Last modified on Oct 08, 2024 10:38

Contributors:  W. Preimesberger (WP, tU Wien), W. Dorigo (WD, tU Wien), T. Frederikse (TF, Planet Labs/ Vandersat), A. Dostalova (AD, EODC), R. Kidd (RK, EODC)

Issued by: EODC/Richard Kidd

Date: 09/11/2023

Ref: C3S2_312a_Lot4.WP1-PDDP-SM-v2_202312_SM_PQAD-v5_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)

Breakthrough requirement: An ECV requirement level set by GCOS which “[…] if achieved, would result in a significant improvement for the targeted application […] at which specified uses within climate monitoring become possible. It may be appropriate to have different breakthrough values for different uses.” GCOS-245 (WMO, 2022)

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).

Fiducial reference measurement: FRMs are defined as fully characterized and traceable ground measurements that follow community agreed guidelines outlined by the Group on Earth Observation (GEO) / Committee in Earth Observation Satellites (CEOS) Quality Assurance for Earth Observation (QA4EO)¹ framework. They provide a fiducial reference for users of satellite-based Earth Observation (EO) products who need validated products with reliable uncertainty estimations. In practice, this is the recommended subset of ground measurements that should be used to evaluate satellite observations. It consists of long time series with low noise ratio and was found to represent soil moisture at the satellite grid scale well.

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 Group for High Resolution Sea Surface Temperature (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: “The change in bias over time” (GCOS-245; WMO, 2022). “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)

Structural breaks: Long-term analyses require consistent time series. Structural breaks are inhomogeneities (over time) in an observation series. This can be for example local changes in noise level, mean (“jumps”) or any other inconsistencies that systematically affect the underlying data distribution. Breaks can therefore lead to observations over time not being comparable to each other anymore, which can lead to errors in derived long-term metrics. There can be natural and artificial causes for such data inconsistencies. Usually, the goal is to detect and correct artificially caused breaks.

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

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

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 .

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, Earth Observation Data Centre (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). The product version assessed in this report is v202312, which is scheduled for production in January 2024.

Executive summary

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. It includes an outlook on planned validation activities, some methodological background and includes a selection of results from the validation of previous versions.

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.

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 2024 (record ID: v202312). 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 and GCOS-245, described in WMO (2016) and WMO (2022), respectively) 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 the 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, focusing 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 (v202312) is compared to previous versions. 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.

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 Soil Moisture (SM) algorithm version 8.

The C3S soil moisture products are generated from data acquired by a set of satellite-borne microwave radiometers and scatterometers.

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

• 2x Active Microwave Instrument (AMI) on European Remote Sensing Satellites (ERS) ERS-1 and -2 (both inactive)
• 3x Advanced SCATterometer (ASCAT) onboard the Meteorological Operational Satellites (MetOp-A, -B, and -C). Metop-A was decommissioned on 15 November 2021; MetOp-B and –C are operational.

The PASSIVE products rely on satellite microwave radiometers, and 14 sensors are currently integrated into the CDR:

• Scanning Multichannel Microwave Radiometer (SMMR)
• 3x Special Sensor Microwave Imager (SSM/I): F08, F11, and F13
• Tropical Rainfall Measuring Mission Microwave Imager (TMI)
• Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)
• Windsat Polarimetric Radiometer
• Soil Moisture and Ocean Salinity (SMOS) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS)
• 3x Feng-Yun (FY) Microwave Radiation Imager: FY-3B, FY-3C, and FY-3D
• Advanced Microwave Scanning Radiometer 2 (AMSR2)
• Global Precipitation Measurement Mission (GPM)
• Soil Moisture Active and Passive mission (SMAP)

The COMBINED products merge Level 3 soil moisture data from all above (active and passive) systems into a single harmonized record to maximize data quality and coverage.

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 the latest CF conventions². 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 using the product given in the Product User Guide and Specifications (PUGS) [D1].

Table 1: Product specifications of the C3S Soil Moisture products, summarized from TRGAD [D5]. For GCOS-based requirements, the “Breakthrough”³ level is used here (which is generally between the “Threshold” and “Target”).

