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学者姓名：苏华
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To scientifically plan and accurately manage the coastal aquaculture industry, it is especially critical to quickly and accurately extract raft aquaculture areas. In the study, the Raft-Former was designed to accurately extract coastal raft aquaculture in Sansha Bay using Sentinel-2 remote sensing imagery. Specifically, a Feature Enhancement Module (FEM) was designed to selectively learn the interest features for solving the omission and mis-extraction caused by changes in the coastal environment. For the boundary adhesion problems caused by the dense distribution of raft aquaculture areas, a Feature Alignment Module (FAM) was developed to enhance edge-aware ability. A Global-Local Fusion Module (GLFM) was introduced to effectively integrate the local features with multi-scale and global features to overcome significant scale differences in aquaculture areas. Numerous experiments show that our method is better than the state-of-the-art models. Specifically, Raft-Former respectively achieves 90.05% and 86.73% mIoU on the Sansha Bay dataset.
Keyword ：
edge-aware edge-aware multi-scale multi-scale Raft extraction Raft extraction Sentinel-2 remote sensing imagery Sentinel-2 remote sensing imagery
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| GB/T 7714 | Su, Hua , Liu, Yuxin , Huang, Zhanchao et al. Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Su, Hua et al. "Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Su, Hua , Liu, Yuxin , Huang, Zhanchao , Wang, An , Hong, Wenjun , Cai, Junchao . Edge-aware transformer for coastal raft aquaculture extraction in optical remote sensing imagery . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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Long time series and accurate subsurface temperature data in the global ocean are essential for ocean warming and climate change studies. The sparse in situ observations in the pre-Argo era hinder the reconstruction of long-time series observational data for the global ocean. This study proposes a novel Adaptive Spatio-TEmporal Neighbors with two-point differences (ASTEN) method for subsurface temperature reconstruction, which adaptively learns and adjusts spatio-temporal neighbors depending on the distribution of in situ observations to ensure robust gaps-filling performance across four dimensions. By integrating geoscience domain knowledge and utilizing spatiotemporal autocorrelation, ASTEN simultaneously learns the spatial pattern and temporal variation of subsurface temperature, and significantly enhances the interpretability and accuracy of ocean temperature reconstructions over a long time series compared to the DINCAE and DINEOF. The ASTEN reconstructed temperature data for the upper 1000 m from 1960 to 2022 can effectively track the ocean warming process for more than six decades. This study demonstrates the ASTEN method is well suited for subsurface temperature reconstruction, and holds great potential in the gaps-filling of sparse ocean observations with high missing rates over a large scale. The new reconstruction of subsurface temperature can effectively reduce the uncertainty of subsurface ocean warming analysis.
Keyword ：
data gaps-filling data gaps-filling Ocean subsurface temperature Ocean subsurface temperature spatio-temporal neighbors adaptive learning spatio-temporal neighbors adaptive learning subsurface ocean warming subsurface ocean warming time series reconstruction time series reconstruction
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| GB/T 7714 | Wang, An , Su, Hua . Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022 [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Wang, An et al. "Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Wang, An , Su, Hua . Spatio-Temporal neighbors adaptive learning with two-point differences for ocean subsurface temperature reconstruction from 1960 to 2022 . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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The recognition of sea ice is of great significance for reflecting climate change and ensuring the safety of ship navigation. Recently, many deep-learning-based methods have been proposed and applied to segment and recognize sea ice regions. However, there are huge differences in sea ice size and irregular edge profiles, which bring challenges to the existing sea ice recognition. In this article, a global-local Transformer network, called SeaIceNet, is proposed for sea ice recognition in optical remote sensing images. In SeaIceNet, a dual global-attention head (DGAH) is proposed to capture global information. On this basis, a global-local feature fusion (GLFF) mechanism is designed to fuse global structural correlation features and local spatial detail features. Furthermore, a detail-guided decoder is developed to retain more high-resolution detail information during feature reconstruction for improving the performance of sea ice recognition. Extensive experiments on several sea ice datasets demonstrated that the proposed SeaIceNet has better performance than the existing methods in multiple evaluation indicators. Moreover, it excels in addressing challenges associated with sea ice recognition in optical remote sensing images, including the difficulty in accurately identifying irregular frozen ponds in complex environments, the broken and unclear boundaries between sea and thin ice that hinder precise segmentation, and the loss of high-resolution spatial details during model learning that complicates refinement.
