SM 2020: Target Requirements and Gap Analysis Document (TRGAD)= title> <!-- @page Section1 { size: 8.5in 11.0in; margin: 1.0in; mso-header-margin: .5in; mso-footer- (2024)

Last modified on J=un 27, 2024 17:54

Contributors: Richard Kidd (EODC GmbH), Christian Briese (EODC GmbH), Wou=ter Dorigo (TU Wien) Tracy Scanlon (TU Wien), Wolfgang Preimesberger (TU Wi=en), Robin van der Schalie (Vandersat)

Issued by: EODC GmbH/Richard A Kidd

Date: 14/06/2021

Ref: C3S_312b_Lot4_D1.S.1-2020_TRGAD_SM_i1.0

Official reference number service contract: 2018/C3S_312b_Lot4_EODC/SC2<=/p>

Level 2 pre-processed (L2P): this is a designation of s=atellite data processing level. "Level 2" means geophysical variables deriv=ed from Level 1 source data on the same grid (typically the satellite swath= projection). "Pre-processed" means ancillary data and metadata added follo=wing GHRSST Data Specification, adopted in the case of LSWT.

Level 3 /uncollated/collated/super-collated (L3U/L3C/L3S): this is a designation of satellite data processing level. "Level 3" ind=icates that the satellite data is a geophysical quantity (retrieval) that h=as been averaged where data are available to a regular grid in time and spa=ce. "Uncollated" means L2 data granules remapped to a regular latitude/long=itude grid without combining observations from multiple source files. L3U f=iles will typically be "sparse" corresponding to a single satellite orbit. ="Collated" means observations from multiple images/orbits from a single ins=trument 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-coll=ated" indicates that (for those periods where more than one satellite data =stream delivering the geophysical quantity has been available) the data fro=m more than one satellite have been gridded together into a single grid-cel=l estimate, where relevant.

Target requirement: ideal requirement which would resul=t in a significant improvement for the target application.

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

This document aims to provide users with the relevant information on req=uirements and gaps for each of the given products within the Land Hydrology= and Cryosphere service. The gaps in this context refer to data availabilit=y to enable the ECV products to be produced, or in terms of scientific rese=arch required to enable the current ECV products to be evolved to respond t=o the specified user requirements.

The ECV products addressed include the three Surface Soil Moisture produ=cts provided by the Soil Moisture Service; derived from merged active micro=wave satellites, derived from merge passive microwave satellites, and a pro=duct generated from merged active and passive microwave sensors.

Initially an overview of each product is provided, including the require=d input data and auxiliary products, a definition of the retrieval algorith=ms and processing algorithms versions; including, where relevant, a comment= on the current methodology applied for uncertainty estimation. The target =requirements for each product is then specified which generally reflect the= GCOS ECV requirements. The result of a gap analysis is provided that ident=ifies the envisaged data availability for the next 10-15 years, the require=ment for the further development of the processing algorithms, and the oppo=rtunities to take full advantage of current, external, research activities.= Finally, where possible, areas of required missing fundamental research ar=e highlighted, and a comment on the impact of future instrument missions is= provided.

Soil Moisture
The C3S soil moisture product comprises a long-term data record called a C=DR which runs from 1978 (PASSIVE and COMBINED) or 1991 (ACTIVE) to December= 2019. This CDR is updated on a dekadal basis (approximately every 10 days)= in an appended dataset called an ICDR. The CDR and ICDR products are provi=ded as NetCDF 4 CF and each of the three products are generated with three =temporal resolutions (daily, dekadal, monthly), meaning that the service pr=ovides a total of 18 soil moisture products.

The ACTIVE products rely on data from the Active Microwave Instrument (A=MI) on ERS -1/2 and the Advanced SCATterometer (ASCAT) onboard the Meteorol=ogical Operational Satellites (MetOp-A, MetOp-B).

The PASSIVE products rely on microwave radiometers, and some 7 sensors a=re currently integrated in the product, with AMSR2 and SMOS based soil mois=ture retrievals forming the basis of the passive microwave near-real-time I=CDR processing.

For the generation of the ACTIVE products the continuation beyond the cu=rrent MetOp program will be provided by the approved MetOp Second Generatio=n (MetOp-SG) program, which will start in 2021/22, and has a goal to provid=e observations until at least 2042.

