H51I-1506
Scaling an in situ network for high resolution modeling during SMAPVEX15
Friday, 18 December 2015
Poster Hall (Moscone South)
Evan J Coopersmith1, Michael H Cosh2, Jennifer M Jacobs3, Thomas J Jackson4, Wade T Crow2, Chandra Holifield Collins5, David C Goodrich5 and Andreas Colliander6, (1)US Department of Agriculture, Beltsville, MD, United States, (2)USDA Agricultural Research Service New England Plant, Soil and Water Research Laboratory, East Wareham, MA, United States, (3)Univ New Hampshire, Durham, NH, United States, (4)USDA ARS, Pendleton, OR, United States, (5)Agricultural Research Service Tucson, Tucson, AZ, United States, (6)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Abstract:
Among the greatest challenges within the field of soil moisture estimation is that of scaling sparse point measurements within a network to produce higher resolution map products. Large-scale field experiments present an ideal opportunity to develop methodologies for this scaling, by coupling in situ networks, temporary networks, and aerial mapping of soil moisture. During the Soil Moisture Active Passive Validation Experiments in 2015 (SMAPVEX15) in and around the USDA-ARS Walnut Gulch Experimental Watershed and LTAR site in southeastern Arizona, USA, a high density network of soil moisture stations was deployed across a sparse, permanent in situ network in coordination with intensive soil moisture sampling and an aircraft campaign. This watershed is also densely instrumented with precipitation gages (one gauge/0.57 km2) to monitor the North American Monsoon System, which dominates the hydrologic cycle during the summer months in this region. Using the precipitation and soil moisture time series values provided, a physically-based model is calibrated that will provide estimates at the 3km, 9km, and 36km scales. The results from this model will be compared with the point-scale gravimetric samples, aircraft-based sensor, and the satellite-based products retrieved from NASA’s Soil Moisture Active Passive mission.
