Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation.
Dong, T., Meng, J., Shang, J., Liu, J., and Wu, B. (2015). "Evaluation of Chlorophyll-Related Vegetation Indices Using Simulated Sentinel-2 Data for Estimation of Crop Fraction of Absorbed Photosynthetically Active Radiation.", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8: Article number 7050317), pp. 4049-4059. doi : 10.1109/JSTARS.2015.2400134 Access to full text
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 ( mathSR705), the Red-Edge Normalized Difference Vegetation Index (mathSR705), 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 mathSR705) had a stronger correlation with crop FPAR than the normalized difference indices (e.g., mathSR705). 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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