Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals

Abstract The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the soil column) soil moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE 3 ) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface soil moisture retrievals, the assimilation of both root- and surface-zone soil moisture estimates reduces the root-mean-square difference between estimated and true root-zone soil moisture by 50% to 35% (assuming instantaneous root-zone soil moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m 3  m −3 ). Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m 3  m −3 ) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R 2 correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface soil moisture and tower-based thermal infrared temperature observations made at the OPE 3 study site.

[1]  Piers J. Sellers,et al.  Relations between surface conductance and spectral vegetation indices at intermediate (100 m2 to 15 km2) length scales , 1992 .

[2]  S. Idso,et al.  Canopy temperature as a crop water stress indicator , 1981 .

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

[4]  R. Jackson.  Canopy Temperature and Crop Water Stress , 1982 .

[5]  Steven A. Margulis,et al.  Feasibility Test of Multifrequency Radiometric Data Assimilation to Estimate Snow Water Equivalent , 2006 .

[6]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[7]  Martha C. Anderson,et al.  Retrieval of an Available Water-Based Soil Moisture Proxy from Thermal Infrared Remote Sensing. Part I: Methodology and Validation , 2009 .

[8]  M. Friedl,et al.  Diurnal Covariation in Soil Heat Flux and Net Radiation , 2003 .

[9]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature , 1995 .

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

[11]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .

[12]  Andrew S. Jones,et al.  Data Assimilation of Satellite-Derived Heating Rates as Proxy Surface Wetness Data into a Regional Atmospheric Mesoscale Model. Part II: A Case Study , 1998 .

[13]  Jeffrey P. Walker,et al.  Extended versus Ensemble Kalman Filtering for Land Data Assimilation , 2002 .

[14]  C. Daughtry,et al.  Using soil moisture and spatial yield patterns to identify subsurface flow pathways. , 2005, Journal of environmental quality.

[15]  Jeffrey P. Walker,et al.  Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study , 2008 .

[16]  G. Hornberger,et al.  A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils , 1984 .

[17]  Thomas J. Jackson,et al.  Utility of Remote Sensing–Based Two-Source Energy Balance Model under Low- and High-Vegetation Cover Conditions , 2005 .

[18]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[19]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

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

[21]  Jetse D. Kalma,et al.  One-Dimensional Soil Moisture Profile Retrieval by Assimilation of Near-Surface Measurements: A Simplified Soil Moisture Model and Field Application , 2001 .

[22]  Randal D. Koster,et al.  Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model , 2005 .

[23]  J. Deardorff.  Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation , 1978 .

[24]  M. Bierkens,et al.  Assimilation of remotely sensed latent heat flux in a distributed hydrological model , 2003 .

[25]  J. D. Tarpley,et al.  Real‐time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project , 2003 .

[26]  William P. Kustas,et al.  Combining Optical and Microwave Remote Sensing for Mapping Energy Fluxes in a Semiarid Watershed , 1998 .

[27]  Toby N. Carlson,et al.  Decoupling of surface and near‐surface soil water content: A remote sensing perspective , 1997 .

[28]  V. Lakshmi,et al.  A simple surface temperature assimilation scheme for use in land surface models , 2000 .

[29]  Assimilation of Gravity Data into a Soil Moisture and Groundwater Column Model , 2004 .

[30]  W. Crow,et al.  The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: a case study based on ESTAR measurements during SGP97 , 2003 .

[31]  D. Entekhabi,et al.  Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment , 2001 .

[32]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology , 2007 .

[33]  J. Norman,et al.  Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover , 1999 .

[34]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[35]  Matthias Drusch,et al.  Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture , 2005 .

[36]  Shafiqul Islam,et al.  Estimation of root zone soil moisture and surface fluxes partitioning using near surface soil moisture measurements , 2002 .

[37]  Wade T. Crow,et al.  Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model , 2008 .

[38]  Thomas J. Jackson,et al.  Soil moisture mapping and AMSR-E validation using the PSR in SMEX02 , 2006 .

[39]  Marco Mancini,et al.  Robust simulation of root zone soil moisture with assimilation of surface soil moisture data , 2001 .

[40]  Wade T. Crow,et al.  Utility of Assimilating Surface Radiometric Temperature Observations for Evaporative Fraction and Heat Transfer Coefficient Retrieval , 2005 .