Last modified on Dec 01, 2023 17:08

Contributrs: W. Dorigo (WD, TU Wien), W. Preimesberger (WP, TU Wien), R. Kidd (RK, EODC), A. Dostalova (AD, EODC)

Issued by: EODC/Richard Kidd

Date: 24/11/2022

Ref: C3S2_312a_Lot4.WP1-PDDP-SM-v1_202206_SM_PQAD-v4_i1.1

Official reference number service contract: 2021/C3S2_312a_Lot4_EODC/SC1

History of modifications

List of datasets covered by this document

Related documents

Acronyms

General definitions

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

Accuracy: The closeness of agreement between a measured quantity value and a true quantity value of a measurand ((JCGM), 2008). The metrics used here to represent accuracy are correlation and unbiased Root Mean Square Difference (ubRMSD). These metrics are commonly used throughout the scientific community as measures of accuracy (Entekhabi et al., 2010).

Backscatter is the measurand of “active” microwave remote sensing systems (radar). As the energy pulses emitted by the radar hit the surface, a scattering effect occurs and part of the energy is reflected back. The received energy is called “backscatter”, with rough surfaces producing stronger signals than smooth surfaces. It comprises reflections from the soil surface layer (“surface scatter”), vegetation (“volume scatter”) and interactions of the two. Under very dry soil conditions, structural features in deeper soil layers can act as volume scatterers (“subsurface scattering”).

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

Brightness Temperature is the measurand of “passive“ microwave remote sensing system (radiometers). Brightness temperature (in degree Kelvin) is a function of kinetic temperature and emissivity. Wet soils have a higher emissivity than dry soils and therefore a higher brightness temperature. Passive soil moisture retrieval uses this difference between kinetic temperature and brightness temperature, to model the amount of water available in the soil of the observed area, while taking into account factors such as the water held by vegetation.

Dekad: the period or interval of 10 days

Degree of saturation: The ratio of the volume of water present in a given soil mass to the total volume of voids in it. It is generally expressed as a percentage, i.e.

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

Hovmoeller diagram: A way of presenting soil moisture (and other meteorological) data over time. In the context of Copernicus Climate Change Service (C3S) soil moisture these usually present changes by month (x-axis) and latitude (y-axis). Data for individual longitudes are averaged; notice that the number of averaged points varies by latitude (due to the uneven distribution of land points), and time (due to available satellites and amount of flagged observations - especially for tropics and frozen soils).

Inter-annual anomalies: Anomalies represent the deviation of the soil moisture signal from the long-term average (climatological) conditions. The reference period used to compute anomalies for C3S Soil Moisture is usually from 1991 to 2020. Anomalies therefore contain information on events deviating from these long-term “normal” conditions, such as (heavy) precipitation or agricultural droughts.

Key Performance Indicators (KPIs): A set of performance measures designed to rate the quality of satellite soil moisture observations. Based on suggestions from the Global Climate Observing System (GCOS), the Climate Modelling User Group (CMUG) and other community agreed standards (more details are given in the “Target Requirements and Gap Analysis Document” [D5])

Koeppen-Geiger Climate Classification: Global classification of regions based on their climates. Contains 5 main classes with multiple sub-classes: A (tropical), B (arid), C (temperate), D (continental), and E (polar) climates. In the context of C3S soil moisture the classification of Peel et al. (2007) is used.

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

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

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

Porosity: The porosity of a given soil sample is the ratio of the volume of voids to the total volume of the given soil mass. It is generally expressed as a percentage. Used to convert between soil moisture expressed in volumetric units and as percent saturation.

Precision: Closeness of agreement between indications or measured quantity values obtained by replicate measurements on the same or similar objects under specified conditions ((JCGM), 2008).

Quality Assurance: Part of quality management focused on providing confidence that quality requirements will be fulfilled (Institution, 2015).

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

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

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

Theil-Sen estimator: A robust (median slope) estimator for fitting trend lines to time series observations (Theil, 1949).

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

Volumetric Soil Moisture: Volumetric water content is the ratio of the volume of water to the total volume of soil. The unit in C3S soil moisture (COMBINED and PASSIVE products) is m³m^-3.

Scope of the document

The purpose of this document is to describe the product Quality Assurance (QA) for the soil moisture products developed by TU Wien, EODC and VanderSat/Planet Labs for the Copernicus Climate Change Service (C3S). The product development has been funded by the European Centre for Medium Range Weather Forecasting (ECMWF), while the scientific development and processor prototyping have been funded by the Climate Change Initiative (CCI) of the European Space Agency (ESA).

This document defines and describes the datasets, validation methods and strategies used for the validation and characterisation of the accuracy and stability of the soil moisture product. Note that, whilst some of the methods described in this document will be implemented routinely each time the product is reprocessed, others will be implemented on an “ad hoc” basis as deemed necessary. The target audience of this document is the users of the C3S soil moisture data products who wish to understand how the results reported in the “Product Quality Assessment Report” (PQAR) [D3] have been derived.

This current document is applicable to the QA activities to be applied on version v202212 of the Climate Data Record (CDR) (planned production in Dec. 2022 / Jan. 2023, filenames have the extension XXX-v202212.0.0.nc). Currently, the methodology does not include details of how the Interim Climate Data Records (ICDRs) will be assessed. This may change with the inclusion of available ERA5T soil moisture fields (available with a 5-day delay from real time) in the validation framework.

