Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess ...Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess and understand the spatial distribution of LE and SH fluxes across different underlying surfaces,it is crucial to verify the validity and reliability of ERA-5,GLDAS,and MODIS data against ground measurements obtained from the Flux Net micrometeorological tower network.This study analyzed the spatial patterns of LE and SH over the Tibetan Plateau using data from ERA-5,GLDAS,and MODIS.The results were compared with ground measurements from Flux Net tower observations on different underlying surfaces,and five statistical parameters(Pearson's r,LR slope,RMSE,MBE,and MAE)were used to validate the data.The results showed that:(1)MODIS LE data and ERA-5 SH data exhibited the closest agreement with ground observations,as indicated by their lowest root mean square error and mean bias area values.(2)The accuracy of ERA-5 SH was the highest in meadows and steppes,while GLDAS SH performed optimally in shrublands.Notably,MODIS LE consistently outperformed the other datasets across all vegetation types.(3)The spatial distribution of LE and SH displayed considerable heterogeneity,contingent upon the specific data sources and underlying surfaces.Notably,there was a contrasting trend between GLDAS and ERA-5,as well as MODIS,in terms of SH distribution in the shrubland.In shrublands and meadows,MODIS SH and LE exhibited more pronounced changes than ERA-5 and GLDAS.Additionally,ERA-5 SH demonstrated the opposite variation in meadow and steppe regions compared to GLDAS and MODIS.展开更多
Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts...Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.展开更多
基金funded by the West Light Scholar of the Chinese Academy of Sciences(xbzg-zdsys-202202)the Natural Science Foundation of Henan(Grant No.232300420165)Integrated Scientific Investigation of the North-South Transitional Zone of China(2017FY100900)。
文摘Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess and understand the spatial distribution of LE and SH fluxes across different underlying surfaces,it is crucial to verify the validity and reliability of ERA-5,GLDAS,and MODIS data against ground measurements obtained from the Flux Net micrometeorological tower network.This study analyzed the spatial patterns of LE and SH over the Tibetan Plateau using data from ERA-5,GLDAS,and MODIS.The results were compared with ground measurements from Flux Net tower observations on different underlying surfaces,and five statistical parameters(Pearson's r,LR slope,RMSE,MBE,and MAE)were used to validate the data.The results showed that:(1)MODIS LE data and ERA-5 SH data exhibited the closest agreement with ground observations,as indicated by their lowest root mean square error and mean bias area values.(2)The accuracy of ERA-5 SH was the highest in meadows and steppes,while GLDAS SH performed optimally in shrublands.Notably,MODIS LE consistently outperformed the other datasets across all vegetation types.(3)The spatial distribution of LE and SH displayed considerable heterogeneity,contingent upon the specific data sources and underlying surfaces.Notably,there was a contrasting trend between GLDAS and ERA-5,as well as MODIS,in terms of SH distribution in the shrubland.In shrublands and meadows,MODIS SH and LE exhibited more pronounced changes than ERA-5 and GLDAS.Additionally,ERA-5 SH demonstrated the opposite variation in meadow and steppe regions compared to GLDAS and MODIS.
基金funded by the Third Xinjiang Scientific Expedition Program(2021xjkk1400)the National Natural Science Foundation of China(42071049)+2 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01C022)the Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Project&Science and Technology Innovation Base Construction Project(PT2107)the Tianshan Talent-Science and Technology Innovation Team(2022TSYCTD0006).
文摘Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.
