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In this paper, based on high-resolution satellite images near an ice bridge in the Canadian Basin, we extracted floe size parameters and analyzed the temporal and spatial variations in the parameters through image processing techniques. The floe area shows a decreasing trend over time, while the perimeter and mean clamped diameter (MCD) exhibit no obvious pattern of change. In addition, the roundness of floes, reflected by shape parameters, generally decreases initially and then increases, and the average roundness of small floes is smaller than that of large floes. To correct the deviations from power law behaviour when assessing the floe size distribution (FSD) with the traditional power law function, the upper-truncated power law distribution function and the Weibull function are selected. The four parameters of the two functions are important parameters for describing the floe size distribution, and Lr and L0 are roughly equal to the maximum calliper diameter and the average calliper diameter of the floes in the region. D in the upper-truncated power law distribution function represents the fractal dimension of the floes, and r in the Weibull function represents the shape parameter of the floes, both of which increase and then decrease with time. In this paper, we investigate the response of the rate of change in the FSD parameter to the differences in the monthly average temperature and find that D, r and air temperature are positively correlated, which verifies the influence of air temperature on the floe size distribution.

The water diversion project in Central Yunnan Province (WDP-YN) is the largest water diversion project under construction in China. However, the ecological effects of this water diversion project are still unclear. This study utilized Sentinel-2 remote sensing data to estimate fractional vegetation cover (FVC), maps spatiotemporal variations of FVC in construction areas from 2017 to 2022, and evaluates the impact of the WDP-YN on regional vegetation coverage using buffer analysis and vegetation type transition matrix methods. The study led to the following findings: (1) From 2017 to 2022, FVC within 10 km of the tunnel construction route showed a slightly downward trend or remained relatively stable with no significant changes in the spatial pattern of FVC. (2) Before and after the construction of WDP-YN, over 60% of the area within 10 km of the tunnel construction route showed no change in FVC. On Construction Route Section I (CRS-I), vegetation improved and/or degraded within 12.90% (14.10%) of the area and the regions with degraded FVC concentrated in the northern CRS-I. For Construction Route Section II (CRS-II), 11.96% and 27.51% of the regions were dominated by improved and/or degraded FVC. Vegetation changes near Groundwater Monitoring Point a (GMPa) were relatively stable. (3) The WDP-YN degraded vegetation within 2 km of both sides of CRS-I, slowing down the increase in FVC, while the WDP-YN improved vegetation within 2–6 km of both sides of CRS-II, the closer the distance to CRS-II, the faster the increase in FVC and the decrease in FVC slowed down within 0–2 km of both sides of CRS-II. This study sheds light on the impacts of water diversion infrastructure on vegetation coverage and provides practical guidance and reference for eco-environment protection and ecological restoration given water conservancy projects in China and other regions of the world.

After the formation of the bend ice cover, the ice thickness of the bend is not uniformly distributed, and an open-water area is usually formed downstream of the bend. The spatial and temporal variation of the ice thickness in seven cross sections was determined via Unmanned Aerial Vehicle Ground Penetrating Radar (UAV-GPR) technology and traditional borehole measurements. The plane morphology change of the open water was observed by Sentinel-2. The results show that the average dielectric permittivity of GPR was 3.231, 3.249, and 3.317 on three surveys (5 January 2022, 16 February 2022, and 25 February 2022) of the Yellow River ice growing period, respectively. The average ice thickness of the three surveys was 0.402 m, 0.509 m, and 0.633 m, respectively. The ice thickness of the concave bank was larger than that of the convex bank. The plane morphology of the open water first shrinks rapidly longitudinally and then shrinks slowly transversely. The vertical boundary of the open water was composed of two arcs, in which the slope of Arc I (close to the water surface) was steeper than that of Arc II, and the hazardous distance of the open-water boundary was 10.3 m. The increased flow mostly affected the slope change of Arc I. Finally, we discuss the variation of hummocky ice and flat ice in GPR images and the physical factors affecting GPR detection accuracy, as well as the ice-thickness variation of concave and convex banks in relation to channel curvature.