Executive summary

This document represents the methodology used to assess the quality of the current C3S soil moisture product (v202212). It includes an outlook on planned validation activities, some methodological background and includes a selection of results from the validation of previous versions. Detailed results of the quality assessment of the newly produced record (v202212) can be found in the PQAR [D3].

The Product Quality Assurance Document includes the definition and description of the datasets, validation methods and strategies used for the validation and characterisation of the accuracy and stability of the soil moisture product. This document is applicable to the QA activities performed on the version of the Climate Data Record (CDR) produced in January 2023 (record ID: v202212). Currently, the methodology does not include details of how the Interim Climate Data Records (ICDRs) will be assessed. Due to the recent availability of ERA5T, ERA5 data could be used in the future as a reference to evaluate the ICDR data stream with a delay of only a few days. Note that, to achieve maximum consistency between CDR and ICDR, both products use the same Level 2 products (based on Near Real Time (NRT) data streams) and thus have very similar quality characteristics.

The QA methodology broadly comprises the following parts: accuracy assessment, stability assessment, a completeness / consistency assessment (spatial and temporal), visual inspection of the product, demonstration of uncertainty analysis and comparison to previous versions of the product. The first two sections focus on demonstrating that the Key Performance Indicators (KPIs) for the product are met. More information on the KPIs and their origins are given in the “Target Requirements and Gap Analysis Document” (TRGAD) [D5]. Note these KPIs take into account Global Climate Observing System (GCOS-200, described in WMO (2016)) and user requirements for the product.

The accuracy assessment is based on the comparison of the C3S soil moisture products to ground reference data from the International Soil Moisture Network (ISMN) as well as the comparison to Land Surface Models (LSMs) including the ERA5/ERA5-Land reanalysis datasets.

The stability assessment computes accuracy metrics over time and quantifies changes in these metrics over time.

The completeness / consistency assessment considers both the temporal and spatial domain, focussing on the coverage of the dataset and taking into consideration whether or not the observations were flagged as valid or not. Optional flags will be evaluated separately and with consideration that they will not be applied by most users.

The visual inspection of the dataset focusses on presenting timeseries and daily global maps of the dataset. Whilst such checks are simple, they do provide insight into the attributes of the dataset and allow simple verification of the data product.

The demonstration of uncertainty estimates (which are based on a combination of triple collocation analysis and error propagation) focuses on showing the evolution of uncertainties over the time series by providing Hovmöeller diagrams of the uncertainties.

The current version of the C3S product (v202212) is compared to previous versions (see detailed results in the PQAR [D3]). The assessment focusses on the differences between the products and how these can be attributed to changes in the input datasets as well as changes in the algorithm. The product is also compared to the ESA CCI SM (Soil Moisture) v07.1 product which is based upon the same algorithm as the C3S CDR v4.0 product (v202212).

1. Validated products

1.1. The C3S Soil Moisture Product

The C3S soil moisture product suite provides PASSIVE, ACTIVE and COMBINED (passive + active) microwave soil moisture products on a daily, dekadal (10-days) and monthly basis. The data are provided on a regular 0.25 degree grid based on the WGS84 reference system. The product is available globally between November 1978 and present day (for both the PASSIVE and COMBINED products) and August 1991 and present day (for the ACTIVE product). For details about the products, we refer to the Product User Guide and Specifications (PUGS) [D1].

The product has been processed by EODC with inputs by TU Wien and Vandersat/Planet Labs based on the ESA CCI SM algorithm version 7.

The C3S soil moisture product is generated from a set of passive microwave radiometers (Scanning Multichannel Microwave Radiometer: SMMR, Special Sensor Microwave Imager: SSM/I, Tropical Rainfall Measuring Mission Microwave Imager: TMI, Advanced Microwave Scanning Radiometer-Earth Observing System: AMSR-E, WindSat, Advanced Microwave Scanning Radiometer 2: AMSR2, Soil Moisture and Ocean Salinity: SMOS, Soil Moisture Active and Passive mission: SMAP, FengYun B/C/D and Global Precipitation Mission: GPM) and active microwave scatterometers (European Remote Sensing Satellite Active Microwave Instrument - Windscat: ERS-1/2 AMI WS and Meteorological Operational Satellite: Metop-A/B/C Advanced Scatterometer: ASCAT). The “ACTIVE” and “PASSIVE” product are created by fusing only scatterometer and radiometer soil moisture products, respectively. The “COMBINED” product is a blended product based on the input data from the two former datasets.

It is noted that, to achieve maximum consistency between Climate Data Record (CDR) and Interim Climate Data Record (ICDR), both products use the same Level 2 products (based on near real time (NRT) data streams) and thus have very similar quality characteristics. The methods described here are implemented on the ACTIVE, PASSIVE and COMBINED daily, dekadal (10-daily) and monthly products.

Data files are provided in NetCDF4 format and comply with CF-1.8 conventions¹. A summary of the specification for the C3S soil moisture products is provided in Table 1 (taken from the TRGAD [D5]).

A detailed description of the product generation is provided in the Algorithm Theoretical Basis Document (ATBD) [D2] with further information on the product given in the Product User Guide and Specifications (PUGS) [D1]. The underlying algorithm is based on that used in the generation of the ESA CCI v07.1 product which is described in relevant documents (Scanlon et al. (2022), Gruber et al. (2019), Liu et al. (2012)). In addition, detailed provenance traceability information can be found in the metadata of the product (i.e. in the global attributes of the NetCDF image files).

