Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing

The potential of airborne hyperspectral remote sensing in crop monitoring and estimation of various biophysical parameters was examined in this study. A field experiment, consisting of four weed control strategies (no weed control, broadleaf control, grass control, and full weed control) as the main plot effect, factorially combined with three nitrogen (AT) fertilization rates (60, 120, and 250 N kg ha(-1)), and replicated four times, was conducted. Hyperspectral data in 72 narrow wavebands (409 to 947 nm) from a Compact Airborne Spectrographic Imager (CASI) sensor were acquired 30 days after planting, at tasseling, and at the fully mature stage. In addition, measurements were made concurrently on various crop physiological parameters: leaf greenness (SPAD readings), leaf area index (LAI), plant height, leaf nitrogen content, leaf chlorophyll content, and associated factors such as soil moisture. Regression models were generated to estimate crop biophysical parameters and yield, in terms of reflectance at one or more wavebands, using the maximum r(2) improvement criterion. The models that best represented the data had five wavebands as independent variables. Coefficients of determination (r(2)) were generally greater than 0.9, when based on the spectral data taken at the tasseling stage. Results were improved when normalized difference vegetation indices (NDVI) were used rather than the five-waveband reflectance values. The wavebands at 701 nm and 839 nm were the most prevalent in the NDVI-based models.