scholarly journals Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response

2019 ◽  
Vol 23 (2) ◽  
pp. 1067-1081 ◽  
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Albert I. J. M. van Dijk ◽  
Paul Tregoning ◽  
Jeffrey P. Walker
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
Open Loop ◽  
Total Water ◽  
Surface Soil Moisture ◽  
Vegetation Greenness

Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. The GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column. However, root-zone soil moisture cannot be separated from GRACE-observed total water storage anomalies without ancillary information on surface water and groundwater changes. In this study, GRACE total water storage anomalies and SMOS near-surface soil moisture observations were jointly assimilated into a hydrological model globally to better estimate the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture estimates through the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For example, the correlation between modelled and in situ measurements of root-zone moisture increased by 0.1 (from 0.48 to 0.58) and 0.12 (from 0.53 to 0.65) on average for grasslands and croplands, respectively. Improved correlations were found between vegetation greenness and soil water storage on both seasonal variability and anomalies over water-limited regions. Joint assimilation results show a more severe deficit in soil water anomalies in eastern Australia, southern India and eastern Brazil over the period of 2010 to 2016 than the open-loop, consistent with the satellite-observed vegetation greenness anomalies. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation conditions.

scholarly journals Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response

2018 ◽  
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Albert I. J. M. van Dijk ◽  
Paul Tregoning ◽  
Jeffrey P. Walker
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Root Zone ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
Open Loop ◽  
Total Water ◽  
Vegetation Response ◽  
Vegetation Greenness

Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. In contrast, GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column, which is often dominated by groundwater variation. However, not all vegetation communities can access groundwater. In this study, satellite-derived water content from GRACE and SMOS were jointly assimilated into an ecohydrological model to better predict the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture prediction though the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For example, the correlation between modelled and in-situ measurements of root-zone moisture increased by 0.1 on average over grasslands and croplands. Improved correlations were found between vegetation greenness and soil water storage derived from the joint assimilation with an increase up to 0.47 over grassland compared to open-loop estimates. Joint assimilation results show a more severe deficit in soil water in eastern Australia, western North America and eastern Brazil over the period of 2010 to 2015 than the open-loop, consistent with the satellite-observed vegetation greenness. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation response.

Catchment-scale connection between vegetation accessible storage and satellite-derived Soil Water Index

2020 ◽  
Author(s):  
Laurène Bouaziz ◽  
Susan Steele-Dunne ◽  
Jaap Schellekens ◽  
Albrecht Weerts ◽  
Jasper Stam ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Water Storage ◽  
Surface Soil Moisture ◽  
Catchment Scale ◽  
Near Surface ◽  
Water Index ◽  
Optimal Values

<p>Estimates of water volumes stored in the root-zone of vegetation are a key element controlling the hydrological response of a catchment. Remotely-sensed soil moisture products are available globally. However, they are representative of the upper-most few centimeters of the soil. For reliable runoff predictions, we are interested in root-zone soil moisture estimates as they regulate the partitioning of precipitation to drainage and evaporation. The Soil Water Index approximates root-zone soil moisture from near-surface soil moisture and requires a single parameter representing the characteristic time length T of temporal soil moisture variability. Climate and soil properties are typically assumed to influence estimates of T, however, no clear quantitative link has yet been established and often a standard value of 20 days is assumed. In this study, we hypothesize that optimal T values are linked to the accumulated difference between precipitation (water supply) and evaporation (atmospheric water demand) during dry periods with return periods of 20 years, and, thus, to catchment-scale vegetation-accessible water storage capacities. We identify the optimal values of T that provide an adequate match between estimated SWI from several satellite-based near-surface soil moisture products (derived from AMSR2, SMAP and Sentinel-1) and modeled time series of root-zone soil moisture from a calibrated process-based model in 16 contrasting catchments of the Meuse river basin. We found that optimal values of T vary between 1 and 98 days with a median of 17 days across the studied catchments and soil moisture products. We furthermore show that T, which was previously known to increase with increasing depth of the soil layer, is positively and strongly related with catchment-scale root-zone water storage capacity, estimated based on long-term water balance data.  This is useful to generate estimates of root-zone soil moisture from satellite-based surface soil moisture, as they are a key control of the response of hydrological systems.</p>

scholarly journals Estimating annual water storage variations in medium-scale (2000–10 000 km<sup>2</sup>) basins using microwave-based soil moisture retrievals

