Using partial least squares (PLS) to estimate canopy nitrogen and biomass of paddy rice in China’s Sanjiang Plain
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| Title: | Main Title: Using partial least squares (PLS) to estimate canopy nitrogen and biomass of paddy rice in China’s Sanjiang Plain |
| Description: | Abstract: Nitrogen (N) is one of the most essential elements in agriculture and ecology due to its direct role in determining crop yield and grain quality, as well as its association with canopy photosynthetic capacity and carbon-nitrogen cycling in the earth ecosystem. Remote sensing provides a useful way to capture canopy nitrogen and biomass with high spatial and temporal resolution. However, seasonal dynamics of plant morphophysiological variation hinder the simultaneous estimation of canopy N concentration (%N) and biomass using a traditional method such as vegetation indices because of the distinct dynamics of canopy biochemical and physical traits. In contrast, multivariate analysis method offers the capability of calibrating a model with multiple dependent variables of interest. Therefore, the main objective of this study was to, simultaneously, estimate canopy %N and biomass of rice using the partial least squares regression (PLSR) model. A field experiment was conducted for paddy rice fertilized with five N rates across five growth stages in 2008, located in the Sanjiang Plain, China. Results showed that the PLS regression model simultaneously explained 84% and 91% of the variation in %N and biomass, respectively, across the five growth stages. Our results also suggest that biomass is the dominant factor that affects the link between canopy dynamical traits and canopy reflectance measures. This study demonstrates that, by incorporating with PLSR for retrieving biophysical and biochemical properties from the full-spectrum analysis, to what extent canopy %N and biomass can be simultaneously estimated from canopy reflectance measurement. |
| Identifier: | 10.5880/TR32DB.KGA94.14 (DOI) |
Responsible Party
| Creators: | Kang Yu (Author), Martin Leon Gnyp (Author), L. Gao (Author), Yuxin Miao (Author), Xinping Chen (Author), Georg Bareth (Author) |
| Contributors: | Juliane Bendig (Editor), Georg Bareth (Editor), Transregional Collaborative Research Centre 32 (Meteorological Institute, University of Bonn) (Data Manager), University of Cologne (Regional Computing Centre (RRZK)) (Hosting Institution) |
| Publisher: | Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten |
| Publication Year: | 2014 |
Topic
| Subjects: | Keywords: Nitrogen, Biomass, Hyperspectral, Remote Sensing, Agriculture DDC: 550 Earth sciences |
File Details
| Filename: | Yu_et_al_2013_KGA94.pdf |
| Data Type: | Text - Book Section |
| Sizes: | 5 Pages 1130 Kilobytes |
| File Size: | 1.1 MB |
| Date: | Issued: 14.04.2014 |
| Mime Type: | application/pdf |
| Language: | English |
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| Download Permission: | Free |
| Licence: | [Creative Commons] Attribution 4.0 International (CC BY 4.0) |
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Metadata Details
| Metadata Creator: | Constanze Curdt |
| Metadata Created: | 17.04.2014 |
| Metadata Last Updated: | 17.04.2014 |
| Subproject: | Z1 |
| Funding Phase: | 2 |
| Metadata Language: | English |
| Metadata Version: | V50 |
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| Page Visits: | 543 |
| Metadata Downloads: | 0 |
| Dataset Downloads: | 56 |
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