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

Alfieri, Joseph G.The Factors Influencing Seasonal Variations inEvaporative Fluxes from Spatially Distributed Agro-EcosystemsAlfieri, Joseph G. 1 ; Kustas, William P. 1 ; Gao, Feng 1 ; Prueger,John H. 2 ; Baker, John 3 ; Hatfield, Jerry 21. HRSL, USDA, ARS, Beltsville, MD, USA2. NLAE, USDA, ARS, Ames, IA, USA3. SWMU, USDA, ARS, St. Paul, MN, USAThe exchange of moisture between the land surface andthe atmosphere is the result of complex network ofinteracting processes, most of which are regulated, at least inpart, by spatially variable atmospheric and surfaceconditions. Because these processes are often stronglynonlinear, scaling measurements collected at one scale toanother remains a nontrivial task. Since field measurementsare commonly used to develop, calibrate, and validate bothnumerical models and remotely sensed products, errors inthe upscaling of point measurements can propagate intoand adversely impact the accuracy and utility of thesemodels and products. In an effort to identify the keyenvironmental drivers controlling the latent heat flux (E)from agro-ecosystems and their potential impacts onupscaling in-situ flux measurements, eddy covariance andmicrometeorological data collected over maize and soy atthree distinct sites located in Maryland, Iowa, andMinnesota, respectively were evaluated. The magnitudes ofthe evaporative fluxes were comparable for measurementscollected during clear-sky days with similar environmentalconditions; on average, the measurements of E agreed towithin 50 W m-2, or 10%. When considered in terms ofevaporative fraction (f e), however, there were markeddifferences among the sites. For example, while themagnitude and diurnal pattern of f efor mature maize at theMinnesota site was nearly constant (f e= 0.66) during the day,(f e) at both the Maryland and Iowa site increased steadilyduring the day from a minimum value near 0.68 at midmorningto peak value of 0.87 in the afternoon. Thesedifferences appear to be primarily linked to differences insoil moisture and vegetation density at the various sites. Theutility of remote sensing data to provide the necessaryvegetation metrics to identify the underlying cause of thedifference in f ewill also be discussed.Allen, Richard G.Conditioning of NLDAS, NARR and arid weatherstation data for estimating evapotranspirationunder well-watered conditionsAllen, Richard G. 1 ; Huntington, Justin 2 ; Irmak, Ayse 3 ;deBruin, Henk 4 ; Kjaersgaard, Jeppe 51. Civil Engineering, University of Idaho, Kimberly, ID, USA2. Desert Research Institute, Reno, NV, USA3. School of Natural Resources, University of Nebraska,Lincoln, NE, USA4. Retired, Wageningen University, Wageningen,Netherlands5. Biological Engineering, South Dakota State University,Brookings, SD, USAMany satellites like MODIS and Landsat have returntimes of multiple days. Therefore, calculation ofevapotranspiration (ET) over extended time periods requiresweather data in between overpasses. NLDAS and NARRgridded weather data systems are produced by the WRFmodel or similar. These gridded sets represent ‘ambient’weather and surface energy balance under rainfallconditions. The water balance is generally not informed onirrigation and associated ET so that weather data generatedby these models represent that occurring under rainfedconditions. Local dryness tends to elevate near surfacetemperature (T) measurements by as much as 5 C, andreduces vapor pressure by up to one-half, when compared tomeasurements made over an evaporating surface. When thedata are entered into standardized reference ET equationsfor estimating crop water requirements in irrigatedagriculture, the higher air temperatures and lower vaporpressures associated with the ambient conditions tend tooverestimate ET anticipated in an irrigated environment byup to 20%. A procedure is described for “conditioning”ambient weather data from ‘arid’ environments having lowvapor fluxes and high sensible heat fluxes so that datarepresent near surface air properties for the same climate,but over an evaporating, “reference” surface. Theseadjustments are necessary before applying ‘referenceevapotranspiration’ methods such as the ASCE-EWRIPenman-Monteith equation that assume equilibriumbetween the reference, evaporating surface and near surfaceair. Following conditioning, the “equilibrium reference ET”can be scaled using crop coefficients or other parameters toestimate actual ET for a variety of vegetation. Theconditioning completes the feedback processes betweensurface evaporation and near surface air properties. Theprocedure extrapolates T, vapor and wind speed profiles toand from a blended height of 50 m using ambient andreference vapor fluxes and sensible heat fluxes. T, humidity,solar radiation and wind speed are utilized and theprocedure can be applied to hourly or daily data. Theprocedure can be applied to measured weather data and togridded weather data from WRF-Noah and other landsimulation models. The approach uses standard M-Osimilarity theory. In applications in southern Idaho, theprocedure estimated a 4 C decrease in 2 m T, doubling of 2m31