Considering the PASSIVE products, although there are sufficient differen=t sources of data, a continuation of L-band based soil moisture could becom=e problematic due to possible data access restrictions for the Water Cycle =Observation Mission (WCOM) and no approved follow-up for the Soil Moisture =Active Passive mission (SMAP) or ESA's Soil Moisture and Ocean Salinity mis=sion (SMOS) as of yet. Nevertheless, within ESA and Copernicus, continuatio=n of L-band radiometer observations either as SMOS follow-up or Copernicus =L-band mission, are being considered.

Whilst the current soil moisture products are already compliant with C3S= target requirements (GCOS 2011 target requirements) and in many cases even= go beyond, there is still a requirement to further develop the retrieval m=ethodology based on user requirements including the needs of the community =expressed in the European Space Agency (ESA) Climate Change Initiative (CCI=). The future development covers algorithm improvements and satellite datas=ets that have already been evaluated, with many of these ongoing research a=ctivities and developments being undertaken within the ESA Climate Change I=nitiative (CCI) and CCI+ programmes.

In the long-term, some fundamental research is required in order to impr=ove the soil moisture products even further. For the ACTIVE products some o=f these areas include intercalibration, estimation of diurnal variability, =improved modelling of volume scattering, backscatter in arid regions, and r=espectively 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 mois=ture dataset, and continuing research on error characterisation and stabili=ty assessment.

The Sentinel-1, SMAP, WCOM satellite missions and the two ESA Copernicus= candidate missions (Microwave Radiometer Mission and an L-Band SAR Mission=) are all expected to have substantial impact on the quality of soil moistu=re retrieval in the coming years. The inclusion of additional measurements =from satellites missions such as GPM and the FengYun program are expected t=o additionally increase data coverage for the C3S Soil Moisture product.

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, e.g. CCI+, H-SAF=, Horizon2020. 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. Soil Moisture ECV Service=

The C3S Soil Moisture production system provides the climate community w=ith a stable source of soil moisture data derived from satellite observatio=ns through the Climate Data Store of the Copernicus Climate Change Service =(C3S). The C3S soil moisture product comprises a long-term data record call=ed a Thematic Climate Data Record. This CDR, product version v201912, is up=dated on a dekadal basis (approximately every 10 days) in an appended datas=et called an Interim Climate Data Record (ICDR). Both the CDR and ICDR cons=ist of three surface soil moisture datasets: ACTIVE, PASSIVE and COMBINED. =The ACTIVE and the PASSIVE product are created by using scatterometer and r=adiometer soil moisture products, respectively; the COMBINED product is a b=lended product based on the former two datasets. The CDRs run from 1978 (PA=SSIVE and COMBINED) or 1991 (ACTIVE) to December 2019.

The target requirements are the same for each of the 18 products produce=d as part of the C3S soil moisture product. The requirements include the fo=rmat of the data, the temporal and spatial resolution of the data, the accu=racy and stability of the product, metadata requirements and other quality =related requirements. The requirements may evolve throughout the product li=fetime; in such a case, this document will be updated to reflect this evolu=tion.

In this document an analysis is made that compares the current performan=ce of the C3S Soil Moisture products against its potential in the future. T=his analysis is performed by (1) evaluating both the risk and opportunities= of current and future satellite coverage and data availability, (2) the cu=rrent fitness-for-purpose compared to the user requirements and how this wi=ll evolve in the upcoming years, and (3) ongoing and future research that w=ould be beneficial for integration into the CDR and ICDR processing algorit=hms.

1.1. Introduction

This section provides the product specifications and target requirements= for the C3S soil moisture product, which have been derived from community =requirements as well as international standards. The purpose of this sectio=n is to provide these requirements independent of any assessments such that= the requirements can be tracked as the product develops. As part of the cy=clical 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.

1.2. Soil Moisture Products

1.2.1. Product descriptions

The C3S soil moisture product comprises a long-term data record called a= CDR which runs from 1978 (PASSIVE and COMBINED) or 1991 (ACTIVE) to Decemb=er 2018. This CDR is updated on a dekadal basis (approximately every =10 days) in an appended dataset called an ICDR. The theoretical algor=ithm and the processing implemented in the CDRs and ICDRs are exactly the s=ame and the data provided is consistent between them.

Both the CDR and ICDR consist of three surface soil moisture datasets: T=he ACTIVE and the PASSIVE product are created by using scatterometer, and r=adiometer soil moisture products, respectively; the COMBINED product is a b=lended product based on the former two datasets. The sensors used in =the generation of the COMBINED product are shown in Figure 1. =For each dataset the Daily, the Dekadal (10-days) mean, and the Monthly mea=n are available as NetCDF-4 classic format, using CF 1.6 conventions (Eaton= et al.), and comprise global merged surface soil moisture images at a 0.25= degree spatial resolution. In total, there are 18 products available, as l=isted in Table 1.

