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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #378546

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Spectral discrimination using infinite leaf reflectance and simulated canopy reflectance.

item Hunt Jr, Earle

Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/20/2020
Publication Date: 1/20/2021
Citation: Hunt Jr, E.R. 2021. Spectral discrimination using infinite leaf reflectance and simulated canopy reflectance. . International Journal of Remote Sensing. 42(8):3039-3055.

Interpretive Summary: Biodiversity is an important indicator of ecosystem health, so an important objective of remote sensing is to identify the dominant plant species living in an area. Different plant species have foliage with similar chemical composition, differing mostly in their relative amounts. However, plants of the same species have differences in leaf reflectance caused by environmental factors, such as light exposure and soil nutrient availability. The question is how to enhance the remotely-sensed signal from foliar chemical composition and at the same time how to suppress the contribution of extraneous environmental factors. Based on physical models for the optics of glass plates, one solution may be the theoretical reflectance for an infinitely-thick stack of leaves, which is related to chemical concentration. Infinite leaf reflectance provides an estimate of plant canopy reflectance at very-high leaf area index. A simulation model created nine groups of leaf and canopy reflectance spectra representing different leaf morphologies and chemical composition. Five different algorithms quantified similarity between infinite leaf reflectances and canopy model simulations. While infinite leaf reflectance may not be used to estimate canopy reflectance, these results indicate that infinite leaf reflectance may be used to compare chemical composition for monitoring biodiversity.

Technical Abstract: An important application of hyperspectral remote sensing is monitoring the diversity of plant species and functional types, which are defined by leaf chemical constituents. Diffuse reflectance of an infinitely-thick leaf (R8) is directly related to chemical absorbance and may be calculated from either leaf spectral reflectance and transmittance based on Kubelka-Munk theory or other radiative transfer models. The PROSPECT-D leaf optics model was used to simulate nine sets of leaf reflectance and transmittance, and R8 were calculated using Stokes’, Lillesaeter’s, Goudriaan’s and Hapke’s equations. Similarity between R8 and PROSAIL canopy reflectances were calculated using five spectral information metrics: Euclidean distance (ED), spectral correlation measure (SCM), spectral angle mapper (SAM), spectral information divergence (SID), and the product of SID × sin(SAM). Differences using ED between canopy reflectance and R8 were large, especially using Stokes’ equation. However, the SCM, SAM, SID, and SID-SAM metrics showed R8 from the Stokes’ equation were the most similar to canopy reflectance. R8 from Goudriaan’s equation was the next most similar to canopy reflectance. The combined SID-SAM metric had higher accuracy compared to the other spectral information metrics. Infinite leaf reflectances did not provide good estimates of canopy reflectance, but could be helpful classifying spectra based on differences of chemical composition