Last modified on Mar 18, 2024 14:47

Contributors: Richard Kidd (EODC GmbH), Christian Briese (EODC GmbH), Wouter Dorigo (TU Wien), Tracy Scanlon (TU Wien), Robin van der Schalie (Vandersat), Christopher Merchant (University of Reading), Laura Carrea (University of Reading), Ross Maidment (University of Reading), Lionel Zawadzki (Collecte Localisation Satellites), Beatriz Calmettes (Collecte Localisation Satellites), Lin Gilbert (University Leeds), Sebastian Bjerregaard Simonsen (DAnish TEchnical University), Jan Wuite (ENVEO)

Issued by: EODC GmbH/Richard A Kidd

Date: 28/11/2019

Ref: C3S_312b_Lot4_D1.S.1-2018_TRGAD_LHC_v1.0

Official reference number service contract: 2018/C3S_312b_Lot4_EODC/SC1

History of modifications

Related documents

Click here to expand the list of related documents (RD1-RD7)

Reference ID	Document
RD.1	Global Climate Observing System (2016) THE GLOBAL OBSERVING SYSTEM FOR CLIMATE: IMPLEMENTATION NEEDS, GCOS-200, https://library.wmo.int/doc_num.php?explnum_id=3417
RD.2	Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P., Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen, C., Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofeiva, V. F. and Wagner, W. (2017) Uncertainty information in climate data records from Earth observation. Earth System Science Data, 9 (2). pp. 511-527. ISSN 1866-3516 doi: https://doi.org/10.5194/essd-9-511-2017
RD.3	Group for High Resolution Seas Surface Temperature Data Specification (GDS) v2, Casey and Donlon (eds.), 2012, https://www.ghrsst.org/wp-content/uploads/2016/10/GDS20r5.pdf
RD.4	W. Dorigo, T. Scanlon, P. Buttlinger, R. Kidd, 2019. C3S D312b Lot 4, D3.SM.5-v1.0, Product User Guide and Specification (PUGS): Soil Moisture (v201812).
RD.5	R. van der Schalie, R. De Jeu, C. Paulik, W. Dorigo, T. Scanlon, C. Reimer, R. Kidd, 2019. C3S D312b Lot 4 D1.SM.2-v1.0,.Algorithm Theoretical Basis Document (ATBD): Soil Moisture (v201812).
RD.6	W. Dorigo, T. Scanlon, W. Preimesberger, P. Buttinger R. Kidd, 2019. C3S D312b Lot 4 D2.SM.1_v1.0 Product Quality Assurance Document (PQAD): Soil Moisture.
RD.7	T. Scanlon, W. Dorigo , P. Buttinger, W. Preimsberger, R. Kidd, 2018. C3S D312b Lot 4, D2.SM.2-v1.0, Product Quality Assessment Report (PQAR): Soil Moisture (v201812) (to be issued in April 2019).

Acronyms

General definitions

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, 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" 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 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.

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

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

Scope of the document

This document aims at providing 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 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. In this current version the target requirements and gap analysis are provided for the ECV products generated by the Soil Moisture Service, the Lakes Service and the Ice Sheets Service. The Glacier Service is part of the LHC service, but Target Requirements and Gap Analysis will only be included in the second version of this document due in December 2019.

The ECV products addressed include the three Surface Soil Moisture products provided by the Soil Moisture Service; derived from merged active microwave satellites, derived from merge passive microwave satellites, and a product generated from merged active and passive microwave sensors. The Lakes Service provide two products; a Lake Surface Water Temperature (LSWT) product, and a Lake Water Level (LWL) product. The Ice Sheets and Ice Shelves Service provide four products, being a Surface Elevation Change product for Greenland (Greenland SEC) and a SEC product for Antarctic (Antarctic SEC), an Ice Velocity (IV) and a Gravimetric Mass Balance (GMB) product.

Within this document each thematic service is addressed separately, but in a similar manner. Initially an overview of each product is provided, including the required input data and auxiliary products, a definition of the retrieval algorithms 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 identifies the envisaged data availability for the next 10-15 years, the requirement for the further development of the processing algorithms, and the opportunities to take full advantage of current, external, research activities. Finally, where possible, areas of required missing fundamental research are highlighted, and a comment on the impact of future instrument missions is provided.

Executive Summary

Soil Moisture
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 December 2018. 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 provided as NetCDF 4 CF and each of the three products are generated with three temporal resolutions (daily, decadal, 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 ERS -1/2 and the Advanced SCATterometer (ASCAT) onboard the Meteorological Operational Satellites (MetOp-A, Metop B).

The PASSIVE products rely upon microwave radiometers, and some 7 sensors are currently integrated in the product, with AMSR2 and SMOS based soil moisture retrievals forming the basis of the passive microwave near-real-time ICDR processing.

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 2021/22, and has a goal to provide observations until at least 2042.

Considering the PASSIVE products, although there are sufficient different sources of data, a continuation of L-band based soil moisture could become 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 mission (SMOS) 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.

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 methodology 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 datasets that have already been evaluated, with many of these ongoing research activities and developments being undertaken within the ESA Climate Change Initiative (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, and backscatter in arid regions. 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, 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 moisture retrieval in the upcoming years.

Lakes
The Lakes Service provides two ECV products, specifically lake surface water temperature (LSWT) and lake water level (LWL). The LSWT climate data record (CDR) is a daily gridded product derived from observations of one or more satellites and is an estimate of the daily mean surface temperature of the lake, from 1996 to 2016, and has been attempted for the 1000 GloboLakes lakes. The LSWT CDR product is brokered to the C3S, whilst the LSWT ICDR is generated within the C3S service, and extends the CDR to autumn 2018. The satellites contributing to the time series are: ATSR-2, AATSR and AVHRR Metop-A.

The LWL CDR, which is both brokered and generated in the Lakes Service, is an estimate of the mean surface height of the lake, wherever at least three valid observations have been made within the intersect between the satellite ground track and a given lake. The LWL product targets 155 lakes worldwide, from 1993 to 2018, with daily to decadal monitoring. The satellites contributing to the time series are: TOPEX/Poseidon, Jason-1/2/3 and Sentinel-3a. The data format for both LSWT and LWL products are netCDF4 classic, adopting relevant CF conventions.

For the LSWT products the availability of historical data, (i.e. ATSR 1991 to 2012) may allow a further temporal extension of the CDR, dependent on methodologies being in place and the availability of new SLSTR data streams, i.e. ongoing Sentinel 3A 2017. However the exploitation of SLSTRs is to be reviewed in spring 2019. The reliance of the product on data from the AVHRR sensor is guaranteed via the Metop and MetOps SG programmes, thereby guaranteeing data up to 2042. The inclusion of data from VIIRS would have significant impact, but research is needed for its exploitation and none is presently planned or proposed.

The requirements for the Lakes Service products are largely reliant upon the statements from GCOS, published literature and experience from other CDR projects. For LWST, the threshold for user requirements are generally already reached, but more in situ data is required in order to be able to provide reliable assessments of product stability. For LWL, either the target or the threshold target has already been reached.

Further development of the retrieval methodologies is required. For the LSWT product, improvements in pixel classification and in the optimal estimation (OE) retrieval algorithm are required, and adaptation to a 0.025^o gridding should be possible and useful if there is genuine user demand. For the LWL product, an automatic version for the geographic extraction zone for altimetry measurements is required along with improvements to the geophysical corrections of the extracted data.

The uncertainty estimation within LSWT has been fully developed within CCI SST activities and is considered to be mature. For LWL the uncertainty variable only estimates the precision of the measurements and not the accuracy, and this will be address in the CCI lakes project once this goes ahead.

In addition to LSWT and LWL, elements of lake surface reflectance, lake area and lake ice cover and thickness are included in the GCOS Lake ECV definition. A review of the opportunity to broker datasets addressing these gap areas is scheduled for early 2020.

Ice Sheets
The Ice Sheets and Ice Shelves Service provide four products: an Ice Velocity (IV) product, a Surface Elevation Change (SEC) product for Greenland (Greenland SEC), a SEC product for the Antarctic (Antarctic SEC), and a Gravimetric Mass Balance (GMB) product.

The Ice Velocity product is a gridded product that represents the annual ice surface velocity (IV) of Greenland in true metres per day. It contains horizontal surface velocities and the vertical velocity of the ice surface and is presented in NetCDF 4 according to the C3S convention Common Data Model (CDM) format. Whilst the IV product has a current reliance on Copernicus Sentinel-1 SLC, the Sentinel-1 constellation will continue to operate well into the next decade with two more satellites (Sentinel-1c and -1d) already in development. This, in combination with other new and planned SAR missions (e.g. SAOCOM , NASA-ISRO NISAR), ensures the long-term sustainability of the CDR.

The SEC products provide estimates of surface elevation change over Antarctic Ice Sheets and Ice Shelves (Antarctic SEC), and for the Greenland Ice Sheet (Greenland SEC), using radar altimeter data from four satellite missions: ERS1, ERS2, Envisat and CryoSat-2. The products are a 25km gridded product, with monthly estimates from 1992 to present day, and are presented as NetCDF 4 according to the C3S CDM.

The Gravimetric Mass Balance (GMB) product provides monthly estimates of mass balance changes of the major drainage basins of Greenland and Antarctica from 2002 to 2017. The product relies solely on data from the Gravity Recovery and Climate Experiment (GRACE) mission, which ceased in October 2017. A GRACE follow-on (GRACE-FO) mission was successfully launched in May 2018 and is currently in an In-Orbit-Checkout (IOC) phase (pre-operational).

The IV product currently relies on data from Copernicus Sentinel-1 SLC, the SEC products are reliant on CryoSat-2, and the GMB will be reliant on the GRACE-FO Mission once it becomes operational.

The user requirements provided by GCOS are in some instances unrealistic for the Ice Sheet Service products considering the current available satellite data, i.e. the target for resolution has been revised to 25km. But, in most cases, the primary user requirements (i.e. horizontal resolution for GMB) are already met.

All products will benefit from further development of the retrieval or processing methodology. A number of possible evolutions have already been identified for the Ice Sheets products. For the IV product, primary focuses are on the provision of sub-annual velocity mosaics, an increased spatial resolution of product from 500m to 100m (to meet GCOS requirements), the inclusion of Sentinel-1 TOPS mode InSAR, and to further develop Sentinel-2 optical IV retrieval. For SEC the following improvements are required: addition of Sentinel 3 (A&B) data streams, update of the processor to the multi-mission cross-calibration algorithm, and minor updates to address stability.

Some fundamental research activities are also required outside of the C3S service, specifically for the IV products, and these focus on the development of Sentinel-1 TOPS mode InSAR to derive ice sheet velocity, to investigate methods for the reduction of the effects of differential ionospheric path delay, and the removal of ionospheric stripes. For the SEC products, scientific research is required to identify ice dynamic trends, and for GMB a research activity is required for the evaluation of the data and products from the GRACE-FO mission.

The inclusion and exploitation of Sentinel-3 data is expected to have a major impact for both IV and SEC products.

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 Service

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 of the Copernicus Climate Change Service (C3S). The C3S soil moisture product comprises a long-term data record called a Thematic Climate Data Record. This CDR, product version v201812, is updated on a dekadal basis (approximately every 10 days) in an appended dataset called an Interim Climate Data Record (ICDR). Both the CDR and ICDR consist of three surface soil moisture datasets: 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 the former two datasets. The CDRs run from 1978 (PASSIVE and COMBINED) or 1991 (ACTIVE) to December 2018.

