Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China

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.

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

10.20944/preprints201904.0216.v1 ◽

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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Towards an operationally capable satellite-based 0-100 cm soil moisture dataset from C3S

10.5194/egusphere-egu21-12268 ◽

2021 ◽

Author(s):

Adam Pasik ◽

Wolfgang Preimesberger ◽

Bernhard Bauer-Marschallinger ◽

Wouter Dorigo

Keyword(s):

Soil Moisture ◽

Soil Profile ◽

Land Surface ◽

Root Zone ◽

Soil Layer ◽

Land Surface Model ◽

In Situ Measurements ◽

Surface Model ◽

T Values

Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.Here we introduce the advances in our work on the first operationally capable SWI-based root-zone soil moisture dataset from C3S Soil Moisture v201912 COMBINED product, spanning the period 2002-2020. The uniqueness of this dataset lies in the fact that T-values (temporal lengths ruling the infiltration) characteristic of SWI were translated into particular soil depths making it much more intuitive, user-friendly and easily applicable. Available are volumetric soil moisture values for the top 1 m of the soil profile at 10 cm intervals, where the optimal T-value (T-best) for each soil layer is selected based on a range of correlation metrics with in situ measurements from the International Soil Moisture Network (ISMN) and the relevant soil and climatic parameters. Additionally we present the results of an extensive global validation against in situ measurements (ISMN) as well as the results of investigations into the relationship between a range of soil and climate characteristics and the optimal T-values for particular soil depths.

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Comparing Assimilation of Soil Moisture and C-band Backscatter in High Resolution Land Surface Model

10.5194/egusphere-egu2020-10277 ◽

2020 ◽

Author(s):

Leqiang Sun ◽

Stéphane Belair ◽

Marco Carrera ◽

Bernard Bilodeau

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Open Loop ◽

In Situ Measurements ◽

Lookup Table ◽

Surface Model ◽

Loop Model ◽

The Impact

Canadian Space Agency (CSA) has recently started receiving and processing the images from the recently launched C-band RADARSAT Constellation Mission (RCM). The backscatter and soil moisture retrievals products from the previously launched RADARSAT-2 agree well with both in-situ measurements and surface soil moisture modeled with land surface model Soil, Vegetation, and Snow (SVS). RCM will provide those products at an even better spatial coverage and temporal resolution. In preparation of the potential operational application of RCM products in Canadian Meteorological Center (CMC), this paper presents the scenarios of assimilating either soil moisture retrieval or outright backscatter signal in a 100-meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on field scale with time interval of three hours. The soil moisture retrieval map was synthesized by extrapolating the regression relationship between in-situ measurements and open loop model output based on soil texture lookup table. Based on this, the backscatter map was then generated with the surface roughness retrieved from RADARSAT-2 images using a modified Integral Equation Model (IEM) model. Bias correction was applied to the Ensemble Kalman filter (EnKF) to mitigate the impact of nonlinear errors introduced by multi-sourced perturbations. Initial results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals. Compared to open loop, both can improve the analysis of surface moisture, particularly in terms of reducing bias. &#160;

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The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0217.1 ◽

2020 ◽

Author(s):

Rolf H. Reichle ◽

Qing Liu ◽

Joseph V. Ardizzone ◽

Wade T. Crow ◽

Gabrielle J. M. De Lannoy ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Root Zone ◽

Land Surface Model ◽

Surface Model ◽

Meteorological Forcing ◽

Earth Observing System ◽

Error Surface

AbstractSoil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 Soil Moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with ¼-degree, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, ½-degree, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the Instrumental Variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10-0.11 compared to an increase of 0.02-0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous U.S. reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.

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Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

10.5194/hess-2019-414 ◽

2019 ◽

Author(s):

Renaud Hostache ◽

Dominik Rains ◽

Kaniska Mallick ◽

Marco Chini ◽

Ramona Pelich ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Large Scale ◽

Hydrological Model ◽

Land Surface Model ◽

Surface Model ◽

Meteorological Model ◽

Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

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Surface soil moisture data assimilation for irrigation amounts and timing estimation in semi-arid regions

10.5194/egusphere-egu21-14594 ◽

2021 ◽

Author(s):

Nadia Ouaadi ◽

Lionel Jarlan ◽

Saïd Khabba ◽

Jamal Ezzahar ◽

Olivier Merlin

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Soil ◽

Land Surface Model ◽

Surface Model ◽

Surface Soil Moisture ◽

Field Scale ◽

In Situ Data ◽

Statistical Metrics

Irrigation is the largest consumer of water in the world, with more than 70% of the world's fresh water dedicated to agriculture. In this context, we developed and evaluated a new method to predict daily to seasonal irrigation timing and amounts at the field scale using surface soil moisture (SSM) data assimilated into a simple&#160; land surface model through a particle filter technique. The method is first tested using in situ SSM before using SSM products retrieved from Sentinel-1. Data collected on different wheat fields grown &#160;in Morocco, for both flood and drip irrigation techniques, are used to assess the performance of the proposed method. With in situ data, the results are good. Seasonal amounts are retrieved with R > 0.98, RMSE <42 mm and bias<2 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation where more than 70% of the irrigation events are detected with a time difference from actual irrigation events shorter than 4 days, when assimilating SSM observation every 6 days to mimics Sentinel-1 revisit time. Over the drip irrigated fields, the statistical metrics are R = 0.70, RMSE =28.5 mm and bias= -0.24 mm for irrigation amounts cumulated over 15 days. The approach is then evaluated using SSM products derived from Sentinel-1 data; statistical metrics are R= 0.64, RMSE= 28.78 mm and bias = 1.99 mm for irrigation amounts cumulated over 15 days. In addition to irrigated fields, the applicationof the developed methodover rainfed fieldsdid not detect any irrigation. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.

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ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3515-2018 ◽

2018 ◽

Vol 22 (6) ◽

pp. 3515-3532 ◽

Cited By ~ 74

Author(s):

Clement Albergel ◽

Emanuel Dutra ◽

Simon Munier ◽

Jean-Christophe Calvet ◽

Joaquin Munoz-Sabater ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Snow Depth ◽

Land Surface Model ◽

Surface Model ◽

Area Index ◽

Model Simulations ◽

In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-22-1931-2018 ◽

2018 ◽

Vol 22 (3) ◽

pp. 1931-1946 ◽

Cited By ~ 4

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 assesses 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 and 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 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 and 5 cm, especially during light rainfall events.

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-6699-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 6699-6724 ◽

Cited By ~ 10

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.

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