Table 1. Product specifications of the C3S Soil Moisture products, summarized from TRGAD [D5]

1.2. Available Products

The C3S soil moisture record comprises of a long-term CDR which runs from 1978 (passive and combined) or 1991 (active) to the end of the year/month indicated in the product name (e.g. 202212). This CDR is updated with data from 10 additional days every 10 days (therefore has a lag of 10 to 20 days to present day) in an appended ICDR. The theoretical algorithm and processing parameters between the CDRs and ICDRs are exactly the same and the data provided is therefore consistent between them. A new version of the CDR may be produced under the following cases:

• There are updates to the algorithm (such as scientific advances).
• Processing parameters are updated.
• New sensors are added to the algorithm.
• Any Near Real Time (NRT) products are changed, making a reprocessing of the archive necessary for consistency.

1.3. Soil Moisture Parameters and Units

The C3S soil moisture products are provided along with associated uncertainties (for the daily product only) and additional ancillary data (such as quality or observation mode flags).

The C3S soil moisture products are normally representing a value in the first 5 cm of depth. However, this is variable and depends on several factors such as the properties of the soil (physical and dielectric) or the characteristics of the sensors used to estimate the data. Therefore, there is not information available in the product about the exact depth of retrieval.

For the passive and combined soil moisture products, the volumetric soil moisture is provided in units of [m³ / m³] (volumetric soil moisture). For the active product, the soil moisture is expressed as degree of saturation [%]. This difference in units is due to the different retrieval algorithms used to derive soil moisture from active (Wagner et al., 2013) and passive sensors (Owe et al., 2008), respectively the scaling to Global Land Data Assimilation System (GLDAS) Noah (Rodell et al., 2004) soil moisture in the COMBINED product (Dorigo et al., 2017). Volumetric soil porosity information may be used to convert between (relative) saturation and volumetric units for soil moisture as described in Eq. (1). (Hillel, 2004).

2. Description of reference datasets

2.1. International Soil Moisture Network (ISMN)

The ISMN (Dorigo et al., 2011, Dorigo et al., 2013) has been established as a centralised data-hosting facility where globally available in-situ soil moisture measurements from operational networks and validation campaigns are collected, harmonised, and made available to users². It exists as a means for the geo-scientific community to validate and improve global satellite observations and modelled products. The network is coordinated by the Global Energy and Water Cycle Experiment (GEWEX) in cooperation with Group on Earth Observations (GEO) and CEOS (Committee for Earth Observation Satellites), and ESA. Operational tasks are currently being transferred to The German Federal Institute of Hydrology (BfG), where the ISMN will be hosted in future. The measurements contributing to the ISMN are heterogeneous in that the technique, depth represented, and other factors, may vary within the network. The locations of the ISMN sites are shown in Figure 1. As of December 2020, the ISMN database integrates data from 65 networks (2678 stations) as listed in Figure 2.

Figure 1: Site locations of datasets distributed by the ISMN (as of February 2020). Taken from (Dorigo et al., 2021)

The data available within the ISMN is subject to quality controls (detailed in Dorigo et al. (2013) and Dorigo et al. (2021)) and provided with quality flags. The quality controls include an assessment against a possible range of important metrological variables, which are applied equally to all datasets.

The ISMN dataset has been utilised in the validation of the ESA CCI and C3S SM products in the past usually employing all usable observations from the ISMN.

Figure 2: Temporal availability of in-situ measurements from networks within the ISMN data base, compared to modelled outputs and EO sensor derivatives (as of October 2022). Active networks are those, which continue to contribute to the ISMN; inactive are networks for which no further updates are expected. Adapted from Dorigo et al. (2021).

2.2. ERA5

ERA5 (Hersbach et al., 2020) is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1950 to present. ERA5 is produced by the Copernicus Climate Change Service (C3S) at ECMWF. ERA5 provides global estimates of variables including soil moisture by combining historical observations with advanced modelling and data assimilation systems. Currently, the data record is available from 1950 up to within 5 days of real time. Soil temperature and water content variables are available with a spatial resolution of 0.25 degrees. Of the original hourly time stamps, data at 0:00, 06:00, 12:00 and 18:00 UTC is used in the validation.

2.3. ERA5-Land

ERA5-Land (Muñoz-Sabater et al., 2021) is the successor to the previously used ERA-Interim/Land reanalysis (Balsamo et al., 2015) data product. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. ERA5-Land Volumetric Soil Moisture and Soil Temperature are available with a spatial resolution of 0.1 degrees and representative of Soil Water and Soil Temperature in 4 layers (0-7 cm, 7-28 cm, 28-100 cm, 100-289 cm). The top layer is used for comparison to C3S SM in the validation process. ERA5-Land is available from 1981 onward.

2.4. ESA CCI Soil Moisture

The CCI project was initiated in 2009 by ESA in response to the United Nations Framework Convention on Climate Change (UNFCCC) and Global Climate Observing System (GCOS) needs for Essential Climate Variable (ECV) databases (Plummer et al., 2017). In 2012, ESA released the first multi-decadal, global satellite-observed soil moisture dataset, named ESA CCI SM, combining various single-sensor active and passive microwave soil moisture products (Dorigo et al., 2017). The latest C3S product will be based (scientifically and algorithmically) on v07.1 of the ESA CCI SM product (released in May 2022).

