Background:The time-series data of the Normalized Difference Vegetation Index(NDVI)is a crucial indicator for global and regional vegetation monitoring.However,the current assessment of global and regional long-term v...Background:The time-series data of the Normalized Difference Vegetation Index(NDVI)is a crucial indicator for global and regional vegetation monitoring.However,the current assessment of global and regional long-term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time-series data sets.Methods:In this study,a long time-series monthly NDVI data set with a spatial resolution of 250m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer(MODIS)and AVHRR(Advanced Very High-Resolution Radiometer)time-series NDVI products using the Random Forest(RF)downscaling model.Results:Compared to the MODIS NDVI product,the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05,respectively,with R^(2)values mostly above 0.7.Conclusions:The long time-series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long-term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.展开更多
Dear Editor,RNA knockdown in vivo carries significant potential for dis-ease modeling and therapies.Despite the emerging approaches of CRISPR/Cas9-mediated permanent knock out of targeted genes,strategies targeting RN...Dear Editor,RNA knockdown in vivo carries significant potential for dis-ease modeling and therapies.Despite the emerging approaches of CRISPR/Cas9-mediated permanent knock out of targeted genes,strategies targeting RNA for disruption are advantageous in the treatment of acquired metabolic disorders when permanent modification of genome DNA is not appropriate,and RNA virus infection diseases when pathogenic DNA is not available(such as SARS-Cov-2 and MERS infections).展开更多
基金Natural Science Foundation of China Projects,Grant/Award Number:41971293。
文摘Background:The time-series data of the Normalized Difference Vegetation Index(NDVI)is a crucial indicator for global and regional vegetation monitoring.However,the current assessment of global and regional long-term vegetation changes is subject to large uncertainties due to the lack of spatiotemporally continuous time-series data sets.Methods:In this study,a long time-series monthly NDVI data set with a spatial resolution of 250m from 1982 to 2020 was developed by combining Moderate Resolution Imaging Spectroradiometer(MODIS)and AVHRR(Advanced Very High-Resolution Radiometer)time-series NDVI products using the Random Forest(RF)downscaling model.Results:Compared to the MODIS NDVI product,the fused product shows RMSE and mean absolute error ranging from 0 to 0.075 and from 0 to 0.05,respectively,with R^(2)values mostly above 0.7.Conclusions:The long time-series NDVI products generated in this study are reliable in terms of accuracy and have great potential for long-term dynamic monitoring of terrestrial ecosystems on the Qinghai–Tibet Plateau.
文摘Dear Editor,RNA knockdown in vivo carries significant potential for dis-ease modeling and therapies.Despite the emerging approaches of CRISPR/Cas9-mediated permanent knock out of targeted genes,strategies targeting RNA for disruption are advantageous in the treatment of acquired metabolic disorders when permanent modification of genome DNA is not appropriate,and RNA virus infection diseases when pathogenic DNA is not available(such as SARS-Cov-2 and MERS infections).
