Efficient and accurate wetland monitoring is of great significance for controlling climate, preventing floods, and maintaining ecological balance. Due to the characteristics of complex wetland features, there are still problems in feature extraction and classifier selection when dealing with wetland mapping using remote sensing data. In this paper, a novel wetland classification method based on Convolutional Neural Network (CNN) and Support Vector Machine (SVM) is proposed. Firstly, multi-source images are constructed by sentinel-1 and 2. Furthermore, deep features are extracted from multi-source images based on pre-trained CNN. Finally, considering the advantages of SVM in remote sensing classification, the softmax is replaced by L2-SVM. To verify the effectiveness of the proposed method, Qilihai Wetland is used in our experiment. Experimental results show that combining multi-source remote sensing images significantly improves wetland classification accuracy. Moreover, the proposed method has superior performance with OA and Kappa of up to 90.3% and 0.870, especially in small sample categories and complex land-cover types.

Synthetic Aperture Radar (SAR) has the ability of all-weather and all-day observation, which is suitable for flood monitoring. However, there still has some challenges in flood monitoring using SAR images, such as high-quality prior knowledge, accumulated errors in time series analysis, and the impact of other land cover changes. To solve these problems, in this paper, a novel unsupervised flood monitoring method using temporal information and statistical characteristics of SAR images is proposed. Firstly, the temporal-spatial-polarization (TSP) dataset is constructed from different SAR data. Furthermore, improved K-means clustering is proposed to fit these constructed datasets to mitigate the error accumulation. Finally, considering the statistical characteristics of SAR data, the Bray-Curtis distance is applied to optimize improved K-means. To verify the effectiveness of the proposed method, the latest flood event in Jingpo Lake in China with temporal Sentinel-1 data is used. The experimental results demonstrate that our method has superior performance in detecting flood regions, with OA and Kappa of up to 97.64% and 0.86.

The new generation of SAR satellites is serving our long-standing demand for high-resolution crustal deformation over various scales. However, the reliability of InSAR measurements is still limited by varying tropospheric conditions between acquisitions, especially when mapping slow-deforming interseismic deformation. We propose here a new phase-based approach for mapping interseismic deformation using short-period interferograms. Our method formulates the InSAR phase after topographic correction as the sum of three components: (1) spatiotemporally varied turbulent tropospheric phase, (2) topography-correlated stratified tropospheric phase, and (3) interseismic-related deformation assumed to be accumulated at a constant rate. We simultaneously solve for the parameters in the model to avoid overestimating the tropospheric phases, especially when interseismic deformation and tropospheric delays are both coupled with elevation in space. Synthetic tests and practical applications to easternmost Altyn Tagh fault demonstrate that the new method can effectively recover the small-amplitude interseismic deformation caused by fault motion even when the interferograms are dominated by strong tropospheric delays.

Long-term continuous monitoring of coal mining activities is conducive to master the development and potential risk factors of the mining area. Radar reflected echo signal contains the intensity and phase information, which is suitable for detecting different types of mining areas. However, relevant coal mining detection research mainly focuses on phase information, which results in the application based on underground coal mines more than open-pit coal mines. In order to take full advantage of Synthetic Aperture Radar (SAR) data, in this paper, a novel mine detection method based on intensity and phase information is introduced to detect the underground and open pit coal mine using the multi-temporal ALOS data in Shenmu County, China. Firstly, Interferometric SAR (InSAR) technology is used to detect the deformation information caused by underground coal mine. Secondly, the coherence information is used to detect the deformation from both underground and open-pit coal mine. Moreover, change detection method based on intensity information is used to detect the open-pit coal mining. Finally, the results of phase, coherence and intensity are combined to detect the underground and open-pit coal mine. The experimental results of Shengdong mine show the effectiveness of the proposed method.

Digital elevation models (DEMs) are vital in the geosciences and many other fields. Interferometric synthetic aperture radar (InSAR), an advanced earth observation technology, has shown its potential in DEM reconstruction. Multi baseline InSAR (MB-InSAR) is currently improving the precision of DEM reconstruction by combining multiple interferograms. However, MB-InSAR for DEM generation can result in severe decorrelation, which may cause significant gaps in the final DEM product. To solve this problem, an improved MB-InSAR DEM reconstruction method is proposed in this study, which we term as the dynamic DEM calculation algorithm. The proposed method can estimate the DEM pixel-by-pixel, which allowed us to select the interferograms dynamically and therefore minimize the void values. For the performance test of the proposed method, 25 ascending and 20 descending TerraSAR-X images over Heifangtai (China) were collected to form repeat-pass interferograms and produce the DEM using the proposed method. Results showed that the number of valid pixels increased by approximately 20% compared with the traditional MB-InSAR DEM reconstruction method without loss of precision, thereby illustrating the feasibility of the proposed method.

Underground mining induced displacements in most areas of China amount to meter-level while with small spatial coverage, spatially discontinuous and temporally nonlinear features. Traditional phase-based InSAR methods can hardly obtain large deformation in the center of land subsidence area due to the phase noise, maximum monitoring ability. This paper systematically studies the offset tracking technique based on SAR image intensity maps with and without pre-installed Corner Reflectors (CRs).The results of this experiment indicate that the high resolution SAR data with the aid of pre-installed CR points can better solve the large gradient deformation monitoring problem. In addition, offset tracking method has the potential to achieve two-dimensional deformation field along the line-of-sight and along the azimuth directions, which can provide more detailed information regarding mining induced deformation, which is complimentary to the traditional phase-based and intensity-based techniques.