A radiative transfer model for characterizing photometric and polarimetric properties of leaf reflection: Combination of PROSPECT and a polarized reflection function

Abstract

Light photometric and polarimetric characteristics are crucial for describing the optical properties of leaf reflections, which play an essential role in investigating biochemical and surface structural trait inversion and radiative balance between vegetation and atmospheric system. Although several physical models are available, research on a comprehensive model that accounts for both photometric and polarimetric characteristics and incorporates biochemical and surface structural traits is still inadequate. In this study, we introduced PROPOLAR, a leaf model that considered leaf reflection in terms of polarized and unpolarized components and linked leaf reflection to leaf traits. PROPOLAR employed PROSPECT to simulate non-polarized component associated with biochemical traits, while used a three-parameter function (linear coefficient, refractive index factor, and roughness of leaf surface) to simulate the polarized component. The model was validated using a dataset (composed of both photometric and polarimetric measurements) collected from 533 samples of 13 plant species under various illumination-viewing geometries. The results showed that PROPOLAR outperformed PROSPECT and PROSPECULAR (a leaf model charactering BRF) in simulating light intensity (R² = 0.98), and effectively simulated bidirectional polarization reflectance factor (BPRF) and degree of linear polarization (Dolp) across a wide spectral range (450–2300 nm) and species, with R² = 0.92, and 0.80, respectively. Furthermore, PROPOLAR enhanced the accuracy of PROSPECT and showed comparable accuracy with PROSPECULAR in the inversion of biochemical traits from the multi-angular polarization measurements, including chlorophyll (R² = 0.89, RMSE = 12.83 μg/cm²), equivalent water thickness (R² = 0.90, RMSE = 0.0032 g/cm²), and leaf mass per area (R² = 0.38, RMSE = 0.0031 g/cm²), due to the incorporation of polarization reflection and a linear coefficient during calibration. Notably, PROPOLAR can invert roughness and showed reasonable consistency with measured roughness (R² = 0.61). These results demonstrated the effectiveness of PROPOLAR in simulating both photometric and polarimetric properties of leaf reflection, as well as its potential for biochemical and surface structural trait inversion. PROPOLAR may advance remote sensing applications in vegetation management by integrating photometric and polarimetric properties.

Plant phenotyping relevance

葉の光学・偏光反射から生化学的および表面構造形質を推定するPROPOLARモデルを開発し、多数試料で検証しており、植物表現型取得・推定手法が中心である。

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