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Beijing has undergone severe settlement in recent years. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technique has been widely used to derive time-series land deformation. However, existing studies have faced two challenges: (1) the nonlinear characteristics of time-series subsidence has not been fully investigated; (2) since PS points are normally distributed in urban areas with high building density, measurement gaps usually exist in nonurban areas. To address the challenges, we presented a new method to reconstruct spatially continuous time-series deformation. First, PS-InSAR was used to retrieve the deformation based on 135 scenes of Envisat ASAR and Radarsat-2 images from 2003 to 2020. Polynomial Curve Fitting (PCF) was then used to model nonlinear time-series deformation for the PS points. In the PS measurement gaps, Iterative Self-Organizing Data Analysis Technique (ISODATA) and Multi-output Least Squares Support Vector Regression (MLS-SVR) were used to estimate the PCF coefficients and then time-series deformation considering 40 features including thickness of the compressible layers, annual groundwater level, etc. The major results showed that (1) compared to linear, quadratic, and quartic models, cubic polynomial model generated better fit for the time-series deformation (R2 ≈0.99), suggesting obvious nonlinear temporal pattern of deformation; (2) the time-series deformation over measurement gaps reconstructed by ISODATA and MLS-SVR had satisfactory accuracy (R2 = 0.92, MAPE < 15%) and yielded higher accuracy (R2 = 0.947) than IDW (R2 = 0.687) and Ordinary Kriging (R2 = 0.688) interpolation methods. The reconstructed results maintain the nonlinear characteristics and ensure the high spatial resolution (120 m) of time-series deformation. Among the 40 predictor variables, ground water level datasets are the most influential predictors of time-series deformation.

Transport of suspended particulate matter (SPM) in estuarine waters plays an important role in regulating erosion-accretion and biogeochemical processes. In the Yellow River Estuary (YRE), artificial water and sediment regulation scheme (WSRS) and coastal engineering structures are the 2 typical anthropogenic activities affecting the spatiotemporal dynamics of estuarine SPM. The monitoring of SPM transport affected by such human activities requires SPM mapping at both high spatial and high temporal resolutions. In this study, we presented an improved Flexible Spatiotemporal Data Fusion (FSDAF) strategy with consideration of highly dynamic SPM variations in estuarine waters, and generated 30-m hourly SPM concentrations based on Landsat 8 OLI and GOCI datasets. The new strategy produced higher SPM estimation accuracy than the original FSDAF, with the relative percentage difference (RPD) decreasing from 29.75% to 5.31% using GOCI-derived hourly SPM as reference. With in situ SPM measurements as reference, the fused SPM concentrations had an RMSE of 12.09 mg/L and an RPD of 27.17%. Investigation of interday SPM variations before, during, and after the WSRS in 2018 revealed that the first WSRS significantly increased the SPM concentration and plume extent; new wetland with an area of 12.56 km2 was formed due to sediment accretion near the river mouth. The two groins offshore from the coastlines on the north and south sides of YRE exhibited obvious sediment trapping effects in that higher SPM concentrations on one side of each groin were found regardless of the turbidity modes and diurnal SPM variations; the trapping effects were associated with the number of groins and groin length. Intraday variations of SPM were influenced by tidal currents, with plume direction following the ebb and flooding tidal current direction. The inter- and intraday characteristics of the 30-m hourly SPM dynamics facilitate the detailed analysis of the sediment transport associated with human activities.

Over the past decades, Spartina alterniflora, one of the top exotic invasive plants in China, has expanded throughout coastal China. In the Yellow River Delta (YRD), the rapid expansion of S. alterniflora has caused serious negative ecological effects. Current studies have concentrated primarily on mapping the distribution of S. alterniflora with medium-resolution satellite imagery at the regional or landscape scale, which have a limited capability in early detection and monitoring of the invasive process at the patch scale. In this study, we proposed a framework for monitoring the early stage invasion of S. alterniflora patches in the YRD using multiyear multisource high-spatial-resolution satellite imagery with various ground sampling distances (WorldView-2, SPOT-6, GaoFen-1, GaoFen-2, and GaoFen-6 from 2012 to 2019). First, we proposed to use deep-learning-based image super-resolution models to enhance all images to submeter (0.5 m) resolution. Then, we adopted stepwise evolution analysis-based image segmentation and object-based classification rules to detect and delineate S. alterniflora patches from the super-resolved imagery. By investigating Super-Resolution Convolutional Neural Networks (SRCNN) and Fast Super-Resolution Convolutional Neural Networks (FSRCNN) and comparing these methods with the conventional bicubic interpolation method for image resolution enhancement, we concluded that FSRCNN was superior in constructing spectral and structural details from the 1 m/1.5 m/2 m resolution images to 0.5 m resolution. FSRCNN, in particular, was more effective and efficient in discerning and estimating the size of small S. alterniflora patches (