Upscaling Flux Observations from Local to Continental Scales Using Thermal Remote Sensing

A number of recent intensive and extended field campaigns have been devoted to the collection of land-surface fluxes from a variety of platforms, with the purpose of inferring the long-term C, water, and energy budgets across large areas (watershed, continental, or global scales). One approach to flux upscaling is to use land–atmosphere transfer schemes (LATS) linked to remotely sensed boundary conditions as an intermediary between the sensor footprint and regional scales. In this capacity, we examined the utility of a multiscale LATS framework that uses thermal, visible and near infrared remote sensing imagery from multiple satellites to partition surface temperature and fluxes between the soil and canopy. We conducted exercises using tower and aircraft flux data collected at three experiment sites in Oklahoma and Iowa, each with a different configuration of instrumentation. Combined, the two flux-monitoring systems were found to be complementary: the towers provided high-spatial-resolution, timecontinuous validation at discrete points within the modeling domain, while with the aircraft data it could be confirmed that the model was reproducing broad spatial patterns observed at specific moments in time. High-resolution flux maps created with the LATS allowed evaluation of differences in footprint associated with turbulent, radiative, and conductive flux sensors, which may be contributing to energy budget closure problems observed with eddy correlation systems. The ability to map fluxes at multiple resolutions (1 m–10 km) with a common model framework is beneficial in providing spatial context to an experiment by bracketing the scale of interest. Multiscale flux maps can also assist in the experimental design stage, in a priori assessments of sensor representativeness in complex landscapes. DURING THE LAST TWO DECADES, several intensive field campaigns have focused on collecting high-quality surface flux measurements from tower and aircraft platforms deployed over a wide variety of landscapes and under many different climatic conditions (e.g., FIFE [Kanemasu et al., 1992], Monsoon ’90 [Stannard et al., 1994],HAPEX-SAHEL[Goutorbeet al., 1994],BOREAS [Sellers et al., 1997], and the Soil Moisture–Atmospheric Coupling Experiment [SMACEX; Prueger et al., 2005]). In addition, routine, interannual flux measurements are being made at an increasing number of locations to facilitate large-scale environmental monitoring. Running et al. (1999), for example, described a global network for monitoring terrestrial C fluxes using tower-based measurements and airborne flask sampling. The flux data sets collected in these experiments will ultimately be upscaled to make inferences regarding the long-term C, water, and energy budgets across large areas (watershed, continental, or global scales). To this end, an upscaling mechanism must be used to fill temporal and spatial gaps that remain unsampled by the deployed flux instrumentation set. Eddy correlation (EC) systems integrate fluxes arising from a “source area” or “footprint” on the land surface only of order 10 to 10 m in dimension, depending on sensor height above ground level (agl). Spatial coverage has been improved in many experiments by flying EC systems on aircraft, often on transects intersecting groundbased tower locations for intercomparison (Desjardins et al., 1997; Samuelson and Tjernström, 1999; Stephens et al., 2000; Mahrt et al., 2001; Song and Wesely, 2003; Prueger et al., 2005). A major challenge for such experiments is to ensure that the combined flux data sets are adequately characterizing the full heterogeneity present in the landscape (Schmid, 1997; Schmid and Lloyd, 1999), and to find reasonable means for comparing data from tower and aircraft sensors, which generally are sampling very different footprints on the land surface (Kaharabata et al., 1997). Furthermore, a reliable procedure must be identified for extrapolating these findings to times and locations outside of the field study domain. One approach to flux upscaling uses LATS linked to remotely sensed boundary conditions as an intermediary between the sensor footprint and regional scales. Modeled flux fields provide a basis for connecting point-like tower data and linear aircraft transect measurements made at height with the two-dimensional patterns of fluxes diagnosed at the land surface. The tower and aircraft fluxes can be backprojected onto the surface source and sink distribution through inversion of an appropriate advection–dispersion model, defining the effective “source weighting function” (see review by Schmid, 2002). If the comparisons are reasonable within the segment of the experimental domain sampled by the measurement sets, gridded model fluxes can be aggregated with some degree of confidence to larger spatiotemporal scales with similar characteristics. M.C. Anderson and W.P. Kustas, USDA-ARS, Hydrology and Remote Sensing Lab., Bldg. 007, BARC West, 10300 Baltimore Ave., Beltsville, MD 20705; and J.M. Norman, Dep. of Soil Science, Univ. of Wisconsin, Madison, WI 53706. Received 1 Apr. 2005. *Corresponding author ([email protected]). Published in Agron. J. 99:240–254 (2007). Special Submissions doi:10.2134/agronj2005.0096S a American Society of Agronomy 677 S. Segoe Rd., Madison, WI 53711 USA Abbreviations: ABL, atmospheric boundary layer; agl, above ground level; ALEXI, Atmosphere–Land Exchange Inverse; ASTER, Advanced Space-borne Thermal Emission Reflectance Radiometer; EC, eddy correlation; ER, El Reno, OK; ET, evapotranspiration; GOES, Geostationary Operational Environmental Satellite; LAI, leaf area index; Landsat, LandRemote-Sensing Satellite; LATS, land–atmosphere transfer scheme; OASIS, Oklahoma Atmospheric Surface-Layer Instrumentation System; RMSD, root mean square difference; SMACEX, Soil Moisture–Atmospheric Coupling Experiment; SPG97, Southern Great Plains Experiment of 1997; TIMS, Thermal Infrared Multispectral Scanner; TSEB, two-source surface energy balance; vis/NIR, visible and near infrared. R e p ro d u c e d fr o m A g ro n o m y J o u rn a l. P u b lis h e d b y A m e ri c a n S o c ie ty o f A g ro n o m y . A ll c o p y ri g h ts re s e rv e d .