AMI) wind scatterometer onboard the European Remote Sensing Satellites (ERS-1 and ERS-2), and the Advanced SCATterometer (ASCAT) onboard the Meteorological Operational Satellites (MetOp). The sensors operate at similar frequencies (5.3 GHz C-band) and share a similar design. ERS AMI has three antennae (fore- mid-, and aft-beam) only on one side of the instrument while ASCAT has them on both sides, which more than doubles the area covered per swath. ERS AMI data coverage is variable spatially and temporally because of conflicting operations with the synthetic aperture radar (SAR) mode of the instrument. In addition, due to the failure of the gyroscopes of ERS-2, the distribution of scatterometer data was temporarily discontinued between January 2001 and May 2003, whereas in June 2003 its tape drive failed, leading to data being redesigned as a "real time" mission. Since then, data were only collected when the satellite was within visibility of some ground stations, leading to data gaps in the retrieved soil moisture products. Previously missing soil moisture retrievals for the time span between 2001 and 2003 were later restored in a reprocessed version of ERS-2, covering the period from 1997 to 2003 with improved spatial resolution. These data are included in the C3S soil moisture product. A detailed description of all events is given in Crapolicchio et al. (2012). Decommissioning of ERS-1 and ERS-2 occurred in 2000 and 2011, respectively.

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Several passive microwave radiometers are available that can be used for the retrieval of soil moisture (Table 4), however due to differences in sensor specifications and data access not all are of interest for direct use within the soil moisture climate data record. In general, lower frequency observations, such as C-band and L-band, are preferred for soil moisture retrievals. For an in-depth overview of the impact of different frequencies on the quality of the soil moisture retrievals in the PASSIVE product, such as those due to vegetation influences or radio frequency interference (RFI), see the C3S ATBD [RD.4].

Currently, AMSR2- SMOS- and SMAP-based soil moisture retrievals form the basis of the passive microwave near-real-time ICDR processing. However, other missions are available for inclusion in future versions of C3S soil moisture, such as GMI (X-Band) and FengYun-3B/C/D, although access restrictions for the latter products (as well as for additional WindSat records) could affect their inclusion in C3S soil moisture as additional historical and/or near real time (NRT) products.

Table 4 also includes a list of future satellite missions and provides insight into the continuation of current satellite programs. Although there are enough different sources of data, a continuation of L-band based soil moisture could be at risk due to possible data access restrictions for WCOM (Shi et al., 2016) and no approved follow-up for SMAP (Entekhabi et al., 2010) or SMOS (Kerr et al., 2010) as of yet. Nevertheless, within ESA and Copernicus, continuation of L-band radiometer observations, either as a SMOS follow-up or Copernicus L-band mission, are being considered.

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Due to the wide range of satellites (both active and passive) currently available and in development for the upcoming decade, and the flexibility of the system as explained by the merging strategy in the C3S ATBD [RD.4] (Chapter “Merging strategy”), there is a negligible risk concerning the extension of the COMBINED product into the future. Furthermore, the quality that has been achieved is expected to be maintained or improved during the upcoming years through a set of initiatives described in the ATBD CCI [RD.7] such as the successful integration of FengYun, GPM and ASCAT-C, the inclusion of daytime observations and various other algorithmic updates.

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This section is based on the PUGS [RD.3]. Table 5 provides the C3S Soil Moisture product target requirements adopted from the GCOS 2011/2016 target requirements and shows to what extent these requirements are currently met by the latest C3S Soil Moisture products (v202012). As one can see, the CDR and ICDR products currently provided by the system are compliant with C3S target requirements and in many cases even go beyond. Further details on product accuracy and stability are provided in PQAD [RD.5] (methodology to assess) and PQAR [RD.6] (assessment).

A short summary of the processing steps is given here, to put the targeted and achieved requirements in Table 5 into context. More information is given in the Algorithm Theoretical Baseline Document ATBD [RD.7].

1. Level 3 soil moisture products are derived from observations of the individual scatterometer and radiometer sensors shown in Figure 1, Table 3 and Table 4. For ASCAT Soil Moisture, the original 12.5 km product provided by H-SAF is re-gridded to the regular 0.25° C3S soil moisture grid. For all passive sensors, the Land Parameter Retrieval Model (LPRM) model retrieves soil moisture at the target resolution. All data are pre-processed and quality flags are assigned.
2. Systematic errors are assessed in all datasets relative to a chosen reference (ASCAT for ACTIVE, AMSRE for PASSIVE and GLDAS Noah for COMBINED).
3. Random errors are assessed in all data sets using Triple Collocation Analysis (TCA) (see chapter 3.3.1).
4. Systematic errors are removed by scaling all satellites to the chosen reference data set using Cumulative Distribution Function (CDF) matching. Multiple observations are merged using weights from the derived error estimates.
5. Additional flags and uncertainty information on the merged product are propagated to the final datasets.
6. 10-daily and monthly aggregates are created by temporally averaging the merged, daily data.

Table 5: Summary of C3S Soil Moisture requirements proposed by the consortium (shown in Table 2), specifications of the current C3S products, and whether the requirements are met.

Computation of accuracy (and stability) metrics requires the use of independent reference data at the moment. In situ measurements of soil moisture are harmonised and distributed by the International Soil Moisture Network⁴. However, it is known that accuracy assessment of satellite measurements using in situ data is affected by the uneven distribution of in situ data and the presence of representativeness errors, which inflate the differences between the satellite and ground measurements (Dorigo et al., 2021). It is also expected that the accuracy of soil moisture retrieval varies, depending on factors such as vegetation density or surface geometry (summarised as differences in land cover). While GCOS-200 targets are expressed as single values, the accuracy goals of C3S Soil Moisture are therefore evaluated separately for different land cover classes and are expected to vary between 0.04 and 0.1 m³/m³. Higher accuracy is expected on homogeneous surfaces (e.g. crop- and grasslands) while larger discrepancies are expected for densely vegetated and mountainous regions and urban areas.