Figure 1=: Sensor time periods used in the generation of the C3S COMBINED soil moist=ure product.

The Daily files are created directly through the merging of microwave so=il moisture data from multiple satellite instruments. The Dekadal and Month=ly means are calculated from these Daily files. The Dekadal datasets featur=e a 10-day mean of a month, starting from the 1st to the 10th, from 11th to= the 20th, and from 21st to the last day of a month, while the Monthly mean= represents the soil moisture mean of all daily observations within each mo=nth.

A detailed description of the product generation is provided in the Algo=rithm Theoretical Basis Document (ATBD) [RD.5] with further information on =the product given in the Product User Guide (PUG) [RD.4].The underlying alg=orithm is based on that used in the generation of the ESA CCI v04.4 product=, which is described in relevant documents ((Dorigo et al., 2017), (Gruber =et al., 2017), (Scanlon et al., 2019), (Liu et al., 2012)). In additi=on, detailed provenance traceability information can be found in the metada=ta of the product.

Table 1:= List of Soil Moisture Products

1.3. Soil Moisture Service: User Requirements

The target requirements are the same for each of the 18 products (listed= in =Table 1) produced as part of the C3S soil moisture product. The require=ments are listed in Table 2.

Many of the requirements are derived from knowledge of the user communit=y including the needs of the community expressed in the European Space Agen=cy (ESA) Climate Change Initiative (CCI) User Requirements Document (URD) (=Haas et al, 2018). Some of the requirements are derived from consideration =of international standards and good practices, for example, the revisit tim=e, product accuracy and product stability are those required by Global Clim=ate Observing System (GCOS) (WMO, 2016).

The key users for the data are from the climate monitoring and modelling= communities as well as policy implementation users. Such users were consul=ted as part of the CCI URD and hence user specific requirements are capture=d here.

Currently there are no threshold values assigned for the defined targets=. Further work will consider the accuracy of the dataset required for diffe=rent land cover classes and this work will consider the threshold targets f=or different cases. It is noted, however, that accuracy assessment using in situ data is complicated by the presence of representati=veness errors, which inflate the differences between the measurements; thes=e will need to be taken into account in setting such thresholds.

As part of the cyclical process employed in the generation of the C3S pr=oduct, the needs of the community, and hence the requirements presented her=e, will be updated as required.

Table 2: Summary of C3S E=CV Soil Moisture requirements showing target requirements

1.4. Soil Moisture Service: 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-base=d observing systems; development of processing algorithms; methods for esti=mating uncertainties; scientific research needs; and opportunities for expl=oiting the new generation of Sentinels.

1.4.1.= Description of past, current and future satellite coverage

F=igure 2 shows spatial-temporal coverage that is used for the constructi=on of the CDR and ICDR for all three C3S Soil Moisture products (ACTIVE, PA=SSIVE, and COMBINED). An extensive description of these instruments and the= data specifications can be found in the C3S ATBD [RD.5] (Chapter 1, Instru=ments). This gives an indication of the continuously changing availability =of sensors over time as used in the production of the soil moisture data re=cords. In the C3S ATBD [RD.5] (Chapter 3.3, Merging strategy) how this vari=ability is taken into account and how this affects the quality of the final= product is explained. The recent developments in the data availability for= both scatterometers and passive radiometers are described in Section 1.4.1=.1 and 1.4.1.2, and how this potentially affects the COMBINED product in 1.=4.1.3.

Figure 2=: Spatial-temporal coverage of input products used to construct the CDR/ICD=R (a) ACTIVE, (b) PASSIVE, (c) COMBINED. Blue colours indicate passive (rad=iometer), red colours active microwave sensors (scatterometers). The period=s of unique sensor combinations are referred to as =E2=80=98blending period==E2=80=99. Modified from Dorigo et al. (2017).

1.4.1.1. Active

Active microwave observations used in the production of C3S soil moistur=e data products (see Table 3) are based on backscatter measurements from th=e European Remote Sensing Satellites (ERS) 1 and 2's Active Microwave Instr=ument (AMI) wind scatterometer, and the Advanced SCATterometer (ASCAT) onbo=ard 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 antennas (fore- mid-, and aft-beam) only on one side of the inst=rument while ASCAT has them on both sides, which more than doubles the area= covered per swath. ERS AMI data coverage is variable spatially and tempora=lly because of conflicting operations with the synthetic aperture radar (SA=R) mode of the instrument. In addition, due to the failure of the gyroscope= of ERS-2, the distribution of scatterometer data was temporarily discontin=ued from January 2001 whereas in June 2003 its tape drive failed. Complete =failure of ERS-1 and ERS-2 occurred in 2000 and 2011, respectively.

