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Satellite-derived bathymetry (SDB) using Ice, Cloud, and Land Elevation satellite-2 (ICESat-2) LiDAR data and remote sensing images faces challenges in the difficulty of uniform coverage of the inversion area by the bathymetric control points due to the linear sampling pattern of ICESat-2. This study proposes a novel virtual control point optimization framework integrating inverse distance weighting (IDW) and spectral confidence analysis (SCA). The methodology first generates baseline bathymetry through semi-empirical band ratio modeling (control group), then extracts virtual control points via SCA. An optimization scheme based on spectral confidence levels is applied to the control group, where high-confidence pixels utilized a residual correction-based strategy, while low-confidence pixels employed IDW interpolation based on a virtual control point. Finally, the preceding optimization scheme uses weighting-based fusion with the control group to generate the final bathymetry map, which is also called the optimized group. Accuracy assessments over the three research areas revealed a significant increase in accuracy from the control group to the optimized group. When compared with in situ data, the determination coefficient (R2), RMSE, MRE, and MAE in the optimized group are better than 0.83, 1.48 m, 12.36%, and 1.22 m, respectively, and all these indicators are better than those in the control group. The key innovation lies in overcoming ICESat-2’s spatial sampling limitation through spectral confidence stratification, which uses SCA to generate virtual control points and IDW to adjust low-confidence pixel values. It is also suggested that when applying ICESat-2 satellite data in active–passive-fused SDB, the distribution of training data in the research zone should be adequately considered.

Global uniform chart datum (CD) surface construction is the basic upon which to realize various vertical datums transformation, and is of great importance for geospatial data expression under the same vertical datum. Generally, the CD level is computed by developing the function between tidal constituents’ harmonic constants and time, i.e., the lowest astronomical tide is taken as the lowest predicted tide level by adopting the major constituents over a 19-a period. The CD surface prescribed in China is the theoretical lowest tide (TLT) and is calculated using 13 tidal constituents, i.e., short -period (Q 1 , O 1 , P 1 , K 1 , N 2 , M 2 , S 2 , K 2 , M 4 , MS 4 and M 6 ) and long-period (Sa and Ssa) tidal constituents. Although the accuracy in determining short-period tidal constituents has improved gradually, the long-period tide has not been studied thoroughly owing to nonstationary and temporal variations. Previous studies have intended to evaluate the effect of Sa and Ssa tides in the determination of the TLT level for the purpose of determining a more accurate CD surface for the China seas and adjacent waters. Here, the parameters of long-period tidal correction and long-period tidal correction rate were treated as the effect of both Sa and Ssa on the TLT, and the TOPEX/Poseidon and Jason series satellite altimetry data ranged from October 1992 to April 2022 were adopted to analyze the contribution of long-period tidal constituents. Results showed that the average long-period correction value is 10.10 cm (range from 8.57 cm to 14.98 cm), and that the average long-period tidal contribution rate is 14.56% (range from 9.09% to 23.97%) in the China seas and adjacent waters. Finally, data from 82 tide gauge station with at least a 1-a record of hourly observations were compared with satellite-derived result. We concluded that the long-period tidal contribution should not be neglected in TLT construction. Furthermore, to reduce tidal datum uncertainty, accurate extraction of long-period tidal constituents should receive closer attentions.

In high-resolution earth observation systems, object detection in high spatial resolution remote sensing images (HSRIs) is the key technology for automatic extraction, analysis and understanding of image information. With respect to the multi-angle features of object orientation in HSRIs object detection, this paper presents a novel HSRIs object detection method based on convolutional neural networks (CNN) with adaptive object orientation features. First, an adaptive object orientation regression method is proposed to obtain object regions in any direction. In the adaptive object orientation regression method, five coordinate parameters are used to regress the object region with any direction. Then, a CNN framework for object detection of HSRIs is designed using the adaptive object orientation regression method. Using multiple object detection datasets, the proposed method is compared with some state-of-the-art object detection methods. The experimental results show that the proposed method can more accurately detect objects with large aspect ratios and densely distributed objects than some state-of-the-art object detection methods using a horizontal bounding box, and obtain better object detection results for HSRIs.

Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods.

