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

used three Landsat-5 TM images (05/07/2009, 21/07/2009and 06/08/2009) and MODIS LAI product to retrieve ETestimates. To provide LST data, Landsat-5 TM images weredownloaded through the USGS Earth Explorer gateway in1T level. In the case of solar bands, radiometric correctionwas carried out following the methodology proposed byPons and Solé-Sugrañes (1995); and we used the methoddeveloped by Cristóbal et al. (2009) to atmosphericallycorrect the thermal band. MODIS LAI product (MOD15)was downloaded from the NASA-WIST gateway. Preliminaryresults show an acceptable agreement between TSEB modeland flux tower data. RMSE obtained in the case of atsatellite pass ET, net radiation, sensible heat flux and soilheat flux were 72, 41.5, 40.8 and 50.8 Wm-2, respectively.Further efforts will be focused on the daily energy fluxintegration by means of the implementation of theALEXi/DisALEXI model (Anderson et al., 2007) as well asthe energy balance computation in snow conditions. Finally,due to the importance of LAI input in TSEB model, LAI willbe directly estimate by means of field sampling and thenmodeled using Landsat-5 TM images.Cunha, LucianaExploring the Potential of Space-Based RemoteSensing in Flood PredictionCunha, Luciana 1 ; Krajewski, Witold 1 ; Mantilla, Ricardo 11. Civil and Env Eng, IIHR - Hydroscience & Engineering,Iowa City, IA, USAAdvances in remote sensing techniques to monitorhydro-meteorological and land surface variables from spacemade it possible to monitor and predict floods on a nearglobalbasis. However, the use of calibration-basedhydrological models prevents the implementation of suchglobal systems since it requires data that are unavailable inmany parts of the world. In this study we present progresstowards development of a calibration-free multi-scalehydrological model based entirely on space-based remotesensing data. The model is based on a faithfuldecomposition of the landscape into hillslopes and riverchannel links, and on the solution of the mass andmomentum equations for each control volume. We avoidcalibration through the use of equations and parametersthat can be directly related to measurable physical propertiesof the landscape. As a research strategy to explore thepotential of space-based products in flood forecasting, wefirst force the model with high-resolution data, whenavailable (i.e. NEXRAD radar rainfall, LIDAR and USGS-NED DEMs, and NLCD land cover). We then investigatehow predictions are degraded by the use of coarserresolution and higher uncertainty products provided bysatellites (PERSIANN, 3B42, and CMOHPH rainfall, ASTERand SRTM DEMs, and MODIS-MOD15A2 land cover).Other datasets used in the model implementation are:potential evapotranspiration (MODIS – MOD16), soilproperties (SURGO), soil initial moisture conditions (LandData Assimilation System-NLDAS 2), leaf area index(MODIS – MOD12Q2), snow cover, and snow melt (AMSR-L3). We demonstrate, for example, that accurate floodpredictions based on satellite rainfall products can be obtainat certain scales if retrieval uncertainties are limited torandom components (no significant bias). In this case,uncertainties on the rainfall field are filtered out by theaggregation effect of the river network. We performedsimulations for two watersheds in Iowa for which highresolutiondata is available: Cedar River (16,800 km2), IowaRiver (7,200 km2). We simulate 7 years of data (2002 to2009) including different hydrological conditions for wetand dry years. Streamflow predictions are validated for 32nested sites with drainage areas ranging from 22 to 16,800km2. Our results demonstrate the feasibility of usingcalibration-free hydrological models to simulate alldominant hydrological processes responsible for floodsacross multiple scales. It is evident, however, that simulationduring dry periods is less accurate due to complex and nonlinearsoil dynamics that are not captured by the currentmodel.Das, NarendraPast, Present and Future of Satellite-based RemoteSensing to Measure Surface Soil Moisture: WithEmphasis on Soil Moisture Active Passive Mission(SMAP) MissionDas, Narendra 1 ; Entekhabi, Dara 2 ; Njoku, Eni G. 11. Water and Carbon Cycle Group, Jet Propulsion Lab,Pasadena, CA, USA2. Department of Earth, Atmospheric and PlanetarySciences, MIT, Cambridge, MA, USADuring the last three decades, remote sensingmeasurements, especially soil moisture has played anincreasing role in the fields of hydrology, ecology,climate/weather studies, agronomy and water managementat regional/global scale. Microwave remote sensing providesa unique capability for direct observation of soil moisture.Passive (radiometer) and active (radar) remote sensingmeasurements using satellite-based sensors from spaceafford the possibility of obtaining frequent, global samplingof soil moisture over a large fraction of the Earth’s landsurface. These capabilities render the satellite-based remotesensing of soil moisture very attractive. In past, most of thesatellite-based sensors operated at C- and X-band including,SSM/I, SMMR, and AMSR-E in passive mode, andRADARSAT, ERS, ENVISAT and JERS in active mode.Currently, the SMOS mission of ESA provides soil moisturemeasurements at L-band that has more sensitivity for soilmoisture. All these past and current satellite-based sensorshave limitation either with spatial resolution or withtemporal resolution, and also with retrieved soil moistureaccuracy over different geophysical conditions. Moreover,most of the current land-based applications like weatherforecast, watershed management and agricultureproductivity need high spatiotemporal resolution soilmoisture measurements with high accuracy. To meet theserequirements, the Soil Moisture Active Passive (SMAP)mission is recommended by the U.S. National Research52