A more thorough understanding of the multi-scale spatial structure of different land cover types 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. Ultimately, such knowledge is essential for understanding how local environments will react to and influence future climate conditions. Specifically, the objectives of this study are to (1) quantify which spatial scales are dominant in determining water fluxes 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. Here, we use 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 satellite sources. We then applied a combined wavelet multi-resolution analysis and 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. 2) But, different sensors give different scalings. This is related to the non-linear interactions across scales between different controlling variables and the model ET. In particular, we note that while the dominant length scale of the vegetation index remains relatively constant across the dates, the contribution of the vegetation index to the derived latent heat flux changes with time. This is particularly noted in the MODIS data. This combination of model output and quantitative analysis will aid in identifying the dominant spatial and temporal scales of local to regional land atmosphere interactions. This knowledge is essential for accurately assessing the responses to climate change across heterogeneous regions of the land surface.
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