Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption

Guo, Biao; Qi, Shuang-Li; Heng, Ya-Rong; Duan, Jianzhao; Zhang, Haiyan; Wu, Yapeng; Feng, Wei; Xie, Ying-Xin; Zhu, Yunji

doi:10.1016/j.eja.2016.10.009

Cited by 44 publications

(27 citation statements)

References 50 publications

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“…This N flux suggests emission of volatile nitrogen compounds from donor oak and uptake by its neighbour, first in the aboveground part. The ability of plants to take up nitrogen by leaves has been demonstrated in several studies (Wittwer and Teubner 1959;Eilers et al 1992;Feng et al 2015;Guo et al 2017). After above-ground uptake, N may have been transferred to roots and translocated from roots to soil during dormancy.…”

Section: N Transfer Between Plants May Contribute To the Growth Facilmentioning

confidence: 99%

Below-ground nitrogen transfer from oak seedlings facilitates Molinia growth: 15N pulse-chase labelling

et al. 2020

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: N Transfer Between Plants May Contribute To the Growth Facilmentioning

confidence: 99%

Below-ground nitrogen transfer from oak seedlings facilitates Molinia growth: 15N pulse-chase labelling

et al. 2020

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“…The correlation coefficient between winter wheat yield and canopy spectral characteristics was extreme in the red edge area, and this kind of spectral variable is often regarded as a sensitive and characteristic spectral region 45 . The red edge position was closely related to crop growth, which is commonly used to characterize the crop growth status [46][47][48][49] . The specific spectral information is obviously concentrated in the red edge region, which showed that the red edge region contains some important information on winter wheat growth after freeze stress.…”

Section: Discussionmentioning

confidence: 99%

Canopy hyperspectral characteristics and yield estimation of winter wheat (Triticum aestivum) under low temperature injury

Xie

Wang

Yang

et al. 2020

Sci Rep

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To evaluate the effect of low-temperature stress in winter wheat during the early growth stages, the response regularity of the canopy spectral reflectance was evaluated. Besides, winter wheat yield during the maturation stage and the relationship between yield and canopy spectral reflectance were also analyzed. Two multivariate methods, namely, the successive projections algorithm (SPA) and multiple linear regression (MLR), were combined to explore the relationship between the spectral reflectance and yield. Our results showed that the green peak and red valley in visible wavelengths altered obviously and the red edge gradually moved towards blue wavebands. The canopy spectral reflectance in the near-infrared wavebands increased with an increase in low-temperature stress intensity. Moreover, the reflectance proved that the red edge region under low-temperature stress is related to winter wheat yield, and approximately 38% of extracted wavebands were concentrated in the red edge region (680-780 nm). Compared with the predictive MLR models, the model calibrated during the flowering period of winter wheat (25 days post low-temperature treatment) had better performance in predicting crop yield. Whole-spectrum predictive models based on the principle component regression (PCR) method and Normalized Difference Vegetation Index (NDVI) models based on MLR were also established. Moreover, the performance of three kinds of calibration methods and the validation result of the field test were compared to select the optimal monitoring stage and technique to estimate the yield in the early growth stage of winter wheat under low-temperature stress. This study could provide a theoretical basis and practical reference for hyperspectral assessment of yield in winter wheat during low-temperature stress.

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“…The red edge spectrum is closely related to various physical and chemical parameters of vegetation and is an important indicator for describing plant pigment color and overall health. Other indicators include: canopy LAI estimation [80], chlorophyll density evaluation [81], and leaf element absorption [82]. Vegetation information was abundant in May, so the corresponding red edge bands and indices scored higher than in other seasons using the single-red edge method.…”

Section: Key Predictor Variables In Mfsmentioning

confidence: 99%

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

Chen

Jia

et al. 2019

Remote Sensing

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Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.

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Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption

Cited by 44 publications

References 50 publications

Below-ground nitrogen transfer from oak seedlings facilitates Molinia growth: 15N pulse-chase labelling

Below-ground nitrogen transfer from oak seedlings facilitates Molinia growth: 15N pulse-chase labelling

Canopy hyperspectral characteristics and yield estimation of winter wheat (Triticum aestivum) under low temperature injury

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

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