Seasonality extraction by function fitting to time-series of satellite sensor data
Seasonality extraction by function fitting to time-series of satellite sensor data
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DOI：
10.1109/tgrs.2002.802519
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发表时间：
2002-08-01
影响因子：
8.2
通讯作者：
Eklundh, L
中科院分区：
文献类型：
--
作者：
Jönsson, P;Eklundh, L
提出了一种从卫星传感器数据时间序列中提取季节性信息的新方法。该方法是基于非对称高斯模型函数的时间序列的非线性最小二乘拟合。然后，平滑模型函数用于定义关键的季节性参数，例如生长季节的数量、季节的开始和结束以及生长和下降的速率。该方法是在一个计算机程序TIMESAT和测试的高级甚高分辨率辐射计（AVHRR）归一化差异植被指数（NDVI）数据在非洲。辅助云数据[来自AVHRR（CLAVR）的云]被用作数据值的不确定性水平的估计。所提出的方法具有一般性，也可适用于新类型的卫星派生的时间序列数据。
A new method for extracting seasonality information from time-series of satellite sensor data is presented. The method is based on nonlinear least squares fits of asymmetric Gaussian model functions to the time-series. The smooth model functions are then used for defining key seasonality parameters, such as the number of growing seasons, the beginning and end of the seasons, and the rates of growth and decline. The method is implemented in a computer program TIMESAT and tested on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data over Africa. Ancillary cloud data [clouds from AVHRR (CLAVR)] are used as estimates of the uncertainty levels of the data values. Being general in nature, the proposed method can be applied also to new types of satellite-derived time-series data.