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A lake ice phenology dataset for the Northern Hemisphere based on passive microwave remote sensing 被引量:3

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摘要 Lake ice phenology(LIP)is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts.Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe,North America,and the Tibetan Plateau,but there is a lack of data for inner Eurasia.In this work,enhanced-resolution passive microwave satellite data(PMW)were used to investigate the Northern Hemisphere Lake Ice Phenology(PMW LIP).The Freeze Onset(FO),Complete Ice Cover(CIC),Melt Onset(MO),and Complete Ice Free(CIF)dates were derived for 753 lakes,including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020.Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF,respectively,and the corresponding values of the RMSE were 11.84 and 10.07 days.The lake ice phenology in this dataset was significantly correlated(P<0.001)with that obtained from Moderate Resolution Imaging Spectroradiometer(MODIS)data-the average correlation coefficient was 0.90 and the average RMSE was 7.87 days.The minimum RMSE was 4.39 days for CIF.The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations.The PMW LIP dataset pro-vides the basic freeze-thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere.The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.
出处 《Big Earth Data》 EI 2022年第4期401-419,共19页 地球大数据(英文)
基金 supported by the the Multi-Parameters Arctic Environmental Observations and Information Services Project(MARIS)funded by Ministry of Science and Technology(MOST)[grant number 2017YFE0111700]and Strategic Priority Research Program of the Chinese Academy of Sciences[grant numbers XDA19070201 and XDA19070102].
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