The Microstructure of Yellow River Ice in the Freezing Period

Accurately determining true ice microstructure and material parameters is a basis for ice disaster theoretical research on the Yellow River. In this work, natural Yellow River ice was collected, and ice crystals parallel and perpendicular to the ice surface were photographed using an orthogonal polarizing mirror. Morphologies of ice microstructure were extracted, and equivalent ice grain sizes were calculated. The results show that Yellow River ice mainly consists of granular ice and columnar ice and vary greatly in different time and space ranges. The ice crystal shape is irregular, and the ice crystal size is larger span, and mainly between 1 mm and 10 mm. Ice crystal initial defects come from bubbles, sediment particles, impurities, and microcracks; among them, bubbles are the most common and have a relatively large impact. In addition, a calculation model of the Yellow River ice microstructure was constructed according to the ice crystal test results. Based on the experimental data and numerical model, the obtained Yellow River ice parameters provide help for analyzing ice disaster mechanisms along the Yellow River.

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.