The Tibetan Plateau houses numerous lakes,the phenology and duration of lake ice in this region are sensitive to regional and global climate change,and as such are used as key indicators in climate change research,par...The Tibetan Plateau houses numerous lakes,the phenology and duration of lake ice in this region are sensitive to regional and global climate change,and as such are used as key indicators in climate change research,particularly in environment change comparison studies for the Earth three poles.However,due to its harsh natural environment and sparse population,there is a lack of conventional in situ measurement on lake ice phenology.The Moderate Resolution Imaging Spectroradiometer(MODIS)Normalized Difference Snow Index(NDSI)data,which can be traced back 20 years with a 500 m spatial resolution,were used to monitor lake ice for filling the observation gaps.Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm,and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps.Through time series analysis of every single lake with size greater than 3 km2 in size,308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset,including 216 lakes that can be further retrieved with four determinable lake ice parameters:Freeze-up Start(FUS),Freeze-up End(FUE),Break-up Start(BUS),and Break-up End(BUE),and 92 lakes with two parameters,FUS and BUE.Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing.The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13.The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations.The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744.展开更多
基金This work was supported by the Chinese Academy of Sciences[XDA19070201]Ministry of Science and Technology of the People’s Republic of China[2017YFE0111700]The Chinese Academy of Sciences[131211KYSB20170041].
文摘The Tibetan Plateau houses numerous lakes,the phenology and duration of lake ice in this region are sensitive to regional and global climate change,and as such are used as key indicators in climate change research,particularly in environment change comparison studies for the Earth three poles.However,due to its harsh natural environment and sparse population,there is a lack of conventional in situ measurement on lake ice phenology.The Moderate Resolution Imaging Spectroradiometer(MODIS)Normalized Difference Snow Index(NDSI)data,which can be traced back 20 years with a 500 m spatial resolution,were used to monitor lake ice for filling the observation gaps.Daily lake ice extent and coverage under clear-sky conditions was examined by employing the conventional SNOWMAP algorithm,and those under cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions through a series of steps.Through time series analysis of every single lake with size greater than 3 km2 in size,308 lakes within the Tibetan Plateau were identified as the effective records of lake ice extent and coverage to form the Daily Lake Ice Extent and Coverage dataset,including 216 lakes that can be further retrieved with four determinable lake ice parameters:Freeze-up Start(FUS),Freeze-up End(FUE),Break-up Start(BUS),and Break-up End(BUE),and 92 lakes with two parameters,FUS and BUE.Six lakes of different sizes and locations were selected for verification against the published datasets by passive microwave remote sensing.The lake ice phenology information obtained in this paper was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13.The present dataset is more effective at detecting lake ice parameters for smaller lakes than the coarse resolution passive microwave remote sensing observations.The published data are available in https://data.4tu.nl/repository/uuid:fdfd8c76-6b7c-4bbf-aec8-98ab199d9093 and http://www.sciencedb.cn/dataSet/handle/744.
