2 July 2017
In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements
Ground-based soil moisture measurements of land surface variables are indispensable in the process of developing, validating, and advancing spatially contiguous data sets derived from satellites or models
While all networks are valuable assets for assessing the skill of soil moisture products under various conditions and scales, their usage is hampered by the diversity of sensors, data formats, quality control, and accessibility mechanisms. The need to bring together and to harmonise available soil moisture data was recognised by the international soil moisture community and expedited by the Global Energy and Water cycle Exchanges (GEWEX) project of the World Climate Research Programme (WCRP) with support of the Committee on Earth Observation Satellites (CEOS), the Global Climate Observing System (GCOS), and the Group on Earth Observations (GEO). In the advent of the SMOS mission, ESA decided to provide the financial impetus to establish a global reference database of in situ soil moisture measurements for the purpose of satellite product development and validation. As a result, the International Soil Moisture Network (ISMN) went online in 2010
The primary objective of the ISMN is to collect in situ soil moisture data sets, shared by various data organisations on a voluntary basis, and make them available in a harmonised format through a centralised free and open web portal (
Data from the ISMN has supported hundreds of scientific papers on soil moisture, satellite product, and land surface model validation in particular (e.g.
Despite the valuable contribution of the ISMN to satellite and climate communities, multiple challenges have yet to be mastered, including the heterogeneous availability in space and time
The scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates, new quality control procedures, and new functionality of the data portal. We also review scientific literature making use of ISMN data to assess the achievements facilitated by the ISMN and to identify current limitations in data availability and functionality and challenges in data provision and use. Based on this review, prerequisites and priorities needed to ensure another decade of this unique community-based data repository are defined.
Although the ISMN may be considered a mere data repository, there is much more to it. Its core functionality includes collecting data from participating data providing networks, harmonising them in terms of units, sampling rates, naming, and metadata, performing automated quality control, storing the data and metadata in a searchable database, and making them available through a web interface. And, from a system perspective, it entails even more, e.g. communication with (potential) data providers and users (Fig.
Locations of ISMN networks and sites plotted with the ISMN package described in the code and data availability section (status in July 2021).
As of July 2021, the ISMN contains 71 networks providing access to a total of 2842 stations, approximately 10 000 soil moisture data sets, and an additional 10 000 data sets of other meteorological variables (collocated with the soil moisture measurements). Although the ISMN is a global network, most networks and stations are located in North America (15), Europe (28), Australia (3), and Asia (19; Fig.
Distribution of ISMN sites per Köppen–Geiger climate classification. Only categories with
Most of the networks originate from scientific initiatives in various disciplines (e.g. remote sensing, soil sciences, agriculture, and meteorology), and only a few are run by operational entities like national weather or environmental services. Consequently, a lack of sustainable project funding has forced several scientific networks to close after some time. As a result, 18 out of the 71 networks contained in the ISMN have become inactive and will no longer provide data set updates (Fig.
Most networks provide data set updates at yearly or irregular intervals. Data sets from six networks (ARM, COSMOS, FMI, SCAN, SNOTEL, U.S. Climate Reference Network (USCRN), and WegenerNet), comprising approximately 900 stations, are updated in near-real time (NRT; status in July 2021), which is currently defined as once per day. While the earliest networks were sampled manually at weekly, fortnightly, or even monthly intervals, most current networks take their measurements using electronic devices at daily, hourly, or even more frequent sampling rates. For more details on the networks, see Sect.
Overview of all available networks, the individual time span of the data availability within the ISMN, their operational status, and their updating frequency (status in June 2021).
Overview of all available temporally dynamic variables stored in the ISMN database.
Overview of all available temporally static variables stored in the ISMN database.
The variables contained in the ISMN (Tables
Metadata information can be divided into two categories, i.e. mandatory metadata, which allow for an unambiguous identification of each network, station, and measurement in the ISMN database (Fig.
If available, data providers can optionally share their own, more detailed, characterisations of land cover, soil, and quality flags with the ISMN. These are stored in addition to the same variables from external sources. All static variables per measurement site and depth are listed in Table
Data collection is done either manually (mostly by email) or is automated, depending on the degree of automation at the data provider side. Although standards for in situ soil moisture data collection are available
The first harmonisation step for all data and metadata involves the conversion of units to internationally agreed scientific units (e.g. metres and degrees Celsius). Then, following the recommendations of the World Meteorological Organization for weather observation and forecasting
All soil moisture measurements provided to the ISMN are converted to volumetric soil moisture in cubic metres by cubic metre (hereafter m
The harmonisation of measurement depths is particularly challenging, as different networks adopt different measurement depths, similar sensors are positioned differently (horizontally vs. vertically), or their measurements represent different observation volumes, which may even differ according to the soil wetness (as for cosmic-ray probes). Thus, harmonising soil moisture measurements to one or several reference depths would require either assumptions on the measurements and soil properties or supplemental soil modelling. Additionally, since there are lots of potential uses for the data, there is no common agreement on the optimum sampling depths. For example, satellite calibration–validation generally requires observations of the 0–5 cm layer, while land surface model evaluation requires reference measurements that are representative for the layers defined in the model
After data harmonisation, the data sets are submitted to extensive quality control procedures (Sect.
The ISMN can be accessed at
The data interface displays the location and information of networks and single stations and allows plotting of the available data to gain an impression of data availability and quality. Data can be selected for time period, area, single networks, or entire continents. Alternatively, the data download can be selected via an advanced SQL query, which allows users to make more specific selections (e.g. for a sensor brand or a certain depth range).