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

In this document an analysis is made that compares the current performance of the C3S Soil Moisture products against its potential in the future. 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 CDR and ICDR processing algorithms.

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 section is to provide these requirements independent of any assessments such that the requirements can be tracked as the product develops. 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.

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 December 2018.  This CDR is updated on a dekadal basis (approximately every 10 days) in an appended dataset called an ICDR.  The theoretical algorithm and the processing implemented in the CDRs and ICDRs are exactly the same and the data provided is consistent between them.

Both the CDR and ICDR consist of three surface soil moisture datasets: 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 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 mean 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 listed in Table 1.

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

The Daily files are created directly through the merging of microwave soil moisture data from multiple satellite instruments. The Dekadal and Monthly means are calculated from these Daily files. The Dekadal datasets feature 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 month.

A detailed description of the product generation is provided in the Algorithm Theoretical Basis Document (ATBD) [RD.5]with further information on the product given in the Product User Guide (PUG) [RD4].The underlying algorithm 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), (Chung et al., 2017), (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

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 requirements are listed in Table 2.

Many of the requirements are derived from knowledge of the user community including the needs of the community expressed in the European Space Agency (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 time, product accuracy and product stability are those required by Global Climate 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 consulted as part of the CCI URD and hence user specific requirements are captured here.

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

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.

Table 2: Summary of C3S ECV 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-based observing systems; development of processing algorithms; methods for estimating uncertainties; scientific research needs; and opportunities for exploiting the new generation of Sentinels.

1.4.1. 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 all three C3S Soil Moisture products (ACTIVE, PASSIVE, and COMBINED). An extensive description of these instruments and the data specifications can be found in the C3S ATBD [RD.5] (Chapter 1, Instruments). This gives an indication of the continuously changing availability of sensors over time as used in the production of the soil moisture data records. In the C3S ATBD [RD.5] (Chapter 3.3, Merging strategy) how this variability 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/ICDR (a) ACTIVE, (b) PASSIVE, (c) COMBINED. Blue colours indicate passive, red colours active microwave sensors. The periods of unique sensor combinations are referred to as ‘blending period’. Modified from Dorigo et al. (2017).

1.4.1.1. Active

Active microwave observations used in the production of C3S soil moisture data products (see Table 3) are based on backscatter measurements from the European Remote Sensing Satellites (ERS) 1 and 2's Active Microwave Instrument (AMI) wind scatterometer, 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 antennas (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 gyroscope of ERS-2, the distribution of scatterometer data was temporarily discontinued 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 same 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 current 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 decades. MetOp-C is not yet integrated in the MetOp-ASCAT CDR used as input to C3S.

Table 3: Table of the current and envisaged 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 sensor 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-band. 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 to 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 these fail several other satellites are available for use. The most important of these sensors is SMAP (Entekhabi et al., 2010), the latest L-band mission, for which first test results show improved overall soil moisture retrievals for the PASSIVE product (Van der Schalie & De Jeu, 2016). This dataset however is still under development and is being tested within the Climate Change Initiative - Soil Moisture framework. The use of other backup datasets could be difficult however, for example with GMI (X-band) only covering 65°N - 65°S and data access restrictions for the FengYun and WindSat missions.

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 become problematic 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 SMOS follow-up or Copernicus L-band mission, are being considered.

Table 4: Table of the historical, current and envisaged radiometers suitable for soil moisture retrievals

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 explained by the merging strategy in the C3S ATBD [RD.5] (Chapter 3.3, Merging strategy), 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 a successful integration of SMAP soil moisture datasets could lead to further improvements in the COMBINED product. 

1.4.2. Development of processing algorithms

This section is based on Chapter 1.4 in the PUGS [RD.4]. Table 5 provides the C3S Soil Moisture product target requirements adopted from the GCOS 2011 target requirements and shows to what extent these requirements are currently met by the latest C3S Soil Moisture products (v201812). 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.6] (methodology 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 highlighted in bold show where the target requirement has been exceeded

1.4.3. 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 (TCA, Gruber et al., 2016). Soil moisture uncertainty is the error standard deviation of the datasets estimated through TCA.

1.4.3.1. Triple Collocation Analysis

This section is based on CCI ATBD (Chapter 6.3.1), CCI PUG (Chapter 6.4.1) 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 assuming that any of them act as a supposedly accurate reference (Gruber et al. 2016a&b). 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:

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 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. In the ESA CCI SM production, TCA is applied to estimate the error variances of ACTIVE and PASSIVE. Unfortunately, TCA cannot be used to evaluate the random error characteristics of COMBINED, since, after blending ACTIVE and PASSIVE, 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 of ACTIVE and PASSIVE through the blending scheme to yield an estimate for the random error variance of the final COMBINED product (Gruber et al., 2017):

where the superscripts denote the COMBINED (c), ACTIVE (a) and PASSIVE (p) datasets, respectively; var(ε) denotes the error variances of the datasets; 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 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. However, the ACTIVE and PASSIVE input datasets of COMBINED are not perfectly collocated in time since the satellites do not provide measurements every day. In fact, there are days when either only ACTIVE or only PASSIVE provides 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 3 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 VOD from AMSR2 C-band observations (Figure 3d) 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). Even though the proposed solution to estimate random uncertainty seems to be accurate, it does not account for seasonally varying uncertainty, e.g. because of changes in vegetation. Therefore, a direct modelling of uncertainty within the production system would be favourable.

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

1.4.4. 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 a 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 (Chung et al., 2017).

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. 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.

1.4.4.1. ACTIVE products

1.4.4.1.1. Higher resolution sampling of ERS-1

An ERS-1 product with an improved spatial sampling (25x25 km) is expected to be provided by ESA in the near future. This would make the ERS-1 product consistent with ERS-2.

1.4.4.1.2. Intercalibration of MetOp-B and metOp-A

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 backward processing of MetOp-B may be performed once intercalibrated data becomes 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 research product. The correction method has not yet been transferred to the NRT product distributed by HSAF. Once the new implementation is transferred to the operational NRT product, this will also be readily ingested into the CDR and ICDR.

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 algorithm as developed for SMOS (Van der Schalie et al., 2016 & 2017) can also be applied to the SMAP observations. First results (Van der Schalie & De Jeu, 2016) show good results, however, this dataset first needs to be tested thoroughly within the testing framework of the CCI Soil Moisture before it can be introduced into the C3S soil moisture CDR.

1.4.4.3. Merging

1.4.4.3.1. All products

1.4.4.3.1.1 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 et al., 2015). Therefore, investigations into the feasibility of blending the climatologies and the anomalies of the datasets separately are being undertaken.

1.4.4.3.1.2 Data density and availability

In the current versions, gaps are only filled if the weight of the available product is above a relatively crudely defined empirical threshold. This threshold will be refined to find a best compromise between data density and product accuracy.

1.4.4.3.2. PASSIVE product

1.4.4.3.2.1 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 (nighttime) 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).

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 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.

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 instrument 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 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; Friesen et al., 2007). 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.

1.4.5.1.3. Improved Modelling of Volume Scattering in Soils

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). Characteristics of backscatter should be explored in more depth in very dry environments to recognie and potentially correct for spurious soil moisture retrievals.

1.4.5.1.4. 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).

1.4.5.1.5. 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.

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 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. Further scientific work is needed to improve the surface temperature derived from microwave observations in order to significantly improve the skill of the soil moisture retrievals. Also, in order to remove model dependency for the L-band soil moisture retrievals that use modelled surface temperature as an input, investigations into combining the L-band observations with Ka-band observations from other satellites 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 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. This is also why research was set up to investigate the possibility of developing an independent ancillary-free soil moisture dataset (Chung et al., 2017). Ancillary data could also have 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 original LPRM soil moisture product, however, these patterns disappear when only the dielectric constant is used. More research is needed to derive soil moisture from the dielectric constant records without making use of any ancillary datasets; with such an approach an independent dataset would be created 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. However, in order to achieve a full model independence of the CCI SM products alternative inter-calibration approaches will be investigated, for instance using lagged-variable based approaches or homogeneity tests (Su et al., 2015, 2016). Also the use of an L-band climatology as scaling reference for the COMBINED product is being investigated (Piles et al., 2018) .

1.4.5.4. Error characterization

1.4.5.4.1. 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.5]. 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, 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 inhomogeneities, the MERRA-2 data is compared to the C3S soil moisture; this procedure is described in the PQAD document (Dorigo et al., 2017). The inhomogeneity testing is achieved by first identifying potential 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 time-series. The stability is then expressed in terms of the longest "stable" time-period within the dataset for each pixel. This gives a qualitative indication of the stability of the dataset, however, in future assessments of the dataset, the stability will be expressed in terms of m³ / m³ / y, thereby allowing demonstration against the 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.

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 soil moisture retrievals that are expected to be launched in the upcoming years. 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 CDR 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-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.

1.4.6.2. SMAP

As described in Section 4.2.2, one of the first steps that should be taken is the further development and integration of a SMAP-based dataset. SMAP (Entekhabi et al., 2010) is the latest L-band mission specifically designed for soil moisture retrievals. Although the radar failed shortly after launch, the radiometer is functioning well and produces L-band brightness temperature with a higher radiometric accuracy then previous L-band missions. Secondly, due to the improved RFI mitigation system, RFI has become a relatively small issue concerning L-band retrievals. This leads to higher quality L-band soil moisture retrievals, including in areas like South-East Asia which used to have many RFI issues. Integration of SMAP based soil moisture retrievals could significantly improve the CDR.

1.4.6.3. Water Cycle Observation Mission (WCOM)

Although there are many uncertainties and concerns around the WCOM (Shi et al., 2016) mission, e.g. potential data accessibility issues, 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, opposed to 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.

1.4.6.4. Copernicus candidate missions under consideration

Two ESA missions that are currently under consideration as Copernicus candidate missions (http://missionadvice.esa.int/), a Microwave Radiometer Mission and an L-Band SAR 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.

2. Lake ECV Service

2.1. Introduction

Section 2.2 briefly presents the Lake ECV products provided in the service - lake surface water temperature (LSWT) and lake water level (LWL) as background to the remainder of the report.

Section 2.3 presents known statements of requirements directly relevant to the products in the context of the C3S, in terms of definitional, coverage, resolution, uncertainty, format and timeliness requirements. The C3S team's view and interpretation of these statements of requirement and their relevance to the C3S service is stated.

Section 2.4 presents an analysis of gaps and opportunities:

• current observational constraints and additional/future sources of satellite data
• known areas for improvement of LSWT and LWL estimation methods
• known areas for improvement of LSWT and LWL uncertainty estimation methods
• lake ECV components not presently delivered by the Hydrology service within the C3S 312b LHC service.

2.2. The Lake ECV products

2.2.1. LSWT v4.0: Brokered CDR

The LSWT climate data record (CDR) brokered to the C3S is a daily gridded product derived from observations of one or more satellites (L3S, level-3 super-collated). The reported LSWT is an estimate of the daily mean surface temperature of the lake, wherever at least one valid observation has been made within the spatial grid cell on a given day. The grid is a regular latitude-longitude one at 0.05 degree intervals.

