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Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.

Assessing well-being with social media text data is a promising method, but besides hedonic well-being, little is known about whether additional well-being dimensions, such as psychological richness and eudaimonic well-being, can be predicted from such data. We compare the predictive accuracy for hedonic well-being, eudaimonic well-being, and the recently proposed construct of psychological richness in a large sample of Facebook users ( n = 2,644), and find that the inclusion of language features incrementally improved model prediction accuracy beyond demographic features for psychological richness, but not for hedonic or eudaimonic well-being. Psychological richness had the lowest overall prediction accuracy ( r = .21) followed by hedonic well-being ( r = .27) and eudeomonic well-being ( r = .29). The linguistic features associated with Psychological Richness were face valid, and in many instances the content and direction of the associations were unique to Psychological Richness, which provides discriminant validity evidence.

The year, 2024, marks the 40th anniversary of Chinese research expeditions in the polar regions and the 25th anniversary of its Arctic research expeditions. China has conducted 14 national Arctic research expeditions. With the increase of understandings on the global impacts of the changes of Arctic climate system, especially on China’s weather and climate, and demands for commercial utilization of the Arctic sea routes, Chinese scientists have made great progresses on in site and remote sensing observation technologies for Arctic Ocean, interaction mechanisms between atmosphere, sea ice, and ocean, the connection mechanism between the Arctic Ocean and other regions, and have achieved a series of research results. This study summarizes the research achievements by Chinese scientists in the above-mentioned aspects or beyond, identifies knowledge gaps, and based on this, discusses prospects and provides suggestions. From a perspective of observation, improving the observation capabilities of the Arctic Ocean in winter and the ocean under the ice, as well as floe-scale processes of sea ice and mesoscale and submesoscale processes of the ocean, is an urgent task to be addressed. Strengthening international cooperation is necessary for building a monitoring network for the Arctic marine environment. From a perspective of numerical simulation, the descriptive ability and parameterization scheme of sub-grid processes based on observational evidence need to be developed. From a perspective of cross-sphere interactions, in addition to the multi-media coupling within the Arctic Ocean that this review focuses on, the interaction between the Arctic Ocean and land or ice sheet (Greenland), especially the water cycle process, is also a scientific domain that needs to be considered, in the context of Arctic warming and humidification. From a perspective of climate effects, the physical mechanisms that affect the robustness of teleconnection need to be clarified.

Accurately quantifying vertical sediment transport rates in large seaward rivers is vital for estimating basin-scale water and sediment fluxes and assessing riverbed evolution. Traditional multi-point velocity and suspended sediment concentration (SSC) measurements are costly and slow, hindering long-term online monitoring. Bidirectional flows in tidal reaches further exacerbate this challenge. We propose a physics-constrained support vector machine (SVM) inversion method to estimate vertical sediment transport rates from single-point measurements. Constrained by modified logarithmic velocity and Rouse suspended sediment concentration profiles, it quantitatively relates single-point hydraulic variables to key parameters governing vertical distributions. Lower Yangtze River tidal reach field data validate the hybrid model’s successful reconstruction of vertical distributions. It accurately captures transient sediment responses across maximum flood and ebb. Inverted transport rates match measurements closely (RMSE = 0.085, NSE = 0.969, PBIAS = 2.50%) and exhibit strong cross-site generalization. Sensitivity analysis identifies 0.4 times the water depth above the riverbed as the optimal single-point sensor position. Although currently validated only in the lower Yangtze River, this low-cost, reliable method supports local basin management, flood control, and disaster mitigation by enabling continuous sediment flux monitoring. However, applying it to other river or estuarine systems may require recalibration or retraining to adapt to different local conditions.

Submerged spur dikes are widely applied in the channel regulation structures of plain sand-bed rivers such as the lower reaches of the Yangtze River; thus, the issue of local scour protection near regulating structures is especially important for structure design engineering. To further scientific research on the local scour of submerged spur dike heads, we investigated rulers describing the variance of the incoming flow dynamic, scale of the spur dike body, width of river bottom protection, etc., responding to the maximum local scouring depth of a submerged spur dike and the distance between the submerged spur dike and dam axis under the conditions of river bottom protection. According to principles of dimensional analysis, we established computational formulas for the maximum local scouring depth of a submerged spur dike and the distance between the submerged spur dike and dam axis, with consideration of bottom protection works for the remaining soft mattress. These research results not only enrich existing research on the calculation of local scour of channel-regulating structures, but they are also a relevant technical reference for the design of water conservancy and waterway engineering.