The selected data are directly extracted from the database, and downloads are organised per network. For each network, the download contains (i) the measurements and their quality flags, (ii) information about the file data organisation, (iii) a description of the ISMN quality flags, (iv) a metadata file compliant with ISO 19115 and INSPIRE (Infrastructure for Spatial Information in the European Community) metadata standards, and (v) information about static site characteristics (e.g. land cover, climate class, and soil characteristics).
The extracted data set files are formatted according to either the CEOP
The ISMN is entirely built on the voluntary, free-of-charge contributions from scientific and operational providers. This prevents the ISMN from being too prescriptive towards the data providers in terms of delivery intervals, automation, and formatting. Hence, a careful balance is needed between inclusiveness, on the one hand, and data quality standards, on the other. The ISMN facilitates between users and data providers by reporting data quality issues and user feedback to the providers every 6 months. This is done by means of a report on data usage statistics for each individual network, e.g. on the number of downloads, the usage of their data in scientific publications, and flagging statistics. Together with obtaining visibility and citations, obtaining feedback on data usage and data quality is one of the primary motivations for data providers to join the ISMN.
More than 3000 active users have registered since 2009 (status in July 2021). Data download is free of charge but user registration (compliant with the latest European Union General Data Protection Regulation, EU GDPR, privacy standards) is required to prevent illegal redistribution of the data or theft of ground equipment and to track (undisclosed) statistics on data usage that are fed back to the data providers, e.g. by regular reports.
News feeds on the ISMN web page and a biannual newsletter inform the users about new networks, new data sets, data quality issues, important publications, workshops, and more. In addition, a dedicated forum and classical email exchange allow users to raise and receive response to issues. Moreover, open-source software packages are available for reading and plotting the data (see the data availability section).
The wide variety of sensor types and installations, measurement protocols, calibration methods, preprocessing, and quality control procedures adopted by the data providers result in data sets with large differences in quality and filtering. In an attempt to harmonise the reliability of the data from different networks and sensors and to allow for the marking of spurious observations in near-real time, the ISMN has adopted automated quality procedures which are applied to all observations feeding into the ISMN
Recently, the following refinements of the original flagging procedures, as described and assessed in The outlier detection method (flag D06) now allows spikes to last 2 consecutive time steps instead of the initial 1 h. This occurs when all conditions of Eqs. (4)–(6) in Flag D07 (negative breaks or drops) was extended with an extra possibility, which detects drops from values greater than 0.05 to exactly 0 m In case more than one soil temperature, air temperature, or precipitation sensor is available at a site, a flag is raised for the soil moisture measurement if the conditions of flags D01, D02, and D04, respectively, are met at least for one of these sensors. This has led to a small overall increase in flags D01, D02, and D04 (
All quality control procedures adopted by the ISMN have been made available under the open-source license agreement (see the data availability section;
We assessed the refined flagging procedures by applying them to 10 networks with hourly data that include stations with multiple soil temperature, air temperature, or precipitation sensors. Despite the very low overall impact of the refined flags, for some networks they are substantial (Fig.
Additionally flagged observations by the network shown as fraction of total number of observations. Note that, in principle, the cumulative sum of fractions can be
Measurements that are detected as erroneous by the quality control procedures are not deleted from the database but tagged as such (Table
Occurrence of flags for all variables and measurements contained in the ISMN (status in May 2021). The soil moisture flags are not exclusive, i.e. an observation can be tagged with multiple flags.
The most commonly raised flag is when one of the ancillary temperature observations, i.e. in situ soil temperature (D01), in situ air temperature (D02), or GLDAS soil temperature (D03), is
Fractions of geophysical dynamic range and consistency quality flags per network. Note that, in principle, the cumulative sum of fractions can be
The second most common flag is C03 (soil moisture above the site-specific saturation point), which is computed from the HWSD soil properties. The site-specific saturation point is usually lower than the globally adopted, less conservative threshold of 0.6 m
Constant values as a result of saturation plateaus (D10) or after a negative break (D09) are the most common spectrum-based flags (Fig.
Fractions of spectrum-based quality flags per network. Notice that, in principle, the cumulative sum of fractions can be
Global validation results of surface soil moisture of ERA and ESA CCI soil moisture against ISMN (masked and unmasked for quality flags) for the period 2001–2019. The results for ESA CCI were produced with the QA4SM validation service (
For a selected ISMN site (SCAN and Mayday station),
Representativeness errors of ISMN networks calculated with triple collocation analysis using top-layer ERA5 volumetric soil water and the ESA CCI SM v05.2 passive product. Values in parentheses show the average number of triplets per time series and the total number of sensors, respectively, for each network. Following
The automated quality control algorithms offer insight into the quality of the respective measurement time series but not necessarily of the usability of the data sets for specific applications.
The results for different networks are quite diverse (Fig.
There is a clear trend of decreasing mean errors with increasing sensor depths (Fig.
Representativeness errors for different sensor depths
Concerning the sensors used, there is a large spread in computed representativeness errors for time domain reflectometry (TDR) and capacitance sensors. While the costly hygrometric sensors have the lowest mean error, the mean error of resistance probes is the highest (Fig.
As mentioned earlier, over 3000 users have registered to the ISMN, while 20–30 new users register each month. Most users are based in the USA, China, India, and Europe (Fig.
Number of users per country (status in July 2021).