Two MetOp satellites (MetOp-A and MetOp-B) are currently flown in the sa=me orbit, while MetOp-C was launched in 2018 to replace MetOp-A from 2022. =From that time, MetOp-A will remain in orbit to serve as backup in case of =failure of one of the other MetOp satellites. Continuation beyond the curre=nt MetOp program will be provided by the approved MetOp Second Generation (=MetOp-SG) program, which will start in 2021/22 and has the goal to provide =continuation of C-band scatterometer and other systematic observations for =another 21 years, i.e., at least until 2042. Thus, no potential gap in data= coverage from C-band scatterometer missions is foreseen for the next two d=ecades. MetOp-C is not yet integrated in the MetOp-ASCAT CDR used as input =to C3S.

Table 3: Current and envi=saged active microwave instruments suitable for soil moisture retrievals

1.4.1.2. Passive

Several passive microwave radiometers are available that can be used for= the retrieval of soil moisture (Table 4), however due to differences in se=nsor specifications and data access not all are of interest for direct use =within the soil moisture climate data record. In general, a lower frequency= observation is preferred for soil moisture retrievals, e.g. C-band and L-b=and. For an in-depth overview of the impact of different frequencies on the= quality of the soil moisture retrievals in the PASSIVE product, e.g. due t=o vegetation influences or radio frequency interference (RFI), see the C3S =ATBD [RD.5] (Chapter 3.1.3, Known limitations).

Currently, AMSR2- and SMOS-based soil moisture retrievals form the basis= of the passive microwave near-real-time ICDR processing. However, when the=se fail several other satellites are available for use. The most important =of these sensors is SMAP (Entekhabi et al., 2010), the latest L-band missio=n, for which first test results show improved overall soil moisture retriev=als for the PASSIVE product (Van der Schalie & De Jeu, 2016). This data=set is already included within the Climate Change Initiative - Soil Moistur=e framework and will be included in the next CDR version (v3.0). Other pass=ive datasets that are foreseen to be included in future CDRs and ICDRs incl=ude GMI (X-Band) and FengYun-3B/D, although access restrictions for the lat=ter products (as well as for additional WindSat records) could affect their= inclusion into C3S soil moisture as additional historical and/or NRT produ=cts.

T=able 4 also includes a list of future satellite missions and provides i=nsight into the continuation of current satellite programs. Although there =are enough different sources of data, a continuation of L-band based soil m=oisture could become problematic due to possible data access restrictions f=or 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 =SMOS follow-up or Copernicus L-band mission, are being considered.

Table 4:= Historical, current and envisaged radiometers suitable for soil moisture r=etrievals

1.4.1.3. Combined

Due to the wide range of available satellites (both active and passive) =now and in the upcoming decade, and the flexibility of the system as explai=ned by the merging strategy in the C3S ATBD [RD.5] (Chapter 3.3, Merging st=rategy), there is very little risk concerning the extension of the COMBINED= product into the future. The current quality is not expected to reduce in =the upcoming years, however , as recent research in the ESA CCI SM project =showed, successful integration of SMAP soil moisture datasets will lead to =further improvements in the COMBINED product after March 2015 (Gruber et al=., 2019).

1.4.2. Developmen=t of processing algorithms

This section is based on Chapter 1.4 in the PUGS [RD.4]. Table 5 provide=s the C3S Soil Moisture product target requirements adopted from the GCOS 2=011 target requirements and shows to what extent these requirements are cur=rently met by the latest C3S Soil Moisture products (v201912). As one can s=ee, the CDR and ICDR products currently provided by the system are complian=t with C3S target requirements and in many cases even go beyond. Further de=tails on product accuracy and stability are provided in PQAD [RD.6] (method=ology to assess) and PQAR [RD.7] (assessment).

Table 5: Summary of C3S Soil Moisture =requirements, the specification of the current C3S products, and the target= proposed by the consortium, Green shading indicates target requirement is =obtained, Yellow shading indicates target requirement is being approached, =Red shading indicates that target requirement is not achieved. Items highli=ghted in bold show where the target requirement has been exceeded

1.4.3. Methods fo=r estimating uncertainties

The soil moisture uncertainty estimates are included in all C3S soil moi=sture products: ACTIVE, PASSIVE and COMBINED. A short overview is provided =of how the uncertainties are estimated through the Triple Collocation Analy=sis (TCA, Gruber et al., 2016). Soil moisture uncertainty is the error stan=dard deviation of the datasets estimated through TCA.

