Estimation of LAI in Winter Wheat from Multi-Angular Hyperspectral VNIR Data: Effects of View Angles and Plant Architecture

Abstract

View angle effects present in crop canopy spectra are critical for the retrieval of the crop canopy leaf area index (LAI). In the past, the angular effects on spectral vegetation indices (VIs) for estimating LAI, especially in crops with different plant architectures, have not been carefully assessed. In this study, we assessed the effects of the view zenith angle (VZA) on relationships between the spectral VIs and LAI. We measured the multi-angular hyperspectral reflectance and LAI of two cultivars of winter wheat, erectophile (W411) and planophile (W9507), across different growing seasons. The reflectance of each angle was used to calculate a variety of VIs that have already been published in the literature as well as all possible band combinations of Normalized Difference Spectral Indices (NDSIs). The above indices, along with the raw reflectance of representative bands, were evaluated with measured LAI across the view zenith angle for each cultivar of winter wheat. Data analysis was also supported by the use of the PROSAIL (PROSPECT + SAIL) model to simulate a range of bidirectional reflectance. The study confirmed that the strength of linear relationships between different spectral VIs and LAI did express different angular responses depending on plant type. LAI-VI correlations were generally stronger in erectophile than in planophile wheat types, especially at the zenith angle where the background is expected to be more evident for erectophile type wheat. The band combinations and formulas of the indices also played a role in shaping the angular signatures of the LAI-VI correlations. Overall, off-nadir angles served better than nadir angle and narrow-band indices, especially NDSIs with combinations of a red-edge (700~720 nm) and a green band, were more useful for LAI estimation than broad-band indices for both types of winter wheat. But the optimal angles much differed between two plant types and among various VIs. High significance (R 2 > 0.9) could be obtained by selecting appropriate VIs and view angles on both the backward and forward scattering direction. These results from the in-situ measurements were also corroborated by the simulation analysis using the PROSAIL model. For the measured datasets, the highest coefficient was obtained by NDSI(536,720) at −35 • in the backward (R 2 = 0.971) and NDSI(571,707) at 55 • in the forward scattering direction (R 2 = 0.984) for the planophile and erectophile varieties, respectively. This work highlights the influence of view geometry and plant architecture. The identification of crop plant type is highly recommended before using remote sensing VIs for the large-scale mapping of vegetation biophysical variables.

Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat

“…Physical and structural components of wheat are often measured with hyperspectral data as an indication of yield rather than the quantification of yield‐limiting factors. These parameters include LAI (Li et al., 2014a; Chen et al., 2018), biomass (Hansen & Schjoerring, 2003; Li, Mistele, Hu, Chen, & Schmidhalter, 2014b), photosynthetic efficiency (Silva‐Perez et al., 2018; El‐Hendawy et al., 2019b), and direct estimates of yield (Aparicio, Villegas, Casadesus, Araus, & Royo, 2000; Babar et al., 2006; Wang, Nie, Xi, Luo, & Sun, 2017). Furthermore, hyperspectral techniques have been used to discriminate different wheat varieties and classes (Mahesh, Manickavasagan, Jayas, Paliwal, & White, 2008; Miralbés, 2008).…”

Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs

“…Red (R) and near-infrared (NIR) bands have been widely used for calculations of VIs because the spectral reflectance at R and NIR bands is sensitive to vegetation phenotypic traits (e.g., LAI and chlorophyll content) [38][39][40][41]. Previous studies have attempted to optimize the construction of VIs by evaluating the central wavelengths and bandwidths of R and NIR bands [39]. Therefore, this study followed this method to improve the performance of UAV-based VIs on LAI estimation at the field scale.…”

Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process

“…The spectral index can indicate the pigment content, moisture change and nutritional status of green vegetation through the combination of specific bands [23]. In this study, to better use the information in each wavelength of hyperspectral data, pairs of bands of hyperspectral reflectance data from UAVs were combined to construct the NDSI [30,31]. The construction form is as follows:…”