Last modified on Dec 01, 2023 17:10

Contributors: R. Kidd (EODC GmbH), C. Briese (EODC GmbH), A. Dostalova (EODC GmbH), W. Dorigo (TU Wien), W. Preimesberger (TU Wien), R. van der Schalie (Vandersat)

Issued by: EODC GmbH/Richard A Kidd

Date: 02/12/2022

Ref: C3S2_312a_Lot4.WP3-TRGAD-SM-v1_202204_SM_TR_GA_i1.1

Official reference number service contract: 2021/C3S2_312a_Lot4_EODC/SC1

History of modifications

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; 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.

Dekad: the period or interval of 10 days

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

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

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

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

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

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

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.

Threshold requirement: minimum requirement to be met to ensure data are useful.

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

Scope of the document

This document aims to provide users with the relevant information on requirements and gaps for each of the given products within the Land Hydrology and Cryosphere service. The gaps in this context refer to data availability to enable the Essential Climate Variable (ECV) products to be produced, or in terms of scientific research required to enable the current ECV products to be evolved to respond to the specified user requirements.

The ECV products addressed include the three Surface Soil Moisture products provided by the Soil Moisture Service: (i) derived from merged active microwave satellites, (ii) derived from merged passive microwave satellites, and (iii) a product generated from merged active and passive microwave sensors.

Executive Summary

This document is structured as follows: Chapter 1 gives a high-level overview of the C3S Soil Moisture products. Chapter 2 describes the target requirements for each product, which generally reflect the Global Climate Observing System (GCOS) ECV requirements. As part of the cyclical process employed in the generation of the C3S product, the needs of the community and hence the requirements presented here will be updated as required. Chapter 3 is a gap analysis, that identifies the envisaged data availability for the next 10-15 years. This analysis is performed by (1) evaluating both the risk and opportunities of current and future satellite coverage and data availability, (2) the current fitness-for-purpose compared to the user requirements and how this will evolve in the upcoming years, and (3) ongoing and future research that would be beneficial for integration into the Climate Data Record (CDR) and Interim Climate Data Record (ICDR) processing algorithms.

The C3S soil moisture product comprises a long-term CDR which runs from 1978 (PASSIVE and COMBINED) or 1991 (ACTIVE) to December 2020. This CDR is updated on a dekadal (10-daily) basis with a lag of 1 dekad. Data is therefore produced with a delay of 10 days (from end of latest available dekad to present) to 20 days (from beginning of latest available dekad to present). These updates are provided as an appended Interim ICDR. The CDR and ICDR products are provided following NetCDF4 Climate Forecast (CF) conventions and each of the three products are generated with three temporal resolutions (daily, dekadal, monthly), meaning that the service provides a total of 18 soil moisture products.

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.

For the generation of the ACTIVE products the continuation beyond the current MetOp program will be provided by the approved MetOp Second Generation (MetOp-SG) program, which will start in 2024, and has a goal to provide observations until at least 2043.

Considering the PASSIVE products, although there are sufficient different sources of data, a continuation of L-band based soil moisture could be at risk due to the delay and possible data access restrictions for the Water Cycle Observation Mission (WCOM) and no approved direct follow-up for the SMAP or European Space Agency's (ESA) SMOS, which have both already surpassed their expected lifetime. L-band missions are of particular interest for satellite derived soil moisture data records (such as C3S soil moisture) as electromagnetic waves in this (lower) frequency domain are in theory more suitable for observing soil moisture due to their ability to penetrate vegetation and observe deeper soil layers compared to measurements in higher frequency bands. Continuation is however only expected in several years' time with the ESA/Copernicus radiometer mission Copernicus Imaging Microwave Radiometer (CIMR) (~2026) and L-band Synthetic Aperture Radar (SAR) mission Radar Observation System for Europe in L-band (ROSE-L) (~2028).

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.

In the long-term, some fundamental research is required in order to improve the soil moisture products even further. For the ACTIVE products some of these areas include intercalibration, estimation of diurnal variability, improved modelling of volume scattering, backscatter in arid regions, and the impact of sub-surface scattering on soil moisture retrieval. For the PASSIVE products, activities include updated temperature from Ka-band observations, development of an independent, ancillary free, soil moisture dataset, and continuing research on error characterisation and stability assessment.

