The International Soil Moisture Network: an open-source data hosting facility in support of meteorology and climate science

Mapping Intimacies ◽

10.5194/ems2021-368 ◽

2021 ◽

Author(s):

Ivana Petrakovic ◽

Irene Himmelbauer ◽

Daniel Aberer ◽

Lukas Schremmer ◽

Philippe Goryl ◽

...

Keyword(s):

Soil Moisture ◽

Real Time ◽

International Cooperation ◽

Land Surface ◽

Prediction Models ◽

Weather Prediction ◽

Source Surface ◽

Federal Institute

The International Soil Moisture Network (ISMN, https://ismn.earth) is international cooperation to establish and maintain a unique centralized global data hosting facility, making in-situ soil moisture data easily and freely accessible (Dorigo et al., 2021). Initiated in 2009 as a community effort through international cooperation (ESA, GEWEX, GTN-H, GCOS, TOPC, HSAF, QA4SM, C3S, etc.), the ISMN is an essential means for validating and improving global satellite soil moisture products, land surface-, climate-, and hydrological models.&#160; The ISMN is a widely used, reliable, and consistent in-situ data source (surface and sub-surface) collected by a myriad of data organizations on a voluntary basis. &#160;The in-situ soil moisture measurements 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. Currently, 71 networks are participating with more than 2800 stations distributed on a global scale and a steadily increasing number of user communities. Long term time series with mainly hourly timestamps from 1952 &#8211; up to near-real-time are stored in the database, including daily near-real-time updates. Besides soil moisture in our database are stored other meteorological variables as well (air temperature, soil temperature, precipitation, snow depth, etc.). The ISMN provides benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S) and Global Land Service (CGLS), and the online validation tool QA4SM. ISMN data is widely used in a variety of scientific fields (e.g., climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc). To validate the land surface representations of meteorological forecasting models soil moisture from the ISMN has often been used. The development of various generations of TESSEL models used both in the Integrated Forecasting Systems and reanalysis products of ECMWF, greatly profited from soil moisture and temperature data from the ISMN. Using ISMN data several studies assessed the soil moisture skill of the Weather Research and Forecasting Model (WRF) and assessed the forecast skill or new implementations of numerical weather prediction models. We greatly acknowledge the financial support provided by ESA through various projects: SMOSnet International Soil Moisture Network, IDEAS+, and QA4EO. To ensure a long-term funding for the ISMN operations, several ideas were perused together with ESA. A partner for this task could be found within the International Center for Water Resources and Global Change (ICWRGC) hosted by the German Federal Institute of Hydrology (BfG).&#160; In this session, we want to give an overview and future outlook of the ISMN, highlighting its unique features and discuss challenges in supporting the hydrological research community in need of freely available, standardized, and quality-controlled datasets.&#160;

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More than 10 years of The International Soil Moisture Network (ISMN) in support of EO science

10.5194/egusphere-egu21-16145 ◽

2021 ◽

Author(s):

Irene Himmelbauer ◽

Daniel Aberer ◽

Lukas Schremmer ◽

Ivana Petrakovic ◽

Wouter A. Dorigo ◽

...

Keyword(s):

Soil Moisture ◽

Real Time ◽

Land Surface ◽

European Space Agency ◽

Data Availability ◽

Data Repository ◽

Source Surface ◽

Web Portal ◽

The Past

The International Soil Moisture Network (ISMN, ) 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.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, > 3200 registered users strong. Time series with hourly timestamps from 1952 &#8211; up to near real time are stored in the database and are available through the ISMN web portal for free (), including daily near-real time updates from 6 networks (~ 1000 stations). About 10&#8217;000 datasets are available through the web portal and the number of 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.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.). 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, functionality and challenges in data usage in order to help shape potential future modes in operation of this unique community- based data repository.

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NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

Remote Sensing ◽

10.3390/rs12172861 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2861

Author(s):

Jifu Yin ◽

Xiwu Zhan ◽

Jicheng Liu

Keyword(s):

Soil Moisture ◽

Real Time ◽

Land Surface ◽

Vital Role ◽

Product System ◽

Global Soil ◽

One Stop ◽

Satellite Sensors ◽

Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

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The S2M meteorological and snow cover reanalysis over the French mountainous areas, description and evaluation (1958–2020)

10.5194/essd-2021-249 ◽

2021 ◽

Author(s):

Matthieu Vernay ◽

Matthieu Lafaysse ◽

Diego Monteiro ◽

Pascal Hagenmuller ◽

Rafife Nheili ◽

...

