Global-scale land cover characterization has advanced from a spatial resolution of 1×1°in the mid-1990s to 30×30 m resolution to date.However,some mapping challenges exist persistently regardless of the...Global-scale land cover characterization has advanced from a spatial resolution of 1×1°in the mid-1990s to 30×30 m resolution to date.However,some mapping challenges exist persistently regardless of the increasing spatial resolution.Data fusion has been proved as an effective way of improving land cover characterization.Here we applied a machine learning-based data integration approach for improving global-scale forest cover characterization.The approach employed six coarse-resolution(250-1000 m)global land cover maps as input and various regional,higher-resolution land cover data-sets as reference to build regression tree models per continent.The average error of 10-fold cross validation of the regression tree models varied between 7.70 and 15.68% forest cover and the r2 varied between 0.76 and 0.94,indicating the robustness of the trained models.As a result of data fusion,the synthesized global forest cover map was more accurate than any input global product.We also showed that other major vegetative land cover types such as cropland,woodland,grassland,and wetland all exhibit similar magnitude of discrepancies as forest among existing land cover maps.Our developed method,because of its type-and scale-invariant feature,can be implemented for other land cover types for improving their global characterization.The ensemble approach can also be internalized for improving data quality when generating a global land cover product,where multiple versions can be produced and subsequently integrated.展开更多
Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with ...Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.展开更多
基金funded by NASA’s Making Earth System Data Records for Use in Research Environments(MEaSUREs)Program[grant number NNX08AP33A]the NASA Earth and Space Science Fellowship(NESSF)Program[grant number NNX12AN92H].
文摘Global-scale land cover characterization has advanced from a spatial resolution of 1×1°in the mid-1990s to 30×30 m resolution to date.However,some mapping challenges exist persistently regardless of the increasing spatial resolution.Data fusion has been proved as an effective way of improving land cover characterization.Here we applied a machine learning-based data integration approach for improving global-scale forest cover characterization.The approach employed six coarse-resolution(250-1000 m)global land cover maps as input and various regional,higher-resolution land cover data-sets as reference to build regression tree models per continent.The average error of 10-fold cross validation of the regression tree models varied between 7.70 and 15.68% forest cover and the r2 varied between 0.76 and 0.94,indicating the robustness of the trained models.As a result of data fusion,the synthesized global forest cover map was more accurate than any input global product.We also showed that other major vegetative land cover types such as cropland,woodland,grassland,and wetland all exhibit similar magnitude of discrepancies as forest among existing land cover maps.Our developed method,because of its type-and scale-invariant feature,can be implemented for other land cover types for improving their global characterization.The ensemble approach can also be internalized for improving data quality when generating a global land cover product,where multiple versions can be produced and subsequently integrated.
基金Meteorological Research in the Public Interest,No.GYHY201106014Beijing Nova Program,No.2010B037China Special Fund for the National High Technology Research and Development Program of China(863 Program),No.412230
文摘Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.
