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学者姓名：苏华
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Abstract ：
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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Estimating the ocean mixed layer depth (MLD) is crucial for studying the atmosphere-ocean interaction and global climate change. Satellite observations can accurately estimate the MLD over large scales, effectively overcoming the limitation of sparse in situ observations and reducing uncertainty caused by estimation based on in situ and reanalysis data. However, combining multisource satellite observations to accurately estimate the global MLD is still extremely challenging. This study proposed a novel Residual Convolutional Gate Recurrent Unit (ResConvGRU) neural networks, to accurately estimate global MLD along with multisource remote sensing data and Argo gridded data. With the inherent spatiotemporal nonlinearity and dependence of the ocean dynamic process, the proposed method is effective in spatiotemporal feature learning by considering temporal dependence and capturing more spatial features of the ocean observation data. The performance metrics show that the proposed ResConvGRU outperforms other well-used machine learning models, with a global determination coefficient (R2) and a global root mean squared error (RMSE) of 0.886 and 17.83 m, respectively. Overall, the new deep learning approach proposed is more robust and advantageous in data-driven spatiotemporal modeling for retrieving ocean MLD at the global scale, and significantly improves the estimation accuracy of MLD from remote sensing observations.
Keyword ：
global ocean global ocean Mixed layer depth Mixed layer depth remote sensing observations remote sensing observations residual convolutional gate recurrent unit residual convolutional gate recurrent unit
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| GB/T 7714 | Su, Hua , Tang, Zhiwei , Qiu, Junlong et al. Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
| MLA | Su, Hua et al. "Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 17 . 1 (2024) . |
| APA | Su, Hua , Tang, Zhiwei , Qiu, Junlong , Wang, An , Yan, Xiao-Hai . Estimating the mixed layer depth of the global ocean by combining multisource remote sensing and spatiotemporal deep learning . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2024 , 17 (1) . |
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叶绿素a浓度可以评估海水富营养化状况，对沿海叶绿素a浓度影响因素的研究在海洋环境保护方面具有重要意义。而现有研究多关注自然因素对沿海叶绿素a浓度的影响，忽视了人为因素的作用。因此实验以夜间灯光遥感数据表征人类活动强度，根据夜间灯光亮度和沿海叶绿素a浓度间的关系将东南沿海的城市分为3个类型，并同时结合海表温度、风速、太阳辐射、降水等自然因素，通过广义相加模型（GAM）分析不同季节下3类城市中人为和自然等多重因素对沿海叶绿素a浓度的影响。结果表明：在北海、汕头等类型Ⅰ城市中自然因素主导叶绿素a浓度的变化，春季的主导因素为风速，夏、秋、冬季为海表温度；而人类活动对叶绿素a浓度的影响较小且没有显著的影响关系。珠海、东莞等类型Ⅱ城市的叶绿素a浓度受自然因素主导，春、秋、冬季的主导因素为风速，夏季为海表温度；而人类活动在夏、秋季对沿海叶绿素a浓度有较大的促进作用。深圳、香港等类型Ⅲ城市中人为因素主导叶绿素a浓度的变化，春、夏、秋季人类活动对叶绿素a浓度的影响最大且为负相关，冬季海表温度对叶绿素a浓度的影响最大。
Keyword ：
东南沿海 东南沿海 人类活动 人类活动 叶绿素a 叶绿素a 广义相加模型(GAM) 广义相加模型(GAM) 自然因素 自然因素
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| GB/T 7714 | 张婧薇 , 陈佐旗 , 苏华 . 基于广义相加模型的东南沿海叶绿素a浓度的多重影响与季节差异 [J]. | 遥感技术与应用 , 2024 , 39 (01) : 134-148 . |
| MLA | 张婧薇 et al. "基于广义相加模型的东南沿海叶绿素a浓度的多重影响与季节差异" . | 遥感技术与应用 39 . 01 (2024) : 134-148 . |
| APA | 张婧薇 , 陈佐旗 , 苏华 . 基于广义相加模型的东南沿海叶绿素a浓度的多重影响与季节差异 . | 遥感技术与应用 , 2024 , 39 (01) , 134-148 . |
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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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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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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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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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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°) for the upper 2000 m from 1993 to 2023. DORS0.25° exhibits excellent reconstruction accuracy, with an average R2 of 0.980 and RMSE of 0.579 °C, and the monthly average accuracy is higher than IAP and ORAS5 datasets. Particularly, DORS0.25° 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° 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. © 2024 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
Keyword ：
Climate change Climate change
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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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Rising sea level caused by global climate change may increase extreme sea level events, flood low-lying coastal areas, change the ecological and hydrological environment of coastal areas, and bring severe challenges to the survival and development of coastal cities. Hong Kong is a typical economically and socially developed coastal area. However, in such an important coastal city, the mechanisms of local sea-level dynamics and their relationship with climate teleconnections are not well explained. In this paper, Hong Kong tide gauge data spanning 68 years was documented to study the historical sea-level dynamics. Through the analysis framework based on Wavelet Transform and Hilbert Huang Transform, non-stationary and multi-scale features in sea-level dynamics in Hong Kong are revealed. The results show that the relative sea level (RSL) in Hong Kong has experienced roughly 2.5 cycles of high-to-low sea-level transition in the past half-century. The periodic amplitude variation of tides is related to Pacific Decadal Oscillation (PDO) and El Nino-Southern Oscillation (ENSO). RSL rise and fall in eastern Hong Kong often occur in La Nina and El Nino years, respectively. The response of RSL to the PDO and ENSO displays a time lag and spatial heterogeneity in Hong Kong. Hong Kong's eastern coastal waters are more strongly affected by the Pacific climate and current systems than the west. This study dissects the non-stationary and multi-scale characteristics of relative sea-level change and helps to better understand the response of RSL to the global climate system.
