Influence of Atmospheric Scattering on the Accuracy of Laser Altimetry of the GF-7 Satellite and Corrections

Satellite laser altimetry can obtain sub-meter or even centimeter-scale surface elevation data over large areas, but it is inevitably affected by scattering caused by clouds, aerosols, and other atmospheric particles. This laser ranging error caused by scattering cannot be ignored. In this study, we systematically combined existing atmospheric scattering identification technology used in satellite laser altimetry and observed that the traditional algorithm cannot effectively estimate the laser multiple scattering of the GaoFen-7 (GF-7) satellite. To solve this problem, we used data from the GF-7 satellite to analyze the importance of atmospheric scattering and propose an identification scheme for atmospheric scattering data over land and water areas. We also used a look-up table and a multi-layer perceptron (MLP) model to identify and correct atmospheric scattering, for which the availability of land and water data reached 16.67% and 26.09%, respectively. After correction using the MLP model, the availability of land and water data increased to 21% and 30%, respectively. These corrections mitigated the low identification accuracy due to atmospheric scattering, which is significant for facilitating satellite laser altimetry data processing

Influence of Atmospheric Scattering on the Accuracy of Laser Altimetry of the GF-7 Satellite and Corrections

Satellite laser altimetry can obtain sub-meter or even centimeter-scale surface elevation data over large areas, but it is inevitably affected by scattering caused by clouds, aerosols, and other atmospheric particles. This laser ranging error caused by scattering cannot be ignored. In this study, we systematically combined existing atmospheric scattering identification technology used in satellite laser altimetry and observed that the traditional algorithm cannot effectively estimate the laser multiple scattering of the GaoFen-7 (GF-7) satellite. To solve this problem, we used data from the GF-7 satellite to analyze the importance of atmospheric scattering and propose an identification scheme for atmospheric scattering data over land and water areas. We also used a look-up table and a multi-layer perceptron (MLP) model to identify and correct atmospheric scattering, for which the availability of land and water data reached 16.67% and 26.09%, respectively. After correction using the MLP model, the availability of land and water data increased to 21% and 30%, respectively. These corrections mitigated the low identification accuracy due to atmospheric scattering, which is significant for facilitating satellite laser altimetry data processing

Reconstruction of spatially continuous time-series land subsidence based on PS-InSAR and improved MLS-SVR in Beijing Plain area

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