Variable Endmember Framework Improves Crop FAPAR Estimation From UAV Imagery
· newswise.com · popularity 20/100 · 0 similar stories today · Agriculture & Food Production (Food, Drink & Agriculture)
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Summary
Estimating the fraction of absorbed photosynthetically active radiation (FAPAR) is essential for tracking crop photosynthesis, growth and yield potential. Unmanned aerial vehicle (UAV) multispectral imagery offers an efficient way to map FAPAR, but vegetation index (VI)-based methods often struggle with mixed pixels, multiple scattering, spectral variability and dynamic canopy-background interactions. This study introduces VE-MLM, a variable endmember-based multilinear mixing framework for FAPAR estimation. The framework builds adaptive foreground and background spectral libraries, iteratively unmixes each pixel with a multilinear mixing model (MLM), and selects the best endmember combination using root mean square error (RMSE). Tests on sorghum and rice show that the derived canopy abundance is strongly correlated with field-measured FAPAR, offering a robust and generalizable approach for precision agriculture.
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