L-Band Temporal Coherence Assessment and Modeling Using Amplitude and Snow Depth over Interior Alaska

Abstract

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.

Can InSAR Coherence and Closure Phase Be Used to Estimate Soil Moisture Changes?

“…Many studies have been introduced to estimate soil moisture changes using the InSAR phase and SAR intensity [15][16][17][18][19][20][21]. However, soil moisture estimation using the InSAR phase has encountered difficulties and uncertainties.…”

Land Subsidence and Its Relations with Sinkhole Activity in Karapınar Region, Turkey: A Multi-Sensor InSAR Time Series Study

“…We also performed interferogram unwrapping and filtering to improve the signal-to-noise ratio [ 46 ]. Temporal coherence is considered as univariate exponential function of time, calculated by taking the random motion of scatterers in a resolution cell [ 47 ]. It ranges from 0, where there is no useful information, to 1, where there is no noise.…”

Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy

“…The nominal spatial resolutions in this mode are 5.4 × 7.9 m in range and azimuth directions, respectively, for incidence angles between 18°and 49°. Both the ALOS-2/PALSAR-2 and RADARSAT-2 data sets have been widely explored for snow studies (Eshqi Molan et al, 2018;Muhuri et al, 2017;Muhuri et al, 2018;Surendar et al, 2017;Usami et al, 2016).…”