Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice.

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ID: 38459
2018
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Ranked #130 of 187 articles by views in Frontiers in plant science

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Abstract
Non-destructive and rapid estimation of canopy variables is imperative for predicting crop growth and managing nitrogen (N) application. Hyperspectral remote sensing can be used for timely and accurate estimation of canopy physical and chemical properties; however, discrepancies associated with soil and water backgrounds complicate the estimation of crop N status using canopy spectral reflectance (CSR). This study established the quantitative relationships between dynamic canopy nitrogen (CN) status indicators, leaf dry weight (LDW), leaf N concentration (LNC), leaf N accumulation (LNA), and CSR-derived new hyperspectral vegetation indices (HVIs), and to access the plausibility of using these relationships to make in-season estimations of CN variables at the elongation (EL), booting (BT), and heading (HD) stages of rice crop growth. Two-year multi-N rate field experiments were conducted in 2015 and 2016 in Hubei Province, China, using the rice cultivar Japonica. The results showed that the sensitive spectral regions were negatively correlated with CN variables in the visible (400-720 nm and 560-710 nm) regions, and positively correlated ( > 0.50, > 0.60) with red and NIR (720-900 nm) regions. These sensitive regions are used to formulate the new (SR, SR) HVIs to predict CN variables at the EL, BT, and HD stages. The newly developed stepwise multiple linear regression (SMLR) models could efficiently estimate the dynamic LDW at the BT stage and LNC and LNA at the HD stage. The SMLR models performed accurately and robustly when used with a validation data set. The projected results offer a suitable approach for rapid and accurate estimation of canopy N-indices for the precise management of N application during the rice growth period.
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din2018estimationfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Din, Mairaj;Ming, Jin;Hussain, Sadeed;Ata-Ul-Karim, Syed Tahir;Rashid, Muhammad;Tahir, Muhammad Naveed;Hua, Shizhi;Wang, Shanqin;
Journal Frontiers in plant science
Year 2018
DOI
10.3389/fpls.2018.01883
URL
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