1.4.3.1. Triple Collocation Analysis

This section is based on CCI ATBD (Chapter 7.2.4), CCI PUG (Chapter 5.4.=1) 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 assumin=g that any of them act as a supposedly accurate reference (Gruber et al. 20=16a&b). This method requires the errors of the three datasets to be unc=orrelated, therefore triplets always comprise of (i)an active dataset=, (ii) a passive dataset, and (iii) the GLDAS-Noah land surface model, whic=h are commonly assumed to fulfil this requirement (Dorigo et al., 2010). Er=ror variance estimates are obtained as:

\[ \sigma^2_{\varepsilon_a} =3D \sigma^2_a - \frac{\sigma_{ap}\=sigma_{am}}{\sigma_{pm}} \] \[ \sigma^2_{\varepsilon_p} =3D \sigma^2_p - \f=rac{\sigma_{pa}\sigma_{pm}}{\sigma_{am}} \]=20

where \( \sigma^2_{\varepsilon} \) denotes the error variance; \( =\sigma^2 \) and \( \sigma \) denote the variances and covariances of =the datasets; and the superscripts denote the active (a), the passive (p), =and the modelled (m) datasets, respectively. For a detailed derivation see =Gruber et al. (2016). Notice that these error estimates represent the avera=ge random error variance of the entire considered time period, which is com=monly assumed to be stationary. Therefore, it only provides a single error =estimate for a larger time period and not for each observation individually=. In the ESA CCI SM production, TCA is applied to estimate the error varian=ces of ACTIVE and PASSIVE. Unfortunately, TCA cannot be used to evaluate th=e random error characteristics of COMBINED, since, after blending ACTIVE an=d PASSIVE, an additional dataset with independent error structures would be= required to complement the triplet. To address this issue, a classical err=or propagation scheme (e.g., Parinussa et al., 2011) is used to propagate t=he TCA-based error variance estimates of ACTIVE and PASSIVE through the ble=nding scheme to yield an estimate for the random error variance of the fina=l COMBINED product (Gruber et al., 2017):

\[ var(\varepsilon_c)=3Dw_a^2=var(\varepsilon_a)+w_p^2var(\varepsilon_p) \]=20

where the superscripts denote the COMBINED (c), ACTIVE (a) and PASSIVE (=p) datasets, respectively; var(=CE=B5) denotes the error variances of the d=atasets; and w denotes the blending weights.

From the equation it can be seen that the error variance of the blended =product is typically smaller than the error variances of both input product=s 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 inp=ut product. However, the ACTIVE and PASSIVE input datasets of COMBINED are =not perfectly collocated in time since the satellites do not provide measur=ements every day. In fact, there are days when either only ACTIVE or only P=ASSIVE provides a valid soil moisture estimate. In C3S, single-category obs=ervations are used to fill gaps in the blended product, but only if the err=or variance is below a certain threshold. Consequently, the random error va=riance of COMBINED on days with single-category observations is typically h=igher than that on days with blended multi-category observations. This resu=lts in an overall average random error variance of COMBINED that lies somew=here in between the random error variance of the single input datasets and =the merged random error variance of all input products (estimated through e=rror propagation) (Gruber et al., 2017).

F=igure 3 shows global maps of the estimated random error variances of AC=TIVE, PASSIVE, and COMBINED in the period where MetOp-A/B ASCAT, AMSR2, and= SMOS are jointly available (July 2012-December 2015). The comparison with =VOD from AMSR2 C-band observations (Figure 3d) shows that, at the global sc=ale, error patterns largely coincide with vegetation density and that error= variances are largely within thresholds defined by the C3S and GCOS user r=equirements (See Table 5). Even though the proposed solution to estimate ra=ndom uncertainty seems to be accurate, it does not account for seasonally v=arying uncertainty, e.g. because of changes in vegetation. Therefore, a dir=ect modelling of uncertainty within the production system would be favourab=le.

Figure 3: Average error variances of ESA CCI SM for ACTIVE (u=pper left), PASSIVE (upper right), and COMBINED (lower left) estimated thro=ugh triple collocation and error propagation for the period July 2012-Decem=ber 2015. Long-term (July 2012-December 2015) VOD climatology (lower right)= from AMSR2 6.9 GHz observations (adapted from Dorigo et al., 2017).

