scholarly journals The International Soil Moisture Network: serving Earth system science for over a decade

2021 ◽  
Vol 25 (11) ◽  
pp. 5749-5804
Author(s):  
Wouter Dorigo ◽  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
...  

Abstract. 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 (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main 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 and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

2021 ◽  
Author(s):  
Wouter Dorigo ◽  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
...  

Abstract. 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 (Dorigo et al., 2011a, b). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonizes them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of December 2020, the ISMN now contains data of 65 networks and 2678 stations located all over the globe, with a time period spanning from 1952 to present.The number of networks and stations covered by the ISMN is still growing and many of the data sets contained in the database continue to be updated. The main 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 and quality control procedures. A comprehensive review of existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage, and to shape priorities for the next decade of operations of this unique community-based data repository.


2009 ◽  
Vol 2 (1) ◽  
pp. 87-98 ◽  
Author(s):  
C. Lerot ◽  
M. Van Roozendael ◽  
J. van Geffen ◽  
J. van Gent ◽  
C. Fayt ◽  
...  

Abstract. Total O3 columns have been retrieved from six years of SCIAMACHY nadir UV radiance measurements using SDOAS, an adaptation of the GDOAS algorithm previously developed at BIRA-IASB for the GOME instrument. GDOAS and SDOAS have been implemented by the German Aerospace Center (DLR) in the version 4 of the GOME Data Processor (GDP) and in version 3 of the SCIAMACHY Ground Processor (SGP), respectively. The processors are being run at the DLR processing centre on behalf of the European Space Agency (ESA). We first focus on the description of the SDOAS algorithm with particular attention to the impact of uncertainties on the reference O3 absorption cross-sections. Second, the resulting SCIAMACHY total ozone data set is globally evaluated through large-scale comparisons with results from GOME and OMI as well as with ground-based correlative measurements. The various total ozone data sets are found to agree within 2% on average. However, a negative trend of 0.2–0.4%/year has been identified in the SCIAMACHY O3 columns; this probably originates from instrumental degradation effects that have not yet been fully characterized.


2019 ◽  
Vol 11 (9) ◽  
pp. 1113 ◽  
Author(s):  
Franklin Paredes-Trejo ◽  
Humberto Barbosa ◽  
Carlos A. C. dos Santos

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted.


2020 ◽  
Author(s):  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Luca Zappa ◽  
...  

&lt;p&gt;&lt;span&gt;The International Soil Moisture Network (ISMN, &lt;/span&gt;&lt;span&gt;&lt;/span&gt;&lt;span&gt;) is an international cooperation to establish and maintain an open-source global data hosting facility, providing in-situ soil moisture data as well as accompanying soil variables. This database is an essential means for validating and improving global satellite soil moisture products as well as land surface -, climate- , and hydrological models.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;For hydrological validation, the quality of used in-situ data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collect their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;The ISMN was created to address the above-mentioned issues. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Since its establishment in 2009 and with continuous financial support through the European Space Agency (ESA), the ISMN evolved into a widely used in situ data source growing continuously (in terms of data volume and users). Historic measurements starting in 1952 up to near&amp;#8211;real time are available through the ISMN web portal. Currently, the ISMN consists of 60 networks with more than 2500 stations spread all over the globe. With a &lt;/span&gt;&lt;span&gt;&lt;span&gt;steadily growing user community more than 3200 registered users strong&lt;/span&gt;&lt;/span&gt;&lt;span&gt; the value of the ISMN as a well-established and rich source of in situ soil moisture observations is well recognized. In fact, the ISMN is widely used in variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.). &lt;/span&gt;&lt;/p&gt;&lt;p&gt; &lt;span&gt;Our partner networks range from networks with a handful of stations to networks that are composed of over 400 sites, are supported with half yearly provider reports on statistical data about their network (e.g.: data download statistic, flagging statistic, etc.). &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;About 10&amp;#8217;000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (GROW observatory ) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In this session , we want to give an overview of the ISMN, its unique features and its support of data provider, who are willing to openly share their data, as well as hydrological researcher in need of freely available datasets.&lt;/span&gt;&lt;/p&gt;


2016 ◽  
Author(s):  
Brecht Martens ◽  
Diego G. Miralles ◽  
Hans Lievens ◽  
Robin van der Schalie ◽  
Richard A. M. de Jeu ◽  
...  

Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include: (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 35-year data set spanning the period 1980–2014 (v3.0a, based on satellite-observed soil moisture, vegetation optical depth and snow water equivalents, reanalysis air temperature and radiation, and a multi-source precipitation product), and two fully satellite-based data sets. The latter two share most of their forcing, except for the vegetation optical depth and soil moisture products, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative data sets) for the first data set (v3.0b, spanning the period 2003–2015) and observations from the Soil Moisture and Ocean Salinity satellite in the second data set (v3.0c, spanning the period 2011–2015). These three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 64 eddy-covariance towers and 2338 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in case of the v3.0a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the two fully satellite-based data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar as the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements between 0.78 and 0.80 for the three different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at http://GLEAM.eu and may be used for large-scale hydrological applications, climate studies and research on land-atmosphere feedbacks.


2018 ◽  
Vol 10 (11) ◽  
pp. 1839 ◽  
Author(s):  
A. Al-Yaari ◽  
S. Dayau ◽  
C. Chipeaux ◽  
C. Aluome ◽  
A. Kruszewski ◽  
...  

Global soil moisture (SM) products are currently available thanks to microwave remote sensing techniques. Validation of these satellite-based SM products over different vegetation and climate conditions is a crucial step. INRA (National Institute of Agricultural Research) has set up the AQUI SM and soil temperature in situ network (composed of three main sites Bouron, Bilos, and Hermitage), over a flat area of dense pine forests, in South-Western France (the Bordeaux–Aquitaine region) to validate the Soil Moisture and Ocean salinity (SMOS) satellite SM products. SMOS was launched in 2009 by the European Space Agency (ESA). The aims of this study are to present the AQUI network and to evaluate the SMOS SM product (in the new SMOS-IC version) along with other microwave SM products such as the active ASCAT (Advanced Scatterometer) and the ESA combined (passive and active) CCI (Climate Change Initiative) SM retrievals. A first comparison, using Pearson correlation, Bias, RMSE (Root Mean Square Error), and Un biased RMSE (ubRMSE) scores, between the 0–5 cm AQUI network and ASCAT, CCI, and SMOS SM products was conducted. In general all the three products were able to reproduce the annual cycle of the AQUI in situ observations. CCI and ASCAT had best and similar correlations (R~0.72) over the Bouron and Bilos sites. All had comparable correlations over the Hermitage sites with overall average values of 0.74, 0.68, and 0.69 for CCI, SMOS-IC, and ASCAT, respectively. Considering anomalies, correlation values decreased for all products with best ability to capture day to day variations obtained by ASCAT. CCI (followed by SMOS-IC) had the best ubRMSE values (mostly < 0.04 m3/m3) over most of the stations. Although the region is highly impacted by radio frequency interferences, SMOS-IC followed correctly the in situ SM dynamics. All the three remotely-sensed SM products (except SMOS-IC over some stations) overestimated the AQUI in situ SM observations. These results demonstrate that the AQUI network is likely to be well-suited for satellite microwave remote sensing evaluations/validations.


2020 ◽  
Author(s):  
Daniel Aberer ◽  
Irene Himmelbauer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Wouter Dorigo ◽  
...  

&lt;p&gt;The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models.&amp;#160;&lt;/p&gt;&lt;p&gt;In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.&lt;/p&gt;&lt;p&gt;The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.&lt;/p&gt;&lt;p&gt;Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community &gt; 3200 registered users strong. Time series with hourly timestamps from 1952 &amp;#8211; up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (&gt; 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).&lt;/p&gt;&lt;p&gt;About 10&amp;#8217;000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.&lt;/p&gt;&lt;p&gt;In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.&lt;/p&gt;


2021 ◽  
Author(s):  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Ivana Petrakovic ◽  
Wouter A. Dorigo ◽  
...  

