期刊
JOURNAL OF EXPERIMENTAL BOTANY
卷 72, 期 2, 页码 341-354出版社
OXFORD UNIV PRESS
DOI: 10.1093/jxb/eraa432
关键词
Bioenergy crop; chlorophyll; CO2-saturated photosynthetic rate; hyperspectral leaf reflectance; maize; nitrogen; partial least squares regression; radiative transfer model
资金
- DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research) [DE-SC0018420]
- Center for Digital Agriculture, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign
- NASA New Investigator Award
- Carbon Monitoring System program [NNX16AI56G, 80NSSC18K0170]
- USDA National Institute of Food and Agriculture (NIFA) Foundational Program award [2017-67013-26253, 2017-68002-26789, 2017-67003-28703]
- NASA [1638464]
- NSF [1638720]
- USDA Hatch award [WIS01874]
- Division Of Environmental Biology
- Direct For Biological Sciences [1638720] Funding Source: National Science Foundation
This study evaluated various models for predicting leaf traits from hyperspectral reflectance, showing that accurate estimation of chlorophyll content and nitrogen concentration can be achieved through remote sensing methods, leading to improved predictions of V-max.
The photosynthetic capacity or the CO2-saturated photosynthetic rate (V-max), chlorophyll, and nitrogen are closely linked leaf traits that determine C-4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V-max can be well predicted from spectra (r=0.81). The integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V-max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V-max prediction.
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