Retrieving the Root-Zone Soil Moisture from Surface Soil Moisture or Temperature Estimates: A Feasibility Study Based on Field Measurements

Calvet, J.‐C.; Noilhan, J.; Bessemoulin, Pierre

doi:10.1175/1520-0450(1998)037<0371:rtrzsm>2.0.co;2

Cited by 114 publications

(101 citation statements)

References 22 publications

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“…However; soil moisture in the root zone could be derived from surface soil moisture through a model of water infiltration into the soil [57][58][59][60][61][62][63].…”

Section: Discussionmentioning

confidence: 99%

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

et al. 2017

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Abstract:Soil moisture mapping at a high spatial resolution is very important for several applications in hydrology, agriculture and risk assessment. With the arrival of the free Sentinel data at high spatial and temporal resolutions, the development of soil moisture products that can better meet the needs of users is now possible. In this context, the main objective of the present paper is to develop an operational approach for soil moisture mapping in agricultural areas at a high spatial resolution over bare soils, as well as soils with vegetation cover. The developed approach is based on the synergic use of radar and optical data. A neural network technique was used to develop an operational method for soil moisture estimates. Three inversion SAR (Synthetic Aperture Radar) configurations were tested: (1) VV polarization; (2) VH polarization; and (3) both VV and VH polarization, all in addition to the NDVI information extracted from optical images. Neural networks were developed and validated using synthetic and real databases. The results showed that the use of a priori information on the soil moisture condition increases the precision of the soil moisture estimates. The results showed that VV alone provides better accuracy on the soil moisture estimates than VH alone. In addition, the use of both VV and VH provides similar results, compared to VV alone. In conclusion, the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol % (volumetric unit expressed in percent). Better results were obtained for soil with a moderate surface roughness (for root mean surface height between 1 and 3 cm). The developed approach could be applied for agricultural plots with an NDVI lower than 0.75.

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“…However; soil moisture in the root zone could be derived from surface soil moisture through a model of water infiltration into the soil [57][58][59][60][61][62][63].…”

Section: Discussionmentioning

confidence: 99%

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

et al. 2017

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“…It depends on the type of soil and of vegetation. It has been widely validated over vegetated and bare ground surfaces (Mahfouf and Noilhan, 1991;Calvet et al, 1998).…”

Section: The Svat Modelmentioning

confidence: 99%

Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

Juglea

Kerr

Mialon

et al. 2010

Hydrol. Earth Syst. Sci.

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Abstract. The main goal of the SMOS (Soil Moisture andOcean Salinity) mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz) radiometry. Within the context of the Science preparation for SMOS, the Valencia Anchor Station (VAS) experimental site, in Spain, was chosen to be one of the main test sites in Europe for Calibration/Validation (Cal/Val) activities. In this framework, the paper presents an approach consisting in accurately simulating a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over the wide VAS surface (50×50 km 2 ). Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT) model, SURFEX (Externalized Surface) -module ISBA (Interactions between Soil-Biosphere-Atmosphere) to simulate the spatial and temporal distribution of surface soil moisture. The calibration as well as the validation of the ISBA model are performed using in situ soil moisture measurements. It is shown that a good consistency is reached when point comparisons between simulated and in situ soil moisture measurements are made.Actually, an important challenge in remote sensing approaches concerns product validation. In order to obtain an representative soil moisture mapping over the Valencia Anchor Station (50×50 km 2 area), a spatialization method is

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“…During the past two decades, various inverse methods have been proposed to indirectly infer land surface state variables and model parameters [10][11][12][13]. In a feasibility study, Calvet et al [11] investigated the issue of estimating root-zone soil moisture from time series of surface soil moisture observations, using a least square fit method.…”

Section: Introductionmentioning

confidence: 99%

“…In a feasibility study, Calvet et al [11] investigated the issue of estimating root-zone soil moisture from time series of surface soil moisture observations, using a least square fit method. For a silt loam crop area, they demonstrated the feasibility of retrieving total soil water content from rather infrequent observations (five observations spread out over a 15-day period) of surface soil moisture.…”

Section: Introductionmentioning

confidence: 99%

Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

Ren

2010

Open Geosciences

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Based on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude. Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies. With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations. Keywords: variational data assimilation • land surface modeling • numerical weather prediction (NWP) model • adjoint technique based 4D-Var

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Retrieving the Root-Zone Soil Moisture from Surface Soil Moisture or Temperature Estimates: A Feasibility Study Based on Field Measurements

Cited by 114 publications

References 22 publications

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

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