4.7 Article

Estimating near-infrared reflectance of vegetation from hyperspectral data

Journal

REMOTE SENSING OF ENVIRONMENT
Volume 267, Issue -, Pages -

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2021.112723

Keywords

Solar-induced chlorophyll fluorescence (SIF); Hyperspectral remote sensing; Soil contamination; Near-infrared reflectance of vegetation (NIRv); Singular value decomposition (SVD); Red edge

Funding

  1. National Aeronautics and Space Administration (NASA) through Remote Sensing Theory and Arctic Boreal Vulnerability Experiment (ABoVE) [80NSSC21K0568, 80NSSC21K1702]
  2. National Research Foundation of Korea [NRF-2019R1A2C2084626]
  3. European Space Agency (ESA) [4000107143/12/NL/FF/If, 4000125731/19/NL/LF]
  4. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2070 - 390732324]

Ask authors/readers for more resources

Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is difficult. Solar Induced chlorophyll Fluorescence (SIF) can help separate vegetation and soil components, with NIRvH showing the smallest offset compared to NIRv and DVI in isolating true NIR reflectance of vegetation. This study highlights the potential of NIRvH in retrieving canopy structure parameters and estimating fluorescence yield using hyperspectral measurements.
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.

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