The aim of this study is to enhance the stability of zein nanoparticles by using poly(γ‐glutamic acid) (γ‐PGA) as a stabilizer. Zein/γ‐PGA nanocomposite particles are produced through a straightforward anti‐solvent precipitation method. The incorporation of γ‐PGA influenced the average particle size, zeta potential, and overall stability of the resulting zein/γ‐PGA nanocomposite particles. These particles exhibit greater resistance to aggregation and sedimentation compared to zein nanoparticles across various environmental conditions, including a wide pH range (3.0–9.0), elevated temperatures (80 °C for 120 min), high ionic strength (1000 mm), and prolonged storage at 4 °C (up to 3 months). Fluorescence spectroscopy reveals significant interactions between zein and γ‐PGA. Fourier transform infrared spectroscopy and zeta potential measurements indicate that hydrogen bonding, hydrophobic interactions, and electrostatic attraction are the primary mechanisms driving these interactions. Importantly, the conditions for forming zein/γ‐PGA nanocomposite particles are effectively utilized to encapsulate a hydrophobic bioactive model (curcumin) with high encapsulation efficiency. The encapsulated curcumin demonstrates improved stability and an amorphous structure compared to free curcumin. Based on these results, zein/γ‐PGA nanocomposite particles can serve as a promising vehicle for hydrophobic active ingredients in food, pharmaceuticals, and cosmetics. Biocompatible zein/poly(γ‐glutamic acid) nanocomposite particles are developed to enhance the stability of zein nanoparticles, which demonstrate excellent stability under a variety of environmental conditions and effective thermal protection for the encapsulated actives.

Introduction Inequity in maternal-child health services is a challenge to global health as it hinders the achievement of Sustainable Development Goals (SDGs) and Universal Health Coverage. Though the Association of Southeast Asian Nations (ASEAN) has made remarkable achievements in maternal-child health, there remain gaps in reaching global goals. This study aimed to compare and investigate the inequity in maternal-child health (MCH) services in ASEAN member states to help guide policy decisions to improve equitable health services in the SDG era and beyond. Methods Using the WHO Health Inequality Monitor, we identified inequity summary measures for five MCH services in ASEAN member states from 1993 to 2021: antenatal care, births attended by skilled health personnel, diphtheria, tetanus and pertussis (DTP3) immunization, measles immunization, and polio immunization. We divided the analysis dimension of inequity into urban–rural inequity, economic status inequity, and sub-regional inequity. Trends of absolute and relative inequity in every dimension of MCH services in ASEAN member states were examined with the principal component analysis (PCA). Results The mean coverages of MCH services are 98.80% (Thailand), 86.72% (Cambodia), 84.54% (Viet Nam), 78.52 (Indonesia), 76.94% (Timor-Leste), 72.40% (Lao PDR), 68.10% (Philippines) and 48.52% (Myanmar) in 2021. Thailand have the lowest MCH services absolute inequity indexes of -1.945, followed by Vietnam (-1.449). Lao PDR and Myanmar have relatively higher MCH services absolute inequity indexes of 0.852 and 0.054 respectively. The service in Cambodia, Indonesia, and the Philippines is pro-specific regions (with subnational region absolute inequity indexes of -0.02, 0.01, and 1.01 respectively). The service in Myanmar is pro-rich (with economic status absolute inequity index of 0.43). The service in Lao PDR and Timor-Leste is pro-urban areas, pro-rich, and pro-specific regions. Conclusion The inequity of MCH services in ASEAN persists but is in a declining trend. Thailand and Vietnam have performed well in ensuring MCH services equity, while Laos and Myanmar are still facing serious inequity dilemmas. The progress of MCH service equity in Myanmar, Cambodia, the Philippines, and Indonesia is uneven. It is acceptable to learn from the successful experiences of Thailand and Vietnam to improve the equities in other ASEAN countries. Policies should be developed according to the specific types of MCH inequity in member states to improve equity levels.

Marine geophysical and geological investigations are crucial for evaluating the construction suitability of artificial fish reefs (AFRs). Key factors such as seabed topography, geomorphology, sub-bottom structure, and sediment type significantly influence AFR design and site selection. Challenges such as material sinking, sediment instability, and scouring effects should be critically considered and addressed in the construction of AFR, particularly in areas with soft mud or dynamic environments. In this study, detailed investigations were conducted approximately seven months after the deployment of reef materials in the AFR experimental zones around Xiaoguan Island, located in the western South Yellow Sea, China. Based on morphological factors, using data from multibeam echosounders and side-scan sonar, the study area was divided into three geomorphic zones, namely, the tidal flat (TF), underwater erosion-accumulation slope (UEABS), and inclined erosion-accumulation shelf plain (IEASP) zones. The focus of this study was on the UEABS and IEASP experimental zones, where reef materials (concrete or stone blocks) were deployed seven months earlier. The comprehensive interpretation results of multi-source high-resolution acoustic images showed that the average settlement of individual reefs in the UEABS experimental zone was 0.49 m, and their surrounding seabed experienced little to no scouring. This suggested the formation of an effective range and height, making the zone suitable for AFR construction. However, in the IEASP experimental zone, the seabed sediment consisted of soft mud, causing the reef materials to sink into the seabed after deployment, preventing the formation of an effective range and height, and rendering the area unsuitable for AFR construction. These findings provided valuable scientific guidance for AFR construction in the study area and other similar coastal regions.

