Despite the recognized importance of satellite soil moisture data products (WMO, 2022), little regard has been given so far to a clear definition of what constitutes a “good” product and how accuracy ought to be assessed. Until this day, the almost universally-cited (Entekhabi et al., 2010) and officially-endorsed (WMO, 2022) satellite retrieval accuracy requirement of 0.04 m3/m3 for satellite soil moisture data products is not justifiable on scientific grounds and usually ill-defined as to how it ought to be assessed (Gruber et al., 2020). To address the lacking consensus on validation practices, the satellite soil moisture community has recently developed good-practice guidelines for the validation of satellite soil moisture products, including recommendations for ground sampling, sensor calibration, in situ--satellite intercomparison, metrics to be used, data preprocessing, and various other issues (Gruber et al., 2020, Montzka et al., 2020). However, statements on ground sampling and calibration only partly complement the recommendations laid out by the World Meteorological Organization (WMO) Guide to Instruments and Methods of Observation (WMO, 2021), which endorse the adherence to metrological practices (BIPM, 2008) but provide little actual guidance regarding calibration, operation, and maintenance other than statements to follow manufacturer's recommendations.  At the same time, awareness about metrology has been growing in the Earth observation (EO) community more generally (Merchant et al, 2017). To foster the application of metrological principles to satellite data validation, the Quality Assurance for Earth Observation (QA4EO; https://qa4eo.org) framework has been established, which is endorsed by the Group on Earth Observations (GEO). QA4EO aims at providing a consistent approach across disciplines to establish fully-traceable Quality Indicators (QIs) for fundamental data records, thematic data products, and ground reference measurements so users can assess the suitability for their applications, i.e., the “fitness for purpose”. A central element of these guidelines is the traceability of uncertainty and so-called Fiducial Reference Measurements (FRMs). ESA defines FRMs as “a suite of independent, fully characterized, and traceable ground measurements that follow the guidelines outlined by the GEO/CEOS QA4EO”. Furthermore, FRMs should “provide the maximum return-of-investment for a satellite mission by delivering, to users, the required confidence in data products, in the form of independent validation results and satellite measurement uncertainty estimation, over the entire end-to-end duration of a satellite mission” (Banks et al., 2020). To that end, FRMs ought to: have documented SI traceability using metrology standards and/or community-recognized best practices; have documented and maintained uncertainty budgets that are openly available; be independent from the satellite geophysical retrieval process; be accompanied by measurement protocols, procedures, and community-wide management practices (measurement, processing, archive, documents, etc.) that are defined, published, and adhered to by FRM instrument operators; be accessible to other researchers allowing the independent verification of processing systems; and be used to quantify the in-orbit uncertainty characteristics of satellite geophysical measurements via independent validation activities.

In recent years, ESA has been funding numerous activities related to the establishment and utilization of FRMs for various land, ocean, and atmosphere variables. The FRM4SM is one such project that leverages the International Soil Moisture Network (ISMN) and the Quality Assurance for Soil Moisture (QA4SM) framework. The ISMN is a centralized data hosting facility for in situ soil moisture measurements. It collects and harmonizes in situ data from data providers worldwide, applies automated quality control procedures, and makes data available in a unified format on a freely accessible online platform.

QA4SM is an online validation service that has been established in 2018 with support of the Austrian Space Application Programme of the Austrian Research Promotion Agency (FFG) [link: https://www.ffg.at/en] . It integrates ISMN data sets and various modelled and remotely sensed data sets, and allows user to apply validation protocols that follow the good-practice recommendations developed by the soil moisture remote sensing community (Gruber et al., 2020; Montzka et al., 2020). Building upon the ISMN and QA4SM, the specific objectives of the FRM4SM project are:

• OBJ-1: Assess ISMN Quality Flags in view of an updated operational flagging system, altogether with the definition of additional Quality Flags (as per the performed initial efforts), including the adaptation of the coding into Python programming language.
• OBJ-2: Create a specific Digital Object Identifier (DOI) for each platform download, ensuring traceability of the datasets and a-posteriori recovery. DOI should report information such as: time series duration, processing version, downloaded networks/stations and related depths, ISMN harmonization and flags used.
• OBJ-3: Characterize the in situ data error through the development of quality indicators for usability of individual stations and datasets. Specifically, the following items shall be addressed: quality of stations; quality of sensors used; outlier detection; correlation between multiple soil moisture timeseries at a single station; detection of erroneous observations/sensors/stations.
• OBJ-4: Define, following metrological institute guidelines, the protocols for FRM for soil moisture at microwave radiometers resolution. This includes guidelines for SI traceability, definition of the measurement procedures (sampling grid, penetration depth, soil type, soil coverage), processing methods, uncertainty budgets estimation, as well as instrument characterization in order to provide the best hardware baseline for the acquisition of soil moisture FRM.
• OBJ-5: Process and quality-control the in situ data following documented FRM procedures as available from OBJ-4 as much as possible with the current set up, in order to approve their use for satellite validation and deliver approved FRM4SM data sets on QA4SM platform.
• OBJ-6: Identify existing suitable soil moisture network sites to become possible candidates for FRM for soil moisture super-site implementing the full set of guidelines defined by OBJ-4 and provide recommendation for implementing FRM for soil moisture super-site.
• OBJ-7: Maintain and evolve the existing QA4SM service through the provision of the right level of documentation to the users, the inclusion of additional soil moisture satellite data products (e.g. ESA SMOS L2 soil moisture), the implementation of the FRM for soil moisture dataset and the soil moisture validation methodology as outcome of the FRM4SM project.
• OBJ-8: Use FRM4SM data to estimate the uncertainty budget for the ESA SMOS level 2 products and for improving the operational algorithm.
• OBJ-9: Consolidate the validation methodology and use the lessons learnt from the project to define and prepare possible future, comprehensive and globally-spread validation campaign.
• OBJ-10: Provide communications and outreach material promoting the FRM4SM activities; this includes the set-up of a dedicated section in the QA4SM platform for the project and the preparation and submission of peer-reviewed journal articles.
• OBJ-11: Identify and implement (at least) 3 R&D case studies within the general context of SMOS SM validation, as per the Mandatory or Optional indications provided.
• OBJ-12: Quantify the sub-footprint variability and vertical stratification influence when validating satellite SM data from microwave instruments against in situ data resolving different spatial scales.
• OBJ-13: Quantify the temporal aliasing influence when validating satellite SM data from microwave instruments against in situ data resolving different temporal scales.
• OBJ-14: Explore the various possibilities of matching-up data for validation, by comparing an “in situ-driven” approach, where the closest satellite estimate is matched to each in situ data, to a “satellite-driven” strategy, where all the in situ values lying within a satellite footprint would be averaged or upscaled properly.
• OBJ-15: Identify candidate geographical areas that could serve as “committed areas” and identify a new benchmark when reporting the future microwave satellite soil moisture data performance metrics. Discuss the potential implications to the standard current validation protocol.

