4.7 Article

Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2015.2400134

Keywords

Chlorophyll-related vegetation indices; crop FPAR; red-edge reflectance; Sentinel-2

Funding

  1. Strategic Priority Research Program-Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Science [XDA05050109]
  2. National High Technology Research and Development Program of China (863 program) [2012AA12A307]
  3. International Science and Technology Cooperation Program of China [2011DFG72280]
  4. China Grains Administration Special Fund for Public Interest [201313009-02]

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In recent years, the impact of chlorophyll content on the estimation of the fraction of absorbed photosynthetically active radiation (FPAR) has attracted increased attention. In this study, chlorophyll-related vegetation indices (VIs) were selected and tested for their capability in crop FPAR estimation using simulated Sentinel-2 data. These indices can be categorized into four classes: 1) the ratio indices; 2) the normalized difference indices; 3) the triangular area-based indices; and 4) the integrated indices. Two crops, wheat and corn, with distinctive canopy and leaf structure were studied. Measured FPAR and Sentinel-2 reflectance simulated from field spectral measurements were used. The results showed that VIs using the near-infrared and red-edge reflectance, including the modified Simple Ratio-2 (mSR2), the red-edge Simple Ratio (SR705), the Red-Edge Normalized Difference Vegetation Index (ND705), MERIS Terrestrial Chlorophyll Index (MTCI), and the Revised Optimized Soil-Adjusted Vegetation Index (OSAVI[705, 750]), had a strong linear correlation with FPAR, especially in the high biomass range. When the red-edge reflectance was used, the ratio indices (e.g., mSR2 and SR705) had a stronger correlation with crop FPAR than the normalized difference indices (e.g., ND705). Sensitivity analysis showed that mSR2 had the strongest linear correlation with FPAR of the two crops across a growing season. Further analysis indicated that indices using the red-edge reflectance might be useful for developing FPAR retrieval algorithms that are independent of crop types. This suggests the potential for high resolution and high-quality mapping of FPAR for precision farming using the Sentinel-2 data.

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