Land cover mapping plays a critical role in monitoring land system changes.Despite advancements in remote sensing technologies,traditional satellite-based approaches are often constrained by cloud cover,coarse tempora...Land cover mapping plays a critical role in monitoring land system changes.Despite advancements in remote sensing technologies,traditional satellite-based approaches are often constrained by cloud cover,coarse temporal resolution,and limitations in capturing fine-scale landscape dynamics,leading to gaps in continuous and real-time monitoring.Near-surface cameras offer a solution by providing high-frequency,ground-level observations that bridge temporal gaps and enhance spatial detail.Therefore,this study makes substantial contributions by pioneering the integration of near-surface camera observations with satellite imagery,addressing key challenges in imaging perspective differences and limited coverage of ground-based observations for enhanced land cover monitoring at a 30-m/10-m scale.A key innovation lies in leveraging near-surface cameras to reconstruct dense satellite data time series and capture daily land cover dynamics,addressing critical temporal gaps in traditional satellite-based approaches.The research further advances the field by implementing state-of-the-art deep learning techniques,particularly the Segment Anything Model(SAM),to achieve precise parcel-level delineation and reduce classification noise at a high-resolution(meter-and submeter level)scale.Furthermore,the framework’s ability to synthesize multimodal data sources(near-surface cameras,Sentinel-1/2,and high-resolution imagery)represents a methodological breakthrough in space and surface sensor integration for real-time land cover change detection,enabling time-sensitive applications and early warning systems for land system changes.展开更多
基金supported by the National Key R&D Program of China(2024YFF1307600)supported by the Investigation Research Program between Ecological Environment and Human Health in Wuyi Mountain(20242120035)+5 种基金the open project of State Key Laboratory of Efficient Utilization of Arable Land in China,the Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences(No.EUAL-2025-03)the Chebaling National Nature Reserve Phenology Monitoring Network Construction and Application Project(CBLHT-2025050)the Xizang Science and Technology Plan Project(XZ202403ZY0018)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)supported by the National Natural Science Foundation of China(42201367)the Fundamental Research Funds for the Central Universities under grant DUT23RC(3)064.
文摘Land cover mapping plays a critical role in monitoring land system changes.Despite advancements in remote sensing technologies,traditional satellite-based approaches are often constrained by cloud cover,coarse temporal resolution,and limitations in capturing fine-scale landscape dynamics,leading to gaps in continuous and real-time monitoring.Near-surface cameras offer a solution by providing high-frequency,ground-level observations that bridge temporal gaps and enhance spatial detail.Therefore,this study makes substantial contributions by pioneering the integration of near-surface camera observations with satellite imagery,addressing key challenges in imaging perspective differences and limited coverage of ground-based observations for enhanced land cover monitoring at a 30-m/10-m scale.A key innovation lies in leveraging near-surface cameras to reconstruct dense satellite data time series and capture daily land cover dynamics,addressing critical temporal gaps in traditional satellite-based approaches.The research further advances the field by implementing state-of-the-art deep learning techniques,particularly the Segment Anything Model(SAM),to achieve precise parcel-level delineation and reduce classification noise at a high-resolution(meter-and submeter level)scale.Furthermore,the framework’s ability to synthesize multimodal data sources(near-surface cameras,Sentinel-1/2,and high-resolution imagery)represents a methodological breakthrough in space and surface sensor integration for real-time land cover change detection,enabling time-sensitive applications and early warning systems for land system changes.