Furthermore, to foster the development of community standards, FRM4SM is proactively cooperating with other FRM-related activities such as the Soil Moisture Metrology (SoMMeT) project.

The outcomes of the FRM4SM activities are summarized in the reports that can be found on the “Project deliverables” page. https://earth.esa.int/eogateway/activities/frm4sm-fiducial-reference-measurements-for-soil-moisture/project-deliverables

References:

Banks, A. C., R. Vendt, K. Alikas, A. Bialek, J. Kuusk, C. Lerebourg, K. Ruddick, G. Tilstone, V. Vabson, C. Donlon, et al. (2020), Fiducial reference measurements for satellite ocean colour (frm4soc), Remote Sensing, 12(8), p. 1322.

Entekhabi, D., R. H. Reichle, R. D. Koster, and W. T. Crow (2010), Performance metrics for soil moisture retrievals and application requirements, Journal of Hydrometeorology, 11(3), p. 832–840.

Gruber, A., G. De Lannoy, C. Albergel, A. Al-Yaari, L. Brocca, J.-C. Calvet, A. Colliander, M. Cosh, W. Crow,W. Dorigo, et al. (2020), Validation practices for satellite soil moisture retrievals: What are (the) errors?, Remote sensing of environment, 244, p. 111,806, doi:10.1016/j.rse.2020.111806.

Merchant, C. J., F. Paul, T. Popp, M. Ablain, S. Bontemps, P. Defourny, R. Hollmann, T. Lavergne, A. Laeng, G. d. Leeuw, J. Mittaz, C. Poulsen, A. Povey, M. Reuter, S. Sathyendranath, S. Sandven, V. Sofieva, and W. Wagner (2017), Uncertainty information in climate data records from earth observation, Earth System Science Data, 9(2), p. 511–527, doi:10.5194/essd-9-511-2017.

Montzka, C., M. Cosh, B. Bayat, A. Al Bitar, A. Berg, R. Bindlish, H. Bogena, J. Bolten, F. Cabot, T. Caldwell, S. Chan, A. Colliander, W. Crow, N. Das, G. De Lannoy, W. Dorigo, S. Evett, A. Gruber, S. Hahn, T. Jagdhuber, S. Jones, Y. Kerr, S. Kim, C. Koyama, M. Kurum, E. Lopez-Baeza, F. Mattia, K.  McColl, S. Mecklenburg, B. Mohanty, P. O’Neill, D. Or, T. Pellarin, G. Petropoulos, M. Piles, R. Reichle, N. Rodriguez-Fernandez, C. R¨udiger, T. Scanlon, R. Schwartz, D. Spengler, P. Srivastava, S. Suman, R. van der Schalie, W. Wagner, U. Wegm¨uller, J.-P. Wigneron, F. Camacho, and J. Nickeson (2020), Soil Moisture Product Validation Good Practices Protocol Version 1.0, In: C. Montzka, M. Cosh, J. Nickeson, F. Camacho (Eds.): Good Practices for Satellite Derived Land Product Validation (p. 123), Land Product Validation Subgroup (WGCV/CEOS), doi:10.5067/doc/ceoswgcv/lpv/sm.001.

WMO (2021), Guide to Instruments and Methods of Observation, WMO-No. 8 2021 Edition - Volume I: Measurement of Meteorological Variables, World Meteorological Organization (WMO), https://library.wmo.int/doc_num.php?explnum_id=11612, accessed June 2023.

WMO (2022), The 2022 GCOS Implementation Plan, GCOS-No. 244, World Meteorological Organization (WMO); United Nations Educational, Scientific and Cultural Organization; Intergovernmental Oceanographic Commission; United Nations Environment Programme; International Science Council, (ISC), https://library.wmo.int/doc_num.php?explnum_id=11317, accessed June 2023.