Diffuse solar radiation (Rd) provides basic data for designing and optimizing solar energy systems. Owing to the notable unavailability in many regions of the world, Rd is traditionally estimated by models through other easily available meteorological factors. However, in the absence of ground weather station data, such models often need to be supplemented according to satellite remote sensing data. The performance of Himawari-7 satellite inversion of Rd was evaluated in the study, and hybrid models were established (XGBoost_DE, XGBoost_FPA, XGBoost_GOA, and XGBoost_GWO), so as to improve the satellite data and achieve a better utilization effect. The meteorological data of 14 Rd stations in mainland China from 2011 to 2015 were used. Four input combinations (L1–L4) and eight input combinations (S1–S8) of meteorological factors corresponding to satellite remote sensing data were used for model simulation, while two optimal combinations (S7 and S8) were selected for cross-station application. The results revealed that the accuracy of Himawari-7 satellite Rd data was low, with RMSE, R2, MAE, and MBE values of 2.498 MJ·m−2·d−1, 0.617, 1.799 MJ·m−2·d−1, and 0.323 MJ·m−2·d−1, respectively. The performance of these coupled models based on satellite data was significantly improved. The RMSE and MAE values increased by 15.5% and 9.4%, respectively, while the R2 value decreased by 10.9 %. Compared with others based on satellite data, the XGBoost_GOA model exhibited optimal performance. The mean values of RMSE, R2, and MAE were 1.63 MJ·m−2·d−1, 0.76 and 1.21 MJ·m−2·d−1, respectively. The XGBoost_GWO model exhibited optimal performance in the cross-station application, and the average RMSE value was reduced by 2.3–10.5% compared with the other models. The meteorological factors input by the models exhibited different levels of significance in different scenarios. Rd_s was the main meteorological parameter that affected the model based on satellite data, while RH exhibited a significant improvement in the XGBoost_FPA and XGBoost_GWO models based on ground weather stations data. Accordingly, the present authors believe that the XGBoost_GOA model has excellent ability for simulating Rd, while the XGBoost_GWO model allows for cross-station simulation of Rd from satellite data.

Unfrozen free and non-free water between ice crystals in flat and hummock ice in the Yellow River exists as water films with varying contents based on ice temperature. These contents can affect the radar wave velocity of the ice despite its theoretical dependence on the crystal structure and ice body components. The unfrozen water content in ice depends on the ice temperature, which is controlled by the air temperature, solar radiation, and ice thickness. Winter air temperature and radar-detected ice thickness data observed at the Shisifenzi bend in the Yellow River from 2020 to 2021 were analyzed. The unfrozen water content in the ice was the primary factor influencing the accuracy of flat ice thickness detection. The heat flux at the ice–water interface in the Yellow River was determined. The evolution of ice thickness and temperature were simulated using a one-dimensional (1D) ice thermodynamic model forced by the local weather station data (i.e., air temperature, solar radiation, wind speed, and cloud cover). On this basis, the measured ice thickness data of 13 drill holes were combined to calculate 1251 thermodynamically simulated ice thicknesses consistent with the ice thickness detection time of the radar; therefore, statistical relationships regarding the influence of air temperature and the combined action of air temperature and ice thickness on the radar wave velocity in granular and columnar ice during air temperature increases and decreases were determined. Finally, the statistical relationship between the combined influence of air temperature and ice thickness on radar wave velocity was selected as a parameterization scheme to dynamically correct the radar wave velocity of flat ice. To enhance the radar detection accuracy for flat ice thickness, the radar wave velocity of ice was parameterized as a function. Given the presence of unfrozen frazil ice and accumulated broken ice blocks in the Yellow River, radar is suggested to detect the thickness of different types of ice in future research.

In order to satisfy the demand of key sea ice parameters, including melt pond depth Hp and underlying ice thickness Hi, in studies of Arctic sea ice change in summer, four algorithms of retrieving Hp and Hi were compared and validated by using optical data of melt ponds from field observations. The Malinka18 algorithm stood out as the most accurate algorithm for the retrieval of Hp. For the retrieval of Hi, Malinka18 and Zhang21 algorithms could also provide reasonable results and both can be applied under clear and overcast sky conditions, while retrievals under clear sky conditions are more accurate. The retrieval results of Hi for Lu18 agreed better with field measurements for thin ice (Hi < 1 m) than that for thick ice, but those results of Hp were not satisfactory. The König20 algorithm was only suitable for clear sky conditions, and underestimated Hp, while showing a good agreement with Hp < 0.15 m. For Arctic applications, Malinka18 and Zhang21 algorithms provided a basis and reference for the satellite optical data such as WoeldView2 to retrieve Hp and Hi. Malimka18 also showed the ability to retrieve Hi, except for the Lu18 algorithm if pond color captured by helicopters and unmanned aerial vehicles were available. This study identifies the optimal algorithm for retrieval of Hp and Hi under different conditions, which have the potential to provide necessary data for numerical simulations of Arctic sea ice changes in summer.

Ice surface albedo is an important factor in various optical remote sensing technologies used to determine the distribution of snow or melt water on the ice, and to judge the formation or melting of lake ice in winter, especially in cold and arid areas. In this study, field measurements were conducted at Wuliangsuhai Lake, a typical lake in the semi-arid cold area of China, to investigate the diurnal variation of the ice surface albedo. Observations showed that the diurnal variations of the ice surface albedo exhibit bimodal characteristics with peaks occurring after sunrise and before sunset. The curve of ice surface albedo with time is affected by weather conditions. The first peak occurs later on cloudy days compared with sunny days, whereas the second peak appears earlier on cloudy days. Four probability density distribution functions—Laplace, Gauss, Gumbel, and Cauchy—were combined linearly to model the daily variation of the lake ice albedo on a sunny day. The simulations of diurnal variation in the albedo during the period from sunrise to sunset with a solar altitude angle higher than 5° indicate that the Laplace combination is the optimal statistical model. The Laplace combination can not only describe the bimodal characteristic of the diurnal albedo cycle when the solar altitude angle is higher than 5°, but also reflect the U-shaped distribution of the diurnal albedo as the solar altitude angle exceeds 15°. The scale of the model is about half the length of the day, and the position of the two peaks is closely related to the moment of sunrise, which reflects the asymmetry of the two peaks of the ice surface albedo. This study provides a basis for the development of parameterization schemes of diurnal variation of lake ice albedo in semi-arid cold regions.