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. 202312). 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 users⁶. 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. The ISMN is hosted and operated by The German Federal Institute of Hydrology (BfG). The measurements contributing to the ISMN are heterogeneous in that the technique, depth represented, and other factors, may vary within the network. As of January 2020, the ISMN database integrates data from 65 networks (2678 stations) as listed in Figure 1.

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 1: 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. Fiducial reference measurements

As part of ESA's Fiducial Reference Measurements for Soil Moisture (FRM4SM) project⁷, a quality index for the "representativeness" of each in-situ time series for the satellite data at radiometer scale (~25 km) was produced from the time series length and the Triple Collocation Analysis (TCA) based Signal-to-Noise-Ratio (SNR). Sufficiently long time series with a high SNR (>3 dB) at 95% confidence are classified as "very representative" or "representative". Applying this quality indicator to all ISMN time series results in a subset of 1083 high-quality sensor time series (as of January 2023). Their location is shown in Figure 2. The so-found subset forms the reference for the evaluation of C3S SM.

Figure 2: Subset of the ISMN database of "(very) representative" Fiducial Reference Measurements (FRMs). Based on the FRM4SM quality indicator (January 2023). Note that points in the graphic often overlap as most networks consist of stations that operate multiple soil moisture sensors.

ISMN stations and Fiducial Reference Measurements (FRMs) are distributed unevenly globally and often appear in clusters. Most measurements are taken within the continental United States and Europe. Validation results are therefore only representative of the environmental regimes covered by these stations (predominantly temperate climates) and mostly exclude (sub)equatorial and (sub)polar regions, highly organic soils as well as deserts.

2.3. ERA5-Land

ERA5-Land (Muñoz-Sabater et al., 2021) is the successor to the 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 1950 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 v08.1 of the ESA CCI SM product (released in May 2023).

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) group LPV: The Land Product Validation group is a sub-group of CEOS (Committee for Earth Observation Satellites): https://lpvs.gsfc.nasa.gov/ (resource validated 12^th July 2023) 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, 2022) 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 versions to show the evolution of the algorithm over time.
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)⁹ platform, which provides robust, traceable validation of different data products against reference data including ISMN and 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])

* Work on the metrics used for stability assessment is ongoing with the aim of demonstrating compliance with these performance targets.
(1) The minimum requirement is 0.1 m³/m³ (C3S), the target is 0.04 m³/m³ (GCOS)
(2) The actually achieved accuracy / stability varies for different land-cover classes, mainly due to vegetation.
(3) The minimum requirement is 0.05 m³/m³ (C3S), the target is 0.01 m³/m³ (GCOS)

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 in-situ point data is assessed by the FRM4SM project. By using the FRM qualified subset of ISMN, a first step towards using the most representative sensors is taken (see Section 2.2). Excluding non-representative ground observations stratifies validation results to better represent actual errors in the satellite observations.

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 three temporal aggregations. As the C3S soil moisture product includes day- as well as night-time observations from various satellites with varying overpass times, it is considered to represent daily average soil moisture conditions. It is expected that the assessment using in-situ data at an (average) daily resolution would provide the best results. Monthly and 10-daily 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. FRMs were classified in depths between 0 and 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 often applied to bring the datasets into a common climatology. A mean standard deviation scaling is applied¹⁰. For tasks that aim to estimate the bias between satellite and reference data, no scaling is applied. (Additive) biases between satellite and reference data are inherently removed when using the unbiased root mean square difference (ubRMSD) as an accuracy estimator.

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 Pearson’s correlation and 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) in large parts of the world (compare Figure 2).

Here, the datasets used for the accuracy assessment are the ISMN, and 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 FRM 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” (“G”) within the time period covered by the C3S product.

The FRM dataset to be used in the validation exercise was summarised in Figure 2. 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 stations and networks in the PQAR [D3] may vary slightly.

C3S data flagged as “good” or “barren grounds” will be used in the evaluation against in-situ data. 

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 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).

3.4.3. Global Scale

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). Global gap-free reanalysis data will be used to quantify the performance of the retrieval algorithms on a larger scale. Reanalysis 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-Land will be used for this purpose.

In addition, a comparison including all three - the in-situ, ERA5-Land, and satellite products - will be used to estimate relative performance differences between the satellite and reanalysis products, with respect to the overall agreement compared to the in-situ observations.

