Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km2) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided.

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The Differential Synthetic Aperture Radar Interferometry (DInSAR) technique is recognized as a potential remote sensing tool for detecting ground surface displacements with less than a centimetre accuracy. The surface soil moisture changes (∆ ) during the time between the two images as an effective parameter on interferometry phase ), leads to incorrect calculation of ground movement . In this research, the amount and the way that ∆ affects on wheat, rapeseed, weed, pea and idle land fields have been investigatedempirically using a regression model. To do this investigation, airborne data UAVSAR (L-band) along with ground-based data in the CanEx-SM10 campaign in 2010 were used. According to the scattergraphs between and ∆ , and observing a direct and approximately linear relationship between them, some hypotheses were taken into consideration in order to use a regression modeling . Comparing the estimated using the calibrated regression model and calculated from the interferometry technique shows that the model provided the best results for the bare field in VV and HH polarizations (RMSE) of 0.3 to 0.6 rad and R2 of 69% to 72%. In general, the results of the regression model showed that without other factors' effects on , this parameter can be modelled ∆ based on a regression function in bare fields. The model also provided acceptable results in vegetated fields (RMS of 0.6 to 0.99 rad and R2 of 40% to 55% depending on the different vegetation types and different polarizations). Comparing polarizations, fluctuations in co-polarizations (HH and VV) showed a higher correlation with ∆ . Consequently, φ is directly affected by ∆ , and significant changes in ∆ brings about a considerable error in displacement estimation.

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