The large uptake of the ISMN for soil moisture studies is particularly due to the simplicity of accessing and using multiple data sets from a wide variety of networks. Initially, the ISMN was established to facilitate the calibration and validation of SMOS-based soil moisture products
Overview of purposes for which ISMN data are used in scientific studies (status in July 2021;
Soil moisture measurements from the ISMN have been widely used as reference data sets for the development and evaluation of satellite soil moisture products, which are mostly global coarse-scale surface soil moisture products (Table
Recently, the ISMN was recognised as validation source for testing algorithms to derive soil moisture from Global Navigation Satellite Systems (GNSS; e.g.
Soil moisture measurements from the ISMN have been used as a training set for various data-driven approaches. In situ observations were ingested into machine learning frameworks together with several ancillary predictor variables, either to simulate soil moisture at a very high spatial resolution
Usage-oriented evaluation studies have focussed on the intercomparison of multiple coarse-scale satellite products, using the ISMN data as a reference, either to select the best performing sensor for a specific application or geographic region (e.g.
Since direct satellite observations of soil moisture are only possible for the surface layer, most studies concentrate on the evaluation of surface soil moisture. Yet, various studies also focus on products derived from surface soil moisture that represent moisture in deeper (root zone) layers, either through exponential filtering (e.g.
Because of its operational nature and advanced quality control procedures, the ISMN has been identified as the primary reference data source for future operational validation systems for global satellite-based soil moisture products In 2005, the Satellite Application Facility on support to operational Hydrology and water management (H SAF) started to operationally produce and validate precipitation, soil moisture, and snow products from satellites operated by EUMETSAT The Climate Change Initiative of the European Space Agency (ESA CCI) uses data from the ISMN to assess, each year, the quality of new soil moisture climate data record releases and their improvements with respect to forerunner versions C3S produces authoritative, quality-assured climate data records of soil moisture and other essential climate variables (ECVs). The satellite soil moisture products produced within C3S are routinely updated every 10 d with the latest available satellite observations. C3S uses the ISMN soil moisture data in combination with the metadata provided to categorise product performance per land cover and climate type Copernicus Global Land Service (CGLS) produces, within 1–2 d after satellite overpass, soil moisture data sets from Sentinel-1 and from a combination of Sentinel-1 and ASCAT
Recently, the Quality Assurance for Soil Moisture (QA4SM) service (
In situ measurements are the most important reference source when assessing the performance of land surface models, reanalyses products, and hydrological models. Although also satellite observations are a valuable validation source (e.g.
In particular for global assessments, the availability of harmonised data over multiple networks makes the ISMN a preferred reference source over data from individual networks
Also, a suite of new products have been assessed against the ISMN, including multi-model ensembles
Besides the evaluation of hydrological or land surface model improvements, the ISMN has also frequently served model development in a more fundamental way. For example,
Soil moisture from the ISMN has often been used to validate the land surface representations of meteorological forecasting models. However, as meteorological forecasts often rely on the latest generation of land surface models, in practice there is often no strict distinction between meteorological and land surface model development as described in the previous section. Notable examples are the various generations of the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) models used both in the Integrated Forecasting Systems and reanalysis products of ECMWF, the development of which greatly profited from soil moisture and temperature data from the ISMN
Several studies have used the ISMN data to assess the forecast skill or new implementations of numerical weather prediction models. For example,
From a more methodological, land–atmosphere perspective,
In a drought monitoring context, ISMN data have frequently been used in a convergence of evidence approach in combination with other drought-related variables or indicators. For example,
ISMN data have also been used to classify new and more traditional drought indices, such as the Standardised Precipitation (Evaporation) Index and the Palmer drought severity index
ISMN data have been used for various other purposes, going beyond what the ISMN was originally developed for. In addition to supporting satellite and land surface model soil moisture product development, the ISMN has played a fundamental role in the validation of a wide range of hybrid observation-based products, including a Soil Moisture Saturation Index
The ISMN has frequently been used to assess the impact of assimilating satellite observations into hydrological models
The wealth of data covering a wide range of surface and climate conditions has been frequently exploited to study soil water dynamics at various spatial and temporal scales and their (climatic) drivers
The ISMN has been used to develop and test new statistical validation approaches
Although the ISMN has facilitated hundreds of scientific studies, it is impossible to quantify precisely to what degree the ISMN has contributed to product improvements and new insights that could not have obtained otherwise. Admittedly, data from several networks are also distributed through other portals, typically providing access to a single network (e.g. SNOTEL data through
The tracked download statistics reveal that approximately one-third (34 %) of the studies making use of the ISMN use multiple networks, the choice of which depends on the scope of the study, the geographical region, period of interest, and the year the study was performed (with more networks becoming available over time). Examples of such studies using data from more than 20 different networks are
Although it is impossible to pin down the exact contribution of the ISMN to process understanding and product improvements, it is very likely that, mainly, studies have discovered flaws in satellite products that would not have been detected without the use of the ISMN. The main reason is that the large number of networks give insight into the product skill in a large variety of climate zones, land cover types, and so on, which no single network could have provided. This allows for the development of, globally, more robust models and products. Second, the data harmonisation and unified quality control minimise the chance that differences in skill potentially observed between locations with different environmental characteristics is an artefact of a different treatment of the data.