Keyword ：
Accuracy Accuracy Climate change Climate change Data mining Data mining Deep learning Deep learning Feature extraction Feature extraction Ice Ice Image segmentation Image segmentation Integrated optics Integrated optics Optical imaging Optical imaging Optical sensors Optical sensors Remote sensing Remote sensing Sea ice Sea ice sea ice recognition sea ice recognition semantic segmentation semantic segmentation Transformer model Transformer model
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| GB/T 7714 | Hong, Wenjun , Huang, Zhanchao , Wang, An et al. SeaIceNet: Sea Ice Recognition via Global-Local Transformer in Optical Remote Sensing Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Hong, Wenjun et al. "SeaIceNet: Sea Ice Recognition via Global-Local Transformer in Optical Remote Sensing Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Hong, Wenjun , Huang, Zhanchao , Wang, An , Liu, Yuxin , Cai, Junchao , Su, Hua . SeaIceNet: Sea Ice Recognition via Global-Local Transformer in Optical Remote Sensing Images . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Estimating high-resolution ocean subsurface temperature has great importance for the refined study of ocean climate variability and change. However, the insufficient resolution and accuracy of subsurface temperature data greatly limits our comprehensive understanding of mesoscale and other fine-scale ocean processes. In this study, we integrated multiple remote sensing data and in situ observations to compare four models within two frameworks (gradient boosting and deep learning). The optimal model, Deep Forest, was selected to generate a high-resolution subsurface temperature dataset (DORS0.25 degrees) for the upper 2000 m from 1993 to 2023. DORS0.25 degrees exhibits excellent reconstruction accuracy, with an average R-2 of 0.980 and RMSE of 0.579 degrees C, and the monthly average accuracy is higher than IAP and ORAS5 datasets. Particularly, DORS0.25 degrees can effectively capture detailed ocean warming characteristics in complex dynamic regions such as the Gulf Stream and the Kuroshio Extension, facilitating the study of mesoscale processes and warming within the global-scale ocean. Moreover, the research highlights that the rate of warming over the past decade has been significant, and ocean warming has consistently reached new highs since 2019. This study has demonstrated that DORS0.25 degrees is a crucial dataset for understanding and monitoring the spatiotemporal characteristics and processes of global ocean warming, providing valuable data support for the sustainable development of the marine environment and climate change actions.
