2012 AGU Chapman Conference on Remote Sensing of the ...

Wood, EricInternal validation of a distributed hydrologicalmodel through land surface temperature fromremote sensingCorbari, Chiara 1 ; Ravazzani, Giovanni 1 ; Masseroni, Daniele 1 ;Mancini, Marco 1 ; Wood, Eric 21. Politecnico di Milano, Milano, Italy2. Princeton University, Princeton, NJ, USAThis presentation presents a validation of a distributedenergy-water balance model through the constraints on aninternal model variable, the pixel-scale equilibriumtemperature, using remote sensing data of land surfacetemperature. The pixels span different test scales fromagricultural fields to the river basin. The model algorithmsolves the system of energy and mass balances in term of theequilibrium pixel temperature or representative equilibriumtemperature (RET) that governs the fluxes of energy andmass over the basin domain. This equilibrium surfacetemperature is compared to land surface temperature (LST)as retrieved from operational remote sensing data atdifferent spatial and temporal resolutions. The LST is acritical model state variable and remote sensing LST thatcan be effectively used, in combination with energy and massbalance modeling, to monitor latent and sensible heatfluxes. The fluxes regulated from this equilibriumtemperature are also compared and controlled from thosecomputed from local eddy covariance tower data. Adiscussion on the representativeness of satellite, eddycovariance and energy water balance fluxes is made, showingscale congruence among these. A number of case studieshave been carried out ranging from agricultural districtareas to river basins using data from operational satellitesensors and specific airborne flight. The case studies includea maize field in Landriano (Italy), the agricultural district ofBarrax (Spain) and the Upper Po river basin (Italy). Thepresent approach contributes to the research directionhighlighted 30 years ago from Jim Dooge, who encouragedthe scientific modeling community to analyze the behaviourof the model internal state variable (e.g. soil moisture and itsproxy) in addition to the traditional external fluxes (e.g.discharge) to obtain better understanding of hydrologicprocess and model analysis. We think that the use of LSTand RET concepts from energy water balance modeling is acontribution in this direction and is synergistic to effortsdone in remote sensing microwave regarding soil moisture.evaporation at the earth surface. Soil moisture also has astrong effect on surface energy exchange. Thus soil moisturetrends may have a great impact on climate change over land.Likewise, soil moisture is clearly important for thehydrologic applications such as flood and droughtmonitoring, weather forecast, water management andagricultural plant growth. There has been a range ofspaceborne remote sensing sensors deployed during the lasttwo decades to retrieve near surface (~ 0 - 2 cm) soilmoisture. The retrieval techniques to derive satellite basedsoil moisture include either physically based algorithms(such as radiative transfer equations) or various indirectstatistical algorithms. Multiple efforts have been devoted inthe community to retrieve the global near surface soilmoisture content from the Advanced Microwave ScanningRadiometer (AMSR-E) 10.7 GHz measurements using sometype of Radiative Transfer Models (RTM). We find that amajor challenge for using a physically based RTM to retrievesurface soil moisture is to determine the suitable values ofRTM parameters such as the surface soil and vegetationproperties, because the default values of these parametersderived from standard approaches, e.g., soil texture or landcover lookup table, often do not reflect the reality and resultin wrong or off-physical-limit soil moisture retrievals. In thisstudy, we calibrate three major static surface parameters, thefraction of vegetation coverage, roughness height and sandfraction, in the Land Surface Microwave Emission Model(LSMEM), such that the surface emissivity predictions fromthe model climatologically match with the AMSR-Emeasurements. The calibration produces robust parameters,i.e., the parameter values remain stable with longer trainingperiods. Additionally, to improve the vegetation thicknessparameter, which is dynamic in space and time and difficultto calibrate, we implement a new scheme developed atUniversity of Montana to calculate the vegetation opticaldepth. The new calibration technique along with theupdated parameterizations in LSMEM produces a reliablesoil moisture estimate from AMSR-E for the period of 2002to 2011 with higher accuracy. The above calibrationtechnique will be applied to other potential past (e.g.Tropical Rainfall Measuring Mission Microwave Imager(TMI); Scanning Multi-channel Microwave Radiometer(SMMR)) and current (e.g. Soil Moisture and Ocean Salinity(SMOS)) sensors to create a consistent global soil moistureclimatic data record.Wood, EricCreating Global Soil Moisture Data Record FromAMSR-E Through Calibration of a RadiativeTransfer ModelPan, Ming 1 ; Sahoo, Alok 1 ; Wood, Eric 11. Dept of CEE, Princeton University, Princeton, NJ, USASoil moisture is a critical element for both global waterand energy budgets. Soil moisture controls theredistribution of rainfall into infiltration, surface runoff and152