SDGSAT-1,the world's first science satellite dedicated to assisting the United Nations 2030 Sustainable Development Agenda,has been operational for over two and a half years.It provides valuable data to aid in imp...SDGSAT-1,the world's first science satellite dedicated to assisting the United Nations 2030 Sustainable Development Agenda,has been operational for over two and a half years.It provides valuable data to aid in implementing the Sustainable Development Goals internationally.Through its Open Science Program,the satellite has maintained consistent operations and delivered free data to scientific and technological users from 88 countries.This program has produced a wealth of scientific output,with 72 papers,including 28 on data processing methods and 44 on applications for monitoring progress toward SDGs related to sustainable cities,clean energy,life underwater,climate action,and clean water and sanitation.SDGSAT-1 is equipped with three key instruments:a multispectral imager,a thermal infrared spectrometer,and a glimmer imager,which have enabled ground-breaking research in a variety of domains such as water quality analysis,identification of industrial heat sources,assessment of environmental disaster impacts,and detection of forest fires.The precise measurements and ongoing monitoring made possible by this invaluable data significantly advance our understanding of various environmental phenomena.They are essential for making well-informed decisions on a local and global scale.Beyond its application to academic research,SDGSAT-1 promotes global cooperation and strengthens developing countries'capacity to accomplish their sustainable development goals.As the satellite continues to gather and distribute data,it plays a pivotal role in developing strategies for environmental protection,disaster management and relief,and resource allocation.These initiatives highlight the satellite's vital role in fostering international collaboration and technical innovation to advance scientific knowledge and promote a sustainable future.展开更多
High-resolution observations of short-term changes in sea ice are critical to understanding ice dynamics and also provide important information used in advice to shipping,especially in the Arctic.Although individual s...High-resolution observations of short-term changes in sea ice are critical to understanding ice dynamics and also provide important information used in advice to shipping,especially in the Arctic.Although individual satellite sensors provide periodic sea ice obser-vations with spatial resolutions of tens of meters,information regarding changes that occur over short time intervals of minutes or hours is limited.In this study,a gridded ice-water classification dataset with a high temporal resolution was developed based on observations acquired by multiple satellite sensors in the Marginal Ice Zone(MIZ).This dataset-DynIceData-which combines Sentinel-1 Synthetic Aperture Radar(SAR)data with Gaofen-3(GF-3)SAR and SDGSAT-1 thermal infrared imagery was used to obtain observations of the MIZ with a range of temporal resolutions ran-ging from minutes to tens of hours.The areas of the Arctic covered include the Kara Sea,Beaufort Sea,and Greenland Sea during the period from August 2021 to August 2022.Object-oriented segmen-tation and thresholding were used to obtain the ice-water classifi-cation map from Sentinel-1 and GF-3 SAR image pairs and Sentinel-1 SAR and SDGSAT-1 thermal image pairs.The time interval between the images in each pair ranged from 1 minute to 68 hours.Ten-kilometer grid sample granules with a spatial resolution of 25 m for the GF-3 SAR data and 30 m for the SDGSAT-1 thermal data were used.The classification was verified as having an overall accuracy of at least 95.58%.The DynIceData dataset consists of 7338 samples,which could be used as reference data for further research on rapid changes in sea ice patterns at different short time scales and provide support for research on thermodynamic and dynamic models of sea ice in combination with other environmen-tal data,thus potentially improving the accuracy of sea ice forecast-ing using Artificial Intelligence.The dataset can be accessed at https://doi.org/10.57760/sciencedb.j00001.00784.展开更多
文摘SDGSAT-1,the world's first science satellite dedicated to assisting the United Nations 2030 Sustainable Development Agenda,has been operational for over two and a half years.It provides valuable data to aid in implementing the Sustainable Development Goals internationally.Through its Open Science Program,the satellite has maintained consistent operations and delivered free data to scientific and technological users from 88 countries.This program has produced a wealth of scientific output,with 72 papers,including 28 on data processing methods and 44 on applications for monitoring progress toward SDGs related to sustainable cities,clean energy,life underwater,climate action,and clean water and sanitation.SDGSAT-1 is equipped with three key instruments:a multispectral imager,a thermal infrared spectrometer,and a glimmer imager,which have enabled ground-breaking research in a variety of domains such as water quality analysis,identification of industrial heat sources,assessment of environmental disaster impacts,and detection of forest fires.The precise measurements and ongoing monitoring made possible by this invaluable data significantly advance our understanding of various environmental phenomena.They are essential for making well-informed decisions on a local and global scale.Beyond its application to academic research,SDGSAT-1 promotes global cooperation and strengthens developing countries'capacity to accomplish their sustainable development goals.As the satellite continues to gather and distribute data,it plays a pivotal role in developing strategies for environmental protection,disaster management and relief,and resource allocation.These initiatives highlight the satellite's vital role in fostering international collaboration and technical innovation to advance scientific knowledge and promote a sustainable future.
基金funded by the National Key Research and Development Program of China(No.2019YFE0105700 and No.2017YFE0111700)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19070201 and No.XDA19070102)+1 种基金the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(No.CBAS2022IRP08)the International Partnership Program of the Chinese Academy of Sciences“Remote Sensing and Modeling of the Snow and Ice Physical Process”(RSMSIP No.313GJHZ2022054MI).
文摘High-resolution observations of short-term changes in sea ice are critical to understanding ice dynamics and also provide important information used in advice to shipping,especially in the Arctic.Although individual satellite sensors provide periodic sea ice obser-vations with spatial resolutions of tens of meters,information regarding changes that occur over short time intervals of minutes or hours is limited.In this study,a gridded ice-water classification dataset with a high temporal resolution was developed based on observations acquired by multiple satellite sensors in the Marginal Ice Zone(MIZ).This dataset-DynIceData-which combines Sentinel-1 Synthetic Aperture Radar(SAR)data with Gaofen-3(GF-3)SAR and SDGSAT-1 thermal infrared imagery was used to obtain observations of the MIZ with a range of temporal resolutions ran-ging from minutes to tens of hours.The areas of the Arctic covered include the Kara Sea,Beaufort Sea,and Greenland Sea during the period from August 2021 to August 2022.Object-oriented segmen-tation and thresholding were used to obtain the ice-water classifi-cation map from Sentinel-1 and GF-3 SAR image pairs and Sentinel-1 SAR and SDGSAT-1 thermal image pairs.The time interval between the images in each pair ranged from 1 minute to 68 hours.Ten-kilometer grid sample granules with a spatial resolution of 25 m for the GF-3 SAR data and 30 m for the SDGSAT-1 thermal data were used.The classification was verified as having an overall accuracy of at least 95.58%.The DynIceData dataset consists of 7338 samples,which could be used as reference data for further research on rapid changes in sea ice patterns at different short time scales and provide support for research on thermodynamic and dynamic models of sea ice in combination with other environmen-tal data,thus potentially improving the accuracy of sea ice forecast-ing using Artificial Intelligence.The dataset can be accessed at https://doi.org/10.57760/sciencedb.j00001.00784.