Keyword ：
Deep forest Deep forest DORS0.25 degrees dataset DORS0.25 degrees dataset High resolution High resolution Ocean warming Ocean warming Remote sensing observations Remote sensing observations Subsurface temperature Subsurface temperature
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| GB/T 7714 | Su, Hua , Zhang, Feiyan , Teng, Jianchen et al. Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2024 , 218 : 389-404 . |
| MLA | Su, Hua et al. "Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 218 (2024) : 389-404 . |
| APA | Su, Hua , Zhang, Feiyan , Teng, Jianchen , Wang, An , Huang, Zhanchao . Reconstructing high-resolution subsurface temperature of the global ocean using deep forest with combined remote sensing and in situ observations . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2024 , 218 , 389-404 . |
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3-D ocean temperature and salinity data are the basis for studying ocean dynamic processes and warming. Satellite remote sensing observations on the ocean surface are abundant and full-coverage, while in situ observations in the ocean interior are very sparse and unevenly distributed. Currently, the remote sensing inversion models of temperature and salinity in the ocean interior are unable to learn both global and local detail information, and modeling layer-by-layer blocks the connection between vertical depth levels, resulting in poor accuracy. In this study, we proposed a novel clustering-guided and knowledge-distillation network (CGKDN) model based on the ocean knowledge-driven model. The model introduced K-means clustering for the partitions of ocean processes, knowledge distillation (KD) fusing global and local detail information, and adaptive depth gradient loss linking the vertical depth dimension, which enhanced the interpretability and accuracy of the model. Comparison of the reconstructions with the existing major publicly available datasets through the validation of 10% EN4 in situ profile observations from 2001 to 2020 reveals that the reconstructions are more accurate. Concretely, the average root mean square error (RMSE) (degrees C) across time-series and vertical levels of CGKDN/Institute of Atmospheric Physics (IAP)/OCEAN5 ocean analysis-reanalysis (ORAS5)/deep ocean remote sensing (DORS) ocean subsurface temperature (OST) is 0.590/0.598/0.690/0.723, and the average RMSE (PSU) of CGKDN/IAP/ORAS5 ocean subsurface salinity (OSS) is 0.101/0.103/0.106, respectively. Furthermore, the downscaled quarter-degree reconstructions present more mesoscale detail signals, consistent with the ARMOR3D data. This study not only improves the estimation accuracy of subsurface temperature and salinity but also serves the study of ocean interior dynamic processes and variabilities and provides valuable references for reconstructing other ocean subsurface physical variables.
Keyword ：
3-D reconstruction 3-D reconstruction Accuracy Accuracy Adaptation models Adaptation models Computational modeling Computational modeling global ocean global ocean Oceans Oceans Ocean temperature Ocean temperature Predictive models Predictive models Remote sensing Remote sensing remote sensing observations remote sensing observations Salinity (geophysical) Salinity (geophysical) Satellites Satellites subsurface thermohaline subsurface thermohaline Temperature sensors Temperature sensors
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| GB/T 7714 | Wang, An , Su, Hua , Huang, Zhanchao et al. Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Wang, An et al. "Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Wang, An , Su, Hua , Huang, Zhanchao , Yan, Xiao-Hai . Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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Tidal flats in northern China are essential parts of the East Asian-Australasian Flyway, the densest pathway for migratory waterbirds, and are of great ecological and economic importance. They are threatened by human activities and climate change, raising the urgency surrounding tracking the spatiotemporal dynamics of tidal flats. However, there is no cost-effective way to map morphological changes on a large spatial scale due to the inaccessibility of the mudflats. In this study, we proposed a pixel-based multi-indices tidal flat mapping algorithm that precisely characterizes 2D/3D morphological changes in tidal flats in northern China using time-series remote sensing data. An overall accuracy of 0.95 in delineating tidal flats to a 2D extent was achieved, with 11,716 verification points. Our results demonstrate that the reduction in sediment discharge from rivers along the coastlines of the Yellow and Bohai Seas has resulted in an overall decline in the area of tidal flats, from 4856.40 km2 to 4778.32 km2. Specifically, 3D analysis showed that significant losses were observed in the mid-to-high-tidal flat zones, while low-elevation tidal flats experienced an increase in area due to the transformations in mid-to-high-tidal flats. Our results indicate that the sediment inputs from rivers and the succession of native vegetation are the primary drivers leading to 2D/3D morphological changes of tidal flats following the cessation of extensive land reclamation in northern China.
