Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications...Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.展开更多
Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
Chlorophyll-a is the most abundant chlorophyll pigment produced by marine phytoplankton,and it bears the isotope signature of the nitrate source assimilated in the N-atoms that are embedded in its porphyrin ring.The c...Chlorophyll-a is the most abundant chlorophyll pigment produced by marine phytoplankton,and it bears the isotope signature of the nitrate source assimilated in the N-atoms that are embedded in its porphyrin ring.The chloropigment and its degradation product,i.e.,pheophytin-a,could be well preserved in marine sediment,usually at nanomolar level.A sensitive and accurate measurement of theδ15N of chloropigment is capable of providing rich information to greatly enhance our understanding of past nitrogen cycling,which therefore is urgently needed.Hereby,we present a successful method based on two-step HPLC separation followed by'denitrifier method'.The N-content in acetone and potassium persulfate(K_(2)S_(2)O_(8))are very critical to the precision and accuracy of the measurements,because they constitute the majority of the N contamination to the Chl-a samples.In this method,the recrystallized K_(2)S_(2)O_(8)that is used as oxidization reagent was discovered to have aδ15N background of-15‰,consolidated by repeated examinations over a period of two months.This 15N background of K_(2)S_(2)O_(8)would cause-1‰–-2‰deviation on theδ^(15)N of sample that contains nanomolar level N,and highlight the need to examine theδ^(15)N of recrystallized K_(2)S_(2)O_(8)when it is used to oxidize samples of organic nitrogen.The overall measurement ofδ^(15)N pigment is reliable and has an average analytical precision better than±0.5‰(1σ).This study establish a sensitive method for accurate measurement of theδ^(15)N of nano-molar level chlorophyll pigment,and with no doubts will advance its wide application in marine nitrogen cycling studying.展开更多
Internal solitary waves(ISWs)are an essential dynamic process in the ocean due to their large amplitude and long propagation distance.Traditional satellite observations provide only twodimensional observations of ocea...Internal solitary waves(ISWs)are an essential dynamic process in the ocean due to their large amplitude and long propagation distance.Traditional satellite observations provide only twodimensional observations of ocean signatures induced by ISWs.The Surface Water and Ocean Topography(SWOT)satellite has drawn significant attention due to its high resolution and threedimensional observation capabilities.SWOT can generate high-precision three-dimensional sea surface topography,capture sea surface undulations,and reveal ISW-related surface oscillations,thus offering a new perspective for studying ISWs.We collected 43 SWOT observations with clear ISW signatures in the Lombok Strait from August 2023 to June 2024.Based on collected data,the ISW imaging characteristics and distributions were analyzed,and the ISW-related sea level anomaly(SLA)data were measured by the SWOT to calculate the ISW amplitude and reveal the amplitude variations during the propagation along the wave crest.The ISW amplitudes generally range between 10 and 100 m,with most ISW amplitudes between 20 and 40 m.By analyzing two consecutive generated ISW packets,we identified the spreading effect along ISW wave crests,which manifests as ISW amplitude decrease with increase in propagation distance,and the amplitude distribution is non-uniform along the wave crest.Further analysis of the propagation paths of the maximum amplitude of ISW moving northward through the Lombok Strait revealed that these maxima are predominantly oriented in northeast direction.Finally,the relationship between the amplitude of ISW and the resulting SLA was analyzed.The Pearson correlation coefficient between these two variables is as high as 0.90,which suggests a strong positive correlation between amplitude and SLA.Furthermore,this relationship is closely related to the water depth,indicating that the three-dimensional sea surface observations provided by SWOT offer crucial observational data for the inversion of amplitudes of ISW.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.42376185,41876111)the Shandong Provincial Natural Science Foundation(No.ZR2023MD073)。
文摘Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金National Science Foundation of China(No.41576082)。
文摘Chlorophyll-a is the most abundant chlorophyll pigment produced by marine phytoplankton,and it bears the isotope signature of the nitrate source assimilated in the N-atoms that are embedded in its porphyrin ring.The chloropigment and its degradation product,i.e.,pheophytin-a,could be well preserved in marine sediment,usually at nanomolar level.A sensitive and accurate measurement of theδ15N of chloropigment is capable of providing rich information to greatly enhance our understanding of past nitrogen cycling,which therefore is urgently needed.Hereby,we present a successful method based on two-step HPLC separation followed by'denitrifier method'.The N-content in acetone and potassium persulfate(K_(2)S_(2)O_(8))are very critical to the precision and accuracy of the measurements,because they constitute the majority of the N contamination to the Chl-a samples.In this method,the recrystallized K_(2)S_(2)O_(8)that is used as oxidization reagent was discovered to have aδ15N background of-15‰,consolidated by repeated examinations over a period of two months.This 15N background of K_(2)S_(2)O_(8)would cause-1‰–-2‰deviation on theδ^(15)N of sample that contains nanomolar level N,and highlight the need to examine theδ^(15)N of recrystallized K_(2)S_(2)O_(8)when it is used to oxidize samples of organic nitrogen.The overall measurement ofδ^(15)N pigment is reliable and has an average analytical precision better than±0.5‰(1σ).This study establish a sensitive method for accurate measurement of theδ^(15)N of nano-molar level chlorophyll pigment,and with no doubts will advance its wide application in marine nitrogen cycling studying.
基金Supported by the National Key Research and Development Program of China(No.2021YFB3901304)the Shandong Provincial Natural Science Foundation(No.ZR2024QD054)+2 种基金the National Key Research and Development Program of China(No.2019YFA0606702)the National Natural Science Foundation of China(Nos.41906157,42306194,42306195)the Oceanographic Data Center,Chinese Academy of Sciences and the platform of Sino-Indonesian Joint Laboratory for Marine Sciences(SIMS)。
文摘Internal solitary waves(ISWs)are an essential dynamic process in the ocean due to their large amplitude and long propagation distance.Traditional satellite observations provide only twodimensional observations of ocean signatures induced by ISWs.The Surface Water and Ocean Topography(SWOT)satellite has drawn significant attention due to its high resolution and threedimensional observation capabilities.SWOT can generate high-precision three-dimensional sea surface topography,capture sea surface undulations,and reveal ISW-related surface oscillations,thus offering a new perspective for studying ISWs.We collected 43 SWOT observations with clear ISW signatures in the Lombok Strait from August 2023 to June 2024.Based on collected data,the ISW imaging characteristics and distributions were analyzed,and the ISW-related sea level anomaly(SLA)data were measured by the SWOT to calculate the ISW amplitude and reveal the amplitude variations during the propagation along the wave crest.The ISW amplitudes generally range between 10 and 100 m,with most ISW amplitudes between 20 and 40 m.By analyzing two consecutive generated ISW packets,we identified the spreading effect along ISW wave crests,which manifests as ISW amplitude decrease with increase in propagation distance,and the amplitude distribution is non-uniform along the wave crest.Further analysis of the propagation paths of the maximum amplitude of ISW moving northward through the Lombok Strait revealed that these maxima are predominantly oriented in northeast direction.Finally,the relationship between the amplitude of ISW and the resulting SLA was analyzed.The Pearson correlation coefficient between these two variables is as high as 0.90,which suggests a strong positive correlation between amplitude and SLA.Furthermore,this relationship is closely related to the water depth,indicating that the three-dimensional sea surface observations provided by SWOT offer crucial observational data for the inversion of amplitudes of ISW.
