Integration of DInSAR and SBAS Techniques to Determine MiningRelated Deformations Using Sentinel1 Data: The Case Study of Ryduxc5x82towy Mine in Poland Underground coal exploitation often results in landsurface subsidence, the rate of which depends on geological characteristics, the mechanical properties of the rocks, and the applied extraction technology. Since miningrelated subsidence is characterized by xe2x80x9cfastxe2x80x9d displacement and high nonlinearity, monitoring this process by using Interferometric Synthetic Aperture Radar (InSAR) is very challenging. The Small BAseline Subset (SBAS) approach needs to predefine an a priori deformation model to properly estimate an interferometric component related to displacements. As a consequence, there is a lack of distributed scatterers (DS) when the selected a priori deformation model deviates from the real deformation. The conventional differential SAR interferometry (DInSAR) approach does not have this limitation, since it does not need any deformation model. However, the accuracy of this technique is limited by factors related to spatial and temporal decorrelation, signal delays due to the atmospheric artifacts, and orbital or topographic errors. Therefore, this study presents the integration of DInSAR and SBAS techniques in order to leverage the advantages and overcome the disadvantages of both methods and to retrieve the complete deformation pattern over the investigated study area. The obtained results were evaluated internally and externally with leveling data. Results indicated that the Krigingbased integration method of DInSAR and SBAS can be effectively applied to monitor miningrelated subsidence. The rootmeansquare Error (RMSE) between modeled and measured deformation by InSAR was found to be 11 and 13 mm for vertical and horizontal displacements, respectively. Moreover, DInSAR technique as a costeffective and complementary method to conventional geodetic techniques can be applied for effective monitoring fast mining subsidence. The minimum and maximum RMSE between DInSAR displacement and specific leveling profiles were found to be 0.9 and 3.2 cm, respectively. Since the SBAS processing failed in subsidence estimation in the area of maximum deformation rate, the deformation estimates outside the maximum rate could only be compared. In these areas, the good agreement between SBAS and DInSAR indicates that the SBAS technique could be reliable for monitoring the residual subsidence that surrounds the subsidence trough. Using the proposed approach, we detected subsidence of up to xe2x88x921 m and planar displacements (eastxe2x80x93west) of up to 0.24 m.

Evaluation of the Trend of Deformation around the Kanto Region Estimated Using the Time Series of PALSAR2 Data In the Kanto region of Japan, a large quantity of natural gas is dissolved in brine. The largescale production of gas and iodine in the region has caused largescale land subsidence in the past. Therefore, continuous and accurate monitoring for subsidence using satellite remote sensing is essential to prevent extreme subsidence and ensure the safety of residences. This study focused on the small baseline subset (SBAS) method to assess ground deformation trends around the Kanto region. Data for the SBAS method was acquired by the Advanced Land Observing Satellite (ALOS)2 Phased Array type Lband Synthetic Aperture Radar (PALSAR)2 from 2015 to 2019. A comparison of our results with reference levelling data shows that the SBAS method underestimates displacement. We corrected our results using linear regression and determined the maximum displacement around the Kujyukuri area to be approximately 20 mmyear; the mean displacement rate for 2015xe2x80x932019 was xe2x88x927.9 xc2xb1 2.9 mmyear. These values exceed those obtained using past PALSAR observations owing to the horizontal displacement after the Great East Japan Earthquake of 2011. Moreover, fewer points were acquired, and the root meansquared error of each timeseries displacement value was larger in our results. Further analysis is needed to address these bias errors.