Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation

Michael Marshall*, Prasad Thenkabail

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

110 Citations (Scopus)

Abstract

Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5-31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3-33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the "red-edge" spectral range (700-740nm) centered at 722nm (bandwidth=10nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400-2500nm) centered at 539nm, 758nm, 914nm, 1130nm, 1320nm (bandwidth=10nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752nm for rice (R2=0.91); 925 and 1104nm for alfalfa (R2=0.81); 722 and 732nm for cotton (R2=0.97); and 529 and 895nm for maize (R2=0.94). The higher spectral resolution of the EO-1 Hyperion hyperspectral sensor and the ability of users to choose distinct HNBs for improved crop biomass estimation outweigh the benefits that come with higher spatial resolution of MSBBs.

Original languageEnglish
Pages (from-to)205-218
Number of pages14
JournalISPRS journal of photogrammetry and remote sensing
Volume108
DOIs
Publication statusPublished - 1 Oct 2015
Externally publishedYes

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID
  • Earth observation
  • Narrowbands
  • Broadbands
  • Remote sensing
  • Crop yield

Fingerprint

Dive into the research topics of 'Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation'. Together they form a unique fingerprint.

Cite this