The Sentinel-1 and WCOM satellite missions and the two ESA Copernicus candidate missions (CIMR and ROSE-L) have the potential to substantially impact the quality of soil moisture retrieval in the coming years. The inclusion of additional measurements from satellites missions such as the Global Precipitation Mission (GPM) and the FengYun program are expected to additionally increase data coverage for the C3S Soil Moisture product in the next CDR version.

Reliance on External Research
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, and Horizon2020, amongst others). Thus, the implementation of new scientific improvements can only be implemented if external funding allows for it. This 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.

1. Product Description

The C3S Soil Moisture production system provides the climate community with a stable source of soil moisture data derived from satellite observations through the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). The C3S soil moisture product comprises a long-term Climate Data Record (CDR). The latest product version of this CDR is the v202012 and is updated on a dekadal basis (with a delay of 10 days and pushed to the CDS with a delay of 3 days, therefore with a time lag of 10-23 days in total) in an appended Interim Climate Data Record (ICDR). Both the CDR and ICDR consist of three surface soil moisture datasets: ACTIVE, PASSIVE and COMBINED. The ACTIVE and the PASSIVE product are created by using scatterometer and radiometer soil moisture products, respectively; the COMBINED product is a blended product based on all inputs from the former two datasets. The latest available CDR consists of 18 products derived from the ACTIVE, PASSIVE and COMBINED datasets, and runs from 1978 (PASSIVE and COMBINED) or 1991 (ACTIVE) to December 2020.

The target requirements are the same for each of the 18 products derived under the C3S soil moisture production system. The requirements define the format of the data, the temporal and spatial resolution, the accuracy and stability of the product, the metadata content and other quality related aspects. The requirements may evolve throughout the product lifetime; in such a case, this document will be updated to reflect this evolution.

In this document a gap analysis is made aimed to assess the current status of the Soil Moisture products in terms of target requirements and identifying present and future gaps that could be addressed by further research activities.

1.1. Soil Moisture Products

The C3S soil moisture product comprises a long-term Climate Data Record (CDR) and an Interim Climate Data Record (ICDR) which is produced on a regular basis. The theoretical algorithm and the processing implemented in the CDR and ICDR are exactly the same and the data provided are consistent between them.

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.

Table 1: List of Soil Moisture Products

2. User Requirements

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.

Some of the technical requirements, such as data and file format and grid definition - while not strictly described by GCOS or CMUG – follow CF conventions (Hassell et al., 2017) and therefore community-wide standards. They are chosen on the one hand to allow integration into the C3S Climate Data Store (CDS) and on the other hand to make use of the data as simple as possible.

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.

Table 2: Summary of C3S ECV Soil Moisture requirements showing target requirements.

3. Gap Analysis

This section provides a Gap Analysis for the soil moisture product. The purpose of this section is to describe the opportunities, or obstacles, to the improvement in quality and fitness-for-purpose of the Soil Moisture CDR. In this section we address the data availability from existing space-based observing systems; development of processing algorithms; methods for estimating uncertainties; scientific research needs; and opportunities for exploiting the new generation of Sentinel satellites.

3.1. 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.

The recent developments in the data availability for both scatterometers and passive radiometers are described in this document in Section 3.1.1 and 3.1.2, and how this potentially affects the COMBINED product in 3.1.3.

3.1.1. Active

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.

Two MetOp satellites (MetOp-B, and MetOp-C) are currently operational, but only one (B) is currently used in the generation of C3S soil moisture. Metop-A was decommissioned in November 2021 and therefore not used in the generation of C3S soil moisture ICDR since then. It is currently planned to include MetOp-C data in the next CDR generation (December 2022).

Continuation beyond the current MetOp program will be provided by the approved MetOp Second Generation (MetOp-SG) program, which will start in 2024 and has the goal to provide continuation of C-band scatterometer and other systematic observations at least until 2043. Thus, no potential gap in data coverage from C-band scatterometer missions is foreseen for the next two decades.

Table 3: Historical, current and envisaged active microwave instruments suitable for soil moisture retrievals

3.1.2. Passive

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.

Table 4: Historical, current and envisaged radiometers suitable for soil moisture retrievals

3.1.3. Combined

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.

3.2. Development of processing algorithms

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.

3.3. Methods for estimating uncertainties

The soil moisture uncertainty estimates are included in all C3S soil moisture products: ACTIVE, PASSIVE and COMBINED. A short overview is provided of how the uncertainties are estimated through the Triple Collocation Analysis (Gruber et al., 2017). Soil moisture uncertainty is the error standard deviation of the datasets estimated through TCA.

