Abstract
Interferometric synthetic aperture radar (InSAR) provides the capability to detect surface deformation. Numerous processing approaches have been developed to improve InSAR results and overcome its limitations. Regardless of the processing methodology, however, temporal decorrelation is a major obstacle for all InSAR applications, especially over vegetated areas and dynamic environments, such as Interior Alaska. Temporal coherence is usually modeled as a univariate exponential function of temporal baseline. It has been, however, documented that temporal variations in surface backscattering due to the change in surface parameters, i.e., dielectric constant, roughness, and the geometry of scatterers, can result in gradual, seasonal, or sudden decorrelations and loss of InSAR coherence. The coherence models introduced so far have largely neglected the effect of the temporal change in backscattering on InSAR coherence. Here, we introduce a new temporal decorrelation model that considers changes in surface backscattering by utilizing the relative change in SAR intensity between two images as a proxy for the change in surface scattering parameters. The model also takes into account the decorrelation due to the change in snow depth between two images. Using the L-band Advanced Land Observation Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) data, the model has been assessed over forested and shrub landscapes in Delta Junction, Interior Alaska. The model decreases the RMS error of temporal coherence estimation from 0.18 to 0.09 on average. The improvements made by the model have been statistically proved to be significant at the 99% confidence level. Additionally, the model shows that the coherence of forested areas are more prone to changes in backscattering than shrub landscape. The model is based on L-band data and may not be expanded to C-band or X-band InSAR observations.
Highlights
Interferometric synthetic aperture radar (InSAR) provides an all-weather, day-or-night capability to remotely sense mm to cm scale surface deformation with a high spatial resolution of tens of meters or better (e.g., [1,2,3,4])
In this paper, using ALOS-2 PALSAR-2 images, we analyzed the temporal decorrelation of forested and shrub landscapes in Delta Junction, Alaska, and introduced a new InSAR coherence model, which takes into account the effects of the temporal variations of surface backscattering on InSAR coherence
Model C takes into account the long-term irreversible/long-term changes in scatterers’ geometry, reversible/seasonal changes in scatterers’ dielectric constant, induced mainly by the change in soil and biomass water content, and reversible/seasonal changes in scatterers’ geometry, i.e., the third term, due to the change in snow depth
Summary
Interferometric synthetic aperture radar (InSAR) provides an all-weather, day-or-night capability to remotely sense mm to cm scale surface deformation with a high spatial resolution of tens of meters or better (e.g., [1,2,3,4]). InSAR coherence decreases with increasing spatial and temporal baselines between two images. In this paper, using ALOS-2 PALSAR-2 images, we analyzed the temporal decorrelation of forested and shrub landscapes in Delta Junction, Alaska, and introduced a new InSAR coherence model, which takes into account the effects of the temporal variations of surface backscattering on InSAR coherence. The second area of interest is in the design of orbit repeat for new satellite missions, which is driven by considering some important factors, such as tolerable error levels, the attainable baseline, and the expected decorrelation with the time of signals from the regions of interest to be mapped [12] In this case, temporal decorrelation models can facilitate a priori assessment of the expected coherence levels of interferograms for a new satellite mission designed for a specific application. Scatter plots of resciodvuear ltycpoehs.erences (observation—model A) for forested (b) and shrub (d) land cover types
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