Arctic absolute sea level variations were analyzed based on multi-mission satellite altimetry data and tide gauge observations for the period of 1993–2018. The range of linear absolute sea level trends were found −2.00 mm/a to 6.88 mm/a excluding the central Arctic, positive trend rates were predominantly located in shallow water and coastal areas, and negative rates were located in high-latitude areas and Baffin Bay. Satellite-derived results show that the average secular absolute sea level trend was (2.53±0.42) mm/a in the Arctic region. Large differences were presented between satellite-derived and tide gauge results, which are mainly due to low satellite data coverage, uncertainties in tidal height processing and vertical land movement (VLM). The VLM rates at 11 global navigation satellite system stations around the Arctic Ocean were analyzed, among which 6 stations were tide gauge co-located, the results indicate that the absolute sea level trends after VLM corrected were of the same magnitude as satellite altimetry results. Accurately calculating VLM is the primary uncertainty in interpreting tide gauge measurements such that differences between tide gauge and satellite altimetry data are attributable generally to VLM.

In this study, to meet the need for accurate tidal prediction, the accuracy of global ocean tide models was assessed in the South China Sea (0°–26°N, 99°–121°E). Seven tide models, namely, DTU10, EOT11a, FES2014, GOT4.8, HAMTIDE12, OSU12 and TPXO8, were considered. The accuracy of eight major tidal constituents (i.e., Q 1 , O 1 , P 1 , K 1 , N 2 , M 2 , S 2 and K 2 ) were assessed for the shallow water and coastal areas based on the tidal constants derived from multi-mission satellite altimetry (TOPEX and Jason series) and tide gauge observations. The root mean square values of each constituent between satellite-derived tidal constants and tide models were found in the range of 0.72–1.90 cm in the deep ocean (depth>200 m) and 1.18–5.63 cm in shallow water area (depth<200 m). Large inter-model discrepancies were noted in the Strait of Malacca and the Taiwan Strait, which could be attributable to the complicated hydrodynamic systems and the paucity of high-quality satellite altimetry data. In coastal regions, an accuracy performance was investigated using tidal results from 37 tide gauge stations. The root sum square values were in the range of 9.35–19.11 cm, with the FES2014 model exhibiting slightly superior performance.

Extracting tidal constituents in coastal regions remains a major challenge due to complex bathymetry, nonlinear shallow-water effects, and land contamination in satellite altimetry measurements. While tide gauges provide high-precision tidal observations, their sparse spatial coverage limits their utility for global coastal studies. Global tidal models, though improved by data assimilation, often suffer from reduced accuracy in coastal zones due to limited spatial resolution and insufficient nearshore constraints. To address these limitations, we utilize the newly released International Altimetry Service 2024 (IAS2024) dataset, which is derived from reprocessed Jason-1/2/3 satellite altimetry data covering the period 2002–2022. We extract ten primary tidal constituents (Q 1 , O 1 , P 1 , K 1 , N 2 , M 2 , S 2 , K 2 , Sa, and Ssa) in global coastal waters using this dataset. The accuracy of IAS2024 tidal extractions is assessed through comparative analysis with four state-of-the-art global tidal models (DTU16, EOT20, FES2014, and FES2022) and 164 tide gauge records. IAS2024 achieves accuracy levels comparable to EOT20 and superior to FES2014 and FES2022, with performance closely matching that of DTU16. For the eight major tidal constituents, the root sum square error of IAS2024 is 11.26 cm, aligning closely with DTU16 (11.23 cm), EOT20 (11.68 cm), and FES2022 (11.26 cm). Relative errors against tide gauge records are 14.16% (O 1 ), 16.6% (M 2 ), 15.4% (K 1 ), and 17.7% (S 2 ), demonstrating competitive accuracy. Notably, IAS2024 significantly outperforms traditional models in resolving long-period constituents, with amplitude correlation coefficients of 0.924 for Sa and 0.701 for Ssa, markedly surpassing EOT20 and FES2022. IAS2024 shows strong performance within 10 km of the coast—where conventional altimetry is often unreliable—highlighting its potential for coastal applications. Its enhanced ability to resolve long-period tidal variations makes it particularly valuable for coastal sea level research, tidal energy assessments, and hydrodynamic modeling. These findings underscore the strengths of IAS2024 in nearshore tidal extraction and its importance as a dataset for advancing both global and regional tidal studies.