The repeat period of SAR data and its side-looking characteristics make InSAR time series analysis useful for water level monitoring applications. The standard approach determines corresponding scatterers by focusing the study area on the multipath radar reflections that include the water level. This paper introduces an alternative approach to identifying such signals using two metrics: cosine similarity and temporal differential coherence. The results show that temporal differential coherence can detect phase variations similar to water level by constantly returning high values even when there is an offset, while cosine similarity yields low scores. Within an urban environment, this approach finds point scatterers corresponding to water level changes in or near water, such as permanent floating objects, bridges, and buildings adjacent to water, where the highest differential coherence value was acquired from a permanent floating restaurant in open water.

We present the preliminary results of an InSAR analysis of peatland surface motion covering a large spatial and temporal extent. This work is the first large scale analysis of the Dutch Green Heart region, and is made possible using a novel distributed scatter (DS) InSAR processing method. This method is designed to handle breakages in the observed interferometric phase time series which occur due to temporal decorrelation, which we designate with the term loss-of-lock.