Monitoring crop growth by seasonal multi-spectral image data

Multi-temporal remote sensing data has potentials for monitoring crops variability in the field, estimating the crop yield and other agricultural applications. This paper introduces a method to collect and analyze high temporal resolution CIR image data for determining the temporal variability of the cronfiled and locate the best time window for acquiring remote sensing images. During the 2004 and 2006, multi-spectral data was collected each day at Morrow Plots at University of Illinois at Urbana-Champaign through the whole season. The field site contained 8 subplots (30ft*30ft) that were planted with corn with different fertilizer treatments. The raw CIR images were geometrically corrected, resampled to 10cm resolution and calibrated to real reflectance. The results from image processing demonstrated the V6-V10 stages are the best period to identify the variations in the corn filed and VT-R4 stages are the best time to estimate the corn yield.