Empirical models have been widely used to retrieve shallow water bathymetry from multispectral/hyperspectral satellite imagery. In traditional studies on deriving the topography and monitoring its temporal changes, a single date satellite image without clouds corresponded to a bathymetric map and multi-date images corresponded to multiple bathymetric maps. The satellite image noise caused by various environmental conditions and satellite sensors can inevitably introduce errors or gaps in deriving bathymetric maps. Also, empirical models are limited in some remote areas due to the lack of prior bathymetric points. In this study, using only satellite data, including multi-temporal Sentinel-2 images and ICESat-2 data, a multi-temporal stacking method was developed to derive highly accurate and cloud free shallow water bathymetry with accuracy of approximately 1 m and the depth range exceeding 22 m. The proposed method was tested and validated by an airborne bathymetric lidar. To be specific, our method using multi-temporal Sentinel-2 images can achieve a mean RMSE of 1.08 m (R 2 = 0.94) by comparing with in-situ airborne lidar data around Ganquan Island, which is better than the result (R 2 = 0.92, RMSE = 1.46 m) derived from single date image based methods. Also, the gaps in a bathymetric map due to clouds or other noise can be avoidable benefitting from the stacking of multiple date satellite images. In the future, this satellite data driven method can be further extended to the globe to produce highly accurate and cloud free bathymetry around clear shallow water benefited from prior ICESat-2 bathymetric data.

Abstract. Flux ropes are frequently observed in the space plasmas, such as solar wind, planetary magnetosphere and magnetosheath etc., and play an important role in the reconnection process and mass and flux transportation. One usually uses bipolar signature and strong core field to identify the flux ropes. We propose here one new method to identify flux ropes based on the correlations between the variables of the data from in situ spacecraft observations and the “target function to be correlated” (TFC) from the ideal flux rope model. Through comparing the correlation coefficients of different variables at different times and scales, and performing weighted-average techniques, this method can derive the scales and locations of the flux ropes. We compare it with other methods and also discuss the limitation of our method.

Research of turbulence embedded in magnetized space plasma has been carried out for more than 4 decades, and its characteristics are gradually revealed. Turbulence plays an essential role in the fundamental physical processes, such as energy dissipation, energy transport, and particle heating/acceleration in the plasma (e.g.

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