3. Description of product quality assurance methodology

3.1. Introduction

QA in the context of Earth Observation (EO) applications and in particular CDR generation can be defined as the processes undertaken to ensure that the data product meets any defined requirements. QA for a CDR product such as the C3S soil moisture product will generally include:

1. Accuracy assessment of the data product, i.e. validation defined by the Land Product Validation (LPV) group³ as: "the process of assessing, by independent means, the quality of the data products derived from the system outputs" (also see Justice et al. (2000)).
2. Stability assessment of the product over long time periods. This refers to the properties of the product remaining constant in time and has been defined for Earth Observation applications (WMO, 2016) as the extent to which the systematic error associated with the product changes.
3. Completeness and consistency checking to demonstrate the continuous nature of the product over the spatial and temporal domains. This includes evaluation of the number of valid observations available in the dataset.
4. Visual inspection of the dataset, which includes plotting maps and time series of the data to allow a check on the spatial and temporal characteristics of the dataset to ensure they are as expected.
5. Uncertainty assessment provides plots of the uncertainties associated with the product.
6. Comparison to previous products includes an assessment of the dataset against previously released C3S (and CCI) versions to show the evolution of the algorithm over time. The dataset is also compared to the CCI dataset upon which it is based to ensure the dataset has been made as expected
7. Verification of the product to ensure that the output files are generated are as expected (note, for the C3S soil moisture product, such activities are described within the System Quality Assurance Document (SQAD) [D4].

QA of soil moisture datasets is important as quality of individual soil moisture observations can be impacted by numerous factors (Dorigo et al., 2017). These factors can be roughly divided into the following categories: (i) sensor properties, (ii) orbital characteristics, (iii) environmental conditions, (iv) algorithm skill (e.g. methods used to correct for vegetation impacts) and (v) post-processing (e.g. resampling). Further details of each of these characteristics are provided in Table 2 (taken from Dorigo et al. (2017)). The majority of these factors add some degree of random error and bias to the obtained estimate (Dorigo et al., 2017).

The QA activities to be undertaken on the C3S soil moisture assess whether the requirements set in the TRGAD [D5] are achieved or approached. This assessment includes all of the steps listed in 1 – 7 above (for further details see Sections 3.4 to 3.10). However, the focus is on determining the accuracy (see Section 3.4) and the stability (see Section 3.5) of the product with respect to defined requirements (summarised in Section 3.2).

The current QA activities will focus on the assessment at the product timescales (daily, dekadal and monthly). Additional work on inter-annual and seasonal variability will be carried out in the QA of this product. The same measures, as well as pre-processing steps to be applied in the assessments, will be implemented consistently between scales; these are described in Section 3.3.

Ideally, the assessments would be performed on different spatial and temporal scales, with the same evaluation measures applied at each scale. Currently, the focus is on the global dataset, with the effect of different land cover and climate types considered where data is accessible.

A similar assessment methodology to that presented here has been previously utilised in the ESA CCI soil moisture project (Mittelback et al., 2014, Mittelbach et al., 2012). This validation methodology was subject to user community acceptance prior to use and as such, it allowed a contribution to the definition of international standards in the soil moisture domain.

The methods for quality assessment of biogeophysical variables have been developed over several decades and there is significant research available on good practices and techniques (Loew et al., 2017, Gruber et al., 2016b, Gruber et al., 2020). The available guidance is taken into account within the methodology. This is complemented by the development of the Quality Assurance for Soil Moisture (QA4SM)⁴ platform, which provides robust, traceable validation of different data products against reference data including ISMN and ERA5/ERA5-Land. The platform will be used in the validation of C3S SM.

The evaluation of the quality of the dataset should be continuously repeated once a new dataset version becomes available to assess the potential impact of improved calibrations and algorithmic changes (Dorigo et al., 2017). The methodology presented here is applied to each dataset version, however there may be updates in the future as quality assessment methods are improved / advanced.

Table 2: Main sensor, observational and environmental factors impacting the quality of the C3S soil moisture products. Taken from Dorigo et al., (2017).

3.2. Product Quality Requirements

The QA process is required to ensure that the soil moisture product meets any requirements which have been set out prior to the development of the product. This section details the Key Performance Indicators (KPIs) for the C3S product (Table 3). These KPIs have been developed taking into account the user requirements from the CCI soil moisture product (Mittelbach et al., 2012) as well as the GCOS requirements (WMO, 2016) and are summarised in the TRGAD [D5]. Note that, for accuracy, the KPIs match the GCOS requirements, but for stability, the GCOS requirements are slightly more stringent (0.01 m³ / m³ / y). The metrics used here to represent accuracy are Spearman rank correlation and unbiased Root Mean Square Difference (ubRMSD). These metrics are commonly used throughout the scientific community as measures of accuracy (Entekhabi et al., 2010, Gruber et al., 2017a).

Note that “in the latest quarter” in Table 3 means the last three months of the product which is available. The assessment method presented here focusses on the CDRs which are generated once a year.

Table 3: Key Performance Indicators for the C3S Soil Moisture Product (adapted from TRGAD [D5])

(1) Work on the metrics used for stability assessment is ongoing with the aim of demonstrating compliance with these performance targets.

3.3. General Evaluation Methods

3.3.1. Pre-Processing

This section discusses how the different datasets are pre-processed to ensure that the parameters being compared are as equivalent as possible, for example, in-situ compared with satellite datasets can have large representativeness errors (Gruber et al., 2013) which may impact any comparisons undertaken (Su et al., 2016) and these need to be accounted for as far as possible.

Masking

Masking of any of the datasets used will be undertaken following the guidelines set out for the use of their own quality flags. For example, for the ISMN data, flags are available which indicate if the data is “good” (see Dorigo et al. (2013) for more details). The masking applied to the C3S data product will be dependent on the individual assessments being undertaken.

Spatial Resolution

The spatial resolution of the C3S product is 0.25 degrees, however the reference datasets have a range of spatial resolutions from point scale upwards. Therefore, significant consideration should be given to bridging the differences in spatial scale between the datasets. This is particularly important as the spatial variability of the soil moisture can be significant due to complex interactions between pedologic, topographic, vegetative and meteorological factors (Crow et al., 2012).

Currently, the nearest-neighbour approach is used, i.e. the latitude and longitude of the reference dataset pixel or point measurement is used to find the nearest grid point in the C3S dataset. The spatial representativeness of the point data (for example ISMN data) will need to be considered in future iterations of this assessment methodology.

In future iterations, a more complex strategy shall be used to derive a statistically representative comparative value. To achieve the optimal practical solution, upscaling methods (such as upscaling enhancement using time stability concepts, block kriging or land surface modelling) may be implemented in all pixels where representative values need to be derived.

Temporal Resolution

The C3S soil moisture dataset is provided at daily, dekadal (10-daily) and monthly temporal resolutions. The assessments will be undertaken at all time steps. As the comparison should consider the closest temporal measurements, it is expected that the assessment at a daily resolution would provide the best results. Monthly data will be particularly useful where long time series are being processed (for example in the stability assessment) where the aggregation to monthly data has a minimal impact on the results of the assessment.

Soil Moisture Depth

As described in Section 1.3, the exact depth that the soil moisture product represents is not available. Therefore, when considering the use of other products, the upper soil moisture (a depth of 0.-5 cm) is usually taken as an appropriate comparable parameter. In the accuracy assessment against ISMN data, two depth ranges of ISMN sensors will be used: 0 to 5 cm and 5 to 10 cm. For the accuracy assessment to ECMWF reanalysis products, data from the first layer (0-7cm) will be used.

Dynamic Scaling

The different datasets utilised in the quality assessment are available in different dynamic ranges, therefore, scaling is applied to bring the datasets into a common climatology. A mean standard deviation scaling is applied⁵. For tasks that aim to estimate the bias between satellite and reference data, no scaling is applied.

Evaluation Measures

The methods applied, in particular for the accuracy assessment, focus on the performance of the scaled absolute values of soil moisture (ABS) (scaled as described above in this section) as well as the long term anomalies (Inter-Annual Anomalies (IAA)).

For IAA, the climatological mean of a specific day is either based on all values of that day of the year, or taking into account a 10-day window around that day to account for potential shortages of data in the specific time period (Nicolai-shaw et al., 2015).

Appropriate statistical measures will be used in the assessment including the Spearman rank and Pearson’s correlation and unbiased root mean square difference (ubRMSD). Such measures have been frequently, and successfully, used in previous inter-comparison studies (Rüdiger et al. (2009); Brocca et al. (2011); Gruhier et al. (2009); (Gruber et al., 2020)).

Intra-annual errors

Errors in the SM estimation can change throughout a year. Time-variant, seasonally dependent factors such as vegetation coverage and density, frozen soil probability or sub-surface scattering effects have a different impact for different parts of the world at different times of the year. In the PQAR [D3], these aspects will be explored to get a better understanding of the stability of the data record within a year.

3.3.2. Presentation of Results

In general, results of correlations will be presented as box-plots showing the median of the correlation values as well as the 95 % confidence interval. An example is shown in Figure 3. Global maps will also be provided to show how the metrics vary spatially.

Figure 3: Example of boxplots (displaying median, interquartile range (IQR), upper (lower) quartile plus (minus) 1.5 times the IQR, and outliers) of the correlations of the publicly released versions of the ESA CCI SM COMBINED and ERA-Interim / Land with globally available in-situ probe observations down to a maximum depth of 5 cm, both for absolute values (a) and long-term soil moisture anomalies (b). Only observations within the period 1991-2010 were considered. Taken from (Dorigo et al., 2017).

3.4. Accuracy

3.4.1. Introduction

Accuracy assessment is undertaken through the comparison of C3S products against reference datasets. However, as discussed in Dorigo et al. (2017), the reliability of such comparisons hinges on the availability of stable, long-term reference datasets, something which is currently still lacking (WMO, 2016).

Here, the datasets used for the accuracy assessment are the ISMN, and ERA5/ERA5-Land datasets (described in Chapter 2). These have been chosen due to their availability over a relatively long time period (albeit with gaps in some periods at some locations for the in-situ data) and because they are publicly available, enabling traceability of the datasets as well as allowing the validation results to be reproduced by a third party.

3.4.2. Point Scale

The point scale accuracy assessment is undertaken against the ISMN dataset. This type of accuracy assessment is particularly useful as it allows the comparison of instruments, which have not been subjected to the rigors of space (radiation, launch forces etc.) with data derived from space-borne sensors. The advantage of this approach is that any calibration and characterisation of the in-situ sensors undertaken in the laboratory will likely be representative of the sensor's performance throughout its life-cycle. In addition, such sensors can be retrieved from the field and routinely re-calibrated / re-characterised as necessary, resulting in ongoing traceability of the sensors.

Table 4: Settings used in the assessment of the C3S soil moisture product against the ISMN as the reference

The settings used in the ISMN assessment are summarised in Table 4. The assessment will be undertaken using all ISMN stations with available data flagged as “good” within the time period covered by the C3S product⁶.

A set of ISMN networks have been chosen based on previous experience with, and knowledge of the networks; a full list of the stations that are planned to be used in the evaluation of C3S SM, is provided in Table 5. From these networks, the respective ISMN stations are selected and compiled, taking into account the pre-processing steps outlined in Section 3.3.1. However, as constantly new data is being added to the in-situ database, and more reference data is generally favourable for validation purpose, the final list of networks in the PQAR [D3] may vary slightly.

