Abstract: A noise detection method for time-series vegetation index (TSVI) derived from remote sensing images. Firstly, unit root test is used to classify observation values of each pixel into a stationary series or a non-stationary series; for the non-stationary, an appropriate mathematical model is used to model discrete TSVI, then differences between actual observation values and prediction values of the model are calculated and recorded as a deviation. As the deviation has removed seasonal components, the non-stationary series is transformed into a stationary series. For a stationary series or deviation data, noise detection is performed based on the assumption that observation values are distributed within a certain range around mean values; then model fitting and noise detection are iteratively carried out with remained observation values—until the iterations reached the maximum number or no noise is detected at one iteration.

Abstract: A noise detection method for time-series vegetation index (TSVI) derived from remote sensing images. Firstly, unit root test is used to classify observation values of each pixel into a stationary series or a non-stationary series; for the non-stationary, an appropriate mathematical model is used to model discrete TSVI, then differences between actual observation values and prediction values of the model are calculated and recorded as a deviation. As the deviation has removed seasonal components, the non-stationary series is transformed into a stationary series. For a stationary series or deviation data, noise detection is performed based on the assumption that observation values are distributed within a certain range around mean values; then model fitting and noise detection are iteratively carried out with remained observation values—until the iterations reached the maximum number or no noise is detected at one iteration.