Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance 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. Data were 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 from Landsat-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")

Ground-Based Hyperspectral Characterization of Alaska Tundra Vegetation along Environmental Gradients

“…Hyperspectral vegetation indices (HVIs) use narrowband features, which can only be captured by hyperspectral instruments [32]. Research by Thenkabail et al [34,70] has shown that, due to data redundancy in the hyperspectral signal, a small number of specific hyperspectral narrowbands (HNBs) have enough information to determine structural vegetation characteristics. Therefore, hyperspectral two-band vegetation indices (HTBVI) based on normalized difference between the bands are used as a data-mining tool in this study.…”

“…In addition, the probability distributions of the broadband and narrowband NDVIs for each site were investigated in order to assess differences between MAT and MNT. The HNBs in the red and NIR wavelength regions for the three narrowband NDVIs have been chosen by assessment of the spectral metrics, based on research in optimal HNBs for vegetation analysis by Thenkabail et al [34,70] (EnMAP band 59, center wavelength 756 nm), the central NIR reflectance plateau (EnMAP band 71, center wavelength 864 nm) and the maximum NIR reflectance (EnMAP band 101, center wavelength 1,020 nm). EnMAP band 59 at the beginning of the NIR reflectance plateau may be specifically sensitive to vegetation structure.…”

“…The main challenge is to find the right HNBs for an application within the large number of redundant bands provided by hyperspectral instruments [70]. Up to now, hyperspectral satellite and airborne data for arctic regions were limited.…”

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

“…These eigenvalues contain the percentage of original data (bands) captured in that component. Band loadings are the coefficient between each variable (band) and any component; therefore, information of the band loading can be used to find the best bands [40,49]. In order to identify the best bands, we decomposed principal components into their band loading values.…”

“…The advent of airborne HSI sensors has raised new opportunities in urban remote sensing applications, due to a combination of high spectral and high spatial resolution in HSI images [3,9]. HSI sensor stores information of a pixel in hundreds of spectral bands which enables the accurate identification of objects or classes which have similar spectral characteristics [13]. HSI collects a wide continuous narrowband reflectance information across the electromagnetic spectrum precisely 400-2500 nm which provides higher separability features for characterizing complex urban objects [9,14].…”