C3S data flagged as “good” will be used in the evaluation against in situ data. The barren ground flag will be evaluated separately and is therefore excluded in the validation (treated as “good”).

Table 5: ISMN networks used in the accuracy assessment of the C3S product⁷ 

The time series of the in-situ observations are then compared with the associated C3S grid cell and Pearson's correlation coefficients and ubRMSD are calculated. These results are presented globally (without aggregation of results by ancillary data) as well as aggregated results for

1. In situ sensor depth levels
2. soil texture (by sand/clay/silt content and soil organic content), respectively granulometry
3. Köppen-Geiger climate classes
4. ESA CCI Land Cover classes.

The data allowing these stratifications of the results are provided within the ISMN dataset (Dorigo, 2011). In future this may also include splitting the results by factors, which may affect the C3S soil moisture product but are not included with the in situ reference data yet, such as vegetation-cover and the proportional composition of land surface features within each pixel.

It should be noted, that not all ISMN stations are equally representative of soil moisture at the satellite scale. Within ESA's Fiducial Reference Measurements for Soil Moisture (FRM4SM) project, a subset of ISMN stations is being identified, which are better applicable for satellite validation than others. This FRM-compatible subset will be used in the evaluation of C3S soil moisture to reduce errors in validation results due to the use of inappropriate reference data.

3.4.3. Regional and Global Scale

Regional and global datasets will be used to quantify the performance of the retrieval algorithms on a larger scale than the point scale measurements. While detailed information is provided by networks such as the ISMN, ground based observations lack sufficient global coverage and consistency for comprehensive Earth system assessments (Dorigo et al., 2017). Therefore, reanalysis LSM products are used to allow the comparison of the relative values of the C3S product over a larger domain, such as global scales and for specific regions as well as over a long time period (Albergel et al., 2013). ERA5/ERA5-Land will be used for this purpose.

3.5. Stability

The stability of ECV products is a topic of research and providing a metric which describes stability in terms of change in the uncertainty of the variable of interest per decade is currently being investigated.

3.5.1. Monitoring Accuracy Metrics

To monitor stability, accuracy metrics are calculated for the C3S data compared against ISMN data for individual time periods of either 1 or 3 years. The trends in the metrics are then used to assess the stability of the product. For example, we can calculate the Theil-Sen estimate of the slope using the means of the correlation and ubRMSD over time.

This method assumes that the uncertainty associated with the data is characterised completely by the comparison to reference data, which is unlikely to be the case. A more thorough approach might include extracting the systematic component of the uncertainty over time and assessing the trends in this variable.

3.6. Spatial and temporal completeness

In addition to accuracy, stability, uncertainty analysis and determining if the product is within expected boundaries based on other similar products, there are several other potential indicators of a product's quality.

The spatial and temporal completeness provides important quality information for many users. As part of the quality assessment, such factors are considered and reported upon. In addition, it is demonstrated how the current product compares to previous versions of the product in terms of these attributes, if applicable.

The results are presented in the form of Hovmöeller diagrams of the valid observations, providing a summary of the fraction of valid observations per latitude. In addition, the fraction of valid observations is also plotted on global maps for different periods within the dataset (based on the merging periods of the different sensors).

3.7. Visual Assessment

Analysis of time series from a small number of locations provides an insight into the behaviour of the product for different climate and land cover types. Five points have been chosen for which ISMN in-situ data is available (and were used in the assessment). Details of the points are provided in Table 6 and are shown on a global map in Figure 4. In addition to timeseries analysis, individual dates of daily images will also be inspected.

Table 6: Details of locations chosen for time series analysis. 

Note: all are classified as having 'medium' soil texture.

Figure 4: Locations of the points used in the time series comparison (points are given at the C3S gpi location).

3.8. Uncertainty Analysis

The C3S soil moisture product is generated with associated uncertainty estimates. These estimates are based on the propagation of uncertainties from the Level 2 to the Level 3 products. This process is described within the ATBD [D2].

Part of the generation process for the product depends on the use of triple collocation analysis which takes estimates of the uncertainty within the Level 2 product. Triple collocation analysis may be used either on the local, regional or global scales. The aim of the analysis is to provide an estimate of the variance of the error term associated with a set of measurements (Gruber et al., 2016a). Within the C3S project, triple collocation analysis is used to determine the weightings assigned to each available sensor for a particular date / time combination.

The triple collocation technique does not require the specification of a “true” reference dataset and instead permits the estimation of the error variable of each sensor provided certain assumptions about the error structure are met (Zwieback et al., 2012); the dependency of the method on these assumptions is considered in recent work (Gruber et al., 2016a). The triple collocation technique assumes that there are three independent sets of measurements describing the same measurement, for example, soil moisture variations over a specific location. It is assumed that the measurement is linked to the true soil moisture value by an additive and multiplicative term together with a random error. The random uncertainty component provided by this process can be expressed as the SNR, which provides useful information by relating the uncertainty to the underlying signal strength.

Within the assessment of the product, global SNR maps for different merging periods will be presented and discussed. In addition, a Hovmöeller diagram of the relative uncertainty (%) will be presented. The latter will provide information both on the availability of uncertainty information within the product as well as the magnitude of the uncertainties over different time periods and latitude bands.