Several studies were identified that not only used the multitude of ISMN data to expose flaws in existing products but also to directly improve data products and models, e.g. by calibrating hydrological model parameters for local site conditions
The in situ measurements of soil moisture data in the ISMN have been collected by a large variety of sampling techniques. The early networks contained in the ISMN (e.g. RUSWET, CHINA, and MONGOLIA) are based on gravimetric sampling, which is still considered the most accurate approach
Nowadays, the most commonly used techniques for systematic in situ sampling are based on the contrasting dielectric properties of soil and water and comprise time domain reflectometry (TDR) and frequency domain reflectometry
Slightly larger soil volumes (diameter of 15 to 60 cm) are observed with the neutron scattering method, where the density of thermal neutrons produced by scattering of fast neutrons on soil hydrogen can be related can be related to soil moisture through a calibration curve
Also, the measurements of the PBO_H2O network, which uses global positioning system (GPS) receivers, can be used to bridge the scales between point and satellite sensors. GPS sensors, initially used for geophysical and geodetic applications, have been found to be well suited for measuring soil moisture
As shown, all measurement techniques have their strengths and limitations
Soil moisture is highly variable in space as a result of complex interactions between soil characteristics, topography, vegetation, and meteorological conditions. Depending on the spatial scale considered, the dominant controlling factor(s) can be different
Also, temporal representativeness issues may exist, but due to the hourly sampling of most data sets, the ISMN usually have a denser sampling than most remote sensing or model data sets. Thus, for most applications, the ISMN can be downsampled to the process or observation timescale of interest. However, some of the older, manually sampled data sets have sampling intervals of about 2 weeks and, thus, may miss many higher-frequency wet or dry spells. On a similar note, data sets with a daily sampling or averaging (e.g. cosmic-ray or GPS reflectometry observations) may miss rainfall peaks and are unsuitable for studying sub-daily variability.
The development and use of low-cost sensing technologies, especially in the environmental sciences, have seen a pronounced increase during the last decade. Such a rise is driven by several factors, e.g. the reduced cost of micro-controllers, electric components, and sensors
The considerably lower cost of these sensors compared to traditional probes makes them suitable for high-density and/or large-scale monitoring of soil moisture. The possibility to map soil moisture (and other environmental variables) with an unprecedented spatial coverage can generate new insights into its dynamics and create new opportunities. For instance, high-density networks of low-cost sensors can be used to reduce spatial representativeness errors by providing numerous observations within a satellite footprint
The ISMN has integrated low-cost sensor measurements from the GROW observatory (see Sect.
Citizen science is defined as the involvement of non-experts in collecting data
An outstanding example of a citizen science project focusing on soil moisture is the GROW Observatory (
The integration of citizen observations in the ISMN is challenging for multiple reasons. Crucial is the long-term engagement of citizens, which needs to be thoroughly addressed from the early stages of designing a citizen observatory. It is necessary to create long-lasting communities that go beyond the duration of the contributory projects
Part of the contributions to the ISMN (7 networks with around 900 stations) are inserted into the ISMN in NRT. This process is fully automated, including data downloaded from data providers, harmonisation, flagging, insertion into the database, and updating metadata tables. However, because of different data recording and handling mechanisms at the provider side and differences in data sharing mechanisms and policies, the ISMN is also partially manually operated and will probably also have to continue in this way in the future. The differences between the fully automated and the manual approach are confined to the data download and data ingestion into the processing chain. Data harmonisation and quality control are automated for all data sets (see Fig.
A major challenge in the automation process is the enormous heterogeneity of input data formats. Moreover, these change over time for individual networks, stations, and even sensors, as sensors may fail or the method for data logging is changed. Thus, error detection and handling is of utmost importance, and frequent adaptation of the system is required to cover changes in input data.
A potential way to promote the automated insertion of new data is to allow only data that comply with a strict, yet to be defined, data standard. At the same time, this may be bear the risk of raising the barrier to contribute to the ISMN too high for several scientists.
Since data sharing with the ISMN is entirely built upon a voluntary basis and data usage is open and free, new networks may be reluctant to join. The ISMN is in contact with several network providers who are happy to collaborate but are restrained by data sharing policies, which does not allow data sharing at all or only after a certain time. Furthermore, for governmentally operated networks it is often not allowed to share data as open-access or it is unclear who can make these decisions.
Solving such issues can be supported by collaborative data-hosting facilities, like the Global Terrestrial Network – Hydrology (GTN-H;
Not all users correctly cite the ISMN and the involved networks, as stated in the terms and conditions for ISMN data use
The ISMN data collection is constantly evolving. New data records are added, and existing ones are extended or, if necessary, reprocessed and corrected. Not only the data but also the underlying code changes. These updates and any retroactive changes made to the data archive are tracked within the ISMN but not yet readily passed on to the users. The updates can lead to differences in analyses on the user side, e.g. when considering obvious changes such as temporal extensions or new stations, and lead to non-reproducible results. Therefore, any update must be traceable and clearly communicated, requiring a system to track and store these changes. Version tracking and digital object identifiers (DOIs) are ways of identifying each database access and, therefore, allow tracing back to past states of the ISMN. Such a mechanism is required to make the ISMN compliant with the FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles
In this study, we reviewed the first decade of operations of the ISMN. Besides satisfactorily fulfilling its initial target, i.e. supporting satellite soil moisture product validation and calibration, many additional more or less foreseen uses have emerged. In addition, an increasing number of services and product development chains have routinely included the use of ISMN data in their operational structure. The ISMN started as research activity funded by ESA, and ever since, ESA have provided continuous financial support for ongoing research, development, and operations. In spring 2021, a milestone was achieved when the German Ministry of Transport and Digital Infrastructure announced that it will commit to permanently fund the ISMN operations and development from late 2021. The execution will be with the German Federal Institute of Hydrology (BfG) and the International Centre for Water Resources and Global Change (ICWRGC) based in Koblenz, Germany. At the same time, all network data sets have always been freely contributed by dedicated researchers. To guarantee the availability of these resources for climate and environmental monitoring also for the next decade, we plead with governments and international bodies for systematic funding of its participating data-providing networks too.
