Empirical regression models for estimating multiyear leaf area index of rice from several vegetation indices at the field scale

Masayasu Maki, Koki Homma

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Leaf area index (LAI) is among the most important variables for monitoring crop growth and estimating grain yield. Previous reports have shown that LAI derived from remote sensing data can be effectively applied in crop growth simulation models for improving the accuracy of grain yield estimation. Therefore, precise estimation of LAI from remote sensing data is expected to be useful for global monitoring of crop growth. In this study, as a preliminary step toward application at the regional and global scale, the suitability of several vegetation indices for estimating multi-year LAI were validated against field survey data. In particular, the performance of a vegetation index known as time-series index of plant structure (TIPS), which was developed by the authors, was evaluated by comparison with other well-known vegetation indices. The estimated equation derived from the relationship between TIPS and LAI was more accurate at estimating LAI than were equations derived from other vegetation indices. Although further research is required to demonstrate the effectiveness of TIPS, this study indicates that TIPS has the potential to provide accurate estimates for multi-year LAI at the field scale.

Original languageEnglish
Pages (from-to)4764-4779
Number of pages16
JournalRemote Sensing
Volume6
Issue number6
DOIs
Publication statusPublished - 2014 Jun
Externally publishedYes

Keywords

  • Leaf area index
  • Multiyear
  • Rice
  • Vegetation index

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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