4.7 Article

Canopy modeling of aquatic vegetation: A geometric optical approach (AVGO)

Journal

REMOTE SENSING OF ENVIRONMENT
Volume 245, Issue -, Pages -

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2020.111829

Keywords

Discrete aquatic vegetation; Geometric optical model; Bidirectional reflectance factor; Wetland remote sensing

Funding

  1. National Natural Science Foundation of China [41971320]
  2. Beijing Natural Science Foundation [8162028]
  3. NERC [nceo020006] Funding Source: UKRI

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Aquatic vegetation is an important component in the ecosystems of wetlands and coastal waters, for example it can take up harmful substances and pollutants from the water. Constructing a remote sensing model of aquatic vegetation can deepen our understanding of the spectral characteristics and directional reflection characteristics of the aquatic vegetation canopy under various environmental conditions. However, the existing aquatic vegetation models are limited to continuously distributed canopies. This paper proposes an aquatic-vegetation-geometric-optical (AVGO) model at canopy scale for discrete aquatic vegetation. After dividing the reflectance of water background into specular components and diffuse components, the reflectances of all reflecting surfaces are classified into different reflectance components based on the differences in their materials and on whether the reflectors are illuminated by direct sunlight. Then the scene bidirectional reflectance factor (BRF) is regarded as an area-weighted sum of different reflectance components. Detailed verifications were conducted to ensure that the simulation model operates as intended. The performance of the AVGO model was qualitatively validated with the WCRM model and showed the physical plausibility of the model prediction. Quantitative validations against an artificially designed experiment were consistent with the bidirectional reflectance and the nadir-viewed spectral reflectance. This model can be used for canopy modeling of discrete emergent or submerged aquatic vegetation. Potential applications include retrieval of biophysical or biochemical parameters and aquatic vegetation abundance based on hyperspectral data.

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