COVID-19 is currently spreading widely around the world, causing huge threats on public safety and global society. This study analyzes the spatiotemporal spread pattern of the COVID-19 in China, reveals China’s epicenters of the epidemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the COVID-19 spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters account for more than 60% of the country’s total on 24 January and 7 February, half on 31 January, and more than 70% from 14 February. The total cases finally stabilized around 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities significantly associated with the total number of the cases, while distances to prefecture cities and inter-city traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission and controlling the global COVID-19 spread.

Using a shape model (SM) is a typical method to determine the phenological phases of crops with long-time-series satellite remote sensing data. The average AGDD-based shape model (AAGDD-SM) takes temperature into account compared to SM, however, the commonly used daily average temperature is not sufficient to determine the exact AGDD owing to the possibly significant changes in temperatures throughout the day. In this paper, a modified shape model was proposed for the better estimation of phenological dates and it is incorporated into the continuous AGDD (CAGDD) which was calculated based on temperatures from a continuous 24 h within a day, different from the calendar day or the average AGDD indicators. In this study, the CAGDD replaced the abscissa of the NDVI growth curve over a 5-year period (2014 to 2018, excluding 2015) for a test site of early rice in Jiangxi province of China. Four key phenological phases, including the reviving, tillering, heading and anthesis phases, were selected and determined with reference to the field-observed phenological data. The results show that compared with the AAGDD-SM, the method proposed in this paper has basically improved the prediction of each phenological period. For those cases where the average temperature is lower than the minimum temperatures (K1) but the effective accumulated temperature is not zero, more accurate AGDD can be calculated according to the method in this paper.

The physiographic map can visualize spatial relations between different landforms, thus providing insights into geologic processes that shaped the present-day Martian landscape. The physiographic map of Mars surface is usually made through image interpretation, which is always labor-intensive and highly depends on the expert knowledge. In this paper, we propose an efficient and automatic classification method for characterization of landforms on Mars by using the Mars Orbiter Laser Altimeter (MOLA) digital elevation data. The proposed method was tested on a region where China's Mars probe Tianwen-1 landed. The study area covers the Nepenthes Mensae, Amenthes Planum, northern Terra Cimmeria, northern Hesperia Planum and southern Utopia Planitia region, having a size of 2250km×2750km centered at 117°E, 6°N. The obtained results confirm the effectiveness of the proposed method in describing different topographic characteristics of the Martian landforms. Note that the proposed method is completely data-driven, which can provide a rapid mapping result in large geographical regions, especially from a global perspective to reveal the Martian landform information.

CryoSat-2 repeatedly collects dense radar altimetry footprints covering high latitudes of the polar ice sheets for over 10 years. The Baseline-D height product of CryoSat-2 was recently upgraded and released in 2019. Based on the internal or locally external evaluation of CryoSat-2 Baseline-D ice heights, we extended the validation both heterogeneously and spatially to accomplish a comprehensive assessment. Firstly, reliable ice surface GNSS point solutions along the 36th Chinese National Antarctic Research Expedition traverse functioned as a height reference for comparison with the CryoSat-2 SAR synthetic aperture radar interferometric mode (SIN) data. Secondly, the Land Ice Along-Track Height product over Lambert-Amery System (LAS) from ICESat-2, was also applied to validate CryoSat-2 SIN and Low-Resolution Mode (LRM) data. As the results indicate, the SIN height accuracy is evaluated to be -1.68 m ± 2.35 m (slope < 0.65°) along the traverse and -0.96 ± 1.95 m (slope < 0.95° & roughness < 3 m) over the margin of LAS. The LRM height accuracy is evaluated to be -0.13 ± 0.46 m (slope < 0.1° & roughness < 1 m) over the interior of LAS. The standard deviation (SD) of the height differences degrades linearly against the limited growth of the terrain slope/roughness at the confidence level of 99%. It is worth noting that the spatially heterogeneous pattern of height differences is correlated with surface topography variations. This study should imply the potential to improve the estimation of mass balance and its uncertainty of polar ice sheets based on radar altimetry data.

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