2017 ◽  
Vol 21 (3) ◽  
pp. 1849-1862 ◽  
Author(s):  
Wade T. Crow ◽  
Eunjin Han ◽  
Dongryeol Ryu ◽  
Christopher R. Hain ◽  
Martha C. Anderson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Budget ◽  
Surface Soil ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
The United States ◽  
Surface Soil Moisture ◽  
Medium Scale ◽  
Annual Water

Abstract. Due to their shallow vertical support, remotely sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (dS ∕ dt). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying dS ∕ dt via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2000–10 000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain statistically significant information concerning dS ∕ dt. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing products to enhance the spatial resolution of dS ∕ dt estimates acquired from gravity remote sensing.

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

scholarly journals Satellite-observed changes in vegetation sensitivities to surface soil moisture and total water storage variations since the 2011 Texas drought

2017 ◽  
Vol 12 (5) ◽  
pp. 054006 ◽  
Author(s):  
Geruo A ◽  
Isabella Velicogna ◽  
John S Kimball ◽  
Jinyang Du ◽  
Youngwook Kim ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Water Storage ◽  
Total Water ◽  
Surface Soil Moisture

Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing

2013 ◽  
Vol 14 (4) ◽  
pp. 1259-1277 ◽  
Author(s):  
C. Albergel ◽  
W. Dorigo ◽  
R. H. Reichle ◽  
G. Balsamo ◽  
P. de Rosnay ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Root Zone ◽  
Microwave Remote Sensing ◽  
Surface Soil Moisture ◽  
Global Trends ◽  
Global Trend ◽  
Grid Points ◽  
Modern Era

Abstract In situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the reliability of three soil moisture products: (i) a revised version of the ECMWF Interim Re-Analysis (ERA-Interim; ERA-Land); (ii) a revised version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis from NASA (MERRA-Land); and (iii) a new, microwave-based multisatellite surface soil moisture dataset (SM-MW). Evaluation of the time series and anomalies from a moving monthly mean shows a good performance of the three products in capturing the annual cycle of surface soil moisture and its short-term variability. On average, correlations (95% confidence interval) are 0.66 (±0.038), 0.69 (±0.038), and 0.60 (±0.061) for ERA-Land, MERRA-Land, and SM-MW. The two reanalysis products also capture the root-zone soil moisture well; on average, correlations are 0.68 (±0.035) and 0.73 (±0.032) for ERA-Land and MERRA-Land, respectively. Global trends analysis for 1988–2010 suggests a decrease of surface soil moisture contents (72% of significant trends are negative, i.e., drying) for ERA-Land and an increase in surface soil moisture (59% of significant trends are positive, i.e., wetting) for MERRA-Land. As the spatial extent and fractions of significant trends in both products differ, the trend reflected in the majority of grid points within different climate classes was investigated and compared to that of SM-MW. The latter is dominated by negative significant trends (73.2%) and is more in line with ERA-Land. For both reanalysis products, trends for the upper layer of soil are confirmed in the root-zone soil moisture (first meter of soil).

scholarly journals Error Propagation from Remotely Sensed Surface Soil Moisture Into Soil Water Index Using an Exponential Filter

10.29007/kvhb ◽  
2018 ◽  
Author(s):  
Domenico De Santis ◽  
Daniela Biondi
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Error Propagation ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Measurements ◽  
Surface Soil Moisture ◽  
Water Index ◽  
Study Sites

In this study an error propagation (EP) scheme was introduced in parallel to exponential filter computation for soil water index (SWI) estimation. A preliminarily assessment of the computed uncertainties was carried out comparing satellite-derived SWI and reference root-zone in situ measurements. The EP scheme has shown skills in detecting potentially less reliable SWI values in the study sites, as well as a better understanding of the exponential filter shortcomings. The proposed approach shows a potential for SWI evaluation, providing simultaneous estimates of time-variant uncertainty.

