Improvement of TOPLATS‐based discharge predictions through assimilation of ERS‐based remotely sensed soil moisture values

In this paper, we investigate the possibility to improve discharge predictions from a lumped hydrological model through assimilation of remotely sensed soil moisture values. Therefore, an algorithm to estimate surface soil moisture values through active microwave remote sensing is developed, bypassing the need to collect in situ ground parameters. The algorithm to estimate soil moisture by use of radar data combines a physically based and an empirical back-scatter model. This method estimates effective soil roughness parameters, and good estimates of surface soil moisture are provided for bare soils. These remotely sensed soil moisture values over bare soils are then assimilated into a hydrological model using the statistical correction method. The results suggest that it is possible to determine soil moisture values over bare soils from remote sensing observations without the need to collect ground truth data, and that there is potential to improve model-based discharge predictions through assimilation of these remotely sensed soil moisture values. Copyright © 2002 John Wiley & Sons, Ltd.

[1]  Peter A. Troch,et al.  Sensitivity of radar backscattering to soil surface parameters: a comparison between theoretical analysis and experimental evidence , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[2]  T. Jackson,et al.  III. Measuring surface soil moisture using passive microwave remote sensing , 1993 .

[3]  J. Famiglietti,et al.  Multiscale modeling of spatially variable water and energy balance processes , 1994 .

[4]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Kamal Sarabandi,et al.  An empirical model and an inversion technique for radar scattering from bare soil surfaces , 1992, IEEE Trans. Geosci. Remote. Sens..

[6]  Eric F. Wood,et al.  A soil‐vegetation‐atmosphere transfer scheme for modeling spatially variable water and energy balance processes , 1997 .

[7]  Peter A. Troch,et al.  Hydrologic controls of large floods in a small basin: central Appalachian case study , 1994 .

[8]  S. Planton,et al.  A Simple Parameterization of Land Surface Processes for Meteorological Models , 1989 .

[9]  Eric F. Wood,et al.  A soil‐vegetation‐atmosphere transfer scheme for the modeling of water and energy balance processes in high latitudes: 2. Application and validation , 1999 .

[10]  P. Milly.  An event-based simulation model of moisture and energy fluxes at a bare soil surface , 1986 .

[11]  T. Mo,et al.  The Effects of Soil Moisture, Surface Roughness, and Vegetation on L-Band Emission and Backscatter , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[12]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[13]  A. Fung.  Microwave Scattering and Emission Models and their Applications , 1994 .

[14]  A. Fung,et al.  Microwave Remote Sensing Active and Passive-Volume III: From Theory to Applications , 1986 .

[15]  J. Philip,et al.  The Theory of Infiltration , 1958 .

[16]  Z. Su,et al.  Remote sensing of bare surface soil moisture using EMAC/ESAR data , 1997 .

[17]  J. Philip,et al.  THE THEORY OF INFILTRATION: 4. SORPTIVITY AND ALGEBRAIC INFILTRATION EQUATIONS , 1957 .

[18]  K. Beven,et al.  Soil moisture estimation over grass-covered areas using AIRSAR. , 1994 .

[19]  Pedro Viterbo,et al.  The land surface‐atmosphere interaction: A review based on observational and global modeling perspectives , 1996 .

[20]  Thomas J. Jackson,et al.  Estimating profile soil moisture from surface-layer measurements: a review , 1993, Defense, Security, and Sensing.

[21]  S. Idso,et al.  Analysis of an empirical model for soil heat flux under a growing wheat crop for estimating evaporation by an infrared-temperature based energy balance equation , 1987 .

[22]  M. Mancini,et al.  Retrieving Soil Moisture Over Bare Soil from ERS 1 Synthetic Aperture Radar Data: Sensitivity Analysis Based on a Theoretical Surface Scattering Model and Field Data , 1996 .

[23]  Keith Beven,et al.  On hydrologic similarity: 2. A scaled model of storm runoff production , 1987 .

[24]  Peter A. Troch,et al.  Effective water table depth to describe initial conditions prior to storm rainfall in humid regions , 1993 .

[25]  W. J. Shuttleworth,et al.  Integration of soil moisture remote sensing and hydrologic modeling using data assimilation , 1998 .

[26]  Adrian K. Fung,et al.  A microwave scattering model for layered vegetation , 1992, IEEE Trans. Geosci. Remote. Sens..

[27]  Peter Troch,et al.  Assimilation of active microwave observation data for soil moisture profile estimation , 2000 .

[28]  D. L. Brakensiek,et al.  Estimation of Soil Water Properties , 1982 .

[29]  Adrian K. Fung,et al.  Backscattering from a randomly rough dielectric surface , 1992, IEEE Trans. Geosci. Remote. Sens..

[30]  Transferts de masse et de chaleur dans un sol stratifié soumis à une excitation atmosphérique naturelle : comparaison : modèles-expérience , 1986 .

[31]  Urs Wegmüller,et al.  Active and passive microwave signature catalog on bare soil (2-12 GHz) , 1994, IEEE Trans. Geosci. Remote. Sens..

[32]  Thomas J. Jackson,et al.  Soil moisture estimation using special satellite microwave/imager satellite data over a grassland region , 1997 .

[33]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models , 1985, IEEE Transactions on Geoscience and Remote Sensing.