Keyword(s):

Snow Cover ◽

Land Surface ◽

Prediction Models ◽

Weather Prediction ◽

Scientific Publications ◽

Mountain Areas ◽

Data Set ◽

Meteorological Observations ◽

Surface Scheme

Abstract. This work introduces the S2M (SAFRAN - SURFEX/ISBA-Crocus - MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2020. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2020) and the best possible set of available in-situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground, and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends), and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in-situ snow depth observations. Further, we describe the technical handling of this open access data set, available at this address: http://dx.doi.org/10.25326/37#v2020.2. Scientiﬁc publications using this dataset must mention in the acknowledgments: "The S2M data are provided by Météo-France - CNRS, CNRM Centre d’Etudes de la Neige, through AERIS" and refer to it as Vernay et al. (2020).

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Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau

Remote Sensing ◽

10.3390/rs12030509 ◽

2020 ◽

Vol 12 (3) ◽

pp. 509 ◽

Cited By ~ 6

Author(s):

Ruodan Zhuang ◽

Yijian Zeng ◽

Salvatore Manfreda ◽

Zhongbo Su

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Land Surface ◽

Root Zone ◽

Cumulative Distribution ◽

Analytical Relationship ◽

The Tibetan Plateau ◽

Climate Systems

It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.

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A Real-Time MODIS Vegetation Product for Land Surface and Numerical Weather Prediction Models

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2013.2255059 ◽

2014 ◽

Vol 52 (3) ◽

pp. 1772-1786 ◽

Cited By ~ 23

Author(s):

Jonathan L. Case ◽

Frank J. LaFontaine ◽

Jordan R. Bell ◽

Gary J. Jedlovec ◽

Sujay V. Kumar ◽

...

Keyword(s):

Real Time ◽

Numerical Weather Prediction ◽

Land Surface ◽

Prediction Models ◽

Weather Prediction ◽

Numerical Weather ◽

Numerical Weather Prediction Models

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The International Soil Moisture Network in assistance of EO soil moisture validation products, services and models

10.5194/egusphere-egu2020-16493 ◽

2020 ◽

Author(s):

Daniel Aberer ◽

Irene Himmelbauer ◽

Lukas Schremmer ◽

Ivana Petrakovic ◽

Wouter Dorigo ◽

...

Keyword(s):

Quality Control ◽

Soil Moisture ◽

Real Time ◽

Land Surface ◽

Measurement Techniques ◽

European Space Agency ◽

Web Portal ◽

Soil Moisture Data ◽

Spatial Coverage

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.&#160;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.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.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 > 3200 registered users strong. Time series with hourly timestamps from 1952 &#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 (> 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.).About 10&#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.In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.

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Cross-evaluation of modelled and remotely sensed surface soil moisture with in situ data in southwestern France

Hydrology and Earth System Sciences ◽

10.5194/hess-14-2177-2010 ◽

2010 ◽

Vol 14 (11) ◽

pp. 2177-2191 ◽

Cited By ~ 78

Author(s):

C. Albergel ◽

J.-C. Calvet ◽

P. de Rosnay ◽

G. Balsamo ◽

W. Wagner ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Soil ◽

Root Zone ◽

Weather Prediction ◽

Remotely Sensed ◽

Surface Model ◽

Surface Soil Moisture ◽

Moisture Profile

Abstract. The SMOSMANIA soil moisture network in Southwestern France is used to evaluate modelled and remotely sensed soil moisture products. The surface soil moisture (SSM) measured in situ at 5 cm permits to evaluate SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km) active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP), issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km gridspacing) by TU-Wien (Vienna University of Technology) over a two year period (2007–2008). A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP) and the Integrated Forecasting System (IFS) analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses.

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Intercomparison of In Situ Sensors for Ground-Based Land Surface Temperature Measurements

Sensors ◽

10.3390/s20185268 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5268

Author(s):

Praveena Krishnan ◽

Tilden P. Meyers ◽

Simon J. Hook ◽

Mark Heuer ◽

David Senn ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Field Experiments ◽

Prediction Models ◽

Weather Prediction ◽

Surface Air Temperature ◽

Near Surface ◽

Oak Ridge

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA’s Air Resources Laboratory (ARL)’s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season.

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The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-1609-2011 ◽

2011 ◽

Vol 8 (1) ◽

pp. 1609-1663 ◽

Cited By ~ 16

Author(s):

W. A. Dorigo ◽

W. Wagner ◽

R. Hohensinn ◽

S. Hahn ◽

C. Paulik ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Water Cycle ◽

Soil Moisture Data ◽

The North ◽

Time Period ◽

Different Depths ◽

Operational Networks

Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status January 2011), the ISMN contains data of 16 networks and more than 500 stations located in the North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.

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Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

Remote Sensing ◽

10.3390/rs10111842 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1842 ◽

Cited By ~ 7

Author(s):

Christof Lorenz ◽

Carsten Montzka ◽

Thomas Jagdhuber ◽

Patrick Laux ◽

Harald Kunstmann

Keyword(s):

Time Series ◽

Soil Moisture ◽

High Resolution ◽

Brightness Temperature ◽

Spatial Resolution ◽

Prediction Models ◽

Weather Prediction ◽

Brightness Temperatures ◽

Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

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