Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness
Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness
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DOI：
10.1080/01431160600967128
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发表时间：
2007-06
影响因子：
3.4
通讯作者：
J. F. Hermance
中科院分区：
文献类型：
--
作者：
J. F. Hermance
傅立叶级数和相关的谐波方法已被证明是有效的识别和表征的季节行为，或物候，各种陆地植被群落使用归一化差异植被指数（NDVI）时间序列从地球轨道卫星。然而，这种应用的最终时间分辨率受到截断或低通滤波谐波级数到相对低阶项的常见做法的限制，以便抑制模型结果中的寄生振荡。这些技术的时间分辨率可以显着提高，如果沿着与跟踪观测数据的上包络线的平方数据残差的和的加权最小化，我们还强制执行最小模型粗糙度的期望，以抑制预测值中的虚假振荡。由此产生的年度模型的决议符合特殊的超越形式，如非对称高斯和逻辑（sigmoidal）函数，最近在文献中报道的应用。
Fourier series and related harmonic methods have been demonstrably effective for identifying and characterizing the seasonal behaviour, or phenology, of a variety of terrestrial vegetation communities using Normalized Difference Vegetation Index (NDVI) time series from Earth‐orbiting satellites. The ultimate temporal resolution of such applications has been limited, however, by the common practice of truncating, or low pass filtering, harmonic series to relatively low order terms, in order to suppress spurious oscillations in the model results. The temporal resolution of these techniques can be significantly improved if, along with a weighted minimization of the sum of the squared data residuals tracking the upper envelope of observed data, we also enforce an expectation of minimum model roughness to dampen spurious oscillations in predicted values. The resulting annual models have resolutions consistent with the application of special transcendental forms, such as asymmetric Gaussian and logistic (sigmoidal) functions, recently reported in the literature.