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Acronyms 

General definitions

Active (soil moisture) retrieval: the process of modeling soil moisture from radar (scatterometer and synthetic aperture radar) measurements. The measurand of active microwave remote sensing systems is called “backscatter”.

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Bias: “Bias is defined as an estimate of the systematic measurement error.” (GCOS-200) [RD1]

Breakthrough requirement: An Essential Climate Variable (ECV) requirement level set by Global Climate Observing System (GCOS) which “[…] if achieved, would result in a significant improvement for the targeted application […] at which specified uses within climate monitoring become possible. It may be appropriate to have different breakthrough values for different uses.” (GCOS-245)

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Stability: “The change in bias over time” (GCOS-245) [RD11]. “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) [RD1]

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

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Uncertainty: “Satellite soil moisture retrievals […] usually contain considerable systematic errors which, especially for model calibration and refinement, provide better insight when estimated separately 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 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 Copernicus Climate Change Service (C3S) Soil Moisture products. Chapter 2 describes the target requirements for each product, which generally reflect the Global Climate Observing System (GCOS) ECV requirements [RD1, RD11]. 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 the end of the year indicated in the version number (e.g. v202312). This CDR is updated on a dekadal (10-daily) basis with a lag of 10 days. Data is therefore produced with a delay of 10 days (from the end of the latest available dekad to present) to 20 days (from the beginning of the latest available dekad to present). These updates are provided as an appended, temporally consistent ICDR. The CDR and ICDR records are provided following NetCDF4 Climate Forecast (CF) conventions and each of the three products (ACTIVE, PASSIVE, COMBINED) are generated with three temporal resolutions (daily, dekadal, monthly), meaning that the service provides a total of 9 soil moisture CDRs and the same number of ICDRs. Temporal coverage and sensor specifications are shown in Figure 1 and Table 3.

The ACTIVE product of C3S soil moisture relies on data from five operational and historic European satellite scatterometer missions:

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SSMR, SSM/I, TMI, FY-3B, and AMSR-E were operational in different periods between 1978 and 2020 and are now decommissioned (compare Figure 1 and Table 4). Windsat data is not freely available and therefore included only between 2007 and 2012.

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Quantitative requirements for soil moisture ECVs are collected by GCOS¹ [RD11] and form the basis of C3S soil moisture requirements. More specific, qualitative requirements with a focus on climate model developers are expressed by the Climate Modelling User Group (CMUG) [RD9]. Requirements for Climate Change Initiative (CCI) and C3S soil moisture products in particular are based on user feedback and usually vary from application to application.

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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 v202312 and is updated every 10 days (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 based on scatterometer and radiometer observations, respectively; the COMBINED product is a blended product based on all inputs from the former two datasets. Each CDR provides averaged daily, monthly or 10-daily satellite soil moisture and runs from 1978 (PASSIVE and COMBINED) or 1991 (ACTIVE) to the date indicated by the version number. 

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In this document, a gap analysis is made aimed to assess the current status of the C3S soil moisture products in terms of target requirements and identifying present and future gaps that could be addressed by further research activities.

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 regularly. The theoretical algorithm and the processing implemented in the CDR and ICDR are the same and the data provided are consistent between them.

The sensors used in the generation of the three C3S soil moisture products are shown in Figure 1.

Figure 1: Sensor periods used in the generation of the C3S ACTIVE (blue sensors), PASSIVE (red sensors), and COMBINED (all sensors) soil moisture product. Sensor frequency bands are inidcated next to each sensor name. Note that the used period can differ from the satellite's lifetime.

Each product is provided at three temporal resolutions: Daily, Dekadal (10-days) mean, and Monthly mean. Those are available in NetCDF-4 format, applying CF 1.8² conventions (Hassell et al., 2017), and comprise global merged surface soil moisture images at a 0.25-degree spatial resolution (~25 km). An overview over available products in each C3S SM version are given in Table 1.

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A detailed description of the product generation is provided in the Algorithm Theoretical Basis Document (ATBD) [RD4] with further information on the product given in the Product User Guide and Specification (PUGS) [RD3]. The underlying algorithm is based on that used in the generation of the ESA CCI v08.1 product, which is described in relevant documents (Dorigo et al. (2017), Gruber et al. (2017), ATBD CCI Soil Moisture [RD7], Liu et al. (2012)). In addition, detailed provenance traceability information can be found in the metadata of the product.

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User Requirements

C3S Soil Moisture aims to provide data that meets the accuracy requirements set by GCOS [RD11]⁴ 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” [RD9]. 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 modeling community. The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup provides the “Validation Good Practice Protocol” [RD10] (Montzka et al., 2020), which is a set of guidelines for data production and evaluation. CEOS also judged the maturity of soil moisture validation activities (assessing the fulfillment requirements) to be very high (stage 4 of 4), meaning that uncertainties in the data are quantified, community-agreed validation practices are defined and reference data and validation tools 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

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.