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This section is based on CCI ATBD [RD.7] CCI PUG [RD.8] and Dorigo et al. (2017).

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The error variance of the blended ACTIVE and PASSIVE products is typically smaller than the error variances of the input products unless they are very far apart, in which case the blended error variance may become equal to, or only negligibly larger than, that of the better input product. The individual sensors are not perfectly collocated in time since the satellites do not provide measurements every day. In fact, there are days when either only the active or only passive sensors provide a valid soil moisture estimate. In C3S, single-category observations are used to fill gaps in the blended product, but only if the error variance is below a certain threshold. Consequently, the random error variance of COMBINED on days with single-category observations is typically higher than that on days with blended multi-category observations. This results in an overall average random error variance of COMBINED that lies somewhere in between the random error variance of the single input datasets and the merged random error variance of all input products (estimated through error propagation) (Gruber et al., 2017).

Figure 2 shows global maps of the estimated random error variances of ACTIVE, PASSIVE, and COMBINED in the period where MetOp-A/B ASCAT, AMSR2, and SMOS are jointly available (July 2012-December 2015). The comparison with vegetation optical depth (VOD) from AMSR2 C-band observations (Figure 2 d) shows that, at the global scale, error patterns largely coincide with vegetation density and that error variances are largely within thresholds defined by the C3S and GCOS user requirements (see Table 5). Starting from C3S v202212, random uncertainties in the satellite products are assessed for individual days of the year using a moving window approach. This way seasonally varying uncertainties, such as due to changes in vegetation, are being accounted for in the merging scheme.

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This section provides a brief overview of improvements that are being considered for introduction into the CDR and ICDR in the short term. This covers algorithm improvements and satellite datasets that have already been evaluated. Many of these ongoing research activities and developments are being undertaken within the ESA Climate Change Initiative (CCI) and CCI+ programmes, the continuation of which has not yet been officially approved. Given the large algorithmic dependency on the CCI programme, many of the following sections are based on the CCI ATBD [RD.7].

Since the C3S programme only supports the implementation, development, and operation of the CDR processor, any scientific advances of the C3S products entirely rely on funding provided by external programmes, such as CCI+, H-SAF, Horizon2020. Thus, the implementation of new scientific improvements can only be implemented if external funding allows for it. The latter depends both on the availability of suitable programmes to support the R&D activities and the success of the C3S contractors in winning potential suitable calls.

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These four sensors are prime candidates for inclusion in the upcoming version of C3S soil moisture, as their positive impact on the PASSIVE and COMBINED products was shown within the ESA CCI SM project [RD.7]. However, due to technical reasons only GPM can be included in the production of the ICDR.

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Another approach to improve the estimation of errors, respectively merging weights and scaling parameters for multiple sensors is to model time variant (e.g. seasonally dependent) biases and errors. In ESA CCI SM [RD.7] this was done and improved the quality especially in early periods of the product (with few operational sensors, respectively low data density).

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In the current ESA CCI SM product (v7), Signal to Noise Ratio (SNR) (merging weights) gaps are filled separately for different landcover types using a regression model derived from the available SNR estimates and globally (i.e. gap free) available information on vegetation density [RD.7]. Assuming that the error in soil moisture retrieval is mainly affected by the density and structure of vegetation, this model can then provide a good estimate to fill gaps in SNR maps using the available vegetation information. This way the required, global, gap-free merging weights are found. Using land cover information to separate regression models computation for different land cover regimes can further improve the accuracy of gap-filled SNR, especially for regions with either no or very dense vegetation.

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To directly compare Level 2 surface soil moisture values retrieved from the ERS-1/2 AMI-WS and MetOp-A/B/C ASCAT, it is a pre-condition that these instruments have more or less exactly the same Level 1 calibration [RD.4]. Unfortunately, this is not yet the case because individual instrument generations underwent a somewhat different calibration procedure. Research is ongoing to improve the calibration between these sensors.

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The current C3S soil moisture product is generated with associated uncertainty estimates.  These estimates are based on the propagation of uncertainties, estimated with the triple collocation analysis, through the processing scheme; this process is described within the ATBD [RD.4]. Notice that these uncertainty estimates represent the average random error variance of the entire considered time period, which is commonly assumed to be stationary in the triple collocation. Future research shall focus on the estimation of the uncertainties of each individual measurement, which is driven by the vegetation canopy density or the soil wetness conditions at the time of observation, for example.

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Although there are many uncertainties and concerns around the WCOM (Shi et al., 2016) mission, such as potential data accessibility, it would be a very interesting mission for the further development of the passive soil moisture retrieval algorithm. As described in Table 4, the payload of the WCOM satellite includes an L-S-C (1.4, 2.4 and 6.8 GHz) tri-frequency Full-polarized Interferometric synthetic aperture microwave radiometer (FPIR) and a Polarized Microwave radiometric Imager (PMI, 6 frequencies between 7.2 to 150 GHz). This wide range of simultaneous observations provide a unique tool for further research on soil moisture retrieval algorithms. Firstly, this allows for simultaneous retrieval of temperature from the Ka-band, which can be used in the soil moisture retrieval from the L-band observation, rather than using modelled temperature. Secondly, this provides an opportunity for the first time to study S-band based soil moisture retrievals. Thirdly and most importantly, it provides a perfect tool for the development of a multi-frequency soil moisture retrieval approach based on L-, S-, C-, and X-bands, potentially leading to improved soil moisture retrievals.

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