Remote Sensing of Pigment Content at a Leaf Scale: Comparison among Some Specular Removal and Specular Resistance Methods

Abstract

Leaf pigment content retrieval is negatively affected by specular reflectance. To alleviate this effect, some specific techniques that take specular reflectance or specular effects into account have been proposed. In this study, continuous wavelet transform (CWT) and specific techniques including some vegetation indices (VIs), radiative transfer (RT), and hybrid models, were examined and compared in the nadir and near the mirror-like direction, with a 30° incident zenith angle. Results show that the RT and hybrid models appeared to be ill-posed, and they were not applicable at this high-incident zenith angle (>20°). Most VIs effectively alleviated the specular disturbance in the forward 35° direction, and comparable accuracy was obtained between the two viewing directions. Multiple linear regression (MLR), derivative transformation, and CWT were effective for specular interference alleviation. The MLR-based methods (reflectance, derivatives, etc., as the independent variables and pigment content as the response) generally obtained higher retrieval accuracies than the VIs. With MLR-based methods, the retrieval was more accurate for chlorophylls than for carotenoids. CWT plus MLR (MLR on wavelet coefficients) was the most prominent among all the methods, and it generally obtained the highest accuracy. The results are 2.68 and 0.88 μg/cm² for chlorophylls and carotenoids, respectively, in the nadir direction, and 2.42 and 0.86 μg/cm² in the forward 35° direction, with reflectance or the first derivative input for CWT. In the retrieval, wavelet coefficients at the optimal decomposition scale may achieve a balance in corresponding to fine, and broad absorption features, and the overall reflectance properties.

Data-Driven Methods for the Estimation of Leaf Water and Dry Matter Content: Performances, Potential and Limitations

“…of reflectances at several bands, with at least one band at which the leaf biochemical material strongly absorbs radiation. The mathematical combination could also suppress the sensitivity of other confounding factors, such as the leaf surface reflection [ 52 ]. Therefore, compared to the reflectance at the whole 900–2400 nm spectral region, vegetation indices thus were preferred as the inputs to the ML techniques.…”

A New Polarization-Based Vegetation Index to Improve the Accuracy of Vegetation Health Detection by Eliminating Specular Reflection of Vegetation

“…Wang et al [46] removed the specular reflection by identifying the highlight area, removing the highlight area and complementing it with surrounding information. Some scholars proposed to use wavelet transform to remove specular reflection components in remote sensing images, and it has been proved that wavelet transform is indeed effective for removing specular reflection [47,48]. Although many of the above studies have attempted to directly remove or mitigate the specular interference of leaves, none of them explicitly evaluate the specular interference of leaves and introduce a vegetation index that removes the specular interference of leaves.…”

Leaf Anthocyanin Content Retrieval with Partial Least Squares and Gaussian Process Regression from Spectral Reflectance Data

“…In addition, these studies should also be further examined for applicability under other circumstances. For chlorophylls and carotenoids, many advanced and sophisticated retrieval techniques have been proposed, such as stepwise multiple linear regression [ 18 ], partial least squares regression (PLSR) [ 19 , 20 ], continuous wavelet analysis [ 21 ], artificial neural network (ANN) [ 22 ], and Gaussian process regression (GPR) [ 23 , 24 ]. However, these methods have not been reported to retrieve the leaf anthocyanin content yet.…”