首页> 外文期刊>International journal of remote sensing >A phenology-based method for identifying the planting fraction of winter wheat using moderate-resolution satellite data
【24h】

A phenology-based method for identifying the planting fraction of winter wheat using moderate-resolution satellite data

机译:一种基于诸如使用中等分辨率卫星数据识别冬小麦种植的方法

获取原文
获取原文并翻译 | 示例
           

摘要

Winter wheat is a staple food crop for most of the world's population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to large errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution (1 km per pixel) satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the ripening stage (June) were investigated. The differences in vegetation index between heading and ripening stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 85% and 60% of the spatial variation in municipal- and county-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references.
机译:冬小麦是世界上大多数人口的主食作物,冬小麦的地区和空间分布是估计作物生产和确保粮食安全的关键要素。然而,冬小麦种植区含有具有大量空间异质性,具有用于粗糙和中等分辨率卫星数据的混合像素,导致作物面积估计的大误差。本研究开发了一种使用适度分辨率(每像素)卫星数据的基于候选的方法,以估算冬小麦的子像素种植分数。基于无人驾驶飞行器(UAV)观察,研究了冬季小麦在升序阶段(5月)和成熟阶段(6月)的低值的冬小麦的独特特征。标题和成熟阶段之间的植被指数的差异随着冬小麦的种植级分而增加,因此通过将给定像素的NDVI差异与预定的纯冬小麦和非冬小麦像素的差异进行比较来估计种植级分。这种方法是在华北山东省密集农业区的航空图像和农业统计数据进行评估。该方法分别解释了市和县级统计数据的85%和60%的空间变化。更重要的是,可以根据其NDVI差异使用MODIS数据自动识别预定的纯冬小麦和非冬小麦像素,这加强了在区域和全球范围内使用这种方法的可能性,而没有任何现场观察。

著录项

  • 来源
    《International journal of remote sensing》 |2020年第18期|6892-6913|共22页
  • 作者单位

    Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing Peoples R China;

    Sun Yat Sen Univ Sch Atmospher Sci Guangzhou Guangdong Peoples R China;

    Agrotech Stn Jinan Shandong Peoples R China;

    Sun Yat Sen Univ Sch Atmospher Sci Guangzhou Guangdong Peoples R China;

    China Inst Water Resources & Hydropower Res IWHR Res Ctr Remote Sensing Minist Water Resources Beijing Peoples R China;

    Sun Yat Sen Univ Sch Atmospher Sci Guangzhou Guangdong Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号