Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Re ectance and Hyperion/EO-1 Data

Abstract

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world’s main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks’ Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Datawere collected for the eight crop types in six distinct growth stages. These included (a) eld spectroradiometer measurements (350–2500 nm) sampled at 1-nm discrete bandwidths, and (b) eld biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classi ed using 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was 25% better than the multi-spectral results possible fromLandsat-7’s Enhanced Thematic Mapper+ or EO-1’s Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of speci c two-band normalized difference HVIs to best model and study speci c biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identi ed in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The ndings of this study will make a signi cant contribution to future hyperspectral missions such as NASA’s HyspIRI.