Sea ice growth and decay are critical processes in the Arctic climate system, but comprehensive observations are very sparse. We analyzed data from 23 sea ice mass balance buoys (IMBs) deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020 to investigate the seasonality and timing of sea ice thermodynamic mass balance in the Arctic Transpolar Drift. The data reveal four stages of the ice season: (I) onset of ice basal freezing, mid-October to November; (II) rapid ice growth, December–March; (III) slow ice growth, April–May; and (IV) melting, June onward. Ice basal growth ranged from 0.64 to 1.38 m at a rate of 0.004–0.006 m d–1, depending mainly on initial ice thickness. Compared to a buoy deployed close to the MOSAiC setup site in September 2012, total ice growth was about twice as high, due to the relatively thin initial ice thickness at the MOSAiC sites. Ice growth from the top, caused by surface flooding and subsequent snow-ice formation, was observed at two sites and likely linked to dynamic processes. Snow reached a maximum depth of 0.25 ± 0.08 m by May 2, 2020, and had melted completely by June 25, 2020. The relatively early onset of ice basal melt on June 7 (±10 d), 2019, can be partly attributed to the unusually rapid advection of the MOSAiC floes towards Fram Strait. The oceanic heat flux, calculated based on the heat balance at the ice bottom, was 2.8 ± 1.1 W m–2 in December–April, and increased gradually from May onward, reaching 10.0 ± 2.6 W m–2 by mid-June 2020. Subsequently, under-ice melt ponds formed at most sites in connection with increasing ice permeability. Our analysis provides crucial information on the Arctic sea ice mass balance for future studies related to MOSAiC and beyond.

The Ma’anshan section of the lower Yangtze River features a complex multi-anabranching system, where the river divides into several branches around mid-channel sandbars, with distinct point bars alternately developing along both banks. Within this morphologically active system, Zhengpu Harbor suffered severe operational disruptions by accelerated siltation at its approach channel, primarily due to its vulnerable location downstream of the expanding Niutun River point-bar on the left bank. To systematically diagnose the mechanisms of siltation, this study integrates multi-method investigations: decadal-scale morphodynamic analysis using long-term bathymetric surveys, numerical modeling to quantify engineering impacts on flow dynamics, and multiple linear regression analysis for the contributions of key influencing factors. The result identifies three primary drivers of siltation, collectively responsible for 70% of the sediment accumulation, including the rightward shift of the thalweg in the Ma’anshan left branch, reduced flow diversion of the left Branch of Central bar, and the expansion of the Niutun River point bar. River engineering structures, such as bridges, contribute approximately 12%, while changes in upstream flow-sediment supply account for approximately 18%. To mitigate siltation at Zhengpu Harbor’s approach channel, this study proposes targeted engineering interventions to enhance local hydrodynamic conditions. The spur dikes were designed to enhance the morphological stabilization of the Central bar head to regulate flow distribution. A diversion channel could also be excavated at the tail of the Niutun River shoal, and emergency dredging was recommended at the harbor front. Numerical modeling indicates that these measures will increase flow velocity by over 0.1 m/s at the harbor front, mitigating the siltation situation. The study concludes that the proposed engineering measures can reduce annual siltation by approximately 30% under normal-year hydrological conditions, demonstrating their feasibility in mitigating siltation trends in multi-anabranching river systems. This research provides a reference for addressing siltation issues in harbors within complex anabranching river systems.

The characteristics of sea ice leads (SILs) in the Weddell Sea are an important basis for understanding the mechanism of the atmosphere–ocean system in the Southern Ocean. In this study, we derived the sea ice surface temperature (IST) of the Weddell Sea from MODIS thermal images and then generated a daily SIL map for 2015 and 2022 by utilizing the iterative threshold method on the optimised MOD35 cloud-masked IST. The results showed that SIL variations in the Weddell Sea presented remarkable seasonal characteristics. The trend of the SIL area exhibited an initial rise followed by a decline from January to December, characterised by lower values in spring and summer and higher values in fall and winter. SILs in the Weddell Sea were predominantly concentrated between 70~78°S and 60~30°W. The coastal spatial distribution density of the SILs exceeded that of offshore regions, peaking near the Antarctic Peninsula and then near Queen Maud Land. The SIL variation was mainly influenced by dynamical factors, and there were strong positive correlations between the wind field, ocean currents, and sea-ice motion.

Large marginal sandbanks in tidal rivers experience periodic splitting processes. In this paper, the morphodynamic evolution of Jingjiang Beach, a sandbank on the Yangtze tidal river, has been investigated based on measured data. The results show that the duration of the splitting process in the middle and lower sections of Jingjiang Beach is 4–6 years. The periodical evolution occurred both in flood season and dry season, with a slight difference in the initial stage of splitting. This paper focuses on the evolution characteristics related to strong human activities since 2003. Ever since the second stage of the 12.5 m Deepwater Channel Project (DCP), the volumes above the 10 m and 12.5 m isobaths of Jingjiang Beach have been generally decreasing. The elevation data in recent years have demonstrated that the upper section, and nearshore side of the middle section, of Jingjiang Beach have tended to be stable. Moreover, the migration distance for a splitting sand body at the tail of Jingjiang Beach appears to have shortened. With the operation of the Three Gorges Reservoir, the number of days with discharge less than 15,000 m3/s has shown a decreasing trend; thus, the development scale of Jingjiang Beach could decline in the future.