Keyword ：
2D/3D morphological changes 2D/3D morphological changes remote sensing remote sensing tidal flats tidal flats time series time series Yellow and Bohai Seas Yellow and Bohai Seas
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| GB/T 7714 | Gan, Zhiquan , Guo, Shurong , Chen, Chunpeng et al. Tracking the 2D/3D Morphological Changes of Tidal Flats Using Time Series Remote Sensing Data in Northern China [J]. | REMOTE SENSING , 2024 , 16 (5) . |
| MLA | Gan, Zhiquan et al. "Tracking the 2D/3D Morphological Changes of Tidal Flats Using Time Series Remote Sensing Data in Northern China" . | REMOTE SENSING 16 . 5 (2024) . |
| APA | Gan, Zhiquan , Guo, Shurong , Chen, Chunpeng , Zheng, Hanjie , Hu, Yuekai , Su, Hua et al. Tracking the 2D/3D Morphological Changes of Tidal Flats Using Time Series Remote Sensing Data in Northern China . | REMOTE SENSING , 2024 , 16 (5) . |
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Tidal wetlands provide a variety of ecosystem services to coastal communities but suffer severe losses due to anthropogenic activities in the Yangtze River Estuary (YRE). However, the detailed dynamics of tidal wetlands have not been well studied with sufficient spatiotemporal resolution. Here, we proposed a rapid classification method that integrates the COntinuous monitoring of Land Disturbance (COLD) algorithm and Median Composite (MC) based on the dense Landsat time series to track the dynamic processes of tidal wetlands in the YRE from 1990 to 2020. The results showed that the COLD-MC demonstrated remarkable effectiveness in detecting the change of tidal wetlands and excellent overall accuracy and kappa coefficient ranging from 90% to 96% and 0.89-0.95, respectively. The overall accuracy of change detection was 97% with an absolute error of 0.4 years. We found that the total area of tidal wetlands experienced a net loss of 59.75 km2 in the YRE, but the gain and loss of the study period were 1556.07 and 1615.82 km2, respectively. Land reclamation, sediment reduction, and Spartina alterniflora invasion pose significant threats to tidal wetlands. Sustainable management could be implemented through the establishment of nature reserves and ecological sediment enhancement engineering projects.
Keyword ：
COLD-MC COLD-MC dynamic equilibrium dynamic equilibrium land reclamation land reclamation landsat time-series landsat time-series sediment starvation sediment starvation Tidal wetlands Tidal wetlands
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| GB/T 7714 | Wu, Wenting , Lin, Zhibin , Chen, Chunpeng et al. Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
| MLA | Wu, Wenting et al. "Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 17 . 1 (2024) . |
| APA | Wu, Wenting , Lin, Zhibin , Chen, Chunpeng , Chen, Zuoqi , Zhao, Zhiyuan , Su, Hua . Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
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The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder-decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model's effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction.
Keyword ：
agricultural parcel extraction agricultural parcel extraction boundary-aware weighted loss boundary-aware weighted loss dual-stream feature extraction (DSFE) dual-stream feature extraction (DSFE) feature compensation restoration (FCR) feature compensation restoration (FCR) global feature fusion module (GFFM) global feature fusion module (GFFM)
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| GB/T 7714 | Xu, Weiming , Wang, Juan , Wang, Chengjun et al. Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images [J]. | REMOTE SENSING , 2024 , 16 (14) . |
| MLA | Xu, Weiming et al. "Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images" . | REMOTE SENSING 16 . 14 (2024) . |
| APA | Xu, Weiming , Wang, Juan , Wang, Chengjun , Li, Ziwei , Zhang, Jianchang , Su, Hua et al. Utilizing Dual-Stream Encoding and Transformer for Boundary-Aware Agricultural Parcel Extraction in Remote Sensing Images . | REMOTE SENSING , 2024 , 16 (14) . |
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As a result of global warming, major ocean basins have witnessed an increase in the number of extreme warm events and a decrease in the number of extreme cold events, increasing the number of marine heatwave (MHW) events. Previous quantification of MHW events has been limited to simple single metrics, which can only recognize some characteristics from a particular aspect. Here, we propose a new marine Heat Wave Comprehensive Index (HWCI) by fusing multiple metrics to characterize the scalable cumulative intensity of MHWs, which exhibits excellent identification reliability and superiority to effectively monitor the evolutionary patterns of various levels of MHW events. We find that five levels of global MHW events have presented an obvious spatial expansion and temporal enhancement pattern since the early 21st century, with the obvious spatial contraction (32.98 %) of weak events followed by the expansion (19.82 %) of extreme events at the highest growth rate of 0.07, primarily in the mid-low-latitude oceans and the Arctic. The results demonstrate that extreme MHW events dominate global MHW evolution patterns and that the expansion and intensification of such episodes have major implications for the event distribution and level structure. The new indicator is promising for directly measuring and identifying MHWs, and contributes to a more comprehensive understanding of the evolution of MHWs in the context of global climate change.
