An Analysis of the Uncertainty in InSAR Deformation Measurements for Groundwater Applications in Agricultural Areas

Interferometric synthetic aperture radar (InSAR) is a remote sensing method that maps relative ground surface deformation. In previous work, we investigated the relationship between deformation and hydraulic head change in the San Luis Valley, CO, USA, and determined that we must quantify the spatially variable uncertainty in the InSAR deformation measurement in order for these data to be used to predict hydraulic head. In this study, we modify a commonly applied multitemporal technique, Small Baseline Subset (SBAS) analysis, to process InSAR data in an area where pumping for crop irrigation creates seasonally variable deformation. We propagate the uncertainty due to decorrelation through the InSAR processing chain and calculate the uncertainty in the deformation for all selected pixels. The standard deviation of the uncertainty in the deformation ranges from 1 to 5 mm. Finally, we investigate how the InSAR coherence affects the standard deviation of the estimated deformation. Through a synthetic study, we show that given the mean coherence and standard deviation of coherence, we can determine the mean standard deviation of the final deformation estimates. This allows us to optimize InSAR processing to identify which pixels can provide the uncertainty desired in the final deformation time series.