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[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: nearsurface 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.

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[1] Many Earth system science and environmental applications require knowledge of mapped evaporation. Satellite remote sensing can indirectly provide these measurements with a spatial coverage that is logistically and economically impossible to obtain through ground-based observation networks. Here a model for surface energy fluxes estimation based on the assimilation of land surface temperature from satellite is presented. The data assimilation scheme provides a useful framework that allows us to combine measurements and models to produce an optimal and dynamically consistent estimate of the evolving state of the system. The assimilation scheme can take advantage of the synergy of multisensor-multiplatform observations in order to obtain estimations of surface fluxes, flux partitioning, and surface characteristics. The model is based on the surface energy balance and bulk transfer formulation. A simplified soil wetness model, which is a filter of antecedent precipitation, is introduced in order to develop a more robust estimation scheme. This approach is implemented and tested over the Southern Great Plain field experiment domain. Comparisons with observed surface energy fluxes and soil moisture maps have shown that this assimilation system can estimate, when compared with the ground truth observations, the surface energy balance and its partitioning among turbulent heat fluxes. The introduction of the simplified soil wetness model forced by precipitation data improved evaporative fraction estimation. Further research is still required to analyze the reliability of retrieved fluxes in periods where radiation is the limiting factor for latent heat flux.

Abstract:Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS-2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring-summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat-ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only.