1.4.4. Opportunities to improve quality and fitness-for-purpose o=f the CDRs

This section provides a brief overview of improvements that are being co=nsidered for introduction into the CDR and ICDR in a short term. This cover=s algorithm improvements and satellite datasets that have already been eval=uated. Many of these ongoing research activities and developments are being= undertaken within the ESA Climate Change Initiative (CCI) and CCI+ program=mes, the continuation of which has not yet been officially approved. Given =the large algorithmic dependency on the CCI programme, many of the followin=g sections are based on the CCI ATBD (Scanlon et al., 2019).

Since the C3S programme only supports the implementation, development, a=nd operation of the CDR processor, any scientific advances of the C3S produ=cts entirely rely on funding provided by external programmes, e.g. CCI+, H-=SAF, Horizon2020. Thus, the implementation of new scientific improvements c=an only be implemented if external funding allows for it. The latter depend=s both on the availability of suitable programmes to support the R&D ac=tivities and the success of the C3S contractors in winning potential suitab=le calls.

1.4.4.1. ACTIVE products

The following issues are currently addressed by ASCAT soil moisture prov=iders (H-SAF) and will improve the quality of C3S soil moisture when includ=ed in the active NRT input data streams.

1.4.4.1.1. Higher resolution sampling of ERS-1

An ERS-1 product with an improved spatial sampling (25x25 km) is provide=d 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 current=ly expected to be done by H-SAF within the CDOP-4 framework (2022-2027).

1.4.4.1.2. Intercalibration of MetOp-B and MetOp-A=span>

Intercalibration between MetOp-B and MetOp-A NRT data is only available =after June 2015, therefore MetOp-B can only be used after this date. A back=ward processing of MetOp-B may be performed once intercalibrated data becom=es available from H-SAF/EUMETSAT.

1.4.4.1.3. Improved vegetation correction for ASCAT=

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

1.4.4.1.4. Correction of artificial wetting trends=span>

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 phe=nomenon is especially visible over cities and is probably related to RFI. A=rtificial trends in the data should be corrected while natural trends must =be preserved. Multiple methods for this correction are currently being test=ed by H-SAF ASCAT SSM providers (Hahn et al. 2019).

1.4.4.1.5. Impact of sub-surf=ace scattering on active soil moisture retrieval

It has long been noted that backscatter measurements over desert areas a=nd semi-arid environments during a long dry spell exhibit an unusual behavi=our that may lead to a situation where soil moisture from scatterometers is= often less accurate than radiometer retrievals (Wagner et al., 2007, Gruhi=er et al., 2009, Hahn et al., 2020). Methods for the correct retrieval of s=oil moisture under the described conditions are currently being explored.=p>

1.4.4.2. PASSIVE products

1.4.4.2.1. Introduction of SMAP soil moisture

As the SMAP observation frequency is similar to SMOS, the current algori=thm as developed for SMOS (Van der Schalie et al., 2016 & 2017) can als=o be applied to the SMAP observations. SMAP SM is included in ESA CCI SM v5= (Pasik et al., 2020) and currently scheduled for inclusion in CDR v3. It i=s expected that the inclusion of SMAP will lead to increased observation de=nsity and soil moisture quality in C3S Soil Moisture.

1.4.4.2.2. Improved flagg=ing 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 retri=eval especially for higher latitudes in future CDR versions. It allows cons=istent, satellite observation based assessment of the probability and subse=quent masking of unreliable measurements due to the absence of liquid water= in the soil under frozen conditions.

<=span class=3D"nh-number">1.4.4.3. Merging

1.4.4.3.1. All products

1.4.4.3.1.1 Separate blending of clim=atologies and anomalies

Currently the merging scheme applies a relative weighting of datasets ba=sed on their relative error characteristics. However, studies have shown th=at different spectral components may be subject to different error magnitud=es (Su et al., 2015). Therefore, investigations into the feasibility of ble=nding the climatologies and the anomalies of the datasets separately are be=ing undertaken.

1.4.4.3.1.1 Data density and a=vailability

In CDR v3 gaps are only filled if the weight of the available product is= above a relatively crudely defined empirical threshold. This threshold wil=l be refined to find a best compromise between data density and product acc=uracy. In the current ESA CCI SM product (v6), SNR (merging weights) gaps a=re filled separately for different landcover types. Assuming that gap filli=ng functions depend on the amount of vegetation present in a grid cell, fit=ting functions to fill missing pixels that fall under similar landcover typ=es separately can improve gap filling results, particularly for regions wit=h either no or very dense vegetation.