In addition to the cell-mean LSWT data, the product contains:

• an uncertainty estimate for the LSWT as an estimate of the daily cell-mean value
• a quality level indicator for the LSWT between 0 (invalid) and 5 (excellent), the recommended quality levels for most applications being 4 and 5
• the number of contributing satellite LSWTs combined to make the gridded estimate
• a flag indicating whether a cross-sensor offset adjustment has been applied to the temperatures
• metadata, including funder and citation instructions
• the main lake ID for each cell (from an internationally accepted ID database)

The data format is netCDF4 classic, adopting relevant CF conventions.

The v4.0 CDR covers the period 1996 to 2016. The satellites contributing to the time series are: ATSR-2, AATSR and AVHRR Metop-A.

2.2.2. LSWT v4.1: Generated ICDR

The generated ICDR v4.1 extends the brokered time series to autumn 2018. The generated ICDR is identical in format and scientific methodology to the v4.0 brokered dataset. The ICDR starts from the day following the last in the CDR, is scientifically the same as the CDR, and is thus intended to be used seamlessly with it. The evolution of the v4.1 ICDR relative to v4.0 is the extension of the set of input satellite data to include AVHRR Metop-B.

2.2.3. LWL V3.0: Brokered and Generated CDR

The LWL climate data record (CDR) brokered to the C3S is a timeseries product derived from observations of one or more satellites. The reported LWL is an estimate of the mean surface height of the lake, wherever at least three valid observations have been made within the intersect between the satellite ground track and a given lake.

In addition to the lake-mean LWL data, the timeseries contains:

• the UTC time of acquisition
• an uncertainty estimate for the mean LWL
• metadata, including lake name in English, location, country, funder and citation instructions

The data format is netCDF4 classic, adopting relevant CF conventions.

The v3.0 CDR covers the period 1993 to 2018 under identical reprocessing, so there is no brokered/extended distinction in this case. The satellites contributing to the time series are: TOPEX/Poseidon, Jason-1/2/3 and Sentinel-3a.

2.3. Lakes Service: User requirements

There not having been a precursor ESA Climate Change Initiative project addressing the Lake ECVs, the is no substantive survey of user requirements for satellite-derived lake products. Presently, this section relies on statements for the Lake ECV from GCOS, published literature, experience from other CDR projects, and requirements emerging from the definition of the service. The requirements will be updated in future versions using requirements that emerge from users of the service and their feedback, and from any user requirements survey that is undertaken in a future CCI+ project.

2.3.1. LSWT (v4.0/v4.1)

2.3.1.1. Definitional requirements

2.3.1.2. Coverage

2.3.1.3. Spatial and temporal resolution

2.3.1.4. Uncertainty requirements

2.3.1.4.1. Communication of uncertainty

2.3.1.4.2. Data uncertainties

2.3.1.5. Format requirements

2.3.1.6. Timeliness requirements

2.3.2. LWL (V3.0)

2.3.2.1. Definitional requirements

2.3.2.2. Coverage

2.3.2.3. Spatial and temporal resolution

2.3.2.4. Data uncertainties

2.3.2.5. Format requirements

2.3.2.6. Timeliness requirements

2.4. Lakes Service: Analysis of gaps and opportunities

2.4.1. Satellite observational constraints and opportunities

2.4.1.1. Lake surface water temperature

The LSWT observing system from space consists of ~1 km resolution infra-red imaging radiometers. In particular, the following sensors can be exploited for LSWT retrieval:

• Along-Track Scanning Radiometers, ATSRs (1991 to 2012): These were satellite systems that had two-point brightness temperature calibration accuracy, mid-morning overpass time and low noise, delivering high LSWT sensitivity. They were dual-view sensors, but currently only the single view is used for LSWT retrieval because (i) the spatial resolution of the forward view is lower and not useful except for lakes with widths exceeding ~10 km² in both directions, and (ii) the current archives are not geolocated with respect to altitude differences, which is needed for lake processing. The ATSR2 and AATSR sensors (nadir view) have been used in the Globolakes LSWT CDR; because of some sensor problems and the eruption of Mt Pinatubo, further R&D is needed to extend the CDR back using ATSR1.
• Sea and Land Surface Temperature Radiometers, SLSTRs (2017 to present day): similar to AATSR but with a wider swath and operational in pairs planned for operation through to ~2030, thus offering a much-improved coverage compared to that which was available previously. The SLSTRs are still at an early stage of operation and the level 1 archive of the Sentinel 3A unit is currently (end 2018) in reprocessing to a stable version. Exploitation within this service is foreseen from 2020, subject to conclusion of a review in spring 2019.
• AVHRRs: these are satellite systems that offer mid-morning single-view measurements and a larger swath with respect to the ATSRs. The global full resolution data (1km, FRAC) are capable of LSWT available from the MetOp A & B platforms. EUMETSAT will maintain MetOp AVHRR up to MetOp C, and thereafter will provide MetImage. The MetOp A AVHRR has been used in the LSWT v4.0 CDR and, due to the large coverage of LSWT per lake, is the reference sensor for the harmonised time series (for the time being: this may switch to SLSTR in future). MetOp B AVHRR is additionally exploited for the C3S extension. Older AVHRR data are only available globally at reduced resolution, although a proposal to research the use of 1 km data collected over Europe to extend the series back in time regionally is under consideration by ESA.
• VIIRS: this satellite system extends and improves upon the AVHRR and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. It is single view, offers a non-traditional LSWT band that could reduce impacts of aerosols on retrievals, has 750 m nadir resolution that would enable better observation of small (few km) lakes, and has a day-night band that would facilitate use of night time data (particularly the cloud detection step). The opportunity afforded by VIIRS for LSWT is significant, but research is needed for exploitation and none is presently planned or proposed. The level 1 data access is a significant practical challenge and also expense.

Summary: with R&D, there are opportunities that would extend the LSWT CDR to earlier times (1991 globally, mid 1980s for Europe) with something like the current resolution and quality. Uncertainties in the contemporary extensions of the record should decrease as Metop B, SLSTR A, SLSTR B and Metop C are brought into the service progressively over the next few years. To capture more small lakes, a better resolution instrument is required, and VIIRS is a possibility here, although presently no mechanism for the necessary R&D and practical measures can be identified to make the progress needed to take advantage of this opportunity. Against the targets, the gap analysis is as summarised, therefore, in Table 6.

Table 6: LSWT Gap Analysis Summary

2.4.1.2. Lake water level

Table 7: LWL Gap Analysis Summary

2.4.2. Improvement of retrieval algorithms

2.4.2.1. Lake surface water temperature

LSWT estimation has three steps:

1. pixel classification to ensure the viewed pixel is suitable for LSWT retrieval
2. LSWT retrieval
3. gridding across sensors to make the multi-sensor L3S product

The priorities for improvement in each area are described in the following.

Classification: (1) The day-time classification of a pixel is based on a combination of threshold tests on the visible (VIS), near-IR (NIR), and short-wave-IR (SWIR) channels. This classification is achieved using a fixed of global thresholds on the VIS, NIR and SWIR channels. Since lakes present different optical properties, there are failures to detect water certain cases such as turbid lakes or shallow salty lakes. Lake-specific thresholds may improve this, although it is a significant R&D task to achieve this for ~1000 lakes. (2) The day-time water detection is not applicable at night-time and for the ATSR1 sensor since the VIS channels are not available. To include night-time LSWT observations requires thermal-only water/cloud/fog/ice discrimination and could almost double the density of observations and reduce uncertainties in gridded daily products. Bayesian methods used for SST have been used for lake observations from ATSRs, and this should be considered for LSWT v5.0.

LSWT retrieval: The optimal estimation (OE) retrieval algorithm will continue to be the retrieval of choice for LSWT, because it is context specific. The main improvement to come will be to switch to ERA-5 as the source of prior information that is used in the radiative transfer model needed for OE. The LSWT records from different sensors are adjusted using overlap periods to be unbiased in the lake mean compared to AVHRR MetOp A. The better calibrated SLSTRs may be considered as a reference for the future (e.g., for LSWT v5.0).

L3 gridding: Adaptation to a 0.025^o gridding should be possible and useful, if there is genuine user demand. This may be addressed as a future evolution of the service after the priority tasks of bringing additional sensors into the data stream are successfully completed.

The context in which R&D to underpin some service evolutions can be pursued is, for LSWT, the ESA Lake CCI project. This project is expected to start in 2019. The R&D elements for LSWT are limited by resources to a few weeks' effort on each of the following:

1. Preconditioning of water detection using a more dynamic water bodies mask – this is mainly retrospective (affecting LSWT v5.0)
2. Explore potential of context-sensitivity water detection thresholds
3. User LSWT v4.0 to inform Bayesian cloud detection for night-time data (affecting LSWT v5.0)
4. Revisit method of harmonisation across sensors
5. Validate LSWT uncertainty estimates
6. Explore retrieval benefits and limitations (mainly spatial resolution) of using dual-view from SLSTR for LSWT

All R&D progress in the ESA Lake CCI will ultimately enter the C3S service via the CCI-generated brokered dataset, and validated transition of the updated research code to generate future annual C3S time series extensions.

2.4.2.2. Lake water level

The current state-of-the-art R&D that lead to the V3.0 CDR relies partly on a manual approach to estimate the geographic extraction zone of altimetry measurements. An automatic version of this R&D is currently being implemented in the frame of the present project to ramp-up the products and be able to provide water level for a wider network of lakes. New lakes should thus be proposed in the Test CDR V2.0. The method relies on a database of lake delineations and a land/water mask (from Global Surface Water Explorer, Pekel et al. 2016), intersected with the theoretical ground-track of the satellites.

Then, the extracted data must be corrected for various propagations (ionosphere, wet troposphere, dry troposphere…etc) and geophysical corrections (geoid, pole tide, solid earth tide…etc) based on models and with limitations. The geoid model, in particular, does not include small wavelengths of the geoid and this must be estimated based on altimetry data and a posteriori corrected. The algorithm is currently being improved to cover both simple (cf Figure 4, left panel) and complex (cf Figure 4, right panel) cases.

Figure 4: Automatic extraction of altimetry measures over specific lakes

These two implementations are performed to improve the number of lakes monitored in the LWL product (see Section 2.4.1.2). Additionally, other R&D algorithms should be developed within the CCI-Lakes project and then be implemented in the C3S-312b-Lot4 products for operational use to improve the quality of the product.

2.4.3. Improvement of uncertainty estimation

2.4.3.1. Lake surface water temperature

L3C uncertainty: A comprehensive approach to estimate the LSWT uncertainty in L3 has been developed within the CCI SST work and it comprises the following components:

1. Propagation of instrument noise (uncorrelated effect): Values for independent random noise in the input satellite brightness temperatures (BTs) are propagated through the retrieval at the level of full resolution. Arising from independent random errors, the resulting uncertainty in the L3 cell average LSWT is straightforward to calculate and depends on the number of pixels in the cell average as well as the noise in each. For L3 cell at higher resolution this component of the uncertainty needs to be re-evaluated.
2. Retrieval uncertainty (locally correlated effect): The inverse solution is always an LSWT selected from a distribution of potential solution LSWTs, all of which could be compatible with the observed BTs and background information to within their uncertainties. The retrieval uncertainty is therefore the dispersion of those potential solution LSWTs. In the optimal estimation, this dispersion is evaluated using standard equations for estimating a posteriori error covariance. This error is highly correlated across pixels at the scale of 0.05°, and thus the uncertainty in this component doesn't reduce when forming a cell average.
3. Sampling uncertainty (uncorrelated effect). Generally, the LSWT in the cell is not fully observed in space because of partial cloud cover. The available information is the standard deviation of the LSWT in the pixels that are observed and the fraction of possible pixels that were in reality clear-sky and retrieved. These two parameters have been shown to be able to give a good estimate of the uncertainty arising from subsampling the cell, and this source of uncertainty is included in the L3 uncertainty estimate.

