Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice
| Autor: | Mairaj Din, Jin Ming, Sadeed Hussain, Syed Tahir Ata-Ul-Karim, Muhammad Rashid, Muhammad Naveed Tahir, Shizhi Hua, Shanqin Wang |
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| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Canopy N-nutrition Soil science Plant Science lcsh:Plant culture 01 natural sciences phenology Japonica Crop Linear regression lcsh:SB1-1110 Cultivar Mathematics Original Research biology dynamic canopy variables Phenology rice Hyperspectral imaging 04 agricultural and veterinary sciences Vegetation biology.organism_classification 040103 agronomy & agriculture 0401 agriculture forestry and fisheries hyperspectral reflectance 010606 plant biology & botany |
| Zdroj: | Frontiers in Plant Science Frontiers in Plant Science, Vol 9 (2019) |
| ISSN: | 1664-462X |
| Popis: | 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 (r > 0.50, r > 0.60) with red and NIR (720–900 nm) regions. These sensitive regions are used to formulate the new (SR777/759, SR768/750) 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. |
| Databáze: | OpenAIRE |
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