3.3.1. Triple Collocation Analysis

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

Triple collocation analysis is a statistical tool that allows the estimate of the individual random error variances of three datasets without assuming that any of them act as a supposedly accurate reference (Gruber et al., 2017). This method requires the errors of the three datasets to be uncorrelated, therefore triplets always comprise of (i) an active dataset, (ii) a passive dataset, 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 shown in Eq. (1).

where σ²_ε denotes the error variance; σ² and σ denote the variances and covariances of the datasets; and the superscripts denote the active (a), the passive (p), and the modelled (m) input sensor datasets, respectively. For a detailed derivation see Gruber et al. (2017). Notice that these error estimates represent the average random error variance of the entire considered time period, which is commonly assumed to be stationary. Therefore, it only provides a single error estimate for a larger time period and not for each observation individually. TCA is applied to estimate the error variances of active and passive satellite products. Unfortunately, TCA cannot be used to evaluate the random error characteristics of the COMBINED product, since, after blending active and passive sensors, an additional dataset with independent error structures would be required to complement the triplet. To address this issue, a classical error propagation scheme (e.g., Parinussa et al. (2011)) is used to propagate the TCA-based error variance estimates through the blending scheme to yield an estimate for the random error variance of the final ACTIVE, PASSIVE and COMBINED product (Gruber et al., 2017).

where S indicates the set of merged sensors (separately applied for only active, only passive and all sensors, for the ACTIVE, PASSIVE and COMBINED product); var(ε) denotes the error variances of the final datasets; σ²_ε,i  and w_i denotes the error variances derived from TCA and blending weights for the individual sensors that are merged.

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.

Figure 2: Average error variances of C3S / ESA CCI SM for ACTIVE (a), PASSIVE (b), and COMBINED (c) estimated through triple collocation and error propagation for the period July 2012-December 2015. Long-term (July 2012-December 2015) VOD climatology (d) from AMSR2 6.9 GHz observations. Adapted from Dorigo et al. (2017).

3.4. Opportunities to improve quality and fitness-for-purpose of the CDRs

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.

3.4.1. ACTIVE products

The following issues are currently addressed by ASCAT soil moisture providers (the Hydrological Satellite Application Facility (EUMETSAT) (H-SAF)) and will improve the quality of C3S soil moisture when included in the active NRT input data streams.

3.4.1.1. Higher resolution sampling of ERS-1

An ERS-1 product with an improved spatial sampling (25x25 km) is provided by ESA and could be used to improve consistency between the derived ERS-1 soil moisture with ERS-2 and ASCAT soil moisture products. This is currently expected to be done by H-SAF within the CDOP-4 framework (2022-2027)⁵.

3.4.1.2. Improved vegetation correction for ASCATCDOP

An improved vegetation correction algorithm has been developed for ASCAT (Vreugdenhil et al., 2016) and is currently employed in the offline research product. The correction method has not yet been transferred to the NRT product distributed by H-SAF. Once the new implementation is transferred to the operational NRT product, this will also be readily ingested into the CDR and ICDR.

3.4.1.3. Correction of artificial wetting trends

It is known that ASCAT soil moisture shows a positive (wetting) trend in some (densely populated) regions such as parts of Europe and Asia. The phenomenon is especially visible over cities and is probably related to RFI. Artificial trends in the data should be corrected while natural trends must be preserved. Multiple methods for this correction are currently being tested by H-SAF ASCAT SSM providers.

3.4.1.4. Impact of sub-surface scattering on active soil moisture retrieval

It has long been noted that backscatter measurements over desert areas and semi-arid environments during a long dry spell exhibit an unusual behaviour that may lead to a situation where soil moisture from scatterometers is often less accurate than radiometer retrievals (Wagner et al., 2007, Gruhier et al., 2009). Methods for the correct retrieval of soil moisture under the described conditions are currently being explored (Wagner et al., 2022).

3.4.1.5. Development of a 6.25 km SSM product from H-SAF ASCAT

To bring the C3S soil moisture product to a higher spatial resolution while avoiding any downscaling methodology applied to the L3 data, the input SM products need to be produced with a smaller spatial sampling. Currently a 6.25 km SSM product from ASCAT (A, B, C) is being developed and could serve as input for C3S in future versions.

3.4.2. PASSIVE products

3.4.2.1. Introduction of GPM and FengYun 3B/C/D soil moisture

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.

