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首页> 外文期刊>Agricultural and Forest Meteorology >An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data
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An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data

机译：一种从MODIS EVI时间序列数据检测草原春季植被物候的改进Logistic方法

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Satellite-derived greenness vegetation indices provide a valuable data source for characterizing spring vegetation phenology over regional or global scales. A logistic function has been widely used to fit time series of vegetation indices to estimate green-up date (GUD), which is currently being used for generating the global phenological product from the Enhanced Vegetation Index (EVI) time-series data provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, we address a violation of the basic assumption of the logistic fitting method that arises from the fact that vegetation growth under natural conditions is controlled by multiple environmental factors and often does not follow a well-defined S-shaped logistic temporal profile. We developed the adaptive local iterative logistic fitting method (ALILF) to analyze the "local range" (i.e., the range of data points where the values in the time series begin to increase rapidly) in the MODIS EVI profile in which GUD is found. The new method adopts an iterative procedure and an adaptive temporal window to properly simulate the trajectory of EVI time series in the local range, and can determine GUD more accurately. GUD estimated by ALILF almost match the date of the onset of the greenness increase well while the traditional logistic fitting method shows errors of even more than 1 month in the same cases. ALILF is a more general form of the logistic fitting method that can estimate GUD both from well-defined S-shaped time series and from non-logistic ones. Besides, it is resistant to a range of noise levels added on the time-series data (Gaussian noise with a mean value of zero and standard deviations ranging from 0% to 15% of the EVI value). These advantages mean ALILF may be widely used for monitoring spring vegetation phenology from greenness vegetation indices. (C) 2014 Elsevier B.V. All rights reserved.
机译：卫星衍生的绿色植被指数为表征区域或全球尺度的春季植被物候提供了宝贵的数据来源。逻辑函数已被广泛用于拟合植被指数的时间序列以估计绿化日期（GUD），目前正用于从植被指数（EVI）提供的时间序列数据生成全球物候产品。中分辨率成像光谱仪（MODIS）。在这项研究中，我们解决了违反逻辑拟合方法的基本假设的问题，该假设是由于自然条件下的植被生长受多种环境因素控制并且通常未遵循明确定义的S形逻辑时态分布这一事实而引起的。我们开发了自适应局部迭代逻辑拟合方法（ALILF），以分析找到GUD的MODIS EVI配置文件中的“局部范围”（即，时间序列中的值开始迅速增加的数据点范围）。该新方法采用迭代过程和自适应时间窗来正确模拟局部范围内EVI时间序列的轨迹，并可以更准确地确定GUD。 ALILF估计的GUD几乎与绿色发生的日期匹配得很好，而在相同情况下，传统的逻辑拟合方法显示的误差甚至超过1个月。 ALILF是逻辑拟合方法的一种更通用的形式，它可以从定义明确的S形时间序列和非逻辑时间序列中估算GUD。此外，它还可以抵抗在时间序列数据上添加的一系列噪声级别（平均值为零且标准偏差为EVI值的0％至15％的高斯噪声）。这些优点意味着ALILF可以广泛用于根据绿色植被指数监测春季植被物候。 （C）2014 Elsevier B.V.保留所有权利。

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