Keyword ：
climate teleconnection climate teleconnection El Nino-Southern Oscillation (ENSO) El Nino-Southern Oscillation (ENSO) Pacific Decadal Oscillation (PDO) Pacific Decadal Oscillation (PDO) relative sea-level change relative sea-level change time series analysis time series analysis
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| GB/T 7714 | Liang, Xindan , Lin, Yinyi , Wu, Renguang et al. Time-frequency analysis framework for understanding non-stationary and multi-scale characteristics of sea-level dynamics [J]. | FRONTIERS IN MARINE SCIENCE , 2023 , 9 . |
| MLA | Liang, Xindan et al. "Time-frequency analysis framework for understanding non-stationary and multi-scale characteristics of sea-level dynamics" . | FRONTIERS IN MARINE SCIENCE 9 (2023) . |
| APA | Liang, Xindan , Lin, Yinyi , Wu, Renguang , Li, Gang , Khan, Nicole , Liu, Rui et al. Time-frequency analysis framework for understanding non-stationary and multi-scale characteristics of sea-level dynamics . | FRONTIERS IN MARINE SCIENCE , 2023 , 9 . |
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As the most relevant indicator of global warming, the ocean heat content (OHC) change is tightly linked to the Earth's energy imbalance. Therefore, it is vital to study the OHC and heat absorption and redistribution. Here we analyzed the characteristics of global OHC variations based on a previously reconstructed OHC dataset (named OPEN) with four other gridded OHC datasets from 1993 to 2021. Different from the other four datasets, the OPEN dataset directly obtains OHC through remote sensing, which is reliable and superior in OHC reconstruction, further verified by the Clouds and the Earth's Radiant Energy System (CERES) radiation flux data. We quantitatively analyzed the changes in the upper 2000 m OHC of the oceans over the past three decades from a multisource and multilayer perspective. Meanwhile, we calculated the global ocean heat uptake to quantify and track the global ocean warming rate and combined it with the Oceanic Nino Index to analyze the global evolution of OHC associated with El Nino-Southern Oscillation variability. The results show that different datasets reveal a continuously increasing and non-decaying global ocean warming from multiple perspectives, with more heat being absorbed by the subsurface and deeper ocean over the past 29 years. The global OHC heating trend from 1993 to 2021 is 7.48 +/- 0.17, 7.89 +/- 0.1, 10.11 +/- 0.16, 7.78 +/- 0.17, and 12.8 +/- 0.26 x 10(22) J/decade according to OPEN, IAP, EN4, Ishii, and ORAS5, respectively, which shows that the trends of the OPEN, IAP, and Ishii datasets are generally consistent, while those of EN4 and ORAS5 datasets are much higher. In addition, the ocean warming characteristics revealed by different datasets are somewhat different. The OPEN OHC dataset from remote sensing reconstruction shows a unique remote sensing mapping advantage, presenting a distinctive warming pattern in the East Indian Ocean. Meanwhile, the OPEN dataset had the largest statistically significant area, with 85.6% of the ocean covered by significant positive trends. The significant and continuous increase in global ocean warming over the past three decades, revealed from remote sensing reconstruction, can provide an important reference for projecting ocean warming in the context of global climate change toward the United Nations Sustainable Development Goals.
Keyword ：
ENSO ENSO global ocean warming global ocean warming Ocean Heat Content (OHC) Ocean Heat Content (OHC) Ocean Heat Uptake (OHU) Ocean Heat Uptake (OHU) remote sensing reconstruction remote sensing reconstruction
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| GB/T 7714 | Su, Hua , Wei, Yanan , Lu, Wenfang et al. Unabated Global Ocean Warming Revealed by Ocean Heat Content from Remote Sensing Reconstruction [J]. | REMOTE SENSING , 2023 , 15 (3) . |
| MLA | Su, Hua et al. "Unabated Global Ocean Warming Revealed by Ocean Heat Content from Remote Sensing Reconstruction" . | REMOTE SENSING 15 . 3 (2023) . |
| APA | Su, Hua , Wei, Yanan , Lu, Wenfang , Yan, Xiao-Hai , Zhang, Hongsheng . Unabated Global Ocean Warming Revealed by Ocean Heat Content from Remote Sensing Reconstruction . | REMOTE SENSING , 2023 , 15 (3) . |
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