Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery

[1] Turbulent heat fluxes from the surface do not have a unique signature that can be detected by remotely deployed instruments. In order to retrieve the fluxes, the measurements need to be merged into models that infer fluxes from their space and time patterns. This study is based on variational assimilation of land surface temperature (LST) into a surface energy balance model with dual-source soil and vegetation flux components. There are two major unknown parameters in the estimation of land evaporation: near-surface air turbulent conductivity (that scales the magnitude of the fluxes) and evaporative fraction (that partitions the total flux into latent and sensible heat flux). This study advances the data assimilation approach in two major new directions. First and foremost, it recasts the variational assimilation system as a multiscale problem with LST estimates from a constellation of satellites. The assimilation system can ingest measurements with varying scales and overlapping coverages. Second, the remotely sensed LST is treated as a combination of contributions from the canopy and the exposed soil surface. Application to a large area within the U.S. Great Plains is shown. Spatial patterns of the retrieved parameters, their correspondence to observed land use maps, and their consistency with seasonal phenology are demonstrated. Finally, the performance of a combined-source formulation is compared with the dual-source model. Remarkably, the spatial patterns of the heat transfer coefficient reflect dominant vegetation patterns, even though there was no vegetation index information used in the combined-source formulation.

[1]  Wilfried Brutsaert,et al.  Daytime evaporation and the self-preservation of the evaporative fraction and the Bowen ratio , 1996 .

[2]  Shafiqul Islam,et al.  Prediction of Ground Surface Temperature and Soil Moisture Content by the Force‐Restore Method , 1995 .

[3]  Fabio Castelli,et al.  Estimation of Surface Turbulent Fluxes through Assimilation of Radiometric Surface Temperature Sequences , 2004 .

[4]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[5]  J. Araus,et al.  Spectral vegetation indices as nondestructive tools for determining durum wheat yield. , 2000 .

[6]  M. S. Moran,et al.  Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index , 1994 .

[7]  Giorgio Boni,et al.  Sampling strategies and assimilation of ground temperature for the estimation of surface energy balance components , 2001, IEEE Trans. Geosci. Remote. Sens..

[8]  Martha C. Anderson,et al.  Estimating land surface energy budgets from space: Review and current efforts at the University of Wisconsin-Madison and USDA-ARS , 2004 .

[9]  Christopher M. U. Neale,et al.  Land surface temperature derived from the SSM/I passive microwave brightness temperatures , 1990 .

[10]  Craig S. T. Daughtry,et al.  Estimation of the soil heat flux/net radiation ratio from spectral data , 1990 .

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

[12]  Carlo Ulivieri,et al.  A split window algorithm for estimating land surface temperature from satellites , 1994 .

[13]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[14]  Thomas J. Jackson,et al.  Soil moisture mapping at regional scales using microwave radiometry: the Southern Great Plains Hydrology Experiment , 1999, IEEE Trans. Geosci. Remote. Sens..

[15]  R. Crago,et al.  Conservation and variability of the evaporative fraction during the daytime , 1996 .

[16]  J. Norman,et al.  Surface flux estimation using radiometric temperature: A dual‐temperature‐difference method to minimize measurement errors , 2000 .

[17]  William E. Nichols,et al.  Evaluation of the evaporative fraction for parameterization of the surface energy balance , 1993 .

[18]  Timothy J. Schmit,et al.  Derived Product Imagery from GOES-8 , 1996 .

[19]  William P. Kustas,et al.  An intercomparison study on models of sensible heat flux over partial canopy surfaces with remotely sensed surface temperature , 1996 .

[20]  Mark A. Friedl,et al.  Modeling land surface fluxes using a sparse canopy model and radiometric surface temperature measurements , 1995 .

[21]  Fabio Castelli,et al.  Mapping of Land-Atmosphere Heat Fluxes and Surface Parameters with Remote Sensing Data , 2003 .

[22]  Fabio Castelli,et al.  Estimation of surface heat flux and an index of soil moisture using adjoint‐state surface energy balance , 1999 .

[23]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[24]  John M. Norman,et al.  Estimating Fluxes on Continental Scales Using Remotely Sensed Data in an Atmospheric–Land Exchange Model , 1999 .

[25]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[26]  Jean-Yves Pontailler,et al.  Estimation of leaf area index using ground-based remote sensed NDVI measurements: validation and comparison with two indirect techniques , 2003 .

[27]  M. S. Moran,et al.  Single- and Dual-Source Modeling of Surface Energy Fluxes with Radiometric Surface Temperature , 1996 .

[28]  M. Derrien,et al.  Automatic cloud detection applied to NOAA-11 /AVHRR imagery , 1993 .

[29]  W. Kustas,et al.  A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e , 1997 .

[30]  Z. Su.  The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[31]  Robert E. Dickinson,et al.  The Force–Restore Model for Surface Temperatures and Its Generalizations , 1988 .

[32]  Thomas J. Jackson,et al.  Verification of Patch- and Regional-Scale Energy Balance Estimates Derived from Microwave and Optical Remote Sensing during SGP97 , 2001 .

[33]  Eva Boegh,et al.  Evaluating evapotranspiration rates and surface conditions using Landsat TM to estimate atmospheric resistance and surface resistance , 2002 .

[34]  Giorgio Boni,et al.  Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation , 2001 .

[35]  Le Jiang,et al.  A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations , 1999 .