首页|N distribution characterization based on organ-level biomass and N concentration using a hyperspectral lidar
N distribution characterization based on organ-level biomass and N concentration using a hyperspectral lidar
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NSTL
Elsevier
? 2022 Elsevier B.V.Accurate estimates of the N concentration and biomass (W) in plant organs provide information regarding the mechanisms of N distribution, which is critical for improving N use efficiency (NUE) and optimizing N management. Dimensionless passive remotely sensed data may lead to the asymptotic saturation problem when extracting W, whereas commercial lidar systems employing only one wavelength band have limited capacity in N retrieval. Combining the advantages of passive remote sensing and traditional lidar, hyperspectral lidar (HSL) has the ability to simultaneously extract structural and spectral information for plants. The objectives of this research were to evaluate the ability of HSL to estimate maize N concentration and W at the organ level, and to test whether HSL can characterize maize N distribution at different growth stages and under different N fertilizer conditions. A wide range of HSL performance for leaf and stem N extraction (R2 = 0.71 – 0.91) was observed based on the partial least squares regression (PLSR) method with spectral indices as inputs. Close relationships (R2 ≥ 0.75) were established between extracted height metrics (stem height and plant height) and organ-level W. The W portioning, dynamics of N concentration, and the variations in N with W accumulation were successfully monitored based on the estimated N concentration and W, thus demonstrating the capacity of HSL to characterize the N distribution pattern within maize plants. Our results show that the novel HSL system holds much potential for monitoring plant N distribution and serving precision agriculture.
Crop nitrogen responseLaser return intensityMaizePrecision agricultureSpectral point cloud
Bi K.、Gao S.、Bai J.、Huang N.、Sun G.、Niu Z.、Xiao S.、Zhang C.
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State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute – Chinese Academy of Sciences and Beijing Normal University
College of Land Science and Technology China Agricultural University
The School of Environment and Geoinformatics China University of Mining and Technology
NSTL
Elsevier
