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Optimizing angular resistant spectral indices to estimate leaf biochemical parameters from multi-angular spectral reflection
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DOI：10.1016/j.agrformet.2024.109916.png)
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Spectral indices are popular and simple mathematical methods for estimating leaf biochemical parameters from spectral reflectance information. However, the estimation accuracy of most spectral indices decreases in high spatial-resolution data. This is primarily because leaf reflectance exhibits multi-angular effects that are controlled by specular reflection and vary with illumination and viewing angles. Therefore, there is a need for robust spectral indices whose predictions of leaf biochemistry are resistant to measurement angles and configurations. The commonly used wavelength optimization method of reducing angular effects by iterating all the possible band combinations is time-consuming and lacks a sound theoretical basis. In this study, we developed an efficient and defensible three-step wavelength selection method for Angular Resistant Spectral Indices (ARSIs) with difference ratio format based on the multi-angular spectral reflection. Firstly, a cumulative Rncrease within the leaf biochemistry-significant wavelength range is used to identify a specular wavelength (angstrom spec) as the wavelength with the highest contribution of specular reflection to total reflection. The impact of specular reflection on the estimation of the biochemical parameters is reduced by subtracting the reflection at angstrom spec from the total reflection. Secondly, a reference wavelength (angstrom ref) that is insensitive to biochemical variations is selected from the valley bottom of the R2 spectrum near its maximal peak. Thirdly, the optimal ARSIs are developed based on the combination of angstrom spec, angstrom ref, and a biochemistry-indicative wavelength (angstrom indi). These ARSIs show strong relationships to leaf chlorophyll content (LCC, R2 = 0.94), leaf carotenoid content (CAR, R2 = 0.61), and equivalent water thickness (EWT, R2=0.89). Validation using several independent datasets and accurate LCC and CAR estimations based on close -range hyperspectral images of plant canopies confirmed that ARSIs outperformed most existing indices. The three-step wavelength selection method for ARSIs is valuable and convenient for plant science, and crop management assessments that need accurate estimates of leaf biochemical parameters.
Keyword:
Multi-angular reflectance factor
Leaf chlorophyll content
Leaf carotenoid content
Leaf water content
Angular resistant spectral index (ARSI)
Close -range hyperspectral imaging
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IF:
5.7
论文数:
6.9K
被引数:
3.2W
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