3.4.2.2. Improved flagging of PASSIVE soil moisture under frozen soil conditions

A recently developed improved flagging strategy for radiometer products used in C3S soil moisture (van der Vliet et al., 2020) will improve SM retrieval especially for higher latitudes in future CDR versions. It allows consistent, satellite observation-based assessment of the probability and subsequent masking of unreliable measurements due to the absence of liquid water in the soil under frozen conditions.

3.4.2.3. Improved flagging for barren ground

In ESA CCI SM v07.1 an optional flag to filter out SM measurements taken from bare grounds under dry conditions, where neither radiometer nor scatterometer measurements are expected to return a strong / meaningful signal was introduced. This flag will also be introduced to C3S soil moisture in a future version.

3.4.2.4. Updated temperature input from Ka-band observations

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 re-evaluated by Parinussa et al. (2016) for daytime observations. An update to the linear regression for land surface temperature showed a significant increase in soil moisture retrieval skill. This research highlighted the importance and impact of correct temperature input into the algorithm. Van der Schalie et al. (2021) developed a method to replace the model dependency in the SM retrieval with a LPRM for L-band observations (SMAP, SMOS) with actual measurements from Ka-band.

3.4.3. Merging

3.4.3.1. All products

3.4.3.1.1. Separate blending of climatologies and anomalies

Currently the merging scheme applies a relative weighting of datasets based on their relative error characteristics. However, studies have shown that different spectral components may be subject to different error magnitudes (Su and Ryu, 2015). Therefore, investigations into the feasibility of blending the climatologies and the anomalies of the datasets separately are being undertaken.

3.4.3.1.2. Intra-annual error estimation and scaling coefficients

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).

3.4.3.1.3. Merging Weights gap filling by land cover class

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.

3.4.3.2. PASSIVE products

3.4.3.2.1. Using both night-time and day-time observations

Based on extensive product validation and triple collocation attempts to address the uncertainty of both ascending (daytime) and descending (night-time) modes will be made. Based on these results, this will guide decisions on how both observation modes can be considered in the generation of a single merged passive product, potentially leading to improved observation frequency with respect to the single descending mode used in the current PASSIVE product. An important step towards this approach was made by (Parinussa et al., 2016). A first version of this is implemented in v07.1 of ESA CCI SM.

3.4.3.2.2. Improved intercalibration of AMSRE and AMSR2 observations

The current constellation of sensors in C3S PASSIVE soil moisture omits an appropriate sensor to bridge the gap between AMSRE and ASMR2 observations. In C3S CDR v3 (v202012) an improved intercalibration was achieved by matching (non-overlapping) sub-periods of the two products. This resulted in the removal of a negative break in the merged passive data. With the planned inclusion of FengYun 3B, data is available to perform this correction on the brightness temperature level.

3.5. Scientific Research needs

In the previous section, research activities that are already in an advanced stage of development and which could potentially be introduced into the CDR and ICDR in the short-term were discussed. However, in the long-term, some fundamental research is needed in order to improve the soil moisture products even further.

3.5.1. ACTIVE products

3.5.1.1. Inter-Calibration of Backscatter Data Records

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.

3.5.1.2. Estimation of Diurnal Variability

ASCAT measurements are performed for descending orbits (equator crossing 09:30, local time) and ascending orbits (equator crossing 21:30, local time). It has been noted that the backscatter measurements and, consequently, the Level 2 (L2) surface soil moisture retrievals from satellite platforms, although not dependent on temperature, show in some regions a difference between morning (i.e., day or sun-lit) and evening (i.e., night or dark) acquisitions (Friesen et al., 2012). Currently, it is not clear if these observed diurnal differences are due to changes in the instrument between ascending or descending passes (e.g. due to the strong temperature differences in the sun-lit or dark orbital phases), shortcomings in the retrieval algorithm (e.g. neglecting diurnal differences in vegetation water content), or if these are just a natural expression of diurnal patterns of the surface soil moisture content. The underlying reasons for diurnal differences are to be investigated by comparing satellite ascending and descending orbit soil moisture retrievals.

