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  1. Article ; Online: Accumulation of wheat phenolic acids under different nitrogen rates and growing environments

    Wenfei Tian / Fengju Wang / Kaijie Xu / Zhaoxing Zhang / Yan, Junliang / Jun Yan / Yubing Tian / Jindong Liu / Yan Zhang / Yong Zhang / He Zhonghu

    Plants

    2023  

    Abstract: The health benefits of whole wheat grains are partially attributed to their phenolic acid composition, especially that of trans-ferulic acid (TFA), which is a powerful natural antioxidant. Breeders and producers are becoming interested in wheat with ... ...

    Abstract The health benefits of whole wheat grains are partially attributed to their phenolic acid composition, especially that of trans-ferulic acid (TFA), which is a powerful natural antioxidant. Breeders and producers are becoming interested in wheat with enhanced health-promoting effects. This study investigated the effects of different nitrogen (N) application rates (0, 42, 84, 126, and 168 N kg ha−1) on the phenolic acid composition of three wheat varieties in four locations for two years. The results indicate that the different N rates did not affect the TFA concentration but that they significantly affected the concentrations of para-coumaric acid, sinapic acid, and cis-ferulic acid in the wheat grains. A statistical analysis suggested that the wheat phenolic acid composition was predominantly determined by wheat variety, though there existed some interaction effect between the wheat variety and environments. The TFA concentration of the variety Jimai 22 was generally higher (with a mean value of 726.04 µg/g) but was easily affected by the environment, while the TFA concentration of the variety Zhongmai 578 (with a mean value of 618.01 µg/g) was more stable across the different environments. The results also suggest that it is possible to develop new wheat varieties with high yield potential, good end-use properties, and enhanced nutraceutical values.
    Keywords wheat ; phenolic acids ; nitrogen ; environment ; antioxidants ; ecology
    Language English
    Publishing date 2023-02-28T09:00:22Z
    Publisher MDPI
    Publishing country fr
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Estimating Leaf Area Index with Dynamic Leaf Optical Properties

    Hu Zhang / Jing Li / Qinhuo Liu / Yadong Dong / Songze Li / Zhaoxing Zhang / Xinran Zhu / Liangyun Liu / Jing Zhao

    Remote Sensing, Vol 13, Iss 4898, p

    2021  Volume 4898

    Abstract: Leaf area index (LAI) plays an important role in models of climate, hydrology, and ecosystem productivity. The physical model-based inversion method is a practical approach for large-scale LAI inversion. However, the ill-posed inversion problem, due to ... ...

    Abstract Leaf area index (LAI) plays an important role in models of climate, hydrology, and ecosystem productivity. The physical model-based inversion method is a practical approach for large-scale LAI inversion. However, the ill-posed inversion problem, due to the limited constraint of inaccurate input parameters, is the dominant source of inversion errors. For instance, variables related to leaf optical properties are always set as constants or have large ranges, instead of the actual leaf reflectance of pixel vegetation in the current model-based inversions. This paper proposes to estimate LAI with the actual leaf optical property of pixels, calculated from the leaf chlorophyll content (Chl leaf ) product, using a three-dimensional stochastic radiative transfer model (3D-RTM)-based, look-up table method. The parameter characterizing leaf optical properties in the 3D-RTM-based LAI inversion algorithm, single scattering albedo (SSA), is calculated with the Chl leaf product, instead of setting fixed values across a growing season. An algorithm to invert LAI with the dynamic SSA of the red band (SSA red ) is proposed. The retrieval index (RI) increases from less than 42% to 100%, and the RMSE decreases to less than 0.28 in the simulations. The validation results show that the RMSE of the dynamic SSA decreases from 1.338 to 0.511, compared with the existing 3D-RTM-based LUT algorithm. The overestimation problem under high LAI conditions is reduced.
    Keywords LAI inversion ; radiative transfer model ; single scattering albedo ; leaf chlorophyll content ; spectral invariant theory ; Science ; Q
    Subject code 550
    Language English
    Publishing date 2021-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data

    Wentao Yu / Jing Li / Qinhuo Liu / Jing Zhao / Yadong Dong / Xinran Zhu / Shangrong Lin / Hu Zhang / Zhaoxing Zhang

    Remote Sensing, Vol 13, Iss 3, p

    2021  Volume 484

    Abstract: High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change. Gap filling is the most important issue in reconstructing NDVI time series from satellites with ... ...

    Abstract High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change. Gap filling is the most important issue in reconstructing NDVI time series from satellites with high spatial resolution, e.g., the Landsat series and Chinese GaoFen-1/6 series. Due to the sparse revisit frequencies of high-resolution satellites, traditional reconstruction approaches face the challenge of dealing with large gaps in raw NDVI time series data. In this paper, a climate incorporated gap-filling (CGF) method is proposed for the reconstruction of Landsat historical NDVI time series data. The CGF model considers the relationship of the NDVI time series and climate conditions between two adjacent years. Climate variables, including downward solar shortwave radiation, precipitation, and temperature, are used to characterize the constrain factors of vegetation growth. Radial basis function networks (RBFNs) are used to link the NDVI time series between two adjacent years with variabilities in climatic conditions. An RBFN predicted a background NDVI time series in the target year, and the observed NDVI values in this year were used to adjust the predicted NDVI time series. Finally, the NDVI time series were recursively reconstructed from 2018 to 1986. The experiments were performed in a heterogeneous region in the Qilian Mountains. The results demonstrate that the proposed method can accurately reconstruct and generate continuous 30 m 8-day NDVI time series using Landsat observations. The CGF method outperforms traditional time series reconstruction methods (e.g., the harmonic analysis of time series (HANTS) and Savitzky-Golay (SG) filter methods) when the raw time series is contaminated with large gaps, which widely exist in Landsat images.
    Keywords gap filling ; time series reconstruction ; NDVI time series ; vegetation growth ; climate data ; radial basis function networks (RBFN) ; Science ; Q
    Subject code 333
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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