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 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.

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 5 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 5. 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 Grid Point Index (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 sensors (with a focus on newly added sensors) 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.

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 v202212 (CDR v4.0) and ESA CCI SM v08.1 (the base algorithm for C3S SM v202312). The actual validation results of v202312 (CDR v5.0) 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 and ubRMSD, stations in temperate climates and grasslands are expected to perform best. Similar results for the latest version will be further investigated and discussed in the full PQAR [D3] for this product.

Figure 5: Correlation (Pearson’s) between absolute values of C3S SM COMBINED and ISMN FRMs for different land cover classes in 0-10 cm depth.

Figure 6: ubRMSD between absolute values of C3S SM COMBINED and ISMN FRMs for different land cover classes in 0-10 cm depth.

It can be seen from Figure 6 that the accuracy target of GCOS (0.04 m³/m³) is reached for some but not all stations. Especially stations in forested areas are often above the GCOS threshold. The median ubRMSD is below 0.05 m³/m³ in all other landcover regimes (further decreasing with newer dataset versions). However, for almost all stations the ubRMSD is below the C3S threshold of 0.1 m³/m³.

4.2. Accuracy – Comparison against Land Surface Models

The COMBINED product has been compared to ERA5-Land in the past. The ubRMSD of C3S SM v202212 is shown in Figure 7. Typical spatial patterns in ubRMSD, with lower values seen in mid- to low-latitudes and values above 0.07 m³/m³ in areas where there is snow or ice cover for many days of the year are apparent. 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 absolute soil moisture values of the C3S SM v202212 COMBINED product and ERA5-Land top-level soil water content (left) and global comparison with the previous C3S SM version (right). White boxes contain the computed validation scores for all ISMN sensors (N), coloured boxes indicate the lower and upper limits of the 95% confidence interval at the same locations.

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 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 9, where the distribution of trends (Theil-Sen slopes) in ubRMSD is shown - again aggregated for stations in croplands and forests. 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 of ubRMSD between C3S v202212 and ISMN soil moisture split by aggregated CCI Land Cover classes: “cropland” (left) and “tree cover” (right). The C3S and ISMN data were divided into yearly subset periods for this assessment.

Figure 9: Distribution of trends in ubRMSD in C3S SM v202212 COMBINED split by aggregated CCI Land Cover classes: “cropland” (left) and “tree cover” (right). Trends are an estimate for the long-term stability of a data set.

4.4. Spatial and temporal completeness and consistency

The number of valid (un-flagged) observations available is shown globally in Figure 10 for the period from April 2015 to December 2022 and per latitude for the entire data product period (Figure 11). The coverage in v202312 is expected to remain on a similar level as in v202212.

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 11). 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 10. Fractional coverage of the C3S v202312 COMBINED soil moisture product for the period from 2015-04-01 to 2022-12-31. Expressed as the total number of daily observations per time period divided by the number of days spanning that time.

Figure 11. Fraction of days per month with observations of soil moisture for each latitude and time period for the v202312 COMBINED product.

Similar assessments on data coverage and completeness will be conducted for all three C3S products in the PQAR [D3]. In addition, a comparison with the previous C3S SM version will be made, to show expected (and unexpected) increase (or decrease) in data coverage over time (e.g. Figure 12).

Figure 12. Change in (relative) number of valid observations between v202012 and v202212 for the PASSIVE C3S SM product. Green indicates an increase compared to the previous version, red a decrease in observations.

4.5. Visual Assessment

The time series for the individual locations from Figure 4 for the ACTIVE, PASSIVE and COMBINED products are given in Figure 13. 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.

The COMBINED product generally shows the best data coverage. Due scaling all sensors to GLDAS Noah model outputs, it is expected that COMBINED is the most stable of the three products.

An updated version of this plot for the new data version is presented in the PQAR [D3].

Figure 13: Time series comparison for the COMBINED, ACTIVE and PASSIVE products of C3S v202212, 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 utilizes 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 14. 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 are considered in the PQAR [D3].

Figure 14. Daily image of the relative soil moisture uncertainty for the COMBINED product of C3S v202212 CDR. Image date: 2022-06-20.

