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Detecting Changes of Wheat Vegetative Growth and Their Response to Climate Change Over the North China Plain

机译:华北平原小麦营养生长变化及其对气候变化的响应

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Wheat vegetative growth, defined as the stage from green-up date to heading date (STAGE), is a crucial phrase, which largely affects crop yield. Previous some studies mainly focused on site-based changes of STAGE. However, there are few studies concerning large-scale changes of STAGE, which limits our understanding of regional changes of STAGE in response to climate change. Here, we introduced robust satellite-based phenological algorithms as well as third-generation global inventory modeling and mapping studies normalized difference vegetation index for the period of 1982-2015 to spatially derive winter wheat green-up date, heading date, and STAGE over croplands of the North China Plain (NCP). Changes of STAGE and their response to climate change were then investigated. Results showed that a strong predicted ability of introduced heading date algorithm was observed with r of 0.88 (p < 0.01), bias of -1.0 day and RMSE of 4.9 days. We found that unlike the patterns of winter wheat green-up and heading date, STAGE spatially decreased from southwestern to northeastern NCP with regional averaged stage of 41.2 +/- 4.6 days. Advanced green-up date faster than advanced heading date induced lengthened stage of entire NCP with 1 day/decade (R-2 = 0.11, p = 0.06). Stage of 23.4% cropland pixels performed significantly lengthened trends (p < 0.05), mainly locating in western NCP. The relationships between STAGE and climatic variables suggested that compared to precipitation, temperature was more responsible for changes of STAGE (r = 0.59, p < 0.01). This study highlights important roles of remote sensing data and satellite-based phenological algorithms for regional crop growth monitoring and management.
机译:小麦营养生长被定义为从绿化期到抽穗期(STAGE)的阶段,是至关重要的短语,在很大程度上影响作物的产量。先前的一些研究主要集中于基于站点的STAGE变化。但是,关于STAGE的大规模变化的研究很少,这限制了我们对STAGE响应气候变化的区域变化的理解。在这里,我们介绍了基于卫星的强大物候算法,以及第三代全球库存建模和制图研究,对1982-2015年期间的标准化植被指数进行了归一化,以空间推算冬小麦的绿化日期,抽穗期和耕地上的STAGE华北平原(NCP)。然后研究了STAGE的变化及其对气候变化的响应。结果表明,引入的航向日期算法具有很强的预测能力,r为0.88(p <0.01),偏差为-1.0天,RMSE为4.9天。我们发现,与冬小麦绿化和抽穗期不同,STAGE在空间上从NCP西南向东北减小,区域平均阶段为41.2 +/- 4.6天。提前绿化日期快于提前抽穗日期诱导的整个NCP延长期,为1天/十年(R-2 = 0.11,p = 0.06)。 23.4%农田像素阶段显着延长趋势(p <0.05),主要位于NCP西部。 STAGE与气候变量之间的关系表明,与降水相比,温度对STAGE的变化影响更大(r = 0.59,p <0.01)。这项研究突出了遥感数据和基于卫星的物候算法在区域作物生长监测和管理中的重要作用。

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