QA4SM – An online tool for satellite soil moisture data validation

2021 ◽  
Author(s):  
Samuel Scherrer ◽  
Wolfgang Preimesberger ◽  
Monika Tercjak ◽  
Zoltan Bakcsa ◽  
Alexander Boresch ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Global Climate ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Space Applications ◽  
Online Tool ◽  
Soil Moisture Data

<p>To validate satellite soil moisture products and compare their quality with other products, standardized, fully traceable validation methods are required. The QA4SM (Quality Assurance for Soil Moisture; ) free online validation tool provides an easy-to-use implementation of community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellites. It sets the basis for a community wide standard for validation studies.</p><p>QA4SM can be used to preprocess, intercompare, store, and visualise validation results. It uses state-of-the-art open-access soil moisture data records such as the European Space Agency’s Climate Change Initiative (ESA CCI) and the Copernicus Climate Change Services (C3S) soil moisture datasets, as well as single-sensor products, e.g. H-SAF Metop-A/B ASCAT surface soil moisture, SMOS-IC, and SMAP L3 soil moisture. Non-satellite data include in-situ data from the International Soil Moisture Network (ISMN: ), as well as land surface model or reanalysis products, e.g. ERA5 soil moisture.</p><p>Users can interactively choose temporal or spatial subsets of the data and apply filters on quality flags. Additionally, validation of anomalies and application of different scaling methods are possible. The tool provides traditional validation metrics for dataset pairs (e.g. correlation, RMSD) as well as triple collocation metrics for dataset triples. All results can be visualised on the webpage, downloaded as figures, or downloaded in NetCDF format for further use. Archiving and publishing features allow users to easily store and share validation results. Published validation results can be cited in reports and publications via DOIs.</p><p>The new version of the service provides support for high-resolution soil moisture products (from Sentinel-1), additional datasets, and improved usability.</p><p>We present an overview and examples of the online tool, new features, and give an outlook on future developments.</p><p><em>Acknowledgements: This work was supported by the QA4SM & QA4SM-HR projects, funded by the Austrian Space Applications Programme (FFG).</em></p>

QA4SM: Development of a traceable online satellite soil moisture validation system

2020 ◽  
Author(s):  
Wolfgang Preimesberger ◽  
Tracy Scanlon ◽  
Doris Baum ◽  
Zoltan Bakcsa ◽  
Alexander Boresch ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Global Climate ◽  
European Space Agency ◽  
Land Surface Model ◽  
Current Status ◽  
Surface Model ◽  
Space Applications ◽  
High Resolution Data ◽  
In Situ Data

<p>The Quality Assurance for Soil Moisture (QA4SM) service is an online validation tool to evaluate and intercompare the performance of state-of-the-art open-access satellite soil moisture data records (https://qa4sm.eodc.eu). QA4SM implements routines to preprocess, intercompare, store and visualise validation results based on community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellite. The focus on traceability in terms of input data, software and validation results improves reproducibility and sets the basis for a community wide standard for future validation studies.</p><p>Within the validation framework a number of up-to-date soil moisture datasets are provided. Satellite data include multi-sensor records such as the European Space Agency’s Climate Change Initiative (ESA CCI) and the Copernicus Climate Changes Services (C3S) Soil Moisture datasets and single sensor products e.g. from SMAP, SMOS or Metop ASCAT. Reference data within the service include the full in-situ data archive of the the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) and land surface model/reanalysis products, e.g. from the European Centre for Medium-Range Weather Forecasts (ECMWF). General validation metrics between dataset pairs (such as correlation or RMSD amongst others) and triples (Triple Collocation) are part of the service. QA4SM allows users to select from a number of input parameters to specify temporal or spatial subsets of data to evaluate and provides options for data filtering, validation of anomalies and the use of different scaling methods.</p><p>Within this study we show the current status of the service, present its scope of operation and give an outlook on future developments such as the integration of high resolution data.</p><p>This work was supported by the QA4SM project, funded by the Austrian Space Applications Programme (FFG).</p>

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Atmospheric Model ◽  
Surface Model ◽  
Soil Moisture Data ◽  
The Impact ◽  
Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation

2018 ◽  
Vol 15 (4) ◽  
pp. 498-502 ◽  
Author(s):  
Clay B. Blankenship ◽  
Jonathan L. Case ◽  
William L. Crosson ◽  
Bradley T. Zavodsky
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Soil Moisture Data

scholarly journals Land surface model performance using cosmic-ray and point-scale soil moisture measurements for calibration

2017 ◽  
Vol 21 (6) ◽  
pp. 2843-2861 ◽  
Author(s):  
Joost Iwema ◽  
Rafael Rosolem ◽  
Mostaquimur Rahman ◽  
Eleanor Blyth ◽  
Thorsten Wagener
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Surface Energy ◽  
Eddy Covariance ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
Point Scale ◽  
Soil Moisture Data

Abstract. At very high resolution scale (i.e. grid cells of 1 km2), land surface model parameters can be calibrated with eddy-covariance flux data and point-scale soil moisture data. However, measurement scales of eddy-covariance and point-scale data differ substantially. In our study, we investigated the impact of reducing the scale mismatch between surface energy flux and soil moisture observations by replacing point-scale soil moisture data with observations derived from Cosmic-Ray Neutron Sensors (CRNSs) made at larger spatial scales. Five soil and evapotranspiration parameters of the Joint UK Land Environment Simulator (JULES) were calibrated against point-scale and Cosmic-Ray Neutron Sensor soil moisture data separately. We calibrated the model for 12 sites in the USA representing a range of climatic, soil, and vegetation conditions. The improvement in latent heat flux estimation for the two calibration solutions was assessed by comparison to eddy-covariance flux data and to JULES simulations with default parameter values. Calibrations against the two soil moisture products alone did show an advantage for the cosmic-ray technique. However, further analyses of two-objective calibrations with soil moisture and latent heat flux showed no substantial differences between both calibration strategies. This was mainly caused by the limited effect of calibrating soil parameters on soil moisture dynamics and surface energy fluxes. Other factors that played a role were limited spatial variability in surface fluxes implied by soil moisture spatio-temporal stability, and data quality issues.

scholarly journals Impact of quality control of satellite soil moisture data on their assimilation into land surface model

2014 ◽  
Vol 41 (20) ◽  
pp. 7159-7166 ◽  
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Youfei Zheng ◽  
Jicheng Liu ◽  
Christopher R. Hain ◽  
...  
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Soil Moisture Data
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