To maximise geographic coverage and data usage, the policy of the ISMN has always been to integrate data sets without strict requirements on sensors, sampling protocol, or data quality. The resulting strongly heterogeneous data set characteristics call for far-reaching quantification and traceability of errors, from sensor calibration and data download to the point measurement and the spatiotemporal support of the application. Thus, besides expanding its coverage to data-poor regions and landscapes, the ISMN shall spend the next few years focusing on developing methodologies to fully characterise the uncertainties and usability associated with the individual data sets. An important step in this direction will be made within the ESA-funded Fiducial Reference Measurements for Soil Moisture initiative. With the foreseen developments, the ISMN will reach a new level of maturity in the coming decade.
A summary of each network is given in Table
Main properties of networks and data contained in the ISMN.
Continued.
AACES stands for the Australian Airborne Cal/val Experiments for SMOS. This campaign network covers a
The AMMA-CATCH observatory gathered data from densely instrumented mesoscale sites in West Africa (Benin, Niger, and Mali). The network is devoted to long-term regional monitoring of global change impacts on the critical zone. Height stations in Benin and four stations in Niger of the network are included in the ISMN from 2006 to the present, including surface soil moisture and root zone soil moisture until 1 m depth. For more information, see
The Atmospheric Radiation Measurement (ARM) programme has three soil sensor networks across north-central Oklahoma and southern Kansas in the USA, including the Soil Water And Temperature System (SWATS), through 2016, and, presently, the Soil Temperature and Moisture Profile (STAMP), and Surface Energy Balance System (SEBS). The SWATS and STAMP have profiles at 5–8 depths up to 175 cm, while the SEBS measure at 2.5 cm. All sites are co-located with a suite of meteorological and radiative measurements available from
The Automated Weather Data Network (AWDN) network is located in Nebraska, USA, and consists of 50 stations. The data sets were collected by the High Plains Regional Climate Center, and data availability is from 1998 to 2010 but varies per station.
The dense soil moisture sites suited for the validation of high-resolution Sentinel-1 soil moisture products were established in the Biebrza Wetlands in northeastern Poland in May 2015
The Bonanza Creek Long-Term Ecological Research (BNZ LTER) network consists of 12 stations located in the boreal forest near Fairbanks, Alaska
The CALABRIA network operates five TDR stations measuring volumetric soil moisture at 30, 60, and 90 cm depths. The stations were installed in 2000 by the Centro Funzionale Decentrato of the Calabria region for hydrometeorological monitoring for flood and landslide risk mitigation. For more information on the performance of the network, see
The CAMPANIA network consisted of two stations located near the city of Naples in southern Italy. It was managed by the Centro Funzionale per la Previsione Meteorologica e il Monitoraggio Meteo-Pluvio-Idrometrico e delle Frane. The ISMN contains data from the operational start in 2000 until the end of 2008. The data sets include soil moisture measured at a depth of 0.30 m, precipitation, and air temperature. For more information on the performance of the network, see
CARBOAFRICA/SD_DEM is located outside El Obeid in Kordofan, Sudan, and has been active since February 2002. It is operated by the Department of Physical Geography and Ecosystem Science at Lund University, Sweden, in cooperation with the Agricultural Research Corporation (ARC) in El Obeid, Sudan. From 2007–2009, eddy covariance measurements were undertaken, which are available from FLUXNET (
This agricultural monitoring campaign from 1981 to 1999 includes 40 stations with soil moisture measurements up to 1 m depth and was hosted by the Institute of Geographical Sciences and Natural Resources Research at the Chinese Academy of Sciences in Beijing
The Cosmic-ray Soil Moisture Observing System (COSMOS;
CTP_SMTMN is a multiscale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau
The DAHRA field site is located in a typical low tree and shrub savanna environment in Senegal. To limit the uncertainty in the comparison of remote sensing products and models, the site was selected to be flat, with homogeneous vegetation cover within a radius of at least 3 km. The site is equipped with two towers, i.e. a meteorological tower with meteorological, hydrological, and radiation sensors and an eddy covariance flux tower. More information can be found in
AMERIFLUX is the North American contribution to the global FLUXNET. At this moment, two sites close to Sacramento, California, i.e. Tonzi and Vaira ranches, are distributed through the ISMN. Both stations provide soil moisture measurements at eight different depths down to 0.60 m. Additionally, soil temperature, air temperature, and precipitation are provided.
