Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

Kaiyi Bi,Zeying Han,Shuai Gao,Ji Wang,Zheng Niu,Shunfu Xiao,Gang Sun,Jie Bai

doi:10.3390/rs13214203

Abstract

High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.

Highlights

Increasing crop productivity is a major target in the 21st century to feed the growing population and respond to global climate change
The results successfully addressed the three points evaluated in the study: (1) Based on the spectral information of 20 wavelength bands, hyperspectral lidar (HSL) has the ability to estimate photosynthetic parameters
The estimation process was based on either the reference-based method or the trait-based method, whereas the trait-based method performed worse than the reflectance-based method for both Analytical Spectral Devices (ASD) and HSL datasets (Table 2). (3) Containing spectral and structural properties of targets, HSL data have the ability to estimate photosynthesis traits at both the leaf level and 3D plant level

Summary

Introduction

Increasing crop productivity is a major target in the 21st century to feed the growing population and respond to global climate change. The current increase rate of crop yield, cannot meet the demand of the global population [1] and will lead to serious food shortages by 2050 [2]. Enhancing photosynthetic ability provides the possibility for pursuing crop yield since harvested crop dry mass mainly comes from photosynthesis [3,4]. Photosynthetic parameters are spatially heterogeneous and differ along with the depth of plant canopy [9], and the lower leaf layers with shading intercept less light compared to the upper layers [5,10].

Methods

Results

Conclusion