Temporal decorrelation model in repeat pass SAR interferometry for detection of volcanic ash

The coherence in SAR interferometry is used as a parameters for change detection. However, it is difficult to quantitatively analyze the coherence value because the decorrelation come from diverse reasons and the unwanted targets naturally changes. In order to isolate the changed region by an accumulation of the volcanic ash, we analyze the reasons of the decorrelation. We estimate the temporal decorrelation using time-series dataset and predict the temporal decorrelation for dataset involving dramatic changes. The estimated and the expected ratio is useful to identify the regions affected by volcanic ash. This method successfully isolate the regions with dramatic change due to volcanic ash from the naturally changing region, easily misinterpreted as a dramatic changed area.