This distributed network of in situ measurement stations gathering information on soil moisture and soil temperature has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station in northern Finland. Between 2010 and 2017, 16 stations were installed around Sodankylä and 3 further north at Saariselkä. Each station covers a vertical measuring profile and two additional horizontal measuring points. The vertical profiles have five sensors placed close to the station at the following depths: 5, 10, 20, 40, and 80 cm in mineral and semi-organic soils and at 5, 10, 20, 30, 40 cm in organic soils. The two additional horizontal measuring points, at depths of 5 and 10 cm, have been installed approximately 10 m from the station in opposing directions to catch small-scale variations in topsoil moisture. A more detailed description is provided in
The FR_AQUI network was set up by INRAE in the Bordeaux–Aquitaine region (southwestern France) in 2012
GROW gathered crowd-sourced observations to assess the temporal and spatial consistency of various satellite-derived soil moisture products. In total, 6500 low cost sensors were deployed in 24 GROW places in 13 countries across Europe. A subset of 150 sensors was transferred to the ISMN
This network is operated by the Geological Survey of Finland (GTK) and contains seven stations throughout the country, with one station north of the polar circle. Measurements are taken from the upper soil layer until 0.9 m depth (soil moisture and soil temperature, as well as air temperature). The data are available from the years 2001 to 2012, but the availability varies per station.
The HiWATER_EHWSN network is located on an irrigated farmland in the middle stream of the Heihe River basin close to the Gobi Desert, China. It consists of short time series between April and September 2012 collected at 174 stations by the Cold and Arid Regions Environmental and Engineering Research Institute (CAREERI) of the Chinese Academy of Science
The Hydrological Open Air Laboratory – Soil Network (HOAL SoilNet) was set up in Petzenkirchen, Austria, as part of a concerted effort to advance the understanding of water-related flow and transport processes in a 66 ha agricultural catchment
HOBE is a hydrological observatory established in the western part of Denmark in the Skjern River catchment
HSC_SELMACHEON was a single station located in South Korea. Data were collected by the Hydrological Survey Center (HSC) and Water Resource and Remote Sensing Laboratory (WRRSL) and are available for the period August–September 2011.
The HYDROL-Net Perugia network
The former Illinois Climate Network (ICN) was operated by the Water and Atmospheric Resources Program of the Illinois State Water Survey and formerly integrated in the Global Soil Moisture Data Bank
The network IIT_KANPUR network consisted of a single station and was managed by the Hydraulics and Water Resources Laboratory at the Indian Institute of Technology Kanpur, India. Soil moisture measurements were made at four depths (10, 25, 50, and 80 cm) between from June 2011 to November 2012. The station was situated in the Ganges River basin, which is the largest river basin in India, and the soil type at the observation site is clayey silt.
The IMA_CAN1 network is operated by the Institute for Agricultural and Earthmoving Machines (IMAMOTER) of the Italian National Research Council (CNR), now part of STEMS-CNR. It is located in an experimental vineyard in Carpeneto, in the hilly Alto Monferrato region, which is a valuable vine growing and wine production area in the Piedmont region in northwestern Italy. The monitored vineyards are part of the Experimental Vine and Wine Centre of Agrion Foundation. The stations in the network provide measurements of soil moisture, precipitation, air temperature and humidity, hourly runoff, and event soil losses. Hourly volumetric soil moisture was measured by 12 Decagon 5TM sensors in the period 2011–2015, both in grassed and tilled vineyards, in correspondence with the track and no-track position, both down and up the hill
The IOWA network was located in two catchments in the southwestern part of Iowa, USA. Soil moisture observations from six stations, until 2.6 m depth, with an interval of twice a month during 1972 to 1994 (April to October) are included in the ISMN
The Instituto Pirenaico de Ecologia (IPE) network runs two stations located in Aragon, northeastern Spain. The stations have been collecting meteorological data with at least an hourly time resolution (air and soil temperatures, soil moisture, relative humidity, radiation, and wind speed) since 2008 in a Mediterranean oak forest (Agüero) and a semi-arid pine forest (Peñaflor). These measurements are related to dendrometer hourly records of changes in the root and stem (Agüero; see
The interactive Roaring Fork Observation Network (iRON) is a series of 10 stations operated by the Aspen Global Change Institute spread across the elevations of the Roaring Fork Watershed, located in the Southern Rocky Mountains of the USA. This data set includes soil moisture at 5, 20, and 50 cm, soil temperature at 20 cm, and additional weather measurements variable by station. Further information can be found in
The KHOREZM network in Uzbekistan is located between the Amu Darya river and the border with Turkmenistan and was part of a project conducted by the University of Würzburg, Germany (Patrick Knöfel). There were seven stations that made soil moisture, soil temperature, air temperature, and surface temperature measurements from 2010 April to 2011 September, and these data are included in the ISMN. Although soil moisture was not observed continuously, the measurements are still a valuable contribution since no other recent observations are available in this region.
The Korea Institute of Hydrological Survey (KIHS) has been running the Cheongmicheon (CMC) network since 2009, with annually returning measurements from March till December. It comprises 56 TDR Buriable Waveguide soil moisture sensors at 18 stations located on an area of approximately
KIHS_SMC is operated by the Environmental and Remote Sensing Lab of the Korea Institute of Hydrological Survey. The 51 soil moisture sensors (depths from 0.1 to 0.6 m) are located on a mountain slope distributed over 19 stations in close proximity to each other.
LAB-net was created as the first soil moisture network in Chile to support remote sensing research on drought and water use conflicts
The MAQU monitoring network
The METEROBS (MET European Research OBServations) network measured soil moisture at a single site between October 2011 October and May 2012. It was located in the Apennines mountains in the rural area of Benevento, Southern Italy. Soil Moisture measurements from five layers until 0.5 m depth have been included in the ISMN.
