Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa

摘要

With increased availability of satellite data products used in mapping surface energy balance and evapotranspiration (ET), routine ET monitoring at large scales is becoming more feasible. Daily satellite coverage is available, but an essential model input, surface temperature, is at 1 km or greater pixel resolution. At such coarse spatial resolutions, the capability to monitor the impact of land cover change and disturbances on ET or to evaluate ET from different crop covers is severely hampered. The effect of sensor resolution on model output for an agricultural region in central Iowa is examined using Landsat data collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This study was conducted in concert with the Soil Moisture Experiment 2002 (SMEX02). Two images collected during a rapid growth period in soybean and corn crops are used with a two-source (soil + vegetation) energy balance model, which explicitly evaluates soil and vegetation contributions to the radiative temperature and to the net turbulent exchange/surface energy balance. The pixel resolution of the remote sensing inputs are varied from 60 m to 120, 240, and 960 m. Model output at high resolution are first validated with tower and aircraft-based flux measurements to assure reliability of model computations. Histograms of the flux distributions and resulting statistics at the different pixel resolutions are compared and contrasted. Results indicate that when the input resolution is on the order of 1000 m, variation in fluxes, particularly ET, between corn and soybean fields is not feasible. However, results also suggest that thermal sharpening techniques for estimating surface temperature at higher resolutions (～250 m) using the visibleear infrared waveband resolutions could provide enough spatial detail for discriminating ET from individual com and soybean fields. Additional support for this nominal resolution requirement is deduced from a geostatistical analysis of the vegetation index and surface temperature images.