The different uncertainties are aggregated; in the products the total uncertainty is provided. The uncertainty can be validated and the various components can be further refined (parameters better estimated and better validated) over time and understanding of the spatial and temporal scales of the error correlations over lakes can be improved. Alternative methods of representing the uncertainties (i.e. ensembles) can potentially also be considered.

L3S uncertainty: The per-lake inter-satellite bias correction generates an uncertainty which is included in the estimation of the L3S LSWT uncertainty.

The uncertainty estimate for LSWT is mature, and the ongoing evolution should focus on determining appropriate parameters to use for additional sensor data streams and updating such parameters for all sensors if reason to do so emerges.

2.4.3.2. Lake water level

The uncertainty variable distributed in the LWL product along the Water Level variable is currently estimated based on the Median Absolute Deviation of the consecutive along-track water level measurements before it is averaged. It estimates the precision of the measurements but not the accuracy part. The improvement of this uncertainty variable depends on the success of the CCI lakes project, but no strategy is currently foreseen to improve this variable.

The ongoing offline validation exercise will provide global statistics on the LWL product and a characterization of the global uncertainty based on:

• Comparison to other altimetry products (e.g. G-REALM)
• Comparison to in situ data (e.g. HYDROLARE)

2.4.4. Lake ECV components not presently in the service

The GCOS definition (RD.1) of the Lake ECV includes, in addition to the LSWT and LWL, the elements of lake surface reflectance, lake area and lake ice cover and thickness. A review of the opportunity to broker datasets addressing these gap areas is scheduled for early 2020.

3. Ice Sheets and Ice Shelves Service

3.1. Introduction

This section aims at providing users with the relevant information on requirements, and gaps for the three products provided by the Ice Sheets and Ice Shelves Service. It is divided into three sections. Section 3.2 describes the products currently provided by the Service. Section 3.3 provides the target requirements for the product. Section 3.4 provides a past, present, and future gap analysis for the product and covers both gaps in the data availability and scientific gaps that could be addressed by further research activities (outside C3S).

3.2. Ice Sheets Product description

The ice sheets and shelves service provides four products.

3.2.1. Ice velocity

The velocity grid represents the average annual ice surface velocity (IV) of Greenland in true metres per day. The geographic extent is the Greenland Ice Sheet, not including peripheral glaciers which are delivered as a separate product. The ice sheet boundaries are based on the latest version of the Randolph Glacier Inventory (RGI 6.0, RGI Consortium, 2017) with updated glacier fronts for marine terminating glaciers. The basic IV product contains the horizontal (V_x, V_y) and vertical (V_z) components of the velocity vector. The horizontal surface velocities are derived from measured displacements in radar geometry (range, azimuth). The vertical velocity is derived from the interpolated height at the end position of the displacement vector minus the elevation at the start position, taken from a DEM (see auxiliary data). The main data variables are defined on a three-dimensional grid (x, y, z), where x and y are defined by the used map projection, i.e. the polar stereographic grid. The velocities are true values and not subject to the distance distortions present in the polar stereographic grid. Along with the ice velocity maps, the products include a valid pixel count map, which provides the number of valid slant range and azimuth displacement estimates at the output pixel position and are used in compiling the averaged map, as well as an uncertainty map (based on the standard deviation).

The IV product is distributed in NetCDF4 format according to the C3S convention Common Data Model format. The files can be readily ingested and displayed by any GIS package (e.g. the popular open-source GIS package QGIS) and are largely self-documenting. The NetCDF files contain the IV fields V_x, V_y, V_z, and V_v (magnitude of the horizontal components) as separate layers converted to metres per day (Figure 5). The pixel count map and uncertainty map are provided as separate layers. The IV maps are gridded at 500 m and are in NSIDC North Polar Stereographic projection with latitude of true scale at 70°N and central meridian at 45°W (EPSG: 3413).

Figure 5: Example IV product covering the Greenland Ice Sheet, depicted are from left to right the easting component, the northing component and the magnitude of velocity. Note: the ice sheet peripheral glaciers and ice caps are not included in the ice sheet product but delivered separately for C3S ECV Glaciers.

3.2.1.1. Instruments

The IV product is primarily derived by applying feature tracking on repeat pass Copernicus Sentinel-1 SLC data. The Sentinel-1 is a C-band synthetic aperture radar (SAR) mission and the constellation currently comprises two identical satellites (Sentinel-1A and -1B) with a repeat cycle of 6/12-days. The Interferometric Wide (IW) swath mode is the standard operation mode over land surfaces and inland ice. It applies the Terrain Observation by Progressive Scans (TOPS) acquisition technology, providing a spatial resolution of about 3 m and 22 m in slant range and azimuth, respectively, with a swath width of 250 km. The Sentinel­1 constellation is the main source for regular and comprehensive monitoring of land ice motion.

3.2.1.2. Algorithm name and version

The ENVEO software package (ESP v2.1) is a state-of-the-art IV retrieval algorithm designed for various SAR sensors (e.g. Sentinel-1, TerraSAR-X, ALOS PALSAR, Cosmo-SkyMed). The processor has been tested rigorously through intercomparisons with other packages and extensive validation efforts. The ESP-IV processing system runs on common Linux operating systems and has successfully been connected to cluster systems utilising several hundreds of Cores. This is especially of interest for campaign processing of big data sets as for Greenland. The existing system for annual IV production for Greenland is fully operational. Further improvements of the software are planned and discussed in section 3.4.

3.2.1.3. Auxiliary data

Auxiliary data needed for input in the IV processor are a digital elevation model (DEM) and polygon shapefiles of the ice sheet boundary.

3.2.1.3.1. DEM

A DEM is needed for geometric co-registration of repeat pass SAR data and geocoding of the final products. This requires an accurate DEM without artefacts as spurious jumps in the derived velocity fields can occur otherwise. For the IV maps produced in the Greenland Ice Sheet CCI, the Greenland Ice sheet Mapping Project (GIMP) DEM (Howat et al., 2014) was used. For C3S a new DEM was compiled and implemented based on the recently released 90 m TanDEM-X Global DEM (Rizzoli et al., 2017). Known issues relating to processing artefacts, outliers and gaps, are filled in using a gap interpolation method. The extent of the DEM is equal to the IV product.

3.2.1.3.2. Ice sheet boundary

The ice sheet and glacier boundaries are based on the latest version of the Randolph Glacier Inventory (RGI 6.0, RGI Consortium, 2017) with updated glacier fronts. The inventory has been compiled from more than 70 Landsat scenes (mostly acquired between 1999 and 2002) using semi-automated glacier mapping techniques (Rastner et al., 2012).

3.2.2. Antarctic surface elevation change

The product provides estimates of surface elevation change over the Antarctic ice sheets and ice shelves, over a long period, using level 2 radar altimeter data from four satellite missions: ERS1, ERS2, Envisat and CryoSat-2. Its algorithms and processing scheme are based on previous work for the ESA Antarctic Ice Sheet Climate Change Initiative, and are guided by the GCOS (Global Climate Observing System) targets for the Ice Sheets Land ECV (Essential Climate Variable).

Data consist of estimates of surface elevation change rate in a 5-year moving window that advances in one-month steps. The first measurements used are taken from phase C of the ERS1 mission, starting in April 1992, and extend to the present. Estimates are made, where possible, for each time period in each cell of a 25km by 25km polar stereographic grid, covering the ice sheets, ice shelves and associated ice rise and island areas. Data gaps are flagged, but not filled.

The product is distributed in NetCDF4 format according to the C3S Common Data Model conventions. The main ECV and its uncertainties are accompanied by a map of surface type, i.e. ice sheet, ice shelf or island/ice rise and a set of flags denoting regions of high surface slope.

Figure 6: Example Antarctic SEC product showing the rates of change derived for the period from 01-07-2007 to 01-07-2012. This merges data from Envisat and CryoSat-2. In this case the data extends only as far south as the Envisat southern orbit limit.

3.2.2.1. Instruments

The instruments used are the ERS1 RA, ERS2 RA, Envisat RA2 and CryoSat-2 SIRAL. The data products used are the ERS1 and ERS2 Reaper L2, the Envisat L2 GDR_v2.1 and the CryoSat-2 L2i LRM (Low Rate Mode) and SIN (Synthetic aperture radar INterferometer).

3.2.2.2. Algorithm name and version

The software package has been assembled and tailored to the C3S requirements from previous work on IMBIE (the Ice sheet Mass Balance Intercomparison Exercise) and various ESA Climate Change Initiative projects. The initial version is called C3S_Ant_Sec_ops_v1.0. The results have been tested against datasets from the previous projects mentioned and validated against the multi-year IceBridge airborne laser altimetry campaigns. The underlying processing system runs on common Linux operating systems.

3.2.2.3. Auxiliary data

Four auxiliary datasets are needed.

3.2.2.3.1. DEM

Radar altimetry over regions of very high slope is generally of poor quality due to confusion over echo provenance. The digital elevation model is used to remove data from areas extremely high slope, i.e. greater than 10°, from the input measurements. It is also used to provide a grid of flags ranking the slope angle in each cell. The model in use is the Slater et al. model based on CryoSat-2 data.

3.2.2.3.2. Ice extent

The processing area consists of all of the Antarctic ice sheets, ice shelves and associated ice rises and island. Its boundaries are based on the IceSAT MODIS (Moderate Resolution Imaging Spectroradiometer) 1km resolution mask, produced for the IMBIE2 project by Zwally et al.

3.2.2.3.3. Glacial isostatic adjustment

Movements of the surface related to glacial isostasy are corrected for using the Ivins et al. model IJ05.

3.2.2.3.4. Tides

Due to the poor resolution of the satellites' land masks in processing Antarctic coastal regions, it is necessary to remove the tides supplied in the L2 products and replace them with a consistent set. The replacements are generated using the Padman et al. CATS 2008a tide model.

3.2.3. Greenland surface elevation change

The Greenland surface elevation change closely follows the Antarctic SEC (Sec. 3.2.2). The main algorithms are based on previous work for the Greenland Ice Sheet CCI and are guided by the GCOS (Global Climate Observing System) targets for the Ice Sheets Land ECV. A full description of the processing approaches and algorithms are found in Sørensen et al. (2018) and Simonsen and Sørensen (2017, LSM5).

The product provides estimates of surface elevation change over the Greenland ice sheet, back to 1992, using level-2 radar altimeter data from the four ESA radar altimeter satellite missions: ERS-1, ERS-2, Envisat and CryoSat-2. Data consist of estimates of surface elevation change rate in a 5-year moving window that advances in one-month steps, for the earlier missions (ERS-1, ERS-2 and ENVISAT), whereas the novel altimeter of CryoSat-2 enabled the 5-year window to be shortened to a 3-year running mean.