A new uncertainty estimation scheme will be introduced in v202312 (Stradiotti et al, in prep.). Triple collocation was done on the full sensor time series before. In the new version, this is applied on a day-of-year basis, meaning that intra-annual errors are better characterized. A higher dynamic in the uncertainties over time is achieved, which better represent uncertainties in the observations due to seasonal effects (Figure 15). This (new) data aspect will be further evaluated in the PQAR [D3].

Figure 15. Difference in SM uncertainties before and after introducing seasonal uncertainty estimation. This plot is made from ESA CCI SM v08.1 data (the base algorithm for C3S SM v202312).

4.7. Long-term trends

Long-term trends in soil moisture records are key to understanding the impact of climate change on global water availability in the future. However, there is rarely an agreement between long-term trends found in different soil moisture data sets (reanalysis, models and satellite measurements). Even between the three C3S SM products, there are large differences as shown in Figure 16.

It is known that the active product of C3S SM is affected by long-term changes in land cover. These are not sufficiently corrected for in the current European Meteorological Satellite (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) ASCAT Surface Soil Moisture (SSM) data products, which often translates into an unrealistic increase in soil moisture over time. H SAF is currently working on a correction algorithm for this. Once this is included in the operational H SAF ASCAT products, it will also be included in the C3S SM ACTIVE (and COMBINED) product.

Long-term (1991-2020) trends are computed and compared as part of the PQAR [D3]. Theil-Sen slopes are calculated based on monthly mean values of the three C3S SM records as well as ERA5-Land. A Mann-Kendall significance test is applied to detect statistically significant trends.

Figure 16: Long-term (1991-2020) trends in C3S v202212 ACTIVE (top) and PASSIVE (bottom). Only statistically significant trends are shown. Note the different scale bars due to different value ranges / units of the products.

4.8. Break correction

A (temporal) break correction algorithm (Preimesberger et al., 2021) will be included for the first time in C3S SM v202312 (for more details see the ATBD [D2]). This algorithm compares differences in statistical moments between sub-periods of each time series with respect to a reference data set (ERA5). Sub-periods are defined by the merged sensors (where no sensor / observation frequency band changes occur). As part of the validation activities, the break detection methods will be applied to the C3S SM COMBINED product. It is expected that fewer breaks are found after the algorithm is applied (the new version) compared to the previous version (compare Figure 17).

Figure 17: Detected breaks (mean and variance) between the sensor periods before/after the transition date 2002-06-19. These plots are based on the ESA CCI SM v08.1 product (the base algorithm for C3S v202312) before (top) and after (bottom) the correction algorithm is applied. Areas with dense vegetation are marked with dark green colour in this plot.

4.9. Other comparisons

4.9.1.1. Assessment of applicability to monitor extreme events

The operational production of C3S SM allows monitoring extreme events (e.g. droughts) with a short delay. In the PQAR [D3], a short overview on recent applications (e.g. anomalies or flooding events as presented in the European State of the Climate report¹²) will be given.

4.9.1.2. Comparison of global statistics

An overview of global mean, median, standard deviation, minimum and maximum soil moisture is given in the PQAR [D3]. This is presented as table and based on the monthly mean values of the ACTIVE, PASSIVE and COMBINED products. The same statistics are computed for the previous dataset version for comparison. The data distributions of both versions are compared. Significant changes in global statistics can be either unexpected (due processing errors, e.g. introducing a bias) or due to updated retrieval algorithms (e.g. improved masking of frozen soils to exclude unrealistically dry soil moisture value).

4.9.1.3. Comparison of daily soil moisture images

Differences in the soil moisture values of a single day between the current and previous version will be shown in the PQAR [D3]. Significant spatial changes in soil moisture (as discussed in Section 4.9.1.2) are visible in this comparison. See for example Figure 18, which shows an expected decrease in soil moisture in high latitudes due to the updated Land Parameter Retrieval Model (LPRM) introduced in this version.

Figure 18. Absolute difference in soil moisture between C3S v202212 and v202012 for the daily COMBINED product on 2019-07-01. Brown areas indicate a decrease from the previous version, blue an increase.

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