Mapping sugarcane biomass using remote sensing

Abstract

Monitoring biophysical features of sugarcane to estimate productivity of growing cane using groundbased crop cut techniques require immense time and equipment. Crop biophysical parameters from representative Mumias Nucleus Estate sugarcane fields were used to characterize biomass by gleaning spectral reflectance values to calculate five vegetation indices and comparing them with ground-truthed data clipped from the fields. Results indicated leaf area index and Red/Near infrared as the best biomass predictors with Coefficients of correlation (r2) of 0.94 while a strong relationship existed between the spectral values and field biomass with predictions of r2 of 0.78 and 0.82 for bands 3 and 4 respectively. Temporal maps developed using transformed values of bands 3 and 4 suggested that yield and biomass could be mapped from ETM+ satellite imagery. A model developed performed well returning a coefficient of efficiency of 0.98 confirming the potential of remote sensing in providing data to estimate crop yield.