Wavelength selection for estimating biomass, LAI, and leaf nitrogen concentration in winter wheat of Gifu prefecture using <i>in situ</i> hyperspectral data
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- KANEMOTO Maiko
- River Basin Research Center, Gifu University
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- TANAKA Shinya
- River Basin Research Center, Gifu University
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- KAWAMURA Kensuke
- National Institute for Agro-Environmental Sciences
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- MATSUFURU Hiroki
- Gifu Prefectural Agricultural Technology Center
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- YOSHIDA Kazuaki
- Gifu Prefectural Agricultural Technology Center
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- AKIYAMA Tsuyoshi
- River Basin Research Center, Gifu University
Bibliographic Information
- Other Title
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- ハイパースペクトルデータによる冬小麦の生育推定のための波長選択
- Wavelength selection for estimating biomass, LAI, and leaf nitrogen concentration in winter wheat of Gifu prefecture using in situ hyperspectral data
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Abstract
Winter wheat (Triticum aestivum L.) is an important rotation crop in southern Gifu prefecture, but rotational use of dried paddy fields often reduces grain quality. Consequently, grain quality varies greatly throughout a single area. This study is intended to evaluate in situ canopy reflectance to predict the total dry matter (DM), leaf area index (LAI), and leaf nitrogen concentration (Nconc). In a field experiment including four levels of N application, hyperspectral reflectance data (400–950 nm) were obtained for winter wheat during five growth stages. Band selection applied using a normalized difference vegetation index (NDVI) formula yielded the best-fitted two-pair waveband. Several paired bands were also indicated, which had higher R2 values of DM (R2 > 0.8), LAI (R2 > 0.4), and Nconc (R2 > 0.5) than those in typical red and near-infrared (NIR) based NDVI(R2 = 0.39, 0.27, and 0.01 respectively in DM, LAI, and Nconc). Partial least squares (PLS) regression analyses using the best two-pair NDVI predictions improved the predictive accuracy for DM (R2 = 0.93), LAI (R2 = 0.80), and Nconc (R2 = 0.83). Furthermore, the model using first derivative reflectance (FDR) more precisely predicted DM (R2 = 0.95) and Nconc (R2 = 0.88) than that using reflectance spectra. These findings suggest that PLS can provide better prediction than conventional methods using a single waveband or band combination. Strong correlations were found in regions with shorter wavelengths: green(525–570 nm), red edge (720–780 nm), and NIR (930–950 nm) regions to DM and LAI; and blue (425–490 nm) and red regions (645–685 nm) to Nconc. These results suggest that important wavelength regions are green, red edge, and NIR regions for DM and LAI, and blue and red regions for Nconc.
Journal
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- Journal of the Japanese Agricultural Systems Society
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Journal of the Japanese Agricultural Systems Society 24 (1), 43-56, 2008
The Japanese Agricultural Systems Society
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Details 詳細情報について
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- CRID
- 1390282679391524352
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- NII Article ID
- 10022579219
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- NII Book ID
- AN10164125
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- ISSN
- 21890560
- 09137548
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- NDL BIB ID
- 9353702
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed