Reconstructing fresh green leaf spectra in the SWIR-2 region (2001–2500 nm) collected in a humid environment by referring to publicly available green leaf spectral databases

Abstract

Leaf spectra (reflectance and transmittance) are commonly measured using a portable spectroradiometer and an integrating sphere or contact probe with an artificial light source. However, spectral data may be obscured due to water vapor and low signal-to-noise ratios, especially in the shortwave infrared-2 region (SWIR-2, 2001–2500 nm). Therefore, we proposed a spectral reconstruction approach to retrieve noise-free SWIR-2 fresh green leaf spectra by referring to the previously published quality-controlled fresh green leaf spectral reference databases. We processed 896 pairs of fresh tea (Camellia sinensis var. sinensis) leaf reflectance/transmittance from Alishan in central Taiwan. We selected a subset (500–1900 nm) of the spectra in the visible, near-infrared, and SWIR-1 regions (VNS-1) that were relatively insensitive to atmospheric conditions. We matched those spectra with publicly available reference green leaf spectral databases, and selected the one that was most similar to each Alishan VNS-1 spectrum. We then used multivariable linear regression, linear parameter multiplication and spectral reversion to reconstruct SWIR-2 spectra. Finally, we used another set of green leaf spectral databases to assess the performance of the proposed method. The performance of the reconstruction approach was satisfactory, with mean (± standard deviation) root-mean-square errors (RMSEs) of 0.0041 ± 0.0019 (reflectance) and 0.0054 ± 0.0027 (transmittance) for each spectrum and RMSEs of 0.0058 ± 0.0027 (reflectance) and 0.0055 ± 0.0043 (transmittance) for each SWIR-2 band. The proposed approach successfully modeled SWIR-2, which could be further improved with the availability of a more comprehensive set of green leaf reference spectral databases.