The C3S-SEC product is distributed in NetCDF4 format according to the C3S Common Data Model conventions, at 25 by 25 km grid resolution. The grid is an equal area grid as defined by the NSIDC North Polar Stereographic projection with latitude of true scale at 70°N and central meridian at 45°W (EPSG: 3413). This projection is the same as used for the Ice velocity product. In addition to the gridded solution of SEC, the following fields are also available: cartesian x-coordinate (x), cartesian y-coordinate (y), geographical longitude and latitude (lon, lat), grid area (accounting for projection errors), relative elevation change since 1992 (dh), start and end times for the altimeter data used (start_time, stop_time), and a number of different accuracy fields for the different parameters.

3.2.3.1. Instruments

The instruments used are the ERS1 RA, ERS2 RA, Envisat RA2 and CryoSat-2 SIRAL. The data products used are the ERS1 and ERS2 Reaper L2, the Envisat L2 GDR_v2.1 and the CryoSat-2 L2i LRM (Low Rate Mode) and SIN (Synthetic aperture radar INterferometer).

3.2.3.2. Algorithm name and version

The software package has been assembled and tailored to the C3S requirements from previous work in ESA Climate Change Initiative projects. The results have been validated against the multi-year NASA Operation IceBridge airborne laser altimetry campaigns, see section 3.4.3.3 and Simonsen et al (2017).

The underlying processing system runs on a common Linux operating system, and are divided in two, one for the older mission (ERS-1, ERS-2 and ENVISAT) and one for Cryosat-2. For the older missions, the processing is brokered from the Greenland CCI and follow the proposed combination of cross-over and repeat-track algorithms for SEC as documented in Sørensen et al. (2018). This method has been independently validated and inter-compared with stat-of-the-art methods in Levinsen et al. (2015). A 5-year running mean window are used to derive an annual SEC solution. The final monthly solution provided for the C3S-product is derived by a temporal-weighted mean of all solutions covering a given month. For CryoSat-2, the least-square-model solution 5 of Simonsen and Sørensen (2017) has been tailored to the requirements of the C3S. The monthly solution is derived based on 1.5-years of CryoSat-2 data on either side of the month in question. This 3-year running-mean window is chosen for stability of the plan-fit solutions, and to limit the imprint of interannual weather variability in the SEC product and predict climatic signals.

3.2.3.3. Auxiliary data

The processing approach for the Greenland SEC are in less degree in need of auxiliary data. However, to provide consistent documentation, a full description of the same auxiliary data as in the Antarctic SEC is provided here. If not used, the reason for not considering them is provided.

3.2.3.3.1. DEM

The Greenland SEC applies the official level-2 data solutions provided by ESA. When this level-2 product is generated by ESA, a DEM is applied in the geolocation of LRM data. For more information refer to the mission specific documentation for the specific DEM used in the geolocation of the echo. No DEM are used for the combined cross-over and repeat-track solutions, however a DEM is used as an initial parameter for the LSM5-method applied for CryoSat-2. The resulting solution from LSM5 is an update to the DEM. In the CryoSat-2 processing the Greenland Ice sheet Mapping Project (GIMP) DEM version 1 is used (Howat, Negrete, and Smith 2017).  

3.2.3.3.2. Ice extent

The processing is done for all Greenlandic grid-cells with an ice-cover of more than 95%, as given by the PROMICE ice-cover product (Citterio and Ahlstrøm 2013).

3.2.3.3.3. GIA

No glacial isostatic adjustment is applied to the dataset, due to the large discrepancy in the model GIA signal in Greenland, and the limited bias in the resulting SEC.

3.2.3.3.4. Tides

As the extent of floating ice shelves is limited in Greenland, no tidal adjustment is added to the product.

3.2.4. Gravimetric mass balance

The Gravimetric mass balance (GMB) relies solely on data from the Gravity Recovery and Climate Experiment (GRACE) mission. The mission consists of two twin satellites, which measure satellite-to-satellite distance. The gravity field of the Earth can then be derived from the change in the distance between the satellites. This precise evaluation of the gravity-field enables monthly solutions of Earth's gravity field anomalies from the launch in March 2002 to the end of its science mission in October 2017. The GRACE mass-con solution from both the Greenland and the Antarctic ice sheet CCI projects are brokered for the C3S-product and provided for the major ice sheet basins. See Barletta, Sørensen and Forsberg (2013) and Groth and Horwath (2016) for the description of the derivation of GMB from the initial level-2, c20, 1-degree GRACE-data. A GIA model and land ice mask are used as auxiliary data, along with the drainage basin definitions.

3.3. Ice Sheets User requirements

The overall requirements for all ice sheet and ice shelf service products are given in table 8 below.

Table 8: GCOS target requirements for ice sheet related ECVs (source: GCOS Implementation Plan, 2016)

*The GCOS resolution target cannot be met with current satellite data, so the C3S project has set a 25km resolution target.
**It should be noted that there is a difference between volume and mass change of the ice sheet, which seems to be undefined in the GCOS implementation plan.

3.3.1. Ice velocity

The primary GCOS requirements for ice velocity are listed in Table 8. In addition, as part of Ice Sheets CCI Phase 1, user requirements were identified through an extensive user survey within the community. The User Requirements Document (URD) from the Ice Sheets CCI Phase 1 project contains a full description of the results from this survey (Hvidberg et al., 2012). The user requirements for the ice velocity are summarised in Table 9.

Table 9: User requirements from Ice_Sheets_cci Phase 1 User Survey (Hvidberg et al., 2012).

3.3.2. Surface elevation change

The primary GCOS requirements for surface elevation change are listed in Table 8. In addition, as part of the Ice Sheets Greenland/Antarctica CCI Phase 1, user requirements were identified through an extensive user survey within the community. The User Requirements Document (URD) generated contains a full description of the results from this survey (Hvidberg et al., 2012, Shepherd et al., 2018) and its first requirement¹ matches the GCOS table (Table 8).

3.3.3. Gravimetric mass balance

The GCOS requirements regarding ice mass change do not adequately follow glaciological considerations, as there is a difference between ice sheet volume change (units: km³/year) and mass change (units: Gt/year). It has been assumed that the requirements should be given in water equivalent volumes, hence the conversion of 1-to-1 from volume to mass in Table 8.

3.4. Ice Sheets Gap analysis

3.4.1. Ice velocity

3.4.1.1. Description of past, current and future satellite coverage

The primary source dataset for the Greenland Ice Sheet (GIS) ice velocity product comprises Sentinel-1 (S1) single look complex (SLC) SAR data acquired in Interferometric Wide (IW) swath mode. One of the unique aspects of the S1 mission is the systematic acquisition planning of polar regions, designed to cover the entire GIS margin and large sections of the Antarctic coast continuously. The ongoing acquisition of ice sheet margins is augmented by dedicated ice sheet-wide campaigns for Greenland (annually) and Antarctica.

When the first S1 data became available, the GIS CCI consortium generated and provided the first complete IV map of Greenland (Nagler et al., 2015) and demonstrated the capabilities of Sentinel-1A (S1A) for mapping ice stream dynamics at 12-day intervals. The launch of Sentinel-1B (S1B) in March 2016 reduced the repeat observation period from 12 to only 6 days, enabling an even denser time series, providing better coverage of fast outlet glaciers and high accumulation areas, as well as opening opportunities for InSAR applications. Since June 2017, also virtually the entire Antarctic perimeter is covered continuously at 6 to 12-day intervals.

For Greenland each year in winter there is a dedicated mapping campaign during which, in the course of about 2 months, the entire ice sheet is covered in IW mode with 4 to 6 acquisitions per track. The S1 mission is currently in its 5th year and production of the 4^rd consecutive ice sheet wide velocity map is nearly completed. The maps provide a detailed snapshot of contemporary ice flow in Greenland. The latest maps include data from both S1A & S1B and are nearly gapless and seamless.

The constellation is currently the primary source for year-round monitoring of IV. There are plans for further expansion of the continuous coverage in Greenland to also include the interior ice sheet. This could provide an opportunity to produce Greenland wide velocity maps at sub-annual, perhaps even monthly, intervals in the near-future. The Sentinel-1 constellation will continue to operate well into the next decade with two more satellites (Sentinel-1c and -1d) already in development. This, in combination with other new and planned SAR missions (e.g. SAOCOM , NASA-ISRO NISAR), ensures the long-term sustainability of the CDR.

3.4.1.2. Development of processing algorithms

The existing system (ESP v2.1) at ENVEO for annual IV production for Greenland is fully operational. ESP is a state-of-the-art IV retrieval algorithm suited to accommodate the ongoing evolution of the Copernicus Sentinel-1 mission data. The primary processor will continue to be developed and updated to accommodate new sensors and requirements. Further technical development activities, ongoing and planned, are described in sections 3.4.1.4and 3.4.1.5.

3.4.1.3. Methods for estimating uncertainties

The error prediction framework described in Mohr and Merryman-Boncori (2008) is applied to derive estimates of the error standard deviation of slant-range and azimuth velocity measurements. The input to the framework consists of the location of the GCPs used for velocity calibration, and in models for the covariance function (or equivalently the structure function) of all error sources, including noise and atmospheric propagation. For a mathematical formulation, the reader is referred to Mohr and Merryman-Boncori (2008).

In speckle tracking, where coherence is required, the noise component can be estimated from the correlation coefficient. For coherent offset tracking, the maximised coherence becomes equal to the interferometric coherence, and the following expression for the standard deviation, σC, of the shift estimate (in units of resolution elements) holds (DeZan, 2014):

where N is the number of pixels in the cross-correlation. For incoherent (intensity-based) offset-tracking applied to a coherent pair, the error becomes (DeZan, 2014):

which for γ→1 approach 1.8σ_C. For these noise error models to apply, it must be known that the signal is coherent, which is often not the case, especially at the outlet glaciers, where only intensity tracking of large features works. For coherent offset-tracking (rarely applied), the noise contribution is estimated by the equation for σC using the maximised correlation coefficient as γ.

For incoherent offset-tracking (the general case), the error is estimated for each pixel by calculating a local offset-map standard deviation in a 5x5 neighbourhood. A plane fit to the offset map in the 5x5 neighbourhood is subtracted prior to calculating the standard deviation, so that an actual velocity gradient is not interpreted as a noise signal. The standard deviation estimate is corrected for any averaging carried out, as well as correlation between neighbouring samples (i.e. if the radar data are oversampled). Each generated map will be accompanied by its associated error standard deviation. The latter is also a map, in the same geometry as the associated measurement, providing a measure of uncertainty on a per-pixel basis.

Additionally, for estimating the quality of IV products a series of standard test/measures are developed providing various levels of validation. Table 10 gives an overview of the QA tests and the metrics that they provide. The tests are described in more detail below.

Table 10: Summary of QA tests and the metrics that it provides.

QA-IV-1 Comparison of satellite derived velocity products with in situ measured velocity data (GPS). The quality metrics of this test provides: Mean and RMSD of the difference in velocity of IV products and in situ data.

QA-IV-2 Comparison of velocity fields generated from independent datasets from different sensors covering roughly the same period. The quality metrics of this test provides: Mean and RMSD of the difference of velocity components (Easting, Northing, Z).

QA-IV-3 The product is evaluated against publicly available products covering the same area. These can be assembled from different sensors or cover a different time. Nevertheless, in the latter case they can still provide a level of quality assurance, in particular in areas where little change is to be expected (e.g. inland ice sheet). The quality metrics of this test provides: Mean and RMSD of the difference of velocity components.