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 [RD4]. 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 [RD4] 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 Sections 3.1.1 and 3.1.2, and how this potentially affects the COMBINED product in 3.1.3.

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 the visibility of some ground stations, leading to data gaps in the retrieved soil moisture products. Previously missing soil moisture retrievals for the period 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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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 ATBD [RD4].

Currently, AMSR2- SMOS-, SMAP-, and GPM-based soil moisture retrievals form the basis of the passive microwave near-real-time ICDR processing. However, other missions are foreseen or already available for inclusion in future versions of C3S soil moisture. The SSM/I constellation comprises multiple satellites, of which only three are used at the moment. This is mainly due to the observation frequency of these sensors (Ku-band) and unstable orbits for some of the (historic) missions that can affect soil moisture retrieval negatively.  However, these sensors, which include SSM/I F10, F14, F15, and F17, will be evaluated for future inclusion and could be used to fill data gaps in earlier periods of the C3S soil moisture records.

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 (passive) 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, a continuation of L-band radiometer observations, either as a SMOS follow-up or Copernicus L-band mission, is being considered.

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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 [RD4] (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 CCI ATBD [RD7].

Development of processing algorithms

This section is based on the PUGS [RD3]. Table 5 provides the C3S Soil Moisture product target requirements adopted from the GCOS 2016/2022 ECV target requirements. Most requirements are met by the latest C3S Soil Moisture products. As one can see, the CDR and ICDR products currently provided by the system are compliant with C3S target requirements and in many cases even go beyond. Further details on product accuracy and stability are provided in PQAD [RD5] (methodology to assess) and PQAR [RD6] (assessment).

A 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 ATBD [RD7].

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) 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, AMSR-E for PASSIVE, and Global Land Data Assimilation System (GLDAS) Noah for COMBINED).
3. Random errors are assessed in all data sets using Triple Collocation Analysis (TCA) (see Section 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 via weighted averaging based on 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 harmonized and distributed by the International Soil Moisture Network (ISMN)⁷. However, it is known that the accuracy assessment of satellite measurements using in-situ data is affected by the uneven global station distribution 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 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 0.01-0.1 m³/m³. Higher accuracy is expected on homogeneous surfaces (e.g. crop- and grasslands) where the target of 0.04 m³/m³ is met, while larger discrepancies are expected for densely vegetated and mountainous regions and urban areas (0.04-0.1 m³/m³).

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.

Triple Collocation Analysis

This section is based on CCI ATBD [RD7] CCI PUG [RD8] 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. There are days when either only 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 ASCAT, AMSR2, and SMOS are jointly available (July 2012-April 2015, bulk case i.e. no error seasonality). 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). Seasonal error estimates vary especially in areas with high vegetation dynamics.

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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 (bulk case, i.e. no seasonal error variations are considered). Long-term (July 2012-December 2015) VOD climatology (d) from AMSR2 6.9 GHz observations. Adapted from Dorigo et al. (2017).

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+ programs. Given the large algorithmic dependency on the CCI program, many of the following sections are based on the CCI ATBD [RD7].

Since the C3S program 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 programs, such as CCI+, H-SAF, and 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 programs to support the R&D activities and the success of the C3S contractors in winning potential, suitable calls.

Retrievals from active sensors

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 operational near-real-time (NRT) input data streams. 

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)⁸.

Improved vegetation correction for ASCAT CDOP

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. If the new implementation is transferred to the operational NRT product, this will also be readily ingested into the CDR and ICDR.

Correction of artificial wetting trends

It is known that ASCAT soil moisture shows a positive (wetting) trend in large regions, mainly parts of Europe and Asia. The phenomenon is probably related to long-term land cover changes. 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 but so far only exist as experimental (offline) products.

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 to flag or even correct retrievals of soil moisture under the described conditions are currently being explored (Wagner et al., 2022).

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 L3 SM products need to be produced with a smaller spatial sampling. Currently, a 6.25 km SSM product from ASCAT is being developed and could serve as input for C3S in future versions.

Retrievals from passive sensors

Soil moisture from all passive sensors used in C3S is retrieved using the Land Parameter Retrieval Model. The following list comprises opportunities to further improve LPRM that would directly benefit the merged C3S soil moisture records.

