Elia Scudiero1, Dennis L. Corwin1, Brian J. Wienhold2, Bruce D. Bosley3, John F. Shanahan4 and Cinthia K Johnson5, (1)USDA-ARS, Riverside, CA (2)UNL, East Campus, USDA-ARS, Lincoln, NE (3)Colorado State University, Sterling, CO (4)DuPont Pioneer, Lincoln, NE (5)Plainview Farms, Inc., Sterling, CO
Crop growth and yield can be efficiently monitored using canopy reflectance. However, the spatial resolution of freely available remote sensing data is often too coarse to fully understand spatial dynamics of crop status. In this study the canopy reflectance obtained from the Landsat 7 (L7) satellite is downscaled from the native resolution of 30×30 m to that typical of yield maps (ca. 5×5 m) over two fields in northeastern Colorado, USA. The fields were cultivated with winter wheat (Triticum aestivum L.) in the 2002-2003 growing season. Geophysical (apparent soil electrical conductivity and bare-soil imagery) and terrain (micro-elevation) data (resolution <5×5 m) were used to represent the soil spatial variability, in particular, of those soil properties influencing crop yield. Geographically weighted regressions (GWRs) were established to study the relationships between L7 reflectance and the geophysical and terrain data at the 30×30 m scale. Geophysical and terrain sensors could describe a large portion of the L7 reflectance spatial variability (0.83 < R2 <0.94). Maps for regression parameters and intercept were obtained at 30×30 m and used to estimate the L7 reflectance at 5×5 m resolution. Landsat 7 multispectral reflectance was therefore provided at the 5×5 m resolution throughout the season (i.e., 11 scenes) allowing monitoring plant-soil relationships at a very fine spatio-temporal scale. To independently assess the quality of the downscaling procedure, yield maps were used. In both fields, the 5×5 m estimated reflectance always showed stronger correlations (average increase in explained variance = 3.2%) with yield than in at the 30×30 m resolution. The proposed methodology can be used to monitor crop status at high resolutions throughout the growing season facing costs remarkably lower than buying and pre-processing high resolution imagery.