The closure phase, which is a circular summation of the phases of the three multilooked interferograms, comprises a geophysical component and phase noise. In agricultural regions of southern Spain, encompassing both open crop fields and greenhouses, the closure phases constructed from Sentinel-1 acquisitions consistently exhibit positive signatures. The evolution of these observations appears to be related to the phenological stages of plants, as evidenced by crop calendars. Moreover, the signatures of closure phases stand out as a potential indicator of vegetation development under dense vegetation conditions when compared with coherence and normalized radar cross section (NRCS). Two existing models, one based on dielectric variation in the subsurface and another on volume scattering combined with perpendicular baselines, do not explain observed time series. Therefore, the presence of these positive closure phases implies the existence of supplementary factors contributing to closure phases associated with plant development. In this context, we explore two potential factors: variations in dielectric properties within crop canopies and the line-of-sight (LoS) motion of crops. These factors are considered to establish connections between temporal changes in vegetation parameters and observed closure phase signatures. Regarding the first factor, we characterize the crop canopies using the dielectric constant of an equivalent medium, thereby capturing changes in wave propagation within the canopies due to leaves and vertical stalks' development throughout the crop growth stages. We then model their contributions to closure phases in a manner analogous to an existing soil moisture model. Using realistic vegetation parameters derived from in situ measurements, this forward model generates synthetic data comparable in magnitude to the observations. As for the second factor, we propose an additional contributing mechanism to closure phases - skewed motion in the radar LoS direction induced by plant growth. This motion model is mathematically verified under a small-motion approximation. Both the models offer valuable insights into the origins of geophysical closure phases.

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Oceans cover a significant part of the Earth's surface. The coupling between the upper ocean and the atmosphere is very complicated with defied theoretical understanding, while it is essential for climate studies, weather prediction, and marine ecosystems. With the advent of spaceborne Synthetic Aperture Radar (SAR) systems, surface signatures of ocean and atmospheric processes have been revealed. As winds blowing over the ocean excite the wind waves, all undulations of the ocean surface are assumed as waves in this study. The primary sources for ocean surface signatures in SAR images are waves that are created by the exertion of the local wind stress. Wind waves cause changes in the backscattered power due to three mechanisms: specular reflections, Bragg scattering, and a contribution from wave breaking. A statistical multi-static normalized radar cross-section (NRCS) background model in terms of the directional wave spectrum is developed, considering both Bragg and non-Bragg mechanisms for various polarization states. As the qualitative comparison between optical and SAR data reveals a significant correlation in sea surface signatures, a synthetic attempt is made to estimate the SAR signals from optical signatures. This is realized with the transformation of the wave spectrum in a nonuniform medium, as a consequence of surface currents, and varying near-surface wind fields. A comparison between modeled NRCS and observations is presented. This modulated NRCS model advances the quantitative interpretation of the upper ocean dynamics from satellite measurements.

River width is an important indication for water storage, with the development of remote sensing techniques, estimating river width in a large area becomes possible. In this report, fully-focused SAR altimetry data with an advantage of high spatial resolution is applied to compute river width for the first time. Methodology developed based on morphologic characters of rivers and data features is validated in two different areas, the Netherlands and Vietnam, with good statistical results.