Abstract
A field-based hyperspectral method was used to estimate leaf protein content in this study. Leaf spectral data were pre-processed such as filtering, reflectance normalization and the first derivative of the reflectance before analysis. In order to reduce redundant wavebands, principal component analysis (PCA) was conducted; PC1 explained about 76% of variability, mostly dominated by the SWIR region. Additionally, a stepwise discriminant analysis was performed to select sensitive bands for a range of leaf protein concentrations by eliminating the influence of other factors, such as variety and treatment. A wavelength at 1514 nm was found to be sensitive to leaf proteins, which was found to be the most recurring band. Different spectral indices were worked out using the noise removed spectral data and their transformed derivatives. The significant correlation was observed between leaf protein and Optimized Soil-Adjusted Vegetation Index at 1510 nm (OSAVI1510) among all indices for estimating leaf protein content of fresh leaves. Thus, the SWIR region of spectrum 1510–1514 nm range can play an important role in estimating leaf protein content.
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Goswami, J., Das, R. & Sarma, K.K. Retrieval of leaf protein content using spectral transformation: proximal hyperspectral remote sensing approach. Vegetos 36, 721–727 (2023). https://doi.org/10.1007/s42535-022-00407-1
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DOI: https://doi.org/10.1007/s42535-022-00407-1