1.4.4.3.2. PASSIVE product

1.4.4.3.2.1 Using both night-t=ime 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 sin=gle merged passive product, potentially leading to improved observation fre=quency with respect to the single descending mode used in the current PASSI=VE product. An important step towards this approach was made by Parinussa e=t al. (2016).

1.4.4.3.2.2 Improved intercali=bration 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 observation=s. In ESA CCI SM v5 (Pasik et al., 2020) an improved intercalibration was a=chieved by matching (non-overlapping) subperiods of the two products. This =resulted in the removal of a negative break in the merged passive data. The= same improvement is planned for inclusion in CDR v3.0. Once additional pas=sive products (FengYun 3B) are included, they can be used instead to bridge= this gap.

1.4.4.3.3. ACTIVE product

1.4.4.3.3.1 Data gaps

In the framework of the C3S work, investigations into the potential use =of ERS to fill gaps in the ASCAT time series will be undertaken.

1.4.5. Scientific Research =needs

In the previous section, research activities that are already in an adva=nced stage of development and which could potentially be introduced into th=e 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.

1.4.5.1. ACTIVE products

1.4.5.1.1. 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.5]. =Unfortunately, this is not yet the case owing to the fact that individual i=nstrument generations underwent a somewhat different calibration procedure.= Research is ongoing to improve the calibration between these sensors.

1.4.5.1.2. 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 tim=e). 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 b=etween morning (i.e., day or sun-lit) and evening (i.e., night or dark) acq=uisitions (Friesen et al., 2012; Friesen et al., 2007). Currently, it is no=t clear if these observed diurnal differences are due to changes in the ins=trument between ascending or descending passes (e.g. due to the strong temp=erature 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 patte=rns of the surface soil moisture content. The underlying reasons for diurna=l differences are to be investigated by comparing satellite ascending and d=escending orbit soil moisture retrievals.

1.4.5.1.3. Dry and Wet Crossover Angles

The crossover angle concept adopted in the retrieval method for scattero=meters, states that at the dry and wet crossover angles, vegetation has no =effect on backscatter (Wagner, 1998). These crossover angles have been dete=rmined empirically based on four study areas (Iberian Peninsula, Ukraine, M=ali, and Canadian Prairies). Nevertheless, the empirically determined dry a=nd wet crossover angles are used on a global scale in the surface soil mois=ture retrieval model. A known limitation of the global use of these crossov=er angles is that, depending on the vegetation type, or more precisely the =evolution of biomass of a specific vegetation type, crossover angles may va=ry 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 no=t be applicable, in particular regions without vegetation cover (i.e., dese=rts). Recent investigations have shown that improved retrievals can be obta=ined by a local optimisation of cross-over angles (Pfeil et al., 2018).

1.4.5.1.4. 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 ref=lect vegetation phenology. However, this cannot be true for desert environm=ents, which are characterised by very limited or no vegetation at all. In p=rinciple, seasonal variations of the dry reference are desirable to account= for backscatter changes induced by vegetation; referred to as vegetation c=orrection. Vegetation correction is based upon changes in the slope paramet=er, which can be also observed in desert environments. These variations see=m to have a big impact particularly in areas with very low backscatter. Hen=ce, it needs to be clarified whether it is a real physical process, noise o=r something else reflected in the slope parameter.

1.4.5.2. PASSIVE products

1.4.5.2.1. =Updated temperature input from Ka=-band observations

Land surface temperature plays a unique role in solving the radiative tr=ansfer model and therefore directly influences the quality of the soil mois=ture 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 l=and surface temperature showed a significant increase in soil moisture retr=ieval skill. This research highlighted the importance and impact of correct= temperature input into the algorithm. Further scientific work is needed to= improve the surface temperature derived from microwave observations in ord=er to significantly improve the skill of the soil moisture retrievals. Also=, in order to remove model dependency for the L-band soil moisture retrieva=ls that use modelled surface temperature as an input, investigations into c=ombining the L-band observations with Ka-band observations from other satel=lites with similar overpass times are needed.