Keyword ：
Climate extremes Climate extremes Global ocean warming Global ocean warming Heat wave comprehensive index (HWCI) Heat wave comprehensive index (HWCI) Marine heat wave (MHW) Marine heat wave (MHW)
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| GB/T 7714 | Chen, Yingliang , Su, Hua , Yan, Xiao-Hai et al. Global oceans suffer extreme heatwaves intensifying since the early 21st century: A new comprehensive index [J]. | ECOLOGICAL INDICATORS , 2024 , 162 . |
| MLA | Chen, Yingliang et al. "Global oceans suffer extreme heatwaves intensifying since the early 21st century: A new comprehensive index" . | ECOLOGICAL INDICATORS 162 (2024) . |
| APA | Chen, Yingliang , Su, Hua , Yan, Xiao-Hai , Zhang, Hongsheng , Wang, Yunpeng . Global oceans suffer extreme heatwaves intensifying since the early 21st century: A new comprehensive index . | ECOLOGICAL INDICATORS , 2024 , 162 . |
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Subsurface density (SD) is a crucial dynamic environment parameter reflecting a 3-D ocean process and stratification, with significant implications for the physical, chemical, and biological processes of the ocean environment. Thus, accurate SD retrieval is essential for studying dynamic processes in the ocean interior. However, complete spatiotemporally accurate SD retrieval remains a challenge in terms of the equation of state and physical methods. This study proposes a novel multiscale mixed residual transformer (MMRT) neural network method to compensate for the inadequacy of the existing methods in dealing with spatiotemporal nonlinear processes and dependence. Considering the spatial correlation and temporal dependence of dynamic processes within the ocean, the MMRT addresses temporal dependence by fully using the transformer's processing of time-series data and spatial correlation by compensating for deficiencies in spatial feature information through multiscale mixed residuals. The MMRT model was compared with the existing random forest (RF) and recurrent neural network (RNN) methods. The MMRT model achieves the best accuracy with an average determination coefficient (R-2) of 0.988 and an average root mean square error (RMSE) of 0.050 kg/m(3) for all layers. The MMRT model not only outperforms the RF and RNN methods regarding reliability and generalization ability when estimating global ocean SD from remote sensing data but also has a more interpretable encoding process. The MMRT model offers a new method for directly estimating SD using multisource satellite observations, providing significant technical support for future remote sensing super-resolution and prediction of subsurface parameters.
Keyword ：
Global ocean Global ocean remote sensing observations remote sensing observations subsurface density (SD) subsurface density (SD) transformer transformer
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| GB/T 7714 | Su, Hua , Qiu, Junlong , Tang, Zhiwei et al. Retrieving Global Ocean Subsurface Density by Combining Remote Sensing Observations and Multiscale Mixed Residual Transformer [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
| MLA | Su, Hua et al. "Retrieving Global Ocean Subsurface Density by Combining Remote Sensing Observations and Multiscale Mixed Residual Transformer" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62 (2024) . |
| APA | Su, Hua , Qiu, Junlong , Tang, Zhiwei , Huang, Zhanchao , Yan, Xiao-Hai . Retrieving Global Ocean Subsurface Density by Combining Remote Sensing Observations and Multiscale Mixed Residual Transformer . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2024 , 62 . |
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