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (Aw, Ap, and Ai, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing Ap, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively.

The 2021 Mw 7.4 Maduo (Madoi) earthquake that struck the northern Tibetan Plateau resulted in widespread coseismic deformation features, such as surface ruptures and soil liquefaction. By utilizing the unmanned aerial vehicle (UAV) photogrammetry technology, we accurately recognize and map 39,286 liquefaction sites within a 1.5 km wide zone along the coseismic surface rupture. We then systematically analyze the coseismic liquefaction distribution characteristics and the possible influencing factors. The coseismic liquefaction density remains on a higher level within 250 m from the surface rupture and decreases in a power law with the increasing distance. The amplification of the seismic waves in the vicinity of the rupture zone enhances the liquefaction effects near it. More than 90% of coseismic liquefaction occurs in the peak ground acceleration (PGA) > 0.50 g, and the liquefaction density is significantly higher in the region with seismic intensity > VIII. Combined with the sedimentary distribution along-strike of the surface rupture, the mapped liquefaction sites indicate that the differences in the sedimentary environments could cause more intense liquefaction on the western side of the epicenter, where loose Quaternary deposits are widely spread. The stronger coseismic liquefaction sites correspond to the Eling Lake section, the Yellow River floodplain, and the Heihe River floodplain, where the soil is mostly saturated with loose fine-grained sand and the groundwater level is high. Our results show that the massive liquefaction caused by the strong ground shaking during the Maduo (Madoi) earthquake was distributed as the specific local sedimentary environment and the groundwater level changed.

Radio determination satellite service (RDSS) is one of the characteristic services of Beidou navigation satellite system (BDS), and also distinguishes with other GNSS systems. BDS-3 RDSS adopts new signals, which is compatible with BDS-2 RDSS signals in order to guarantee the services of old users. Moreover, the new signals also separate civil signals and military signals which are modulated on different carriers to improve their isolation and RDSS service performance. Timing is an important part of RDSS service, which has been widely used in the field of the power, transportation, marine and others. Therefore, the timing accuracy, availability and continuity is an important guarantee for RDSS service. This paper summarizes the principle of one-way and two-way timing, and provides the evaluation method of RDSS timing accuracy, availability and continuity. Based on BDS-3 RDSS signal measurements of system, the performance of one-way timing and two-way timing is analyzed and evaluated for the first time. The results show that: (1) the accuracy of one-way timing and two-way timing is better than 30 ns and 8 ns respectively, which are better than the official claimed accuracy; (2) the RMS of one-way timing accuracy is 5.45 ns, which is 20% smaller than BDS-2, and the availability and continuity are 100%; (3) the RMS of two-way timing accuracy is 3.59 ns, which is 34% smaller than one-way timing, and both of the availability and continuity are 100%; (4) the orbit maneuver of GEO satellite make the one-way timing has 7.68 h recovery, but has no affection on the two-way timing.

Studying the stoichiometric characteristics of plant C, N, and P is an effective way of understanding plant survival and adaptation strategies. In this study, 60 fixed plots and 120 random plots were set up in a reed-swamp wetland, and the canopy spectral data were collected in order to analyze the stoichiometric characteristics of C, N, and P across all four seasons. Three machine models (random forest, RF; support vector machine, SVM; and back propagation neural network, BPNN) were used to study the stoichiometric characteristics of these elements via hyperspectral inversion. The results showed significant differences in these characteristics across seasons. The RF model had the highest prediction accuracy concerning the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.88, 0.95, 0.97, and 0.92, respectively. According to the root mean square error (RMSE) results, the model error of total C (TC) inversion is the smallest, and that of C/N inversion is the largest. The SVM yielded poor predictive results for the stoichiometric properties of C, N, and P. The R2 of the four-season models was greater than 0.82, 0.81, 0.81, and 0.70, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The BPNN yielded high stoichiometric prediction accuracy. The R2 of the four-season models was greater than 0.87, 0.96, 0.84, and 0.90, respectively. According to RMSE results, the model error of TC inversion is the smallest, and that of C/P inversion is the largest. The accuracy and stability of the results were verified by comprehensive analysis. The RF model showed the greatest prediction stability, followed by the BPNN and then the SVM models. The results indicate that the accuracy and stability of the RF model were the highest. Hyperspectral data can be used to accurately invert the stoichiometric characteristics of C, N, and P in wetland plants. It provides a scientific basis for the long-term dynamic monitoring of plant stoichiometry through hyperspectral data in the future.