ABSTRACT Time series Normalized Difference Vegetation Index (NDVI) fitting, which describes the discrete time series observations with a mathematical model, can remove the residual noise, fill in the missing data, and produce a noise-free continuous time series; therefore, numerous time series fitting methods have been developed, but most of them focused on regularly sampled, noise-suppressed data, as NDVI time series derived from medium-spatial remote sensing images has irregular sampled and distortion-intensive characteristic, limiting the applicability of traditional methods. To address this problem, an automatic distortion-suppressed time series fitting method for irregular sampled NDVI is proposed in this paper. First, the observation quality is evaluated: (I) the observations on a fully clear image are identified as clear observations. (II) According to the prior knowledge that the NDVI is underestimated, the observations in the upper convex hull and above the line of two temporal-adjacent clear observations (identified by fully clear image and upper convex) are labelled as clear observations. (III) As the temporal evolution of NDVI is a slow and continuous process, deep-V observations, which has experienced an abrupt decrease and increase consecutively, are identified as noisy observations. Second, different weights are assigned to observations based on the quality assessment result, and the NDVI time series is weighted fitted. To highlight the observations above the fitting line and suppress those below the fitting line, the weights are updated according to the direction and distance between the actual observation and fitting prediction value. Then, fitting and reweighting processes are repeated until a stable fitting result is obtained. We use images acquired by the Multi-Spectral Imager (MSI) onboard Sentinel-2 satellite in Shouxian, Anhui Province to construct NDVI time series and test the method. The result demonstrates that our method can suppress the distortion and realize time series fitting automatically, compared with the state of art method, our method can obtain reasonably overestimation of the NDVI, providing an alternative for fitting irregular sampled NDVI.

中文翻译：

---

不规则采样NDVI的自动失真抑制时间序列拟合方法

摘要 时间序列归一化差分植被指数（NDVI）拟合，用数学模型描述离散时间序列观测，可以去除残留噪声，填充缺失数据，产生无噪声连续时间序列；因此，已经开发了许多时间序列拟合方法，但大多数都集中在规则采样、噪声抑制的数据上，因为来自中等空间遥感图像的 NDVI 时间序列具有不规则采样和失真密集的特性，限制了其适用性。传统方法。针对这一问题，本文提出了一种不规则采样的NDVI自动失真抑制时间序列拟合方法。首先，评估观察质量：（I）将完全清晰图像上的观察识别为清晰观察。（二）根据NDVI被低估的先验知识，将上凸包内和两个时间相邻的清晰观测（由全清晰图像和上凸识别）连线以上的观测标记为清晰观测。(III) 由于 NDVI 的时间演化是一个缓慢而连续的过程，因此将经历了突然减少和连续增加的深 V 观测确定为噪声观测。其次，根据质量评估结果为观测值分配不同的权重，并对NDVI时间序列进行加权拟合。为了突出拟合线以上的观测值并抑制拟合线以下的观测值，根据实际观测值与拟合预测值之间的方向和距离更新权重。然后，重复拟合和重新加权过程，直到获得稳定的拟合结果。我们使用安徽省寿县哨兵二号卫星上的多光谱成像仪（MSI）获取的图像构建NDVI时间序列并测试该方法。结果表明，我们的方法可以抑制失真并自动实现时间序列拟合，与现有方法相比，我们的方法可以合理地高估NDVI，为拟合不规则采样的NDVI提供了一种替代方法。