QA-IV-4 This is an internal QA method. Within the processing chain of the IV product generation, local quality measures of the IV retrieval are estimated, such as the number of valid matches and STD (described above) of available values for each pixel. These measures quantify the quality of the local IV estimates and are attached to each product.

QA-IV-5 Another internal QA method widely applied for quality assessment of velocity products is the analysis of stable ground where no velocity is expected. This gives a good overall indication for the bias introduced by the end-to-end velocity retrieval including co-registration of images, velocity retrieval, etc. After performing the matching for the entire region covered by the image pair, the results for the ice covered (moving) area will be separated from ice-free (stable) ground. The masking is done using a polygon of the glacier/rock outline. The quality metrics of this test provides Mean and RMSD of the velocity over stable terrain; mean values should be close to 0.

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

Regarding ice velocity (IV), the current CDR constitutes an annually averaged Greenland Ice Sheet velocity map based on continuous processing of all acquired Sentinel-1 data (6- and 12-day repeats). These existing measurements can be further exploited to assemble and merge IV maps at higher temporal frequency and compile sub-annual (e.g. seasonal, monthly, weekly) velocity mosaics. This option becomes particularly interesting as the current acquisition plan for Sentinel-1 is planned to be extended to also cover the interior ice sheet continuously, permitting comprehensive monitoring of the full Greenland Ice Sheet.

Technical developments of the IV retrieval algorithm are foreseen, building on the processing line developed in GIS CCI and AIS CCI projects. Below follows a brief description of on-going and planned research activities that provide opportunities to improve the contemporary data version.

Firstly, the processing system is planned to be adapted to increase the spatial resolution of the velocity product from 500 m towards 100 m, corresponding to the GCOS target requirement. This will greatly increase the versatility of the IV data sets, in particular for smaller outlet glaciers and shear margins. When successfully implemented the existing IV archive can be reprocessed.

The launch of Sentinel-1B in 2016 and subsequent reduction in satellite revisit time has opened new opportunities for InSAR applications. A key research activity/opportunity is therefore to extend the IV processor for supporting Sentinel-1 TOPS mode InSAR. Combining ascending and descending crossing orbit pairs, this development is expected to significantly improve the accuracy of the ice velocity, in particular for slower moving areas.

A third ongoing research activity is to further develop Sentinel-2 optical IV retrieval and exploit the operational synergies of Sentinel-1 and Sentinel-2 derived ice motion products to fill in temporal and spatial gaps in the surface velocity field. As previous investigations have shown, this is particularly relevant during summer periods when surface melt leads to coherence loss and hampering SAR IV retrieval. This leaves gaps in an otherwise complete and dense (Sentinel-1 derived) velocity time-series at time periods when ice flow is usually at its peak. From a science perspective, these gaps are undesirable as they can bias scientific analyses (e.g. modelling, ice discharge). When cloud-free scenes are available the optical trackers can be superior in such cases. The velocity fields can be merged to generate a consistent velocity product suitable for studying ice sheet dynamics. Procedures are developed and tested for integrating ice velocity products from Sentinel-1 and Sentinel-2 data. Figure 7 illustrates the improvement of the Sentinel-1 derived ice velocity field achieved by combining ice velocity products from both sensors. The large gaps at the ice sheet margins and glacier terminus are effectively filled in by merging the Sentinel-1 and Sentinel-2 derived flow fields.

Figure 7: Ice velocity map of Nioghalvfjerdsbrae/79Fjord-Glacier and Zachariae Isbræ from Sentinel-1 only (left) and merged product based on Sentinel-1 and Sentinel-2 (right).

3.4.1.5. Scientific research needs

As mentioned in section 3.4.1.5a key research need is the development of Sentinel-1 TOPS mode InSAR to derive ice sheet velocity. InSAR is capable of providing high precision and high-resolution velocity over large areal extents and can significantly improve the accuracy of the ice velocity in slower moving areas. The retrieval of ice velocity from TOPS InSAR is, however, challenging and requires additional investigation, particularly for the removal of phase discontinuities and burst boundaries. These are caused by azimuth motion and different line of sight direction at the transitions of adjacent bursts. The phase jumps get more significant with increasing azimuth motion. Additional developments are needed that include taking the variation of the line of sight within bursts into account and requiring separation of azimuth and slant range components of velocity. Additionally, a strategy for performing burst wise phase unwrapping needs to be implemented.

Another research need required for improving the processing algorithm is reduction of the effects of differential ionospheric path delay and removal of ionospheric stripes. These stripes are clearly evident as streaks in the retrieved velocity (particularly over northern Greenland) that are aligned slightly oblique to the LOS direction. Ionospheric disturbances are one of the main sources of error in the IV maps and hinder applications. As the repeat cycle for S1 is short, the potential impact of ionosphere-induced noise on the velocity is high. A way to compensate the ionospheric effects is the implementation of the split-spectrum method in the processor, which permits separating the ionospheric and the non-dispersive phase terms.

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

As mentioned in section 3.1, further expansion of continuous acquisition coverage in Greenland of Sentinel-1 provides an opportunity to produce Greenland-wide velocity maps at high temporal resolution. Additionally, the increased temporal coverage in the interior could reduce the error in the annual maps and facilitate the removal of ionospheric stripes.

3.4.2. Antarctic surface elevation change

3.4.2.1. Description of past, current and future satellite coverage

The Antarctic SEC data initially comes from four satellite missions, and it is expected that at least one more will be added in subsequent evolutions of the system.

Table 11: Mission summary

To retrieve surface elevation change data, a crossover method is used. This has to be applied where repeated orbits intersect, which creates a net of data sites that are closer together at more southerly latitudes. The spacing depends on the satellite repeat cycles. ERS1 changed orbit several times, and only mission phases C (April 1992 to December 1993) and G (March 1995 to mission end) are suitable for crossover analysis. CryoSat-2's long cycle nearly repeats every 30 days, but in effect the net 'drifts' slowly, making long-term timeseries comparison more difficult. To mitigate this, a large 25km by 25km polar stereographic grid is used to accumulate data spatially while retaining a monthly temporal sampling rate. This basic data is then combined into timeseries for each grid cell and a surface elevation change rate found, where possible, for a 5-year window advancing in steps of one month.

Spatially, data gaps can occur if too little data is available, for example in coastal regions or rugged terrain (notably the Antarctic Peninsula) where an altimeter can lose lock and fail to take measurements. When taken in combination with its long repeat cycle, this especially affects CryoSat-2. No data can be taken closer to the south pole than the orbital inclination of each satellite allows. Only CryoSat-2 approaches within 2° of the pole, the others are approximately 8.5° away. This affects the temporal gaps as well, as sufficient data to be representative of the 5-year surface elevation change rate in the region only CryoSat-2 can observe is limited to the central timespan of its mission.

In the future, the Sentinel 3 mission will return coverage to an Envisat-like configuration.

In the data product there are no temporal data gaps. At each timestamp a varying pattern of grid cells contain no data. Estimation of the missing data values may be undertaken with care, considering the underlying geophysics of the Antarctic.

3.4.2.2. Development of processing algorithms

The current system, C3S_Ant_Sec_ops_v1.0, will be used to make the initial data product. Its modular layout allows for alterations throughout the processing chain, but it is expected that upgrades will be minor for purposes of stability. There is a planned update to the multi-mission cross-calibration algorithm in 2019. The system is ready to process new releases of data (Envisat v3 and CryoSat-2 Baseline D) when they become available. It can be configured to incorporate new data from other missions, e.g. Sentinel 3, needing only a level 2 ingestion routine and the addition of some metadata to the control script.

3.4.2.3. Methods for estimating uncertainties

The uncertainty in each surface elevation change rate is calculated from three components summed in quadrature. These components are independent of each other and independent in all grid cells. They are:

• the epoch uncertainty, derived from the supplied input data
• the cross-calibration uncertainty, derived from the cross-calibration method
• the model uncertainty, derived from the trend fitting

The epoch uncertainty is the standard deviation of the geophysically-corrected height measurements used in calculating the crossover height. The cross-calibration uncertainty is the standard deviation of the difference between the least-squares linear trends fitted to the last two years of a mission and the first two years of the subsequent mission. The model uncertainty is the standard deviation of the linear least-squares fit used to model the surface elevation change rate.

The GCOS user-requirement target metric for measurement uncertainty (0.1 m/yr) applies to the total uncertainty. The target metric for stability (also 0.1 m/yr) applies to the model uncertainty.

The C3S project has mandated two key performance indicators, which are:

• the percentage coverage of the Antarctic Ice Sheet
• the uncertainty of the surface elevation change rate at drainage basin level

The coverage depends more directly on the performance parameters of the individual satellites, as discussed in section 3.2.1 above. The target is 65% coverage.

Amalgamating grid cells to basin level is a process that can be achieved with increasing levels of sophistication depending on how data gaps are handled. At a basic level, an elevation change timeseries can be derived from the known elevation change rates in each cell of the basin, and these can be averaged to create an effectively mean-filled basin timeseries. At a basic level, an elevation change timeseries can be derived from the given elevation change rates in each cell of the basin, and these can be averaged to create an effectively mean-filled basin timeseries, from which a change rate can be derived. This may not be appropriate for all basins, depending on whether data gaps are randomly spread across the basin or not, and on how much coverage there is altogether, e.g. performance of all satellites over the Antarctic Peninsula is poor because of its rugged terrain.

Figure 8 shows results from a preliminary test on a shorter-period version of the initial dataset.

Figure 8: Test results on a dataset produced using data from May 1992 to May 2018

The pixel-level uncertainty distribution peaks within the target value, but has a long tail outside the target. The dominant component is the epoch uncertainty, which relates to the input satellite measurements.

The basin-level uncertainties, which incorporate both ice sheet and ice shelf basins, have a peak distribution outside but close to the target. Some of the basins, e.g. all four land basins on the Antarctic Peninsula, are very difficult to observe and thus are poorly sampled in the pixel data. The uncertainties shown above were estimated using the simple mean-filling approach and may be reduced by a more sophisticated algorithm. It is suggested that users should create algorithms appropriate to their own projects when using amalgamated pixel data.

The coverage target is met in approximately one-third of the SEC time periods. This is very much a function of the satellite orbits and the observation area, which incorporates both the near-pole regions and hard-to-observe rugged terrain. The distribution is split into peaks depending on which and how many satellites' data were used in each time period. The 'pole hole' for ERS1, ERS2 and Envisat covers 20.0% of the total observation area and will be similar for Sentinel-3A and 3B. Thus, the maximum possible coverage is 80% most of the time. The pole hole for CryoSat-2 covers only 1.1% of the area, but its drifting orbit makes data retrieval more difficult at the Antarctic coast and ice shelves.

Validation is provided by comparison to NASA's Operation IceBridge airborne laser altimetry campaigns. These have been flying over the Antarctic since 2002 and are scheduled to continue indefinitely. They provide a level 4 surface elevation change rate data product, available from https://nsidc.org/icebridge/portal/map.

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

As the product is dependent on a long time-series, and overlapping missions with different orbital characteristics will cover more area, the addition of Sentinel-3 data will improve product data from 1996 onwards. Planned for next 2019 is an upgrade to the cross-calibration methods, which should allow the retrieval of extra pixels that contained data but could not be adequately cross-calibrated.

3.4.2.5. Scientific research needs

In order to identify ice dynamic trends the main emphasis for scientific research is in a long period of continuous acquisition. Progressive improvements in instrumentation allow for greater accuracy and areal coverage and thus a better focus on interesting regions at the sub-drainage-basin scale.

