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

Hanyue Chen,Liming Zhang,Zheng Niu,Wenjiang Huang,Shihe Xing,Wang Li

doi:10.3390/rs10101630

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 (R2 > 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 (R2 = 0.971) and NDSI(571,707) at 55° in the forward scattering direction (R2 = 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.

Highlights

Leaf area indiex (LAI) is one of the key vegetation structural variables for understanding the biophysical processes of crop canopies [1,2] and provides important information for the identification of the early assessment of crop conditions, prediction of crop yields, and even scheduling of irrigation [3]
Danner et al [14] found anisotropic effects of sensor view angle on reflectance were relatively strong for early growth stages of the winter wheat canopy, which influenced the retrieval of the leaf area index (LAI) based on PROSAIL model
Galvão et al [15] and Breunig et al [7] tested the effect of view directions on the normalized difference vegetation index (NDVI)–soybean yield and NDVI–LAI relationships, respectively, and both showed that backward scattering performed better than the forward scattering direction

Summary

Introduction

Leaf area indiex (LAI) is one of the key vegetation structural variables for understanding the biophysical processes of crop canopies [1,2] and provides important information for the identification of the early assessment of crop conditions, prediction of crop yields, and even scheduling of irrigation [3]. VIs derived from multi-angular remote sensing data may provide additional information for enhancing the retrieval of canopy structure variables since they offer a means to characterize the anisotropy of surface reflectance, which may allow us to describe three-dimensional vegetation structures [4,5,6]. Studies with respect to the multi-angular approach often concluded that off-nadir angles improved the performance of VIs in monitoring the ecosystem when compared to nadir viewing, since they better accounted for structural heterogeneity and canopy shading [20]

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