The MOL-RAO network is operated by the German Meteorological Service (DWD) and is part of the operational measuring programme of the MOL-RAO (Meteorological Observatory Lindenberg – Richard Aßmann Observatory). The network is situated in the northeast of Germany and consists of two stations. While the station at Falkenberg has a grass-type vegetation, Kehrigk is situated in a pine forest
Soil moisture data sets for 44 stations were collected by the National Agency of Meteorology, Hydrology, and Environment Monitoring in Ulaanbaatar. All observations were taken using the gravimetric technique and initially provided as volumetric plant-available water (in percent). Volumetric soil moisture (m
The Malaysian Soil Moisture Network (MySMNet) has been operational since 2014. It deploys seven stations (four on an oil palm plantation, two on shrubland, and one on an orchard) that collect soil moisture at 5, 50, and 100 cm depth, soil temperature at 5 cm, air temperature, and relative humidity, all on an hourly basis. The soil moisture sensors used are the WaterScout SM100
The NAQU network is part of the Tibetan Plateau observatory of plateau-scale soil moisture and soil temperature (Tibet-Obs) and consists of 11 stations located in a cold semi-arid climate in Tibet at elevations over 4500 m. Soil moisture and soil temperature have been measured at five different depths (5, 10, 20, 40, and 80 cm) from 2010 onwards
The NGARI network is part of the Tibetan Plateau observatory of plateau-scale soil moisture and soil temperature (Tibet-Obs) and consists of 23 stations located in a cold arid climate in Tibet at elevations between 4200–4700 m. Soil moisture and soil temperature have been measured at five different depths (5, 10, 20, 40, and 80 cm) from 2010 onwards
This is a network of monitoring stations of the Norwegian Water Resources and Energy Directorate (NVE). Soil moisture, soil temperature, and air temperature are measured. Currently, three out of eight stations are accessible through the ISMN. The stations are situated in the Trøndelag region in eastern Norway. Data are available from 2012 to 2019 at more than five different depth layers and down to 1.5 to 2 m depth.
The ORACLE network includes six stations. The data sets reach back to the year 1985 and are available until 2013. ORACLE is a research observatory east of Paris used to study the Grand Morin and Petit Morin river catchments, particularly floods, low water periods, water quality, and the impact of human activities on the environment.
OzNet was established in 2001 with eight sites across the Murrumbidgee River catchment and a cluster of five further sites in the Adelong and Kyeamba catchments. This was further extended in 2003 to include a total of 11 sites at Kyeamba, for a GRACE validation experiment, and 13 sites at Yanco, for SMOS pre-launch algorithm development and post-launch calibration-validation. The Yanco site was further extended to have clusters of stations across 9 and 3 km grids in crop and grassland areas for SMAP algorithm development, calibration, and validation
The PBO_H2O network is a former near-real time network of the ISMN hosted by the University of Colorado Boulder, USA. It consisted of 159 stations distributed in the west of the USA, the Bahamas, the Dominican Republic, Puerto Rico, Colombia, South Africa, and Saudi Arabia. Soil moisture (measured using GPS reflections), precipitation, air temperature, and snow depth from 2004 to 2017 is stored in the ISMN database
The Patitapu Soil Moisture Network (PTSMN) was established in 2016 on the hill country landscapes of the east coast of New Zealand's North Island. PTSMN was deployed to capture spatiotemporal soil moisture trends on various topographical positions distributed over a 13.8 km
REMEDHUS is the University of Salamanca Soil Moisture Measurement Stations Network and was installed in March 1999 with a set of old TDR stations and manual measurements. It is one of the first soil moisture networks in Europe. The network was automated and upgraded with capacitance probes in 2005. The REMEDHUS data available in the ISMN cover the period since its automation. REMEDHUS is located in an agricultural area in the central part of the Duero basin (Spain). The network currently has 20 stations that measure soil moisture and soil temperature hourly in the 0–5 cm layer
The Real-time In-Situ Soil Monitoring for Agriculture (RISMA) network was established in 2011 by Agriculture and Agri-Food Canada at agricultural locations in Ontario, Manitoba, and Saskatchewan
The Romanian Soil Moisture Network (RSMN) consists of 19 stations homogeneously distributed over Romania. The network is managed by the Romanian National Meteorological Administration and is part of the ASSIMO project, which aims to create a framework for the evaluation of current and future satellite microwave-derived soil moisture products.
The Ru_CFR network includes two stations located on the territory of the Central Forest Reserve (CFR), Tver region, Russia. Since 2015, half-hourly continuous measurements of soil moisture have been carried out. Both stations provide measurements of soil moisture at four different depths and measurements of soil temperature, air temperature, and precipitation.
