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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Adjusting for Long-Term Anomalous Trends in NOAA''s Global Vegetation Index Data Sets
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Adjusting for Long-Term Anomalous Trends in NOAA''s Global Vegetation Index Data Sets

机译：调整NOAA全球植被指数数据集的长期异常趋势

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摘要

The weekly 0.144$^{circ}$ resolution global vegetation index from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) has a long history, starting late 1981, and has included data derived from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA-7, -9, -11, -14, -16, -17, and -18 satellites. Even after postlaunch calibration and mathematical smoothing and filtering of the normalized difference vegetation index (NDVI) derived from AVHRR visible and near-infrared channels, the time series of global smoothed NDVI (SMN) still has apparent discontinuities and biases due to sensor degradation, orbital drift [equator crossing time (ECT)], and differences from instrument to instrument in band response functions. To meet the needs of the operational weather and climate modeling and monitoring community for a stable long-term global NDVI data set, we investigated adjustments to substantially reduce the bias of the weekly global SMN series by simple and efficient algorithms that require a minimum number of assumptions about the statistical properties of the interannual global vegetation changes. Of the algorithms tested, we found the adjusted cumulative distribution function (ACDF) method to be a well-balanced approach that effectively eliminated most of the long-term global-scale interannual trend of AVHRR NDVI. Improvements to the global and regional NDVI data stability have been demonstrated by the results of ACDF-adjusted data set evaluated at a global scale, on major land classes, with relevance to satellite ECT, at major continental regions, and at regional drought detection applications.
机译：美国国家海洋与大气管理局（NOAA）国家环境卫星，数据和信息服务（NESDIS）每周提供的分辨率为0.144 $ ^ circ的全球植被指数具有悠久的历史，始于1981年后期，并且包括从NOAA-7，-9，-11，-14，-16，-17和-18卫星上的高级超高分辨率辐射计（AVHRR）传感器。即使在从AVHRR可见光和近红外通道启动后进行校准，数学归一化植被指数（NDVI）的数学平滑和滤波之后，由于传感器退化，轨道变化，全局平滑NDVI（SMN）的时间序列仍然存在明显的间断和偏差。漂移[赤道穿越时间（ECT）]，以及不同仪器在频带响应功能方面的差异。为了满足运营天气和气候建模与监测界对稳定的长期全球NDVI数据集的需求，我们研究了调整方法，以通过简单有效的算法来减少每周全球SMN系列的偏差，该算法需要最少数量的NMN。关于年际全球植被变化的统计特性的假设。在测试的算法中，我们发现调整后的累积分布函数（ACDF）方法是一种均衡的方法，可以有效消除AVHRR NDVI的大部分长期全球年度趋势。 ACDF调整后的数据集在全球范围内，主要土地类别上与卫星ECT相关，在主要大陆地区以及在区域干旱检测应用中进行了评估，结果证明了全球和区域NDVI数据稳定性的改善。

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