3.4.2.6. Opportunities from exploiting the Sentinels and any other relevant satellites

The Sentinel-3 mission will continue the data acquisition timestream. It will allow comparison with CryoSat-2 data that is geographically and temporally close, giving an opportunity to research cross-calibration algorithms in greater detail than usual. Its tandem phase will allow the exploration of the effect of small variations in instrument and orbit on the measurement data.

3.4.3. Greenland Surface elevation Change

3.4.3.1. Description of past, current and future satellite coverage

The satellite coverage for the GrIS SEC is the same as for the AntIS (see Sec. 3.4.2.1 , and Table 11). However, as the north-pole is covered by ocean and not ice sheet, there is a more complete coverage of the GrIS as it is only the northernmost part of the ice sheet that is not covered by the orbit inclination of the older radar satellites (81.5 degree north for ERS-1, ERS-2 and ENVISAT). However, the radar altimeter has difficulties in providing observation over high-sloping areas, as encountered at the margins of the GrIS.

3.4.3.2. Development of processing algorithms

The current system will be used to make the initial data product, and its modular layout in terms of missions allows for alterations throughout the processing chain. Minor upgrades for purposes of stability are expected.The system will be ready to process new releases of data (Envisat v3 and CryoSat-2 Baseline D) when they become available with new data streams will have to undergo an integration test before being deemed operational. Regarding Sentinel-3, options on how to incorporate this satellite to explore the truly multi-mission altimetry era we have had since 2016 are being explored.

3.4.3.3. Methods for estimating uncertainties

The uncertainty given in the product are the epoch uncertainty (derived from the supplied input data) and the model uncertainty (derived from the plan-fitting) combined. Figure 9 shows the annual count of grid-cells with a given uncertainty estimate. It is noted that with the introduction of the CryoSat-2 data and the shortening of the data-record used, the percentage of data points which meet the GCOS reequipments are significantly reduced. This is mainly due to the product being more subjected to inter-annual weather variability, which is reflected in the model uncertainty. In addition, the Cryosat-2 satellite SARIn-mode is able to observe closer to the margin of the ice sheet and therefore map higher rates of elevation change. Therefore, a 0.1 m/yr requirement at the margin of the ice sheet is lowering the tolerance of the percentage error to be much stricter than in the interior of the ice sheet. The real error estimate and the number that is needed to fulfil the user-requirements needs to be found by applying independent validation of the SEC-product.

Figure 9: Summed Grid-cell accuracy in the current processing version (C3S_GrIS_RA_SEC_vers1_2018-12-29.nc)

This independent validation is provided by comparison to NASA's Operation Ice Bridge (OIB) airborne laser altimetry campaigns. OIB started in 2009, however similar instrumentation has been operated in Greenland since 1993 and is included in the level-4 rate-of-surface elevation data product, available from https://nsidc.org/icebridge/portal/map. This product is the ideal dataset for judging if the user requirements are fulfilled. Figure 10 shows the result of the inter-comparison between the OIB and C3S-Greenland SEC, with the mean bias of all observations of 0.1 m/yr. This exactly fulfils the GCOS requirements.

Figure 10: Difference in the rate elevation change from OIB vs. the C3S Greenland SEC product. As the OIB level 4 data consist of data from all repeats of older flight paths, the years in the figure refer to the first year of observations, e.g. 1993 includes data for all repeats of the 1993 flightpath until 2017. In the upper-left panel both the mean and standard derivation are reported. The upper-right panel show the complete distribution for all years, which are shown for individual years in the lower panel.

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

As mentioned, the opportunities are expanding with addition of Sentinel-3 data. However, the ongoing research in the ESA CCI+ project will also be closely followed to refine the products by merging the processing chain with deployment of a dedicated ICESat-2 processing chain or brokering of SEC data from the laser altimeter.

3.4.3.5. Scientific research needs

The scientific research needs for the SEC product over Greenland are the same as for the Anatarctic SEC product. (please see section 3.4.2.5

3.4.3.6. Opportunities for exploiting the Sentinels and any other relevant satellites

These are the same as for the Anatarctic SEC product.See section 3.4.2.6

3.4.4. Gravimetric mass balance

3.4.4.1. Description of past, current and future satellite coverage

The GRACE mission ended in October 2017, resulting in a data-gap until data are released from the GRACE-Follow-On mission. GRACE-FO was launched on May 22, 2018 and is promising to continue the data record left by GRACE, and provide GMB.

3.4.4.2. Development of processing algorithms and methods for estimating uncertainties

The GRACE solution provided for the major drainage basins are brokered from the Greenland and the Antarctic ice sheet CCI projects. For both processing algorithms and uncertainty estimates refer to Barletta, Sørensen and Forsberg (2013), and Groth and Horwath (2016).

The primary GCOS requirements for Gravimetric mass balance are met in terms of horizontal resolution (Table 8). If typical ice densities are assumed, the measurement uncertainties are at present about twice the requirement. This emphasises the outstanding scientific question of how to deal with the signal leakages between changing bodies of mass, such as individual drainage basins and peripheral glaciers and ice caps.

3.4.4.3. Opportunities to improve quality, fitness-for-purpose of the CDRs

In addition to understanding the signal leakage, a major opportunity lies with the R&D activity anticipated in the community in relation to the GRACE-FO mission. The new mission will be able to provide GMB for the ice sheets and continue the long time-series of GRACE. However, the merging/bridging of GRACE and GRACE-FO provide an outstanding scientific need.

References

Soil Moisture

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.

BAUER-MARSCHALLINGER, B., PAULIK, C., HOCHSTÖGER, S., MISTELBAUER, T., MODANESI, S., CIABATTA, L., MASSARI, C., BROCCA, L. & WAGNER, W. 2018. Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sensing, , 10, 1030-1 - 1030-26.

CHUNG, D. 2017. C3S Product User Guide (PUG): Soil Moisture.

CHUNG, D., DORIGO, W., REIMER, C., HAHN, S., MELZER, T., PAULIK, C., VREUGDENHIL, M., WAGNER, W. & KIDD, R. 2017. ESA Climate Change Initiative Phase II - Soil Moisture: Algorithm Theoretical Baseline Document (ATBD).

DORIGO, W., WAGNER, W., ALBERGEL, C., ALBRECHT, F., BALSAMO, G., BROCCA, L., CHUNG, D., ERTL, M., FORKEL, M., GRUBER, A., HAAS, E., HAMER, P. D., HIRSCHI, M., IKONEN, J., DE JEU, R., KIDD, R., LAHOZ, W., LIU, Y. Y., MIRALLES, D., MISTELBAUER, T., NICOLAI-SHAW, N., PARINUSSA, R., PRATOLA, C., REIMER, C., VAN DER SCHALIE, R., SENEVIRATNE, S. I., SMOLANDER, T. & LECOMTE, P. 2017. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment.

EATON, B., GREGORY, J., DRACH, B., TAYLOR, K. & HANKIN, S. NetCDF Climate and Forecast (CF) Metadata Conventions.

GRUBER, A., DORIGO, W., CROW, W. & WAGNER, W. 2017. Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions of Geoscience and Remote Sensing.

GRUHIER, C., DE ROSNAY, P., HASENAUER, S., HOLMES, T., DE JEU, R., KERR, Y., MOUGIN, E., NJOKU, E., TIMOUK, F., WAGNER, W. & ZRIBI, M. 2009. Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site. Hydrology and Earth System Sciences Discussions, 6, 5303-5339.

LIU, Y. Y., DORIGO, W. A., PARINUSSA, R. M., DE JEU, R. A. M., WAGNER, W., MCCABE, M. F., EVANS, J. P. & VAN DIJK, A. I. J. M. 2012. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment, 123, 280-297.

MITTELBACH, H., KIDD, R., DORIGO, W., DE JEU, R., WAGNER, W., SENEVIRATNE, S. I. & HAAS, E. 2013. ESA CCI Soil Moisture - User Requirements Document (URD).

Haas, E, Dorigo, W., Kidd, R., 2018. ESA CCI Soil Moisture - User Requirements Document (URD), version 1.4

PARINUSSA, R., DE JEU, R., VAN DER SCHALIE, R., CROW, W., LEI, F. & HOLMES, T. 2016. A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input. Climate, 4, 50.

PFEIL, I., VREUGDENHIL, M., HAHN, S., WAGNER, W., STRAUSS, P. & BLÖSCHL, G. 2018. Improving the Seasonal Representation of ASCAT Soil Moisture and Vegetation Dynamics in a Temperate Climate Remote Sens., 10.

PILES, M., SCHALIE, R. V. D., GRUBER, A., MUÑOZ-MARÍ, J., CAMPS-VALLS, G., MATEO-SANCHIS, A., DORIGO, W. & JEU, R. D. Global Estimation of Soil Moisture Persistence with L and C-Band Microwave Sensors. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018), 2018-07-22 - 2018-07-27 2018 Valencia, Spain. IEEE, 8259 - 8262.

PLUMMER, S., LECOMTE, P. & DOHERTY, M. 2017. The ESA Climate Change Initiative (CCI): A European contribution to the generation of the Global Climate Observing System. Remote Sensing of Environment.

VAN DER SCHALIE, R. 2017. C3S Algorithm Theoretical Basis Document: Soil Moisture.

WAGNER, W., DORIGO, W., DE JEU, R., FERNANDEZ, D., BENVENISTE, J., HAAS, E. & ERTL, M. 2012. Fusion of Active and Passive Microwave Observations To Create an Essential Climate Variable Data Record on Soil Moisture. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, I-7, 315--321.

WAGNER, W., NAEIMI, V., SCIPAL, K., DE JEU, R. & MARTINEZ-FERNANDEZ, J. 2007. Soil moisture from operational meteorological satellites. Hydrogeology Journal, 15, 121-131.

WMO 2016. The Global Observing System for Climate (GCOS): Implementation Needs.

 Chung, D., W. Dorigo, R.A.M de Jeu, S. Hahn. T. Melzer. R.M. Parinussa, C. Paulik. C. Reimer, M. Vreugdenhil, B. Barrett, W. Wagner and R. Kidd (2017), “Algorithm Theoretical Baseline Document (ATBD) D2.1 Version 03.2 – Executive Summary”, http://www.esa-soilmoisture-cci.org

De Nijs, A.H., Parinussa, R.M., De Jeu, R.A.M., Schellekens, J. and Holmes, T.R. (2015), “A methodology to determine radio-frequency interference in AMSR2 observations”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 9, pp. 5148-5159.

Dorigo, W.A., Scipal, K., Parinussa, R.M., Liu, Y.Y., Wagner, W., De Jeu, R.A.M., and Naeimi, V. (2010), “Error characterisation of global active and passive microwave soil moisture datasets”, Hydrology and Earth System Sciences, vol. 14, pp. 2605 – 2616, doi: 10.5194/hess-14-2605-2010.

Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., et al. (2017), “ESA CCI Soil Moisture for improved Earth system understanding: state-of-the art and future directions”, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2017.07.001

Entekhabi, D., Njoku, E.G., O’Neill, P.E., Kellogg, K.H., Crow, W.T., et al. (2010), “The Soil Moisture Active Passive (SMAP) Mission”, Proc. of the IEEE, vol. 98, no. 5, pp. 704-716, doi: 10.1109/JPROC.2010.2043918.