3.9. Comparison to Previous Versions

The comparison of the C3S soil moisture product to previous versions of the same product can be useful for ensuring that there are no unexpected, unrealistic or unphysical changes within the individual data points and over the time series. In essence, these comparisons can act as a "sanity check" for the data and can provide a useful insight into the comparative performance of different soil moisture dataset releases. A particular focus will be given to assessing the differences between the C3S and ESA CCI product generated with the same algorithm (but slightly different input ASCAT data streams).

The comparison between different versions is undertaken for each of the quality aspects discussed within this report, i.e. accuracy assessment is performed on the different versions. The spatial and temporal completeness of the products are compared between products to determine if the data coverage of the product is improving.

3.10. Verification

Verification of the produced files includes technical checks on the generated output files and ensures that standard (NetCDF4) software can open them without errors. This step includes ensuring that all expected variables and meta data are correctly stored and available in all files, respectively that files are not corrupted or empty. Some verification steps are also performed during production of the data sets.

4. Summary of most recent validation results

In this section, some examples of the validation methodology and their results are provided in relation to v202012 (CDR v3.0). The actual validation results of v202212 (CDR v4.0) and their in-depth analysis will be made available in the PQAR document [D3].

4.1. Accuracy – Comparison against ISMN

The results of the comparison to ISMN are shown for different climate classes and land cover types in Figure 5 and Figure 6 respectively.

The results are expected to be in line with other versions of the data set (both C3S and CCI products). The metrics are shown to vary between different climate and land cover classes. In terms of correlation, Csx/Dsx and grassland perform best. In terms of ubRMSD, urban areas perform the best. Similar results for the latest version will be further investigated and discussed in the full PQAR [D3] for this product. 

Figure 5: Correlation (a) and ubRMSD (b) between C3S v202012 and ISMN (0-5 cm) for different climate classes (B=arid, C=warm temperate, D=snow, S=steppe, f=fully humid, s=summer dry, x=any type of temperature). The boxplots show the mean value and interquartile range and the number of points per class.

Figure 6: Correlation (a) and ubRMSD (b) between C3S v202012 and ISMN (0-5 cm) for different land cover classes. The boxplots show the mean value and interquartile range and the number of points per class.

It can be seen from Figure 5 (b) and Figure 6 (b) that the accuracy target of GCOS (0.04 m³/m³) is reached for many but not all stations. Especially stations in forested areas are often above the GCOS threshold. However, for most stations the ubRMSD is below the threshold of 0.1 m³/m³.

4.2. Accuracy – Comparison against Land Surface Models

The COMBINED product has been compared to ERA5 in the past and the ubRMSD is shown in Figure 7. Similar assessments will be performed using ERA5-Land, potentially making use the higher spatial resolution of these data. Figure 7 shows spatial patterns in ubRMSD, with lower values seen in mid- to low-latitudes and values above 0.1 m³/m³ in areas where there is snow or ice cover for many days of the year. The patterns seen here are similar to those in other data products (C3S and CCI, but also other satellite SM products, such as from SMAP).

Figure 7: ubRMSD between C3S v202012 COMBINED product and ERA5 top layer soil moisture (swvl1 variable) over the time period 1979-01-01 to 2020-12-31.

4.3. Stability – Accuracy Metric Trends

Comparison metrics are calculated against ISMN data for individual time periods to monitor stability (e.g. ubRMSD by year in Figure 8 (a) and (b), using only stations in croplands and forests respectively). Changes in ubRMSD over time at each station could then be used to assess the overall stability of the product. This is shown in bottom row of Figure 8, where the distribution of trends (Theil-Sen slopes) in ubRMSD is shown - again aggregated for stations in croplands and forests, as shown in Figure 8 (c) and (d). The change in ubRMSD can be interpreted as the product stability, and is within the threshold of 0.01 m³ / m³/ year for most stations. It can also be seen that dense vegetation leads to a slightly less stable product, i.e. a larger spread in ubRMSD slopes. However, as there are currently no formal ways to evaluate the stability of merged satellite soil moisture products, different approaches for this will be tested (e.g. using the uncertainty information provided together with the soil moisture data) and if applicable presented in the PQAR [D3].

Figure 8: Stability monitoring through trend assessment of ubRMSD split by aggregated CCI Land Cover classes ("cropland" (a), (c) and "tree cover" (b), (d)) using ISMN reference data. The C3S and ISMN data were divided into yearly subset periods for this assessment.

4.4. Spatial and temporal completeness and consistency

The number of valid (un-flagged) observations available is shown globally in Figure 9 for the ASCAT / SMOS / AMSR2 / SMAP merging period (starting in April 2015) and per latitude for the entire data product period (Figure 10). Due to the inclusion of multiple additional sensors and overpasses, it is expected that the coverage in v202212 will increase significantly.

The figures show that the coverage is better in Europe, South Africa and the continental US than some in other parts of the world as well as the improvement in the availability of data post-2007 as new sensors became available (see Figure 10). This is as expected for the product due to the orbital paths of the satellites resulting in higher coverage in equatorial regions. The reduced coverage in boreal and tropical region is as expected due to the high VOD expected in these areas. In addition, the coverage of the northern most latitudes in snow and ice for long periods also reduces the coverage in these areas.

Figure 9: Fractional coverage of the C3S v202012 COMBINED soil moisture product for the ASCAT / SMOS / AMSR2 / SMAP period (2015-04-01 to 2020-12-31). Expressed as the total number of daily observations per time period divided by the number of days spanning that time.

 

Figure 10: Fraction of days per month with valid (i.e. un-flagged) observations of soil moisture for each latitude and time period for the COMBINED product of v202012.

