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Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics

机译:每日MODIS 500 m反射率各向异性直接广播(DB)产品,用于监测植被物候动态

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摘要

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.
机译:地表植被物候学是一种有效的生物指标,可用于监测生态系统对气候因素变化的响应。本文的主要目的是研究每日MODIS 500 m反射率各向异性直接广播(DB)产品在监测选定作物,果园和森林地区植被物候趋势变化方面的实用性。尽管许多模型拟合的卫星数据已被广泛用于评估地表物候模式的时空分布,以了解物候过程和现象,但目前正在努力研究物候趋势的细节,特别是对于短时间发生的自然物候变化遥感挑战以及卫星数据源缺乏各向异性校正不足以解决这些问题。每天使用MODIS 500 m反射率各向异性乘积来获取加利福尼亚杏仁园和中国东北冬麦田的1年周期以及落叶林的2年周期的每日植被指数(VI)美国新罕布什尔州的地区。与这些地区的地面记录相比,从无云和经过大气校正的MODIS Nadir BRDF(双向反射分布函数)调整反射率(NBAR)得出的VI轨迹不仅捕获了物候事件的详细足迹和主要属性(例如(如开花期和开花期),而且还存在较大的年际变化。这项研究表明,每天500 m的MODIS反射率各向异性DB产品可用于提供每日VI,用于监测和检测自然植被物候的变化,例如包括小麦,杏仁树和落叶林在内的研究区域。

著录项

  • 来源
    《International journal of remote sensing》 |2013年第16期|5997-6016|共20页
  • 作者单位

    ERT Inc. at the Biospheric Sciences Laboratory of NASA's Goddard Space Flight Center, Greenbelt, MD 20771, USA;

    School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USA;

    Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA,NOAA/NESDIS/STAR, College Park, MD, USA;

    Center for Remote Sensing, Department of Earth and Environment, Boston University, MA 02215, USA;

    Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD 57007, USA;

    US Geological Survey, DOI North Central Climate Science Center, Fort Collins, CO 80525, USA;

    School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USA;

    Sigma Space Corporation at the Terrestrial Information Systems Laboratory of NASA's Goddard Space Flight Center, Greenbelt, MD 20771, USA;

    Sigma Space Corporation at the Terrestrial Information Systems Laboratory of NASA's Goddard Space Flight Center, Greenbelt, MD 20771, USA;

    Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA;

    State Key laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing, China,School of Geography and Remote Sensing Science, Beijing Normal University, Beijing 100875, China;

    State Key laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing, China,School of Geography and Remote Sensing Science, Beijing Normal University, Beijing 100875, China;

    State Key laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China,Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing, China,School of Geography and Remote Sensing Science, Beijing Normal University, Beijing 100875, China;

    Space Science and Engineering Center (SSEC/UW-Madison), Madison, WI 53706, USA;

    Space Science and Engineering Center (SSEC/UW-Madison), Madison, WI 53706, USA;

  • 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
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