The regular acquisition and relatively short revisit time of Sentinel-1 satellite improve the capability of a persistent scatterer interferometric synthetic aperture radar (PS-InSAR) as a suitable geodetic method of choice for measuring ground surface deformation in space and time. The SAR instrument aboard the Sentinel-1 satellite supports operation in dual polarization (HH-HV, VV-VH), which can be used to increase the spatial density of measurement points through the polarimetric optimization method. This study evaluates the improvement in displacement mapping by incorporating the information obtained from the VH channel of Sentinel-1 data into the PS-InSAR analysis. The method that has shown great success with different polarimetric data performs a search over the available polarimetric space in order to find a linear combination of polarization states, which yields the optimum PS selection criterion using the amplitude dispersion index (ADI) criterion. We applied the method to a dataset of 50 dual-polarized (VV-VH) Sentinel-1 images over Trondheim city in Norway. The results show overall increase of about 186% and 78% in the number of PS points with respect to the conventional channels of VH and VV, respectively. The study concludes that, using the ADI optimization, we can incorporate information from the VH channel into the PS-InSAR analysis, which otherwise is lost due to its low amplitude.

Combining persistent scatterers (PS) and distributed scatterers (DS) is important for effective displacement monitoring using time-series of SAR data. However, for large stacks of synthetic aperture radar (SAR) data, the DS analysis using existing algorithms becomes a time-consuming process. Moreover, the whole procedure of DS selection should be repeated as soon as a new SAR acquisition is made, which is challenging considering the short repeat-observation of missions such as Sentinel-1. SqueeSAR is an approach for extracting signals from DS, which first applies a spatiotemporal filter on images and optimizes DS, then incorporates information from both optimized DS and PS points into interferometric SAR (InSAR) time-series analysis. In this study, we followed SqueeSAR and implemented a new approach for DS analysis using two-sample t-test to efficiently identify neighboring pixels with similar behaviour. We evaluated the performance of our approach on 50 Sentinel-1 images acquired over Trondheim in Norway between January 2015 and December 2016. A cross check on the number of the identified neighboring pixels using the Kolmogorov–Smirnov (KS) test, which is employed in the SqueeSAR approach, and the t-test shows that their results are strongly correlated. However, in comparison to KS-test, the t-test is less computationally intensive (98% faster). Moreover, the results obtained by applying the tests under different SAR stack sizes from 40 to 10 show that the t-test is less sensitive to the number of images.

Iran is experiencing significant water challenges that have now turned water security into a national priority. By estimating secular trend groundwater storage in Iran between 2002 and 2017, we see that there is an intensive negative trend, even −4400 Mm³ in some areas. These estimations show shifting in the climate and extra extraction from aquifers for agricultural use in some areas in Iran. The secular trend of groundwater storage changes across the whole of Iran inferred from observation well data is −20.08 GT/yr. The secular trends of GWS changes based on observation well data are: −11.55 GT/yr for the Central Plateau basin, −3.60 GT/yr for the Caspian Sea basin, −3.0 GT/yr for the Persian Gulf and Oman Sea basin, −0.53 GT/yr for the Urmieh Lake basin, −0.57 GT/yr for the Eastern Boundary basin, and −0.83 GT/yr for the Gharaghom basin. The most depleted sub-basin (Kavir Markazi) has secular trends of GWS changes of −4.503 GT/yr. This study suggests that groundwater depletion is the largest single contributor to the observed negative trend of groundwater storage changes in Iran, the majority of which occurred after the drought in 2007. The groundwater loss that has been accrued during the study period is particularly alarming for Iran, which is already facing severe water scarcity.

Flood detection and produced maps play essential roles in policymaking, planning, and implementing flood management options. Remote sensing is commonly accepted as a maximum cost-effective technology to obtain detailed information over large areas of lands and oceans. We used remote sensing observations from Global Navigation Satellite System-Reflectometry (GNSS-R) to study the potential of this technique for the retrieval of flood maps over the regions affected by the recent flood in the southeastern part of Iran. The evaluation was made using spaceborne GNSS-R measurements over the Sistan and Baluchestan provinces during torrential rain in January 2020. This area has been at a high risk of flood in recent years and needs to be continuously monitored by means of timely observations. The main dataset was acquired from the level-1 data product of the Cyclone Global Navigation Satellite System (CYGNSS) spaceborne mission. The mission consisted of a constellation of eight microsatellites with GNSS-R sensors onboard to receive forward-scattered GNSS signals from the ocean and land. We first focused on data preparation and eliminating the outliers. Afterward, the reflectivity of the surface was calculated using the bistatic radar equations formula. The flooded areas were then detected based on the analysis of the derived reflectivity. Images from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used for evaluation of the results. The analysis estimated the inundated area of approximately 19,644 km² (including Jaz-Murian depression) to be affected by the flood in the south and middle parts of the Sistan and Baluchestan province. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we showed the capability of detecting floods in the study area. The sensitivity of the spaceborne GNSS-R observations, together with the relatively short revisit time, highlight the potential of this technique to be used in flood detection. Future GNSS-R missions capable of collecting the reflected signals from all available multi-GNSS constellations would offer even more detailed information from the flood-affected areas.

For the first time, rain effects on the polarimetric observations of the global navigation satellite system reflectometry (GNSS-R) are investigated. The physical feasibility of tracking the modifications in the surface roughness by rain splash and the surface salinity by the accumulation of freshwater is theoretically discussed. An empirical analysis is carried out using measurements of a coastal GNSS-R station with two side-looking antennas in right- and left-handed circular polarizations (RHCP and LHCP). Discernible drops in RHCP and LHCP powers are observed during rain over a calm sea. The power drop becomes larger at higher elevation angles. The average LHCP power drops by $\approx ~5$ dB at an elevation angle of 45°. The amplitude of the correlation sum shows a dampening, responding to rain rate systematically. The LHCP observations show higher sensitivity to rainfall compared to RHCP observations. The retrieved standard deviation of surface heights shows a steady increase with the rain rate. The derived surface salinity shows a decrease at rains higher than 10 mm/h. This study confirms the potential under environmental conditions of the GNSS-R ground-based station, e.g., with salinity mostly lower than 30 psu, over a calm sea, being a starting point for future investigations.

The observation of ocean wave parameters is necessary to improve forecasts of ocean wave conditions. In this paper, we investigate the viability of using a single GPS receiver to measure ocean-surface waves, and present a method to enhance the accuracy of the estimated wave parameters. The application of high-pass filtering to GPS data in conjunction with directional wave spectral theory is a core concept in this article. Laboratory experiments were conducted to test the viability and accuracy measurements of wave parameters made by a single GPS receiver buoy. These tests identified an error of less than 1% for the rotational arm measurement (wave height) and an error of 1% in verifications of the wave direction and wave period, and showed a 0.488 s bias; this is sufficiently accurate for many specific purposes. These results are based on the best cut-off frequency value derived in this study. A moored-sea GPS buoy on the Taiwanese coast was used to estimate the GPS-derived wave parameters. Our results indicate that data from a single GPS receiver, processed with the presented method to reduce the error of the estimated parameters, can provide measurements of ocean surface wave to reasonable accuracy.