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Title:   Author  item
                                            BRUNSELL, NATE - University Of Kansasitem
                                            Anderson, Martha
  Submitted to: Biogeosciences
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 8/17/2011
                                    Publication  Date: 8/22/2011
                                    Citation: Brunsell, N., Anderson, M.C. 2011. Characterizing the multi–scale spatial structure of remotely sensed evapotranspiration with information theory. Biogeosciences. 8:2269-2280.Interpretive Summary: One important question in remote sensing is: what scale (spatial resolution) of satellite imagery is required to map a given quantity to the desired level of accuracy?  This depends on the scale at which important variables driving the process in question vary across the landscape, and how these variables interact in determining the output quantity. For evapotranspiration (ET), important driving variables include vegetation amount and surface temperature (both of which can vary at the field scale across agricultural landscapes), and the solar radiation providing the energy for evaporation (which varies at the cloud scale).  In this paper, information theory is applied to maps of ET generated at 3 different scales using satellite imagery from Landsat (~100m), the Moderate Resolution Imaging Spectroradiometer (MODIS; 1km) and the Geostationary Operational Environmental Satellites (GOES; 10km) over an agricultural area in Fort Peck, Montana, to determine the resolution  threshold at which significant information is lost, and how that threshold resolution varies with season and crop phenology.  This threshold can be an indicator of the “optimal” observing resolution, balancing information content with computing demands required for processing high-resolution imagery.  The result demonstrate that MODIS at 1km resolution misses important structure in evaporative fluxes over this landscape, with typical field size on the order of 500 m.  This argues for maintaining continuity in the Landsat observing program – the only routine source of surface temperature data at sub-field scales.
                                Technical Abstract: A more thorough understanding of the multi-scale spatial structure of land surface heterogeneity will enhance understanding of the relationships and feedbacks between land surface conditions,mass and energy exchanges between the surface and the atmosphere, and regional meteorological and climatological conditions. The objectives of this study were to (1) quantify which spatial scales are dominant in determining the evapotranspiration flux between the surface and the atmosphere and (2) to quantify how different spatial scales of atmospheric and surface processes interact for different stages of the phenological cycle. We used the ALEXI/DisALEXI model for three days (DOY 181, 229 and 245) in 2002 over the Ft. Peck Ameriflux site to estimate the latent heat flux from Landsat, MODIS and GOES satellites. We then applied a multiresolution information theory methodology to quantify these interactions across different spatial scales and compared the dynamics across the different sensors and different periods. We note several important results: 1) spatial scaling characteristics vary with day, but are usually consistent for a given sensor, but 2) different sensors give different scalings, and 3) the different sensors exhibit different scaling relationships with driving variables such as fractional vegetation and near surface soil moisture. In addition, we note that while the dominant length scale of the vegetation index remains relatively constant across the dates, but the contribution of the vegetation index to the derived latent heat flux varies with time. We also note that length scales determined from MODIS are consistently larger than those determined from LandSAT. These results aid in identifying the dominant cross-scale nature of local to regional biosphere–atmosphere interactions.


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