4.5. Visual Assessment

The time series for the individual locations for the ACTIVE, PASSIVE and COMBINED products are given in Figure 11. In general, the time series appear to follow expected seasonal cycles at each location, i.e. winters are wetter and summers drier and, in the case of GPI 890047 (which is located in Alaska), there are gaps in the data where the location is covered by snow each winter.

Compared to the previous versions, the inter-calibration of passive sensors after 2010 has been improved. For previous versions, a sudden drop in SM due to insufficient scaling of AMSR2 was obvious (similar to the drop that is still observable for GPI 733335). This issue has been improved in version v202012 and is expected to be further corrected in future due to additional passive sensors (FengYun, GPM) that allow better inter-calibration of passive input products in the upcoming version. It is noted that the COMBINED product has not been affected by this issue due to the scaling of the data to GLDAS v2.1, and due to the inclusion of active sensors in this time period. It is therefore considered the most stable product of the three.

Figure 11: Time series comparison for the COMBINED, ACTIVE and PASSIVE products of C3S v202012, for the GPIs and land cover types stated for each plot. Note: here, the ACTIVE product is divided by 100 to allow it to be plotted on the same axis.

4.6. Uncertainty analysis

The algorithm used to develop the C3S soil moisture product utilises triple collocation analysis to generate weightings for the combination of different soil moisture observations (Gruber et al., 2017b). In combination with error propagation techniques, a per-pixel uncertainty is provided within the C3S soil moisture product in the “sm_uncertainty” field (same unit as the soil moisture variable).

The relative uncertainty for the date 2022-06-20 in the COMBINED product is provided as a percentage (i.e ) in Figure 12. This shows the relative uncertainty for the product is higher in drier areas and lower in those regions where the VOD is higher. Further analysis of the uncertainty associated with the product will be considered in the PQAR [D3].

Figure 12: Daily image of the relative soil moisture uncertainty for the COMBINED product of C3S v202012 ICDR. Image date: 2022-06-20.

4.7. Comparison to ESA CCI SM v07.1

C3S v202212 will based on the processor of ESA CCI SM v07.1 (data released in May 2022). The only difference between the datasets is the ASCAT data, which is ingested into the processor; for C3S the NRT data stream is used and for ESA CCI SM the H-SAF H119 and H-SAF 120 have been used.

When comparing the relative data coverage of the ACTIVE C3S SM v202012, and the current ESA CCI SM v07.1 (which will be the basis of the next C3S SM version), some differences are visible (Figure 13). There are multiple expected reasons for this:

• Due to the use of this different data stream (ASCAT B is not used in C3S SM before 2015), differences between the ESA CCI SM and C3S SM data are to be expected in the COMBINED and, foremost, ACTIVE product, mainly in the period between 2012 and July 2015.
• Due to a new approach introduced in ESA CCI SM v06.1 (which is also used in v07.1) to mask out SM in the ACTIVE data for days when any PASSIVE product is flagged due to frozen soils ("cross-flagging").
• A new version of HSAF ASCAT SSM is used in ESA CCI SM v07.1, with less data in some areas (coastal regions amongst others) compared to the record used in C3S SM.

These aspects of data coverage will be further evaluated in the PQAR [D3].

Figure 13: Comparison of valid observations Hovmöller diagrams for the ACTIVE products of C3S v202012 (a) and ESA CCI SM v07.1 (b). Note the difference in the period from 2013 to 2015 due to different ASCAT data streams in C3S and ESA CCI SM. ASCAT-A was decommissioned in Nov. 2021, ASCAT C is implemented in ESA CCI SM but not in C3S SM. Changes in the observation time stamp of ASCAT A are visible in this plot (2019, 2020).

The COMBINED product however is expected to show an increase in number of valid observations in C3S v202212 due to the ingestion of FengYun and GPM data, and the use of day-time observations for all passive sensors. Figure 14 shows the improved observational coverage in ESA CCI SM v07.1, compared with C3S SM 202012, which does not include FengYun, GPM and day-time observation data. C3S v202212 is expected to show similar improved coverage.

Figure 14: Comparison of the valid observation Hovmöeller diagrams for the COMBINED products of C3S v2012012 (a) and ESA CCI SM v07.1 (b). Note the increase in data coverage due to additional sensors and inclusion of daytime observations.

4.7.1. Improved representation of anomalies

Most changes to the soil moisture data are expected to come from the radiometer data (i.e. affect mainly the PASSIVE, but also the COMBINED product). Figure 15 shows that the anomalies in PASSIVE SM between the versions are comparable. The main differences are found in northern Latitudes, where fewer extreme values are found in the latest version. This and other aspects relevant for climate analyses (e.g. long-term trends), will be evaluated in more detail in the PQAR [D3].

Figure 15: SM anomalies in the PASSIVE product of C3S SM v202012 / ESA CCI SM v05.3 (a) and ESA CCI SM v07.1, the baseline for C3S SM v202212 (b).

Figure 16: Daily image of the difference in soil moisture at 2021-06-30 for the COMBINED products of C3S v202012 and ESA CCI SM v07.1 (CCI minus C3S).

The difference between the soil moisture in the C3S and CCI datasets (COMBINED) are shown in Figure 16 for the date 2021-06-30. This shows that the updates introduced to ESA CCI SM made a difference of around +-0.1 [m³/m³] in some regions. In later periods, SM in the new version is expected to be below the SM from the previous version (compare Figure 15, especially in northern Latitudes). More details will be given in the PQAR [D3] for the latest C3S SM product.

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