Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies

Jindong Wu, Dong Wang, Marvin E. Bauer

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI-LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI-LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI-LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI-LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.

Original languageEnglish (US)
Pages (from-to)33-42
Number of pages10
JournalField Crops Research
Volume102
Issue number1
DOIs
StatePublished - Apr 30 2007

Keywords

  • Leaf area index
  • QuickBird
  • Remote sensing
  • Sensitivity
  • Stability
  • Vegetation index

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