In this study,in-situ soil moisture measurements are used to evaluate the accuracy of three AMSR-E soil moisture prod ucts from NASA(National Aeronautics and Space Administration),JAXA(Japanese Aerospace Exploration A...In this study,in-situ soil moisture measurements are used to evaluate the accuracy of three AMSR-E soil moisture prod ucts from NASA(National Aeronautics and Space Administration),JAXA(Japanese Aerospace Exploration Agency)and VUA(Vrije University Amsterdam and NASA)over Maqu County,Source Area of the Yellow River(SAYR),China.Re sults show that the VUA soil moisture product performs the best among the three AMSR-E soil moisture products in the study area,with a minimum RMSE(root mean square error)of 0.08(0.10)m3/m3 and smallest absolute error of 0.07(0.08)m3/m3 at the grassland area with ascending(descending)data.Therefore,the VUA soil moisture product is used to describe the spatial variation of soil moisture during the 2010 growing season over SAYR.The VUA soil moisture product shows that soil moisture presents a declining trend from east south(0.42 m3/m3)to west north(0.23 m3/m3),with good agreement with a general precipitation distribution.The center of SAYR presents extreme wetness(0.60 m3/m3)dur ing the whole study period,especially in July,while the head of SAYR presents a high level soil moisture(0.23 m3/m3)in July,August and September.展开更多
Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-drive...Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.展开更多
基金supported in part by the Programs of National Natural Science Foundation of China (41675157, 91537212)
文摘In this study,in-situ soil moisture measurements are used to evaluate the accuracy of three AMSR-E soil moisture prod ucts from NASA(National Aeronautics and Space Administration),JAXA(Japanese Aerospace Exploration Agency)and VUA(Vrije University Amsterdam and NASA)over Maqu County,Source Area of the Yellow River(SAYR),China.Re sults show that the VUA soil moisture product performs the best among the three AMSR-E soil moisture products in the study area,with a minimum RMSE(root mean square error)of 0.08(0.10)m3/m3 and smallest absolute error of 0.07(0.08)m3/m3 at the grassland area with ascending(descending)data.Therefore,the VUA soil moisture product is used to describe the spatial variation of soil moisture during the 2010 growing season over SAYR.The VUA soil moisture product shows that soil moisture presents a declining trend from east south(0.42 m3/m3)to west north(0.23 m3/m3),with good agreement with a general precipitation distribution.The center of SAYR presents extreme wetness(0.60 m3/m3)dur ing the whole study period,especially in July,while the head of SAYR presents a high level soil moisture(0.23 m3/m3)in July,August and September.
基金This work was financially supported by National Natural Science Foundation of China(41972262)Hebei Natural Science Foundation for Excellent Young Scholars(D2020504032)+1 种基金Central Plains Science and technology innovation leader Project(214200510030)Key research and development Project of Henan province(221111321500).
文摘Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.
