scholarly journals Triple Collocation Evaluation of In Situ Soil Moisture Observations from 1200+ Stations as part of the U.S. National Soil Moisture Network

2020 ◽  
Vol 21 (11) ◽  
pp. 2537-2549
Author(s):  
Trent W. Ford ◽  
Steven M. Quiring ◽  
Chen Zhao ◽  
Zachary T. Leasor ◽  
Christian Landry
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Model ◽  
National Scale ◽  
Model Soil ◽  
Soil Moisture Data ◽  
Spatial Coverage ◽  
Soil Moisture Monitoring

AbstractSoil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.

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

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

<p>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. </p><p>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.</p><p>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.</p><p>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 – 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.).</p><p>About 10’000 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.</p><p>In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.</p>

scholarly journals Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

2010 ◽  
Vol 7 (5) ◽  
pp. 6699-6724 ◽  
Author(s):  
Y. Y. Liu ◽  
R. M. Parinussa ◽  
W. A. Dorigo ◽  
R. A. M. de Jeu ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cumulative Distribution ◽  
Surface Model ◽  
Carbon Cycles ◽  
In Situ Data ◽  
Spatial Coverage ◽  
Global Soil

Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

scholarly journals Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model

2015 ◽  
Vol 16 (2) ◽  
pp. 917-931 ◽  
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Youfei Zheng ◽  
Jicheng Liu ◽  
Li Fang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Spatial Coverage ◽  
In Situ Observations ◽  
The Impact

Abstract Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

scholarly journals Evaluation of a multi-satellite soil moisture product and the Community Land Model 4.5 simulation in China

2015 ◽  
Vol 12 (5) ◽  
pp. 5151-5186 ◽  
Author(s):  
B. Jia ◽  
J. Liu ◽  
Z. Xie
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Eastern China ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Community Land Model ◽  
Soil Moisture Data ◽  
In Situ Observations

Abstract. Twenty years of in situ soil moisture data from more than 300 stations located in China are used to perform an evaluation of two surface soil moisture datasets: a microwave-based multi-satellite product (ECV-SM) and the land surface model simulation from the Community Land Model 4.5 (CLM4.5). Both soil moisture products generally show a good agreement with in situ observations. The ECV-SM product has a low bias, with a root mean square difference (RMSD) of 0.075 m3 m-3, but shows a weak correlation with in situ observations (R = 0.41). In contrast, the CLM4.5 simulation, forced by an observation-based atmospheric forcing data, produces better temporal variation of surface soil moisture (R = 0.52), but shows a clear overestimation (bias = 0.05 m3 m-3) and larger RMSD (0.09 m3 m-3), especially in eastern China, caused by inaccurate descriptions of soil characteristics. The ECV-SM product is more likely to be superior in semi-arid regions, mainly because of the accurate retrievals and high observation density, but inferior over areas covered by dense vegetation. Furthermore, it shows a stable to slightly increasing performance in China, except for a decrease during the 2007–2010 blending period. Results from this study can provide comprehensive insight into the performances of the two soil moisture datasets in China, which will be useful for their improvements in merging algorithms or model simulations and for applications in soil moisture data assimilation.

scholarly journals Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

2017 ◽  
Author(s):  
Sibo Zhang ◽  
Jean-Christophe Calvet ◽  
José Darrozes ◽  
Nicolas Roussel ◽  
Frédéric Frappart ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Ground Surface ◽  
Satellite System ◽  
Surface Model ◽  
Global Navigation Satellite ◽  
Grassland Site

Abstract. This work aims to assess the estimation of surface volumetric soil moisture (VSM) using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 or 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show a good agreement (R2 = 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 cm and 5 cm, especially during light rainfall events.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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