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

future scientific analysis and research conducted to supportpolicymakers and Federal civil agency missions to inform thepublic. Publically released imagery can be downloaded andfreely used and distributed (source URLs are: gfl.usgs.govand gfp.usgs.gov). Released images are orthorectified andprovided in a GeoTIFF format with supporting metadata.gfp.usgs.gov and gfl.usgs.govMolotch, Noah P.Remote Sensing of the Mountain Snowpack:Integration of Observations and Models toSupport Water Resource Management andEcosystem ScienceMolotch, Noah P. 1, 2 ; Guan, Bin 2 ; Durand, Michael 3 ; Dozier,Jeff 41. Geography / INSTAAR, Univ of CO-Geological Sciences,Boulder, CO, USA2. Jet Propulsion Laboratory - CAL TECH, Pasadena, CA,USA3. Earth Sciences, The Ohio State University, Columbus,OH, USA4. Donald Bren School of Environmental Science andManagement, University of California, Santa Barbara,CA, USAThe impacts of climate change on water sustainability inmountainous regions is inherently linked to changes inmountain snow accumulation and snowmelt timing whichsustains agricultural and municipal water demands for 60million people in the U.S. and one billion people globally.Hence, accurate estimates of the volume of snowpack waterstorage are critical for supporting water resource planningand management. While snow extent is one of the earliestobserved land surface variables from space, hydrologicapplications of these data have been limited as the variableof interest for water management is snow water equivalent(SWE) which is not remotely observable at the fine-scaleresolution needed in the mountains. Since the early 1980’s,several works have illustrated the connection between runoffvolume and snow cover depletion patterns as observed fromsatellite. In this regard, we present a series of experimentswhich illustrate that patterns of snow cover depletion can becoupled to spatially distributed snowmelt models toreconstruct the spatial distribution of SWE. In this regard,we present a proof-of-concept for a global product, providingdaily estimates of snow water equivalent at 500-m scale forthe observation record of the Moderate Resolution ImagingSpectroradiometer (MODIS). Estimates of the reconstructedSWE are validated against observed SWE from extensivesnow surveys across the Sierra Nevada and Rocky Mountainswith adequate spatial sampling, and compared to theoperational Snow Data Assimilation System (SNODAS)SWE product produced by the U.S. National WeatherService. Snow survey SWE is underestimated by 4.6% and36.4%, respectively, in reconstructed and SNODAS SWE,averaged over 17 surveys from sites of varying physiography.Corresponding root-mean-square errors are 0.20 m and 0.25m, respectively, or 2.2 and 2.6 mean standard deviation ofthe snow survey SWE. Comparison between reconstructedand snow sensor SWE suggests that the current snow sensornetwork in the U.S. inadequately represents the domain SWEdue to undersampling of the mid-lower and upperelevations. Correlation with full natural flow is better withreconstructed SWE than with ground-based snow sensors, orwith SNODAS SWE on average; particularly late in thesnowmelt season after snow stations report zero values butsnow persists at higher elevations. These results indicate thatinclusion of remotely sensed snow cover depletion patternsdramatically improves estimates of snow distribution inmountainous regions. Example applications for improvingwater resource management and understanding ecosystemresponse to water availability will be shown.http://instaar.colorado.edu/mtnhydroMonsivais-Huertero, AlejandroOptimal use of active/passive microwaveobservations at L-band for improving root zone soilmoistureMonsivais-Huertero, Alejandro 1 ; Judge, Jasmeet 2 ; Nagarajan,Karthik 21. ESIME Ticoman, Instituto Politecnico Nacional, MexicoCity, Mexico2. Department of Agricultural and Biological Engineering,University of Florida, Gainesville, FL, USAAccurate knowledge of root zone soil moisture (RZSM)is crucial in hydrology, micrometeorology, and agriculturefor estimating energy and moisture fluxes at the landsurface. Soil Vegetation Atmosphere Transfer (SVAT) modelsare typically used to simulate energy and moisture transportin soil and vegetation. Although SVAT models capture thebiophysics of dynamic vegetation fairly well, RZSM estimatesstill diverge from reality due to errors in computation. Thus,uncertainties in model parameters, forcings, and initialconditions should be considered. The model estimates ofRZSM can be significantly improved by assimilatingremotely sensed observations that are sensitive to soilmoisture changes, such as microwave brightness andbackscatter at frequencies < 10 GHz.. For soil moisturestudies, observations at L-band frequencies of 1.2 – 1.4 GHzare desirable due to larger penetration depth and systemfeasibility. The near-future NASA Soil MoistureActive/Passive (SMAP) mission will include active andpassive microwave sensors at L-band (1.2 – 1.4 GHz) toprovide global observations, with a repeat coverage of every2-3 days [3]. In this study, an Ensemble Kalman Filter(EnKF)-based assimilation algorithm was implemented tosimultaneously update states and parameters every 3 days,matching the interval of satellite revisit, by assimilating L-band microwave brightness temperature (TB) andbackscattering coefficient (sigma0) into the SVAT modellinked with a forward active/passive (AP) microwave model.We use a Land Surface Process (LSP) model to estimate theSM profile and ST profile and an AP model to estimate TBand sigma0 during bare soil conditions in an agriculturalfield located at North Florida. Field observations were104