&lt;p&gt;&lt;span&gt;The International Soil Moisture Network (ISMN, &lt;/span&gt;&lt;span&gt;) is a unique centralized global and open freely available in-situ soil moisture data hosting facility. Initiated in 2009 as a community effort through international cooperation (ESA, GEWEX, GTN-H, WMO, etc.), with continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN is more than ever an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Following, building and improving standardized measurement protocols and quality techniques, the network evolved into a widely used, reliable and consistent in-situ data source (surface and sub-surface) collected by a myriad off data organizations on a voluntary basis. 66 networks are participating (status January 2021) with more than 2750 stations distributed on a global scale and a steadily increasing number of user community, &gt; 3200 registered users strong. Time series with hourly timestamps from 1952 &amp;#8211; up to near real time are stored in the database and are available through the ISMN web portal for free (&lt;/span&gt;&lt;span&gt;), including daily near-real time updates from 6 networks (~ 1000 stations). &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;About 10&amp;#8217;000 datasets are available through the web portal and t&lt;/span&gt;&lt;span&gt;he number of&lt;/span&gt; &lt;span&gt;networks and stations covered by the ISMN is still growing as well as most datasets, that are already contained in the database, are continuously being updated.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;The ISMN evolved in the past decade into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS), the online validation service Quality Assurance for Soil Moisture (QA4SM) and many more applications, services, products and tools. In general, ISMN data is widely used in a variety of scientific fields with hundreds of studies making use of ISMN data (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.). &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In this session, we want to inform ISMN users about the evolution of the ISMN over the past decade, including a description of network and dataset updates and new quality control procedures. Besides, we provide a review of existing literature making use of ISMN data in order to identify current limitations in data availability&lt;/span&gt;&lt;span&gt;, &lt;/span&gt;&lt;span&gt;functionality and challenges in data usage in order to help shape potential future modes in operation of this unique community- based data repository.&lt;/span&gt;&lt;/p&gt;


2016 ◽  
Vol 20 (10) ◽  
pp. 4191-4208 ◽  
Author(s):  
Markus Enenkel ◽  
Christoph Reimer ◽  
Wouter Dorigo ◽  
Wolfgang Wagner ◽  
Isabella Pfeil ◽  
...  

Abstract. The soil moisture dataset that is generated via the Climate Change Initiative (CCI) of the European Space Agency (ESA) (ESA CCI SM) is a popular research product. It is composed of observations from 10 different satellites and aims to exploit the individual strengths of active (radar) and passive (radiometer) sensors, thereby providing surface soil moisture estimates at a spatial resolution of 0.25°. However, the annual updating cycle limits the use of the ESA CCI SM dataset for operational applications. Therefore, this study proposes an adaptation of the ESA CCI product for daily global updates via satellite-derived near-real-time (NRT) soil moisture observations. In order to extend the ESA CCI SM dataset from 1978 to present we use NRT observations from the Advanced Scatterometer on-board the two MetOp satellites and the Advanced Microwave Scanning Radiometer 2 on-board GCOM-W. Since these NRT observations do not incorporate the latest algorithmic updates, parameter databases and intercalibration efforts, by nature they offer a lower quality than reprocessed offline datasets. In addition to adaptations of the ESA CCI SM processing chain for NRT datasets, the quality of the NRT datasets is a main source of uncertainty. Our findings indicate that, despite issues in arid regions, the new CCI NRT dataset shows a good correlation with ESA CCI SM. The average global correlation coefficient between CCI NRT and ESA CCI SM (Pearson's R) is 0.80. An initial validation with 40 in situ observations in France, Spain, Senegal and Kenya yields an average R of 0.58 and 0.49 for ESA CCI SM and CCI NRT, respectively. In summary, the CCI NRT product is nearly as accurate as the existing ESA CCI SM product and, therefore, of significant value for operational applications such as drought and flood forecasting, agricultural index insurance or weather forecasting.


2021 ◽  
Vol 13 (9) ◽  
pp. 1712
Author(s):  
Thomas Ramsauer ◽  
Thomas Weiß ◽  
Alexander Löw ◽  
Philip Marzahn

Soil moisture is a key variable in the terrestrial water and energy system. This study presents an hourly index that provides soil moisture estimates on a high spatial and temporal resolution (1 km × 1 km). The long established Antecedent Precipitation Index (API) is extended with soil characteristic and temperature dependent loss functions. The Soilgrids and ERA5 data sets are used to provide the controlling variables. Precipitation as main driver is provided by the German weather radar data set RADOLAN. Empiric variables in the equations are fitted in a optimization effort using 23 in-situ soil moisture measurement stations from the Terrestial Environmental Observatories (TERENO) and a separately conducted field campaign. The volumetric soil moisture estimation results show error values of 3.45 Vol% mean ubRMSD between RADOLAN_API and station data with a high temporal accordance especially of soil moisture upsurge. Further potential of the improved API algorithm is shown with a per-station calibration of applied empirical variables. In addition, the RADOLAN_API data set was spatially compared to the ESA CCI soil moisture product where it altogether demonstrates good agreement. The resulting data set is provided as open access data.


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