scholarly journals Estimating Annual Water Storage Variations Using Microwave-based Soil Moisture Retrievals

2016 ◽  
Author(s):  
Wade T. Crow ◽  
Eunjin Han ◽  
Dongryeol Ryu ◽  
Christopher R. Hain ◽  
Martha C. Anderson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Budget ◽  
Surface Soil ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
The United States ◽  
Surface Soil Moisture ◽  
Annual Variations ◽  
Annual Water

Abstract. Due to their shallow vertical support, remotely-sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (S). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying annual variations in S via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2,000–10,000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain significant information concerning relative inter-annual variations in S. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing to enhance the spatial resolution of S estimates acquired from gravity remote sensing. However, challenging calibration issues regarding the relationship between S and surface soil moisture must be resolved before the approach can be used for absolute water budget closure.

Groundwater dynamics retrievals in Africa using SMOS soil moisture measurements

2020 ◽  
Author(s):  
Thierry Pellarin ◽  
Laurent Oxarango ◽  
Jean-Martial Cohard ◽  
Alban Depeyre ◽  
Basile Hector ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Groundwater Recharge ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Soil Moisture ◽  
Hydrodynamic Parameters ◽  
Grace Satellite ◽  
Moisture Dynamics

&lt;p&gt;ESA&amp;#8217;s SMOS mission is celebrating 10 years of measurements in 2020 and is still producing soil moisture data of interest for many applications. One of the successes of this mission is its unexpected applications of soil moisture, such as thin ice sheets over the ocean, above ground biomass and carbon stocks, crop yields or rainfall estimation. We believe that knowledge of soil moisture time series contains information that are closely related to the functioning of the hydrosphere (infiltration, evaporation, groundwater recharge) and the biosphere (vegetation development, crop yield, carbon storage). These two compartments are traditionally studied using models forced by precipitation rates and atmospheric variables. However, beyond the difficulty of measuring the precipitation rate accurately from space, a non-negligible portion of rain does not infiltrate the soil either because it is intercepted by vegetation or because of the surface runoff.&lt;/p&gt;&lt;p&gt;In this study, we assume that SMOS retrieved soil moisture dynamics (0-5 cm) can inform us on much deeper soil horizons. Given that the water that reaches the root zone (0-200cm) and groundwater necessarily transits at some point through the surface, we can hypothesize that surface soil moisture dynamics intrinsically contains information on water dynamics in deeper layers.&lt;/p&gt;&lt;p&gt;To test this idea, we used Richards' 1D model and forced the first layer of the model with 5-cm in-situ soil moisture measurements from the AMMA-CATCH observatory sites in West-Africa. A variation of soil moisture at the surface generates moisture variations in the deeper layers according to the hydrodynamic parameters of the model: soil conductivity at saturation (Ks), shape parameters of the retention curve (&amp;#945; and m), soil porosity (&amp;#952;&lt;sub&gt;sat&lt;/sub&gt;). For highly permeable soils, water rapidly infiltrates the soil column and creates a groundwater table with its seasonal dynamics. For more impermeable soils, water remains close to the surface and there is no groundwater recharge. This approach satisfyingly compares with in-situ measurements concerning both root zone soil moisture profiles and water table dynamics.&lt;/p&gt;&lt;p&gt;In a second step, the proposed methodology was applied to measurements derived from the SMOS satellite over the whole of Africa. To substitute in situ measurements, the GRACE satellite gravity data is used to compare with simulated soil water variations. This comparison allows to reject a lot of hydrodynamic parameters, and to select the best combination of the 4 parameters. Finally, the method makes it possible to produce maps of water table depths and their temporal dynamics at the scale of the African continent from information on surface soil moisture from SMOS (0-5cm) and soil water content from GRACE satellite.&lt;/p&gt;

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 5 (3) ◽  
pp. 1603-1640 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automatic weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve the root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a climate effect exists.

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