Friesen, J., Steele-Dunne, S.C., and Van de Giesen, N. (2012), “Diurnal differences in global ERS scatterometer backscatter observations of the land surface”, IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 7, doi: 10.1109/TGRS.2012.2193889.

Friesen, J., Winsemius, H.C., Beck, R., Scipal, K., Wagner, W., and Van De Giesen, N. (2007), “Spatial and seasonal patterns of diurnal differences in ERS Scatterometer soil moisture data in the Volta Basin, West Africa”, IAHS Publication, vol. 316.

Gruber, A., Dorigo, W., Crow, W., and Wagner, W. (2017), “Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals”, IEEE trans. geosc. rem. sens., DOI: 10.1109/TGRS.2017.2734070

Gruber, A., Su, C.H., Crow, W.T., Zwieback, S., Dorigo, W.A., & Wagner, W. (2016a). Estimating error cross-correlations in soil moisture data sets using extended collocation analysis. Journal of Geophysical Research: Atmospheres, 121(3), 1208-1219.

Gruber, A., Su, C.H., Zwieback, S., Crow, W.T., Wagner, W., & Dorigo, W. (2016b). Recent advances in (soil moisture) triple collocation analysis. International Journal of Applied Earth Observation and Geoinformation, 45, 200-211.

Gruber, A., Dorigo, W., van der Schalie, R., de Jeu, R., & Wagner, W. (in prep.). Improved blending of multi-satellite products into the ESA CCI soil moisture climate data record

Gruhier, C., de Rosnay, P., Hasenauer, S., Holmes, T. R. H., de Jeu, R. A. M., Kerr, Y., ... Zribi, M. (2009). Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site. Hydrology and Earth System Sciences Discussions, 6, 5303-5339. DOI: 10.5194/hessd-6-5303-2009

Kerr, Y.H., Waldteufel, P., Wigneron, J.P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M.J., Font, J., Reul, N., Gruhier, C., Juglea, S.E., Drinkwater, M.R., Hahne, A., Martin-Neira, M., and Mecklenburg, S. (2010), “The SMOS mission: New tool for monitoring key elements of the global water cycle”, Proceedings of the IEEE, vol. 98, no. 5, doi: 10.1109/JPROC.2010.2043043.

Owe, M., de jeu, R., & Holmes, T. (2008). Multisensor historical climatology of satellite-derived global land surface moisture. Journal of Geophysical Research-Earth Surface, 113, F01002

Parinussa, R.M., De Jeu, R.A.M., Van der Schalie, R., Crow, W.T., Lei, F., and Holmes, T.R.H. (2016), “A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input”, Climate, vol. 4, no. 50, doi:10.3390/cli4040050.

Parinussa, R.M., Meesters, A., Liu, Y.Y., Dorigo, W., Wagner, W., & de Jeu, R.A.M. (2011). Error Estimates for Near-Real-Time Satellite Soil Moisture as Derived From the Land Parameter Retrieval Model. IEEE Geoscience and Remote Sensing Letters, 8, 779-783

Shi et al. (2016), "The water cycle observation mission (WCOM): Overview," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, pp. 3430-3433. doi: 10.1109/IGARSS.2016.7729886

Su, C.-H., and Ryu, D. (2015), “Multi-scale analysis of bias correction of soil moisture”, Hydrol. Earth Syst. Sci., vol. 17, doi:10.5194/hess-19-17-2015.

Su, C.-H., Zhang J., Gruber, A., Parinussa, R., Ryu, D., Crowd, W.T., and Wagner, W. (2016), “Error decomposition of nine passive and active microwave satellite soil moisture data sets over Australia”, Remote Sensing of Environment, vol. 182, doi: https://doi.org/10.1016/j.rse.2016.05.008.

Van der Schalie, R., and De Jeu, R.A.M. (2016), “SMAP Soil Moisture and Vegetation Optical Depth from the Land Parameter Retrieval Model”, as presented at the 3rd Satellite Soil Moisture Validation and Application Workshop, New York

Van der Schalie, R., De Jeu, R.A.M., Kerr, Y.H., Wigneron, J.-P., Rodriguez-Fernandez, N.J., Al-Yaari, A., Parinussa, R.M., Mecklenburg, S., and Drusch, M. (2017), “The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E”, Remote Sensing of Environment, vol. 189, doi: http://dx.doi.org/10.1016/j.rse.2016.11.026.

Van der Schalie, R., Kerr, Y.H., Wigneron, J.P., Rodriguez-Fernandez, N.J., Al-Yaari, and De Jeu, R.A.M. (2016), “Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model”, Int. J. Appl. Earth Observ. Geoinf., doi: http://dx.doi.org/10.1016/j.jag.2015.08.005.

Van der Schalie, R., Parinussa, R.M., De Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodriguez-Fernandez, N.J., Al-Yaari, A., Mecklenburg, S., and Drusch, M. (2018), “Evaluation of three different data fusion approaches for the development of a soil moisture climate record from microwave satellite sensors”, Remote Sensing, ACCEPTED.

Vreugdenhil, M., Dorigo, W.A., Wagner, W., De Jeu, R.A.M., Hahn, S., and Van Marle, M.J.E. (2016), “Analyzing the Vegetation Parameterization in the TU-Wien ASCAT Soil Moisture Retrieval”, IEE Transaction of Geoscience and Remote Sensing, vol. 54, no. 6., doi: 10.1109/TGRS.2016.2519842.

Wagner, W. (1998). Soil Moisture Retrieval from ERS Scatterometer Data. In, Institute for Photogrammetry and Remote Sensing. Vienna: Technical University of Vienna

Wagner, W., Blöschl, G., Pampaloni, P., Calvet, J.-C., Bizzarri, B., Wigneron, J.-P., Kerr, Y. (2007), “Operational readiness of microwave remote sensing of soil moisture for hydrologic applications”, Nordic Hydrology, vol. 38, no. 1.

Ice Sheets

Barletta, V.R.; Sørensen, L.S.; Forsberg, R. Scatter of mass changes estimates at basin scale for Greenland and Antarctica. The Cryosphere 2013, 7, 1411–1432. 757 42.

Citterio, M. and Ahlstrøm, A. P.: Brief communication "The aerophotogrammetric map of Greenland ice masses", The Cryosphere, 7, 445-449, https://doi.org/10.5194/tc-7-445-2013, 2013.

De Zan, F: Accuracy of incoherent Speckle Tracking for Circular Gaussian Signals, IEEE Geoscience and Remote Sensing Letters, Vol 11, No 1, January 2014

Howat, I. M., Negrete, A., and Smith, B. E.: The Greenland Ice Mapping Project (GIMP) land classification and surface elevation data sets, The Cryosphere, 8, 1509-1518, https://doi.org/10.5194/tc-8-1509-2014, 2014.

Groh, A.; Horwath, M. The method of tailored sensitivity kernels for GRACE mass change estimates.  Geophysical Research Abstracts 2016, 18, EGU2016–12065.

Howat, I., A. Negrete, and B. Smith. 2017. MEaSUREs Greenland Ice Mapping Project (GIMP) Digital Elevation Model from GeoEye and WorldView Imagery, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/H0KUYVF53Q8M.

Hvidberg, C.S., et al., User Requirements Document for the Ice_Sheets_cci project of ESA's Climate

Change Initiative, version 1.5, 03 Aug 2012. Available from: http://www.esa-icesheets-cci.org/

Mohr, J.J. & J.P. Merryman Boncori, An Error Prediction Framework for Interferometric SAR Data, IEEE Trans. Geosc. Rem. Sens., Vol 46, pp. 1600-1613, Jun. 2008.

Ivins E. R., James T. S., Wahr J., Schrama O., Ernst J., Landerer F. W., and Simon K. M. (2013), Antarctic contribution to sea level rise observed by GRACE with improved GIA correction, J. Geophys. Res. Solid Earth, 118(6), 3126–3141.

J.F. Levinsen, K. Khvorostovsky, F. Ticconi, A. Shepherd, R. Forsberg, L.S. Sørensen, A. Muir, N. Pie, D. Felikson, T. Flament, R. Hurkmans, G. Moholdt, B. Gunter, R.C. Lindenbergh & M. Kleinherenbrink (2015) ESA ice sheet CCI: derivation of the optimal method for surface elevation change detection of the Greenland ice sheet – round robin results, International Journal of Remote Sensing, 36:2, 551-573, DOI: 10.1080/01431161.2014.999385

Nagler, T., Rott, H., Hetzenecker, M., Wuite, J., Potin, P.: The Sentinel-1 Mission: New Opportunities for Ice Sheet Observations. Remote Sens. 2015, 7, 9371-9389.

Padman, L., H. A. Fricker, R. Coleman, S. Howard, and S. Erofeeva, 2002: A new tidal model for the Antarctic ice shelves and seas, Ann. Glaciol., 34, 247-254.

Rastner, P., Bolch, T., Mölg, N., Machguth, H., Le Bris, R., and Paul, F.: The first complete inventory of the local glaciers and ice caps on Greenland, The Cryosphere, 6, 1483-1495, https://doi.org/10.5194/tc-6-1483-2012, 2012.

RGI Consortium (2017). Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0: Technical Report, Global Land Ice Measurements from Space, Colorado, USA. Digital Media. DOI: https://doi.org/10.7265/N5-RGI-60

Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Borla Tridon, D., Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T., Huber, M., Wessel, B., Krieger, G., Zink, M., and Moreira, A. (2017): Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS Journal of Photogrammetry and Remote Sensing, Vol 132, pp. 119-139.

Shepherd et al, Antarctic Ice Sheet (AIS) Essential Climate Variable (ECV) User Requirements Document (URD), ST-UL-ESA-AISCCI- URD-001, version 3.0, 28th June 2018 at http://esa-icesheets-antarctica-cci.org/

Slater, T., Shepherd, A., McMillan, M., Muir, A., Gilbert, L., Hogg, A. E., Konrad, H., and Parrinello, T.: A new digital elevation model of Antarctica derived from CryoSat-2 altimetry, The Cryosphere, 12, 1551-1562, https://doi.org/10.5194/tc-12-1551-2018, 2018.

Simonsen, S.B.; Sørensen, L.S. Implications of changing scattering properties on Greenland ice sheet volume change from Cryosat-2 altimetry. Remote Sensing of Environment 2017, 190, 207–216.

Sørensen, LS.; Simonsen, SB.; Forsberg, R.; Khvorostovsky, K.; Meister, R.; Engdahl, M.E. 25-years of elevation changes of the Greenland Ice Sheet from ERS, Envisat, and CryoSat-2 radar altimetry. Earth and Planetary Science Letters 2018, 495, 234–241.

Zwally, H. Jay, Mario B. Giovinetto, Matthew A. Beckley, and Jack L. Saba, 2012, Antarctic and Greenland Drainage Systems, GSFC Cryospheric Sciences Laboratory, at http://icesat4.gsfc.nasa.gov/cryo_data/ant_grn_drainage_systems.php.

Related articles

• Page:
  SM 2020: Target Requirements and Gap Analysis Document (TRGAD)
• Page:
  SM v202012: Algorithm Theoretical Basis Document (ATBD)
• Page:
  SM v201912: Product Quality Assessment Report (PQAR)
• Page:
  SM v 201912: Algorithm Theoretical Basis Document (ATBD)
• Page:
  SM v201812: Algorithm Theoretical Basis Document (ATBD)