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

approach to estimate soil water content from thermalinertia. Agricultural and Forest Meteorology, 149, 1693-1698Wang, J.F., & Bras, R.L. (2011). A model ofevapotranspiration based on the theory of maximumentropy production. Water Resources Research, 47 Xue, Y., &Cracknell, A.P. (1995). Adavanced thermal inertia modeling.International Journal of Remote Sensing, 16, 431-446Négrel, JeanRiver surface roughness sensitivity to windconditions : in situ measurement technique,processing method and resultsNégrel, Jean 1 ; Kosuth, Pascal 1 ; Caulliez, Guillemette 2 ;Strauss, Olivier 3 ; Borderies, Pierre 4 ; Lalaurie, Jean-Claude 5 ;Fjortoft, Roger 51. UMR TETIS, IRSTEA, Montpellier, France2. IRPHE, CNRS, Marseille, France3. LIRMM, Université Montpellier 2, Montpellier, France4. DEMR, ONERA, Toulouse, France5. DCT/SI/AR, CNES, Toulouse, FranceWater surface roughness strongly impacts microwavebackscattering process over rivers. Therefore, developpingradar interferometry techniques over continental waters todetermine river slope (cross-track interferometry) or surfacevelocity (along-track interferometry) requires a detailedcharacterization of water surface roughness, its relation withriver flow and wind conditions, its influence on backscattercoefficient. In situ measurement of river surface roughness isa complex task as surface topography is rapidly changingand sensitive to obstacles or contact measurements.Laboratory measurements, although highly informative, fallshort to represent the diversity of river flow and windconditions. In the framework of the SWOT missionpreparatory phase, a method was developped for fieldmeasurement of river surface roughness. It was tested andvalidated in laboratory conditions, and implemented innatural conditions on the Rhône river, under various windintensities. The method is based on the acquisition of waterpressure time series by a network of immersed pressuresensors, with synchronized 10 Hz sampling and 0,1 mbaraccuracy (1mm water elevation). A preliminary low frequencyfiltering is applied to each sensor pressure time series toremove water level trends. Mean depth h, frequencyspectrum, dominant frequency f and standard deviation ofwater pressure Pare determined per time interval (180s). Amultisensor analysis is realised to determine directional wavecelerity c and directional correlation length L corr. Finallystandard deviation of surface water level Zis determined foreach sensor by applying a correction factor, taking intoaccount the signal damping with depth. Z=e 2.pi.h.f/c . P(h)Laboratory tests validated the measurement technique,provided that the correction factor does not exceed 10 (i.e.the immersed sensor records more than 10% of the surfacesignal) which is achievable by limiting the sensor depth.Field measurements were realised during a campaign on theRhône river (may 2011). The sensor network was located10m from the river bank and 0,15m under the surface water.It recorded during 7 hours at 10 Hz. Wind measured at 2mheight had the same direction as river flow (southward),with intensity changing progressively from 8m/s to 0m/s.Surface roughness was characterized, per 3 minute timeintervals, by Frequency spectrum, dominant frequency,standard deviation of surface water level Z, directional wavecelerity c and directional correlation distance L corr. Itssensitivity to wind conditions was analysed and modelled.The method is currently being adapted on a floating supportfor intensive river surface roughness measurement.Njoku, Eni G.Soil Moisture Active Passive (SMAP) MissionScience and Data Product DevelopmentNjoku, Eni G. 1 ; Entekhabi, Dara 2 ; O’Neill, Peggy E. 3 ;Jackson, Thomas J. 41. Jet Propulsion Laboratory, California Institute ofTechnology, Pasadena, CA, USA2. Massachusetts Institute of Technology, Cambridge, MA,USA3. NASA Goddard Space Flight Center, Greenbelt, MD, USA4. USDA ARS Hydrology and Remote Sensing Laboratory,Beltsville, MD, USAThe Soil Moisture Active Passive (SMAP) mission,planned for launch in late 2014, has the objective offrequent, global mapping of near-surface soil moisture andits freeze/thaw state. The SMAP measurement systemutilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The instruments will operateon a spacecraft in a 685-km polar orbit with 6 am/6 pmnodal crossings, viewing the surface at a constant 40-degreeincidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments will yieldglobal maps of soil moisture and freeze/thaw state at 10 kmand 3 km resolutions, respectively, every two to three days.The 10-km soil moisture product will be generated using acombined radar and radiometer retrieval algorithm. SMAPwill also provide a radiometer-only soil moisture product at40-km spatial resolution and a radar-only soil moistureproduct at 3-km resolution. The relative accuracies of theseproducts will vary regionally and will depend on surfacecharacteristics such as vegetation water content, vegetationtype, surface roughness, and landscape heterogeneity. TheSMAP soil moisture and freeze/thaw measurements willenable significantly improved estimates of the fluxes ofwater, energy and carbon between the land and atmosphere.Soil moisture and freeze/thaw controls of these fluxes arekey factors in the performance of models used for weatherand climate predictions and for quantifying the global111