3.5.1.3. Dry and Wet Crossover Angles

The crossover angle concept adopted in the retrieval method for scatterometers, states that at the dry and wet crossover angles, vegetation has no effect on backscatter (Wagner, 1998). These crossover angles have been determined empirically based on four study areas (Iberian Peninsula, Ukraine, Mali, and Canadian Prairies). Nevertheless, the empirically determined dry and wet crossover angles are used on a global scale in the surface soil moisture retrieval model. A known limitation of the global use of these crossover angles is that, depending on the vegetation type, or more precisely the evolution of biomass of a specific vegetation type, crossover angles may vary across the globe, which is not yet considered in the model. Furthermore, for some regions on the Earth's surface the crossover angle concept may not be applicable, in particular regions without vegetation cover (i.e., deserts). Recent investigations have shown that improved retrievals can be obtained by a local optimisation of cross-over angles (Pfeil et al., 2018).

3.5.1.4. Backscatter in Arid Regions

In arid regions, or more specifically in desert environments, it appears that the dry reference shows seasonal variations, which are assumed to reflect vegetation phenology. However, this cannot be true for desert environments, which are characterised by very limited or no vegetation at all. In principle, seasonal variations of the dry reference are desirable to account for backscatter changes induced by vegetation; referred to as vegetation correction. Vegetation correction is based upon changes in the slope parameter, which can be also observed in desert environments. These variations seem to have a big impact particularly in areas with very low backscatter. Hence, it needs to be clarified whether it is a real physical process, noise or something else reflected in the slope parameter.

3.5.2. COMBINED products

3.5.2.1. Independency from model scaling reference

Within the climate community there is a strong preference for climate records that are solely satellite based. Any additional dataset that is used in a soil moisture retrieval algorithm could potentially lead to a dependency between a model and an observation and make the data inappropriate (e.g. for use in model data assimilation). This topic is currently under research in the ESA CCI SM programme (Piles et al., 2018). Madelon et al. (2021) showed that it is viable to use L-band data as a replacement for the modelled SM in the merging scheme of CCI/C3S soil moisture.

3.5.2.2. Improved homogeneity between sensor sub periods

The long-term consistency of the dataset can be improved through the implementation of break-detection and correction methods (Preimesberger et al., 2021). Inconsistencies in the soil moisture time series at sensor transitions in the COMBINED product can remain after the merging/scaling process and should be corrected in a separate post-processing step. However, the described methods rely on the use of additional (reanalysis) reference data for improved scaling.

3.5.3. Error characterisation

3.5.3.1. Estimation of random uncertainty per observation

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.

3.5.3.2. Stability assessment and correction

To describe the change in errors of a satellite over time, stability metrics are calculated. These are currently based on changes over time (trend) in performance metrics (unbiased RMSD) and expressed in terms of m³ / m³ / y, thereby allowing demonstration against the key performance indicators (KPIs). In addition, it is currently being investigated whether a break, once detected, can be corrected for. In this way, the "stable" time period can be extended.

3.6. Opportunities from exploiting the Sentinels and any other relevant satellite

As described in 3.1 there are many upcoming satellites relevant for soil moisture retrievals that are expected to be launched in the upcoming years. This section gives a more in-depth description of the instruments that could have a substantial impact on the quality of the soil moisture CDR and ICDR.

3.6.1. Sentinel-1

Soil Moisture retrieved through Sentinel-1 at 1km spatial resolution is currently in development at the Copernicus Global Land Service (Bauer-Marschallinger et al., 2019). Integration of a dataset like this could drastically improve the spatial resolution of the CDR, but only for data after 2014. So, for the data to be used, a strategy for handling a CDR with changing spatial resolution over time has to be developed. Sentinel-1 also has the potential to improve the soil moisture record spatial resolution using downscaling approaches together with other sensors. The combination with the ASCAT sensor seems promising (Bauer-Marschallinger et al., 2018) but, for integration into a CDR, the current approaches still need to overcome issues with temporal and spatial consistency.

3.6.2. Water Cycle Observation Mission (WCOM)

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.

3.6.3. L-Band follow-on mission to SMAP/SMOS

An additional L-Band mission would be an important step forward in safeguarding the future of the soil moisture climate records. With the upcoming MetOp-SG and Sentinel-1s, the active soil moisture retrievals have an expected satellite support up to 2040. However, for the passive soil moisture retrievals, and especially the development of long-term L-band based climate data records, the future is uncertain after SMOS and SMAP. For C-band frequencies and above, there is also some uncertainty after GCOM-W2. Therefore, these missions could form an important step in safeguarding the continuation of soil moisture climate data records with at least the same level of quality in the upcoming decades. With the potential gap in L-band soil moisture missions, new challenges to provide consistent records will arise.

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