The three historical RUSWET networks were agricultural prediction campaigns conducted by the State Hydrological Institute of the former Soviet Union within the area of present-day Russia. Measurements were taken from 1952 until 2002 and initially distributed through the Global Soil Moisture Data Bank. Altogether, the networks operated 337 sites at which soil moisture was measured 3 times per month via gravimetric sampling. At RUSWET-VALDAI, soil temperature, precipitation, and air temperature were also collected. RUSWET contributes both the northernmost (on McClintock Island) and the earliest (8 June 1952) observations to the ISMN
The Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) monitoring network commenced commissioning in late 2002, with a total of 26 stations in operation by 2003 across a 6500 km
The Natural Resources Conservation Service (NRCS) operates the comprehensive, USA-wide Soil Climate Analysis Network (SCAN). SCAN supports natural resource assessments and conservation activities through its network of automated climate monitoring and data collection sites. SCAN focuses primarily on agricultural areas of the USA, Puerto Rico, and the Virgin Islands. The network consists of 216 stations located across the USA and reports soil moisture, soil temperature, precipitation, temperature, and other climatic variables hourly. Soil sensors are situated at 5, 10, 20, 50, and 100 cm depths
The SKKU network was located at an evenly and moderately vegetated botanical garden in South Korea. It was operated by Sungkyunkwan University (SKKU) from 2014 to 2016, as part of a project for evaluating Cosmic-Ray Neutron Probe (CRNP) soil moisture
The SMOSMANIA network was installed in southern France by Météo-France, the French national meteorological service, in order to monitor in situ soil moisture and soil temperature in contrasting soil and climatic conditions at operational automatic weather stations
NRCS instals, operates, and maintains an extensive, automated data collection network called SNOTEL
The Soil moisture Sensing Controller And oPtimal Estimator (SoilSCAPE) is a wireless soil moisture sensor network for measurements of surface-to-root-zone profiles of soil moisture
The Soil Water and Energy exchange – Poland (SWEX_POLAND) network was operated between 2000 and 2013 by the Institutes of Agrophysics, Polish Academy of Sciences, in Lublin. The network consisted of six stations located in the wetlands of Poleski Park Krajobrazowy to support SMOS product calibration and validation. Soil moisture and temperature measurements were taken down to 1 m depth, along with precipitation observations
The SW-WHU network was hosted by Wuhan University
The Trans-African Hydro-Meteorological Observatory (TAHMO;
TERENO consists of four terrestrial observatories that represent typical landscapes in Germany and central Europe and are considered to be highly vulnerable to the effects of global and climate change
The former German UDC_SMOS network was hosted by the Department of Geography at the University of Munich, in cooperation with the Bavarian State Research Center for Agriculture, and funded by the German Aerospace Centre (DLR). It was located in grassland in the Bavarian region around Munich as an official European SMOS calibration/validation test site. In total, 11 stations provided soil moisture data from 2007 until 2011, up to 40 cm depth, as measured by several types of sensors
This soil moisture monitoring network in the north of the Umbria region, in the upper Tiber River basin, operates three stations in real time (Torre dell'Olmo, Petrelle, and Cerbara). Each station measures at 10, 20, and 40 cm depth. Additional stations of the network have not been operational since 2015 due to a lack of resources for their maintenance. Provided financial resources become available, the stations that are no longer functioning will be restored
UMSUOL (Umidita del Suolo) is a one-station network located close to Bologna, northern Italy. Soil moisture measurements at seven different depths are provided by the Agenzia Regionale Prevenione Ambiente (ARPA). The ISMN contains data from the years 2009 and 2010.
The U.S. Climate Reference Network (USCRN) contains 114 stations sparsely distributed across the contiguous USA. Each station has three sets of soil moisture/temperature probes at five depths (5, 10, 20, 50, and 100 cm), in addition to air and surface temperature, precipitation, relative humidity, and solar radiation
The U.S. Department of Agriculture Agricultural Research Service (USDA ARS) operates a number of Long-Term Agroecosystem Research (LTAR) sites, some of which have spatial coverage of soil moisture and soil temperature. As experimental sites, the locations and configuration of the stations can change depending on the current scientific questions being addressed. A description of the sites can be found in
The Valencia Anchor Station (VAS) network is operated by the Climatology from Satellites Group and Jucar River Basin Authority of the University of Valencia, Spain. The network is located in Spain and consists of three stations. The data sets are available for the years 2010 and 2011.
The VDS network is run by VanderSat, a Dutch company that specialises in providing global satellite-observed data and services over land. The VDS network consists of four stations located near the city of Bago in Myanmar. The network was installed to validate satellite soil moisture products in the tropics. The network has two measurement periods, i.e. one from June 2017 to July 2018 and one from March 2020 onwards.
The WegenerNet, located in the foreland of the southeastern Austrian Alps, is a long-term weather and climate monitoring facility comprising 155 hydrometeorological stations in a dense grid, with one station every about 2 km
The Wales Soil Moisture Network (WSMN) was founded in July 2011. It consists of a total of nine monitoring sites located in mid-Wales representing a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and the most typical land use/cover types. The data set acquired in the network is composed of 0–5 (or 0–10) cm soil moisture, soil temperature, precipitation, as well as other ancillary data
Detailed system overview of the ISMN (status in July 2021).
Overview of data availability per depth layers showing the total number of individual sensor brands, data sets, and stations integrated in the ISMN database. The calculations have been made with respect to horizontally and vertically placed sensors for each depth layer.
Overview of all mandatory metadata information available for each station within the ISMN database.
Overview of scientific studies using ISMN data for satellite product evaluation and development.
Continued.
Overview of scientific studies using ISMN data for model evaluation and development.
Continued.
Scatterplots of
Upon registration, all data and metadata described in this paper can be downloaded for free from
WD conceived the paper and wrote it together with IH, DA, LS, IP, LZ, and WP. All other authors contributed data to the ISMN and provided feedback on the paper.
The authors declare that they have no conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors greatly acknowledge the financial support provided by ESA through various projects including the following: SMOSnet International Soil Moisture Network (grant no. 4000102722/10/NL/FF/fk) and Operations (grant no. 3-13185/NL/FF/fk), IDEAS
The authors greatly acknowledge the financial support provided by ESA through various projects including the following: SMOSnet International Soil Moisture Network (grant no. 4000102722/10/NL/FF/fk) and Operations (grant no. 3-13185/NL/FF/fk), IDEAS
This paper was edited by Thom Bogaard and reviewed by Jan Friesen and Mirko Mälicke.