Biomass estimation of crops and natural shrubs by combining red-edge ratio with normalized difference vegetation index

Biomass is a critical biophysical parameter used to monitor agricultural and terrestrial ecosystems. Operational difficulties in measuring biomass on the field scale led to use of remote sensing techniques. Numerous vegetation indices (VIs) have been developed for this purpose, most of which utilize bands in the VIS-NIR spectral region. However, a significant number of them exhibit saturation at leaf area index (LAI) higher than 2. We propose an innovative vegetation index red-edge ratio normalized difference vegetation index (RERNDVI), that combines a VI sensitive to moderate to high LAI with VI sensitive to low to moderate LAI. An empirical assessment of the performance of our new index compared with nine existing spectral indices was conducted using hyperspectral and biomass data collected by Center for Advanced Land Management Information Technologies for maize and soybean fields, and Sentinel-2 data for semi-arid shrubland sites for which biomass was estimated based on the allometric model. The coefficient of determination (r(2)) of RERNDVI with green biomass for maize and soybean is high, but equally important, its noise equivalence is significantly lower than that obtained for all other indices for the full range of biomass levels. Nevertheless, RERNDVI-biomass relationships vary for different crops and shrubs, suggesting that generalizing these relationships will require information regarding canopy structure parameters as well. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

Automated summary

Biomass is an important parameter for monitoring agricultural and terrestrial ecosystems. Remote sensing techniques, such as the use of vegetation indices, have been employed due to the difficulties in field-scale measurements of biomass. The proposed red-edge ratio normalized difference vegetation index (RERNDVI) combines indices sensitive to varying levels of leaf area index (LAI) and offers improved estimation accuracy for biomass. The study demonstrates that RERNDVI outperforms other indices in terms of noise equivalence and can accurately estimate biomass for different crops and shrubs.