Combination of Spectral Angle Cosine and Band Depth Can Reduce the Specular Reflection Effects in Leaf Biochemical Parameters Estimation From Multiangular Hyperspectral Data

Leaf chlorophyll content (LCC) and equivalent water thickness (EWT) are critical indicators of plant physiological state and photosynthetic capacity. Hyperspectral measurements provide technical support for the efficient estimation of these leaf biochemical parameters. However, most current studies ignored the influence of leaf surface specular reflection, which reduces estimation accuracy, in the multi-angular measurements. This study proposed the spectral angle cosine-band depth indices (SACI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BD</sub>), a method that transforms additive specular reflection into multiplicative differences using band depth (BD) and eliminates them via spectral angle cosine (SAC). Based on a multi-angular calibration dataset, the optimal spectral intervals (OSI) for SACI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BD</sub> were identified. Then, SACI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BD</sub> was validated across five independent leaf datasets and canopy imaging datasets obtained through close-range camera and LESS simulated scene. Results demonstrated that SACI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BD</sub> efficiently captured biochemical-sensitive spectral absorption shape features (OSI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCC</sub>: 658-748 nm and OSI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EWT</sub>: 1872-2026 nm) and eliminated the specular reflection influence on biochemical estimation. Strong linear relationships with both LCC (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.9) and EWT (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.94) as well as validation results of five leaf datasets (LCC: RMSE = 5.98 μg/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, n = 2143; EWT: RMSE = 0.0031 g/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, n = 1919) confirmed the applicability of SACI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BD</sub> for various measurement strategies and plant species in the estimation of leaf biochemical parameters. When the study was extended to the canopy level, SACI<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BD</sub> achieved similarly good accuracy (LCC: RMSE = 6.98 μg/cm², n = 104; EWT: RMSE = 0.0044 g/cm², n = 34). Thus, the novel combination of SAC and BD provides an effective tool for quantitative plant biochemistry research.