1.4.5.2.2. Development of a= solely satellite based PASSIVE soil moisture data record

Within the climate community there is a strong preference for climate re=cords that are solely satellite based. Any additional dataset that is used =in a soil moisture retrieval algorithm could potentially lead to a dependen=cy between a model and an observation. This is also why research was set up= to investigate the possibility of developing an independent ancillary-free= soil moisture dataset (Scanlon et al., 2019). Ancillary data could also ha=ve a strong impact on the spatial distribution of soil moisture. Artificial= patterns of the 1 degree FAO soil property map are still visible in the or=iginal LPRM soil moisture product, however, these patterns disappear when o=nly the dielectric constant is used. More research is needed to derive soil= moisture from the dielectric constant records without making use of any an=cillary datasets; with such an approach an independent dataset would be cre=ated that could be used as a benchmark for different modelled soil moisture= datasets.

1.4.5.3. COMBINED products

1.4.5.3.1. Improved sensor inter-calibration

Currently, inter-calibration between active and passive datasets is done= using CDF-matching against a long-term consistent land surface model. Howe=ver, in order to achieve a full model independence of the CCI SM products a=lternative inter-calibration approaches will be investigated, for instance =using lagged-variable based approaches or hom*ogeneity tests (Su et al., 201=5, 2016). Also the use of an L-band climatology as scaling reference for th=e COMBINED product is being investigated (Piles et al., 2018).

1.4.5.3.2. Improved hom*ogeneity between sensor s=ub periods

The long-term consistency of the dataset can be improved through the ope=rational implementation of break-detection and correction methods (Preimesb=erger et al., 2020). Inconsistencies in the soil moisture time series at se=nsor transitions in the COMBINED product can remain after the merging/scali=ng process and should be corrected in a separate post-processing step. Howe=ver, the described methods rely on the use of additional (reanalysis) refer=ence data for improved scaling.

1.4.5.4. Error characterisation

1.4.5.4.1. Estimation of random uncertainty per= observation

The current C3S soil moisture product is generated with associated uncer=tainty estimates. These estimates are based on the propagation of unc=ertainties, estimated with the triple collocation analysis, through the pro=cessing scheme; this process is described within the ATBD [RD.5]. Notice th=at these uncertainty estimates represent the average random error variance =of the entire considered time period, which is commonly assumed to be stati=onary in the triple collocation. Future research shall focus on the estimat=ion of the uncertainties of each individual measurement, which is driven, e=.g., by the vegetation canopy density or the soil wetness conditions at the= time of observation.

1.4.5.4.2. Stability assessment and correction

To test for inhom*ogeneities, the MERRA-2 data is compared to the C3S soi=l moisture; this procedure is described in the PQAD document (Dorigo et al.=, 2020). The inhom*ogeneity testing is achieved by first identifying potenti=al locations of breakpoints in the time-series (for example where a change =in sensors used occurs). Where the discontinuity values are greater than 1 =% it is considered that this indicates a potential discontinuity in the tim=e-series. The stability is then expressed in terms of the longest "stable" =time-period within the dataset for each pixel. This gives a qualitative ind=ication of the stability of the dataset, however, in future assessments of =the dataset, the stability will be expressed in terms of m³ / m<=sup>3 / y, thereby allowing demonstration against the KPIs. In additi=on, it is currently being investigated whether a break, once detected, can =be corrected for. In this way, the "stable" time period can be extended.

1.4.6. Opportunities from exploiting the Sentinels and any= other relevant satellite

As described in 1.4.1, there are many upcoming satellites relevant for s=oil moisture retrievals that are expected to be launched in the upcoming ye=ars. This section will give a more in-depth description of the instruments =that could have a substantial impact on the quality of the soil moisture CD=R and ICDR.

1.4.6.1. Sentinel-1

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

1.4.6.2. Water Cycle Observation Mission (WCOM)=h5>

Although there are many uncertainties and concerns around the WCOM (Shi =et al., 2016) mission, e.g. potential data accessibility issues, it would b=e a very interesting mission for the further development of the passive soi=l 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) a=nd 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, whi=ch can be used in the soil moisture retrieval from the L-band observation, =opposed to using modelled temperature. Secondly, this provides an opportuni=ty for the first time to study S-band based soil moisture retrievals. Third=ly 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.

1.4.6.3. Copernicus candidate missions under =consideration

Two ESA missions that are currently under consideration as Copernicus ca=ndidate missions (http://missionadvice.esa.int=/=), a Microwave Radiometer Mission and an L-Band SAR Mission, would b=e an important step forward in safeguarding the future of the soil moisture= climate records. With the upcoming MetOp-SG and Sentinel-1s, the active so=il moisture retrievals have an expected satellite support up to 2040. Howev=er, for the passive soil moisture retrievals, and especially the developmen=t of long-term L-band based climate data records, the future is uncertain a=fter SMOS and SMAP. For C-band frequencies and above, there is also some un=certainty 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.

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