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

<p>ESA’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.</p><p>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.</p><p>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 (α and m), soil porosity (θ<sub>sat</sub>). 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.</p><p>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.</p>

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 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 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.

scholarly journals The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

scholarly journals Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3109
Author(s):  
Roïya Souissi ◽  
Ahmad Al Bitar ◽  
Mehrez Zribi
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Soil Texture ◽  
Surface Soil ◽  
Root Zone ◽  
Data Filtering ◽  
Surface Soil Moisture ◽  
The Mean ◽  
Artificial Neural

This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future.

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

2010 ◽  
Vol 14 (11) ◽  
pp. 2177-2191 ◽  
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.

scholarly journals The Indian COSMOS Network (ICON): Validating L-Band Remote Sensing and Modelled Soil Moisture Data Products

2021 ◽  
Vol 13 (3) ◽  
pp. 537
Author(s):  
Deepti B Upadhyaya ◽  
Jonathan Evans ◽  
Sekhar Muddu ◽  
Sat Kumar Tomer ◽  
Ahmad Al Bitar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Surface Soil ◽  
Root Zone ◽  
Cosmic Ray ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Scale Difference

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR).

scholarly journals Satellite soil moisture data assimilation for improved operational continental water balance prediction

2020 ◽  
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Robert C. Pipunic ◽  
Julien Lerat ◽  
Wendy Sharples ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Observation ◽  
Surface Soil Moisture ◽  
Time Step ◽  
Australian Water

Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is the sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model in a post-analysis adjustment after the state updating at each time step. In this study, we apply the data assimilation framework to the Australian Water Resources Assessment Landscape model (AWRA-L) and evaluate its impact on the model's accuracy against in-situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in-situ observation increases from 0.54 (open-loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed vegetation time series across cropland areas results in significant improvements of 0.11 correlation units. The improvements gained from data assimilation can persist for more than one week in surface soil moisture estimates and one month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

scholarly journals Satellite soil moisture data assimilation for improved operational continental water balance prediction

2021 ◽  
Vol 25 (8) ◽  
pp. 4567-4584
Author(s):  
Siyuan Tian ◽  
Luigi J. Renzullo ◽  
Robert C. Pipunic ◽  
Julien Lerat ◽  
Wendy Sharples ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Surface Soil ◽  
Root Zone ◽  
In Situ Observation ◽  
Surface Soil Moisture ◽  
Time Step ◽  
Australian Water

Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is a Kalman-filter-type sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model using tangent linear modelling theory in a post-analysis adjustment after the state updating at each time step. In this study, we assimilate satellite soil moisture retrievals from both Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions simultaneously into the Australian Water Resources Assessment Landscape model (AWRA-L) using the proposed framework and evaluate its impact on the model's accuracy against in situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in situ observation increases from 0.54 (open loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed Enhanced Vegetation Index (EVI) time series across cropland areas results in significant improvements from 0.52 to 0.64 in correlation. The improvements gained from data assimilation can persist for more than 1 week in surface soil moisture estimates and 1 month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

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).

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