Estimating near-infrared reflectance of vegetation from hyperspectral data

• Published in: Remote sensing of environment 2021-12, Vol.267, p.112723, Article 112723
• Main Authors: Zeng, Yelu, Hao, Dalei, Badgley, Grayson, Damm, Alexander, Rascher, Uwe, Ryu, Youngryel, Johnson, Jennifer, Krieger, Vera, Wu, Shengbiao, Qiu, Han, Liu, Yaling, Berry, Joseph A., Chen, Min
• Format: Article
• Language: English
• Subjects: Canopies; Chlorophyll; Complications; Ecophysiology; Environmental Sciences; Evaluation; Fluorescence; Herbivores; Hyperspectral remote sensing; I.R. radiation; Imaging spectrometers; Inclination angle; Infrared reflection; Leaf area; Leaf area index; Leaves; Near infrared radiation; Near-infrared reflectance of vegetation (NIRv); Photons; Photosynthesis; Radiative transfer; Red edge; Reflectance; Remote sensing; Residual soils; Satellites; Singular value decomposition (SVD); Soil contamination; Solar-induced chlorophyll fluorescence (SIF); Vegetation; Vegetation index
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2021.112723

Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0–0.037), as compared to an intermediate offset of 0.047–0.062 from NIRv, and the largest offset of 0.089–0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements. •Previous VIs have not accounted for the shape of the soil spectrum at the red edge.•We propose the NIRvH approach for estimating the true NIR reflectance of vegetation.•NIRvH reduces the soil contamination using the SVD method and a logistic function.•Compared to DVI and NIRv, the proposed NIRvH has the best agreement with SIF.•NIRvH is promising for the anisotropic correction of directional observed