Remote sensing time-series have been applied successfully in various fields

such as vegetation change monitoring

phonological （seasonality） information extraction

land use dynamic classification etc. It is one of the most important data sources for kinds of research work & engineering project. However

due to the effect of sensor

cloud

and atmospheric conditions

there are serious residual noise in time-series data. Therefore

prior to further applications

it is need to filter residual noise to reconstruct the series. Many methods have been developed to solve this problem. In this paper

the following methods are summarized first

including Maximum Value Composite （MVC）

Best Index Slope Extraction Algorithm （BISE）

Temporal Windows Operation （TWO）

Asymmetric Gaussian Function Fitting Approach （AGFF）

Savitzky-Golay Filtering （S-GF）

Harmonic Analysis Algorithm （HAA）

Local Maximum Fitting （LMF）. MVC is more acceptable than other methods because it is useful when producing remote sensing time-series products. But the products are the primary ones and contain much residual noise. This method is helpless to reconstruct the series further. In fact

the data needed to reconstruct before applications of the products made according to MVC. BISE uses a sliding time window to capture local maxima. It requires the determination of the sliding period and a threshold for acceptable percentage increase. TWO can reduce the noise caused by cloud and atmosphere without auxiliary data. However

the requirement of the parameters from experience limits its application. AGFF and S-GF are two strategies developed in recent years. LMF

compared with HAA

first filters noise and then reconstructs the data processed. Then three most frequently-used approaches

AGFF

S-GF and HAA

are introduced in detail in terms of the basic theories

application steps and advantages & disadvantages. AGFF employs more than two combinative Gauss-shape curves to fit the series. Every combination simulates a cycle of plant life. Finally

these combinative fitting curve are joined together to represent the reconstructed data. By this strategy

every combination is independent. AGFF which is more flexible than other methods could avoid curve distortion. But the parameter

the running window used to find a consistent set of maxima and minima

is difficult to retrieve. Thus it makes the further process less reliable and steadier. S-GF is a process of smoothing and filtering in essence. Two parameters control the result; one is the scale of the smoothing window

and the other is convoluting integer. The two parameters are all obtained depending on experience. Meanwhile

a new variable

fitting-effect index is introduced to control the iteration stop. It is more advanced than the other methods which terminate the iteration by a given threshold. S-GF has received more concern because its product can clearly reflect long time trendy and local change information. Meanwhile its products are less subject to special scale and remote sensor. HAA is a term standing for a methods set which use harmonic to fit data while simulate the seasonal regulation. These methods make use of sine or cosine to fit the data and simulate the seasonal regulation which is much concerned. Two main methods of HAA are Seller Algorithm and HANTS. At last

some comments are given

discussing the defects of the approaches

and which aspects need to be improved and how to. For example

most of parameters needed in these reconstruction methods have to be decided by experience. Thus subjective errors from operator would affect the reliance and stability of products. So new strategy or improved ones need to solve this problem.