Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages

Abstract

Normalized Difference Vegetation Index and Ratio Vegetation Index obtained with the fixed band GreenSeeker active multispectral canopy sensor (GS-NDVI and GS-RVI) have been commonly used to non-destructively estimate crop growth parameters and support precision crop management, but their performance has been influenced by soil and/or water backgrounds at early crop growth stages and saturation effects at moderate to high biomass conditions. Our objective is to improve estimation of rice (Oryza sativa L.) aboveground biomass (AGB) with hyperspectral canopy sensing by identifying more optimal measurements using one or more strategies: (a) soil adjusted Vegetation Indices (VIs); (b) optimized narrow band RVI and NDVI; and (c) Optimum Multiple Narrow Band Reflectance (OMNBR) models based on raw reflectance, and its first and second derivatives (FDR and SDR).Six rice nitrogen (N) rate experiments were conducted in Jiansanjiang, Heilongjiang province of Northeast China from 2007 to 2009 to create different biomass conditions. Hyperspectral field data and AGB samples were collected at four growth stages from tillering through heading from both experimental and farmers’ fields. The results indicate that six-band OMNBR models (R2=0.44–0.73) explained 21–35% more AGB variability relative to the best performing fixed band RVI or NDVI at different growth stages. The FDR-based 6-band OMNBR models explained 4%, 6% and 8% more variability of AGB than raw reflectance-based 6-band OMNBR models at the stem elongation (R2=0.77), booting (R2=0.50), and heading stages (R2=0.57), respectively. The SDR-based 6-band OMNBR models made no further improvements, except for the stem elongation stage. Optimized RVI and NDVI for each growth stage (R2=0.34–0.69) explained 18–26% more variability in AGB than the best performing fixed band RVI or NDVI. The FDR- and SDR-based optimized VIs made no further improvements. These results were consistent across different sites and years. It is concluded that with suitable band combinations, optimized narrow band RVI or NDVI could significantly improve estimation of rice AGB at different growth stages, without the need of derivative analysis. Six-band OMNBR models can further improve the estimation of AGB over optimized 2-band VIs, with the best performance using SDR at the stem elongation stage and FDR at other growth stages.