Coastal areas are usually densely populated, economically developed, ecologically dense, and subject to a phenomenon that is becoming increasingly serious, land subsidence. Land subsidence can accelerate the increase in relative sea level, lead to a series of potential hazards, and threaten the stability of the ecological environment and human lives. In this paper, we adopted two commonly used multi-temporal interferometric synthetic aperture radar (MTInSAR) techniques, Small baseline subset (SBAS) and Temporarily coherent point (TCP) InSAR, to monitor the land subsidence along the entire coastline of Guangdong Province. The long-wavelength L-band ALOS/PALSAR-1 dataset collected from 2007 to 2011 is used to generate the average deformation velocity and deformation time series. Linear subsidence rates over 150 mm/yr are observed in the Chaoshan Plain. The spatiotemporal characteristics are analyzed and then compared with land use and geology to infer potential causes of the land subsidence. The results show that (1) subsidence with notable rates (>20 mm/yr) mainly occurs in areas of aquaculture, followed by urban, agricultural, and forest areas, with percentages of 40.8%, 37.1%, 21.5%, and 0.6%, respectively; (2) subsidence is mainly concentrated in the compressible Holocene deposits, and clearly associated with the thickness of the deposits; and (3) groundwater exploitation for aquaculture and agricultural use outside city areas is probably the main cause of subsidence along these coastal areas.

Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channelρ${\mathsf{\rho }}_{\mathrm{HHVV}}$, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.

This study presents a demonstration of the applicability of machine learning techniques for the retrieval of crop height in corn fields using space-borne PolSAR (Polarimetric Synthetic Aperture Radar) data. Multi-year RADARSAT-2 C-band data acquired over agricultural areas in Canada, covering the whole corn growing period, are exploited. Two popular machine learning regression methods, i.e., Random Forest Regression (RFR) and Support Vector Regression (SVR) are adopted and evaluated. A set of 27 representative polarimetric parameters are extracted from the PolSAR data and used as input features in the regression models for height estimation. Furthermore, based on the unique capability of the RFR method to determine variable importance contributing to the regression, a smaller number of polarimetric features (6 out of 27 in our study) are selected in the final regression models. Results of our study demonstrate that PolSAR observables can produce corn height estimates with root mean square error (RMSE) around 40–50 cm throughout the growth cycle. The RFR approach shows better overall accuracy in corn height estimation than the SVR method in all tests. The six selected polarimetric features by variable importance ranking can generate better results. This study provides a new perspective on the use of PolSAR data in retrieving agricultural crop height from space.

Land subsidence is a common geological hazard in urban areas. It can be caused by human activities such as land reclamation and subway construction. However, rapid urbanization can accelerate the progress of these activities. Therefore, spatial-temporal analysis of land subsidence can provide a guarantee for rapid urbanization progress. In this study, Shenzhen, one of the fastest urbanizing cities in China, is selected for continued land subsidence monitoring. We collected 153 Sentinel-1A images from December 2015 to July 2021 and utilized an interferometric point target analysis (IPTA) method to generate the land subsidence in Shenzhen. Additionally, we analyzed the relationship between land subsidence and typical urbanization progress: land reclamation, transportation network, and urban construction. The results show that land subsidence mostly occurs in the western reclamation area, with a maximum rate of -74.9 mm/a in Qianhai Bay. Areas with larger quaternary marine sediment thickness in the range of 10∼20 m are more likely to experience subsidence. Obvious subsidence (<-20 mm/a) along the subway mainly occurred on Lines 11 and Line 5, which is inversely proportional to the distance from the metro. The correlation coefficient is -0.197. Areas with low road densities have a high probability of experiencing great subsidence. A relationship between land subsidence and building construction in the Guangming district is found. Obvious subsidence (<-20 mm/a) mainly occurred in the building areas, with a percentage of 43.48%.

External Digital Elevation Models (DEMs) with different resolutions and accuracies cause different topographic residuals in differential interferograms of Multi-temporal InSAR (MTInSAR), especially for the phase-based StaMPS-PS. The PS selection and deformation parameter estimation of StaMPS-PS are closely related to the spatially uncorrected error, which is directly affected by external DEMs. However, it is still far from clear how the high resolution and accurate external DEM affects the results of the StaMPS-PS (e.g., PS selection and deformation parameter calculation) on different platforms (X band TerraSAR, C band ENVISAT ASAR and L band ALOS/PALSAR1). In this study, abundant synthetic tests are performed to assess the influences of external DEMs on parameter estimations, such as the mean deformation rate and the deformation time-series. Real SAR images, covering Shenzhen city in China, are also selected to analyze the PS selection and distribution as well as to validate the results of synthetic tests. The results show that the PS points selected by the 5 m TanDEM-X DEM are 10.32%, 4.25% and 0.34% more than those selected by the 30 m SRTM DEM at X, C and L bands SAR platforms, respectively, when a multi-look geocoding operation is adopted for X band in the SRTM DEM case. We also find that the influences of external DEMs on the mean deformation rate are not significant and are inversely proportional to the wavelength of the satellite platforms. The standard deviations of the mean deformation rate difference for the X, C and L bands are 0.54, 0.30 and 0.10 mm/year, respectively. Similarly, the influences of external DEMs on the deformation time-series estimation for the three platforms are also slight, except for local artifacts whose root-mean-square error (RMSE) $\ge$ 6 mm. Based on these analyses, some implications and suggestions for external DEMs on StaMPS-PS processing are discussed and provided.

Over the past two decades, the Leizhou Peninsula has suffered from many geological hazards and great property losses caused by land subsidence. However, the absence of a deformation map of the whole peninsula has impeded the government in making the necessary decisions concerning hazard prevention and mitigation. This study aims to provide the evolution of land deformation (subsidence and uplift) in the whole peninsula from 1992 to 2010. A modified stacking procedure is proposed to map the surface deformation with JERS, ENVISAT, and ALOS1 images. The map shows that the land subsidence mainly occurs along the coastline with a maximum velocity of 32 mm/year and in a wide range of inland arable lands with a velocity between 10 and 19 mm/year. Our study suggests that there is a direct correlation between the subsidence and the surface geology. Besides, the observed subsidence in urban areas, caused by groundwater overexploitation for domestic and industrial use, is moving from urban areas to suburban areas. In nonurban areas, groundwater extraction for aquaculture and arable land irrigation are the main reason for land subsidence, which accelerates saltwater intrusion and coastline erosion if regular surface deformation measurements and appropriate management measures are not taken.