Literature DB >> 25047279

Dynamic variability of the heading-flowering stages of single rice in China based on field observations and NDVI estimations.

Zhao Zhang1, Xiao Song, Yi Chen, Pin Wang, Xing Wei, Fulu Tao.   

Abstract

Although many studies have indicated the consistent impact of warming on the natural ecosystem (e.g., an early flowering and prolonged growing period), our knowledge of the impacts on agricultural systems is still poorly understood. In this study, spatiotemporal variability of the heading-flowering stages of single rice was detected and compared at three different scales using field-based methods (FBMs) and satellite-based methods (SBMs). The heading-flowering stages from 2000 to 2009 with a spatial resolution of 1 km were extracted from the SPOT/VGT NDVI time series data using the Savizky-Golay filtering method in the areas in China dominated by single rice of Northeast China (NE), the middle-lower Yangtze River Valley (YZ), the Sichuan Basin (SC), and the Yunnan-Guizhou Plateau (YG). We found that approximately 52.6 and 76.3 % of the estimated heading-flowering stages by a SBM were within ±5 and ±10 days estimation error (a root mean square error (RMSE) of 8.76 days) when compared with those determined by a FBM. Both the FBM data and the SBM data had indicated a similar spatial pattern, with the earliest annual average heading-flowering stages in SC, followed by YG, NE, and YZ, which were inconsistent with the patterns reported in natural ecosystems. Moreover, diverse temporal trends were also detected in the four regions due to different climate conditions and agronomic factors such as cultivar shifts. Nevertheless, there were no significant differences (p > 0.05) between the FBM and the SBM in both the regional average value of the phenological stages and the trends, implying the consistency and rationality of the SBM at three scales.

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Year:  2014        PMID: 25047279     DOI: 10.1007/s00484-014-0877-6

Source DB:  PubMed          Journal:  Int J Biometeorol        ISSN: 0020-7128            Impact factor:   3.787


  10 in total

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  10 in total
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