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Jing Tang

Researcher

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Generation of Spectral-Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications

Author

  • Caroline Gevaert
  • Juha Suomalainen
  • Jing Tang
  • Lammert Kooistra

Summary, in English

Precision agriculture requires detailed crop status information at high spatial and temporal resolutions. Remote sensing can provide such information, but single sensor observations are often incapable of meeting all data requirements. Spectral-temporal response surfaces (STRSs) provide continuous reflectance spectra at high temporal intervals. This is the first study to combine multispectral satellite imagery (from Formosat-2) with hyperspectral imagery acquired with an unmanned aerial vehicle (UAV) to construct STRS. This study presents a novel STRS methodology which uses Bayesian theory to impute missing spectral information in the multispectral imagery and introduces observation uncertainties into the interpolations. This new method is compared to two earlier published methods for constructing STRS: a direct interpolation of the original data and a direct interpolation along the temporal dimension after imputation along the spectral dimension. The STRS derived through all three methods are compared to field measured reflectance spectra, leaf area index (LAI), and canopy chlorophyll of potato plants. The results indicate that the proposed Bayesian approach has the highest correlation (r = 0.953) and lowest RMSE (0.032) to field spectral reflectance measurements. Although the optimized soil-adjusted vegetation index (OSAVI) obtained from all methods have similar correlations to field data, the modified chlorophyll absorption in reflectance index (MCARI) obtained from the Bayesian STRS outperform the other two methods. A correlation of 0.83 with LAI and 0.77 with canopy chlorophyll measurements are obtained, compared to correlations of 0.27 and 0.09, respectively, for the directly interpolated STRS.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • MERGE: ModElling the Regional and Global Earth system

Publishing year

2015

Language

English

Pages

3140-3146

Publication/Series

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volume

8

Issue

6

Document type

Journal article

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Agricultural Science
  • Remote Sensing

Keywords

  • Crop phenology
  • data fusion
  • hyperspectral imaging
  • image resolution
  • precision agriculture

Status

Published

ISBN/ISSN/Other

  • ISSN: 2151-1535