LRPM soil moisture uncertainty estimates

All uncertainty estimates in C3S soil moisture are currently based on TCA (see Section 3.3.1). However, to provide a full end-to-end uncertainty estimation, one has to take into account uncertainties starting from raw satellite observations. This includes errors in observed brightness temperatures (radiometric accuracy, etc.), errors in temperature input (both radiometric accuracy and model uncertainty), and finally uncertainty introduced by the assumptions in the soil moisture retrieval model (in this case LPRM). One of the foreseen options is by using an analytical solution that was developed by Parinussa (2011) for earlier versions of LPRM but is currently not used. This will be revised and - if applicable - used in a new version of LPRM.

Improved flagging for heavy precipitation

Similar to barren soils, which are flagged in C3S SM since version v202212, heavy precipitation can lead to unreliable estimates of soil moisture from radiometer measurements. These cases are currently not sufficiently flagged by LPRM. Different statistical / machine-learning methods are tested to identify, flag, and potentially mask affected retrievals in future LPRM versions.

Improved spatial resolution of passive retrievals

Following GCOS requirements, C3S Soil Moisture aims to provide data at a 10 km / 0.1-degree resolution in the future. This resolution is especially challenging for radiometer-based products, which, due to their measurement principle, require aggregation of observations over large areas, leading to generally lower spatial resolution compared to radar-based products (which are already available globally and in NRT with 12.5 km sampling). However, methods to overcome this physical limitation to a certain degree exist and will be tested to gather insights into the ability of the historical LPRM data record to go towards 0.1-degree resolution in future C3S versions.

Merged products

Subdaily sampling of merged products

With the recent inclusion of additional satellites, with varying overpass times, as well as the use of daytime observations, it has become viable to work towards an aggregated, sub-daily C3S soil moisture data record that still manages to provide sufficient (spatial) coverage. However, the main challenge here is the uneven distribution of available satellite platforms over the C3S soil moisture period (from 1978 onwards, compare Figure 1). While in the last 10-15 years abundance of data is available, this is not the case for earlier periods.

Independence 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). Madelon et al. (2021) showed that it is viable to use L-band data as a replacement for the modeled soil moisture in the merging scheme of CCI/C3S soil moisture. An experimental product was created to replace GLDAS Noah with a merged L-band SMOS+SMAP data record in the CDF matching/scaling step of the merging framework. However, while GLDAS Noah is available for the whole C3S period, L-band satellite soil moisture products are only available from 2010 onwards, and hence also the model-independent COMBINED product would be. Providing a product like this over the full period of over 40 years would therefore require either significantly changing the scaling/merging scheme or creating a scaling reference that also includes observations from other frequency bands.

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.

Scientific Research needs

In the previous section, research activities that are already in an advanced stage of development and that 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 to improve the soil moisture products even further.

Retrievals from active sensors

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 the same Level 1 calibration [RD4]. 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.

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.

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 does not affect 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 optimization of cross-over angles (Pfeil et al., 2018).

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 characterized 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.

Error characterization and merging

 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 / decade, thereby allowing demonstration against the key performance indicators (KPIs). However, a community agreement on these metrics is still lacking, and they can therefore only be considered as "best effort".

Opportunities from exploiting the Sentinels and any other relevant satellite

As described in chapter 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.

Sentinel-1

Soil Moisture retrieved through Sentinel-1 at 1 km spatial resolution over Europe is currently provided by the Copernicus Global Land Service (CGLS) (Bauer-Marschallinger et al., 2019). While the CGLS is focused on small-scale land changes, the focus of C3S SM is on large-scale, long-term climate (change) applications, for which the time series length is more relevant than the spatial resolution (for many applications a lower spatial resolution is even preferred). So, for the Sentinel-1 data to be used directly, a strategy for handling a CDR with changing spatial resolution over time would have to be developed. Sentinel-1 can also be used to improve the spatial resolution of data from other satellites such as ASCAT or SMAP via downscaling approaches (Bauer-Marschallinger et al., 2018; Das et al., 2019). This is however especially challenging for the historic missions that don't have a temporal overlap with Sentinel-1.

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Therefore, while inclusion of Sentinel-1 in C3S SM could drastically improve the spatial resolution of the CDR after 2014, it might lead to a reduced temporal consistency of the record, which is not in line with the requirements of the climate community. Factors such as increased data size and subsequently required processing ressources could further impede the applicability of C3S SM data for climate assessments.

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 provides 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 modeled 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.

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 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. ESA's ROSE-L SAR mission could form an important step in safeguarding the continuation of L-band soil moisture climate data records, but can certainly not be seen as a "drop-in" replacement for the passive SMAP and SMOS systems.

References

BAUER-MARSCHALLINGER, B., FREEMAN, V., CAO, S., PAULIK, C., SCHAUFLER, S., T. STACHL, MODANESI, S., MASSARI, C., CIABATTA, L., BROCCA, L. & WAGNER, W. 2019. Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles. IEEE Transactions on Geoscience and Remote Sensing, 57, 520-539.

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