Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing da...Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.展开更多
Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this stu...Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this study,a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer(MODIS)characterized by coarse spatial resolution and high temporal resolution and Gaofen-1(GF-1)with fine spatial resolution and coarse temporal resolution.This method was applied to the Huangpu district of Guangzhou,Guangdong,China.The results showed that compared to field observation,the predicted values of LAI had an acceptable accuracy of 73.12%.Using Moran’s I index and Kolmogorov-Smirnov tests,it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions.Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38%compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging.Moreover,the regression models that accounts for the relationship between NDVI(Normalized Difference Vegetation Index)and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870.Finally,the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps,which showed reasonably spatial and temporal variability.The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products.展开更多
A real-time,long-term surface meteorological blended forcing dataset(SMBFD)has been developed based on station observations,satellite retrievals,and reanalysis products in China.The observations are collected at natio...A real-time,long-term surface meteorological blended forcing dataset(SMBFD)has been developed based on station observations,satellite retrievals,and reanalysis products in China.The observations are collected at national and regional automatic weather stations,satellite data are obtained from the Fengyun(FY)series satellites retrievals,and the reanalysis products are obtained from the ECMWF.The 90-m resolution digital terrain elevation data in China are obtained from the Shuttle Radar Topographic Mission(SRTM)for temperature and humidity elevation adjustment.The dataset includes 2-m air temperature and humidity,10-m zonal and meridional winds,downward shortwave radiation,surface pressure,and precipitation.The spatial resolution is 1 km,and the temporal resolution is 1 h.During the data processing procedure,various data fusion techniques including the space–time multiscale variational analysis,the discrete ordinates radiative transfer(DISORT)model,the hybrid radiation estimation model,and a terrain correction algorithm are employed.Dependent and independent evaluations of the dataset are performed against observations.The SMBFD dataset is also compared with similar datasets produced in other major meteorological operational centers in the world.The results are as follows.(1)All variables show reasonable geographic distribution features and realistic spatial and temporal variations.(2)Dependent and independent evaluations both indicate that the gridded SMBFD dataset is close to the observations,while the dependent evaluation yields better results than the independent evaluation.(3)Compared with similar datasets produced in other meteorological operational centers,the real-time and retrospective surface meteorological fusion data obviously have higher quality.The dataset introduced in the present study is in general stable and accurate,and can be applied in various practice such as meteorology,agriculture,ecology,environmental protection,etc.Meanwhile,this dataset has been used as the atmospheric forcing data to drive the operational High-resolution Land Data Assimilation System of China Meteorological Administration.The dataset with the network Common Data Form(NETCDF)can be decoded by various programming languages,and it is freely available to non-commercial users.展开更多
For a scintillating-fiber array fast-neutron radiography system,a point-spread-function computing model was introduced,and the simulation code was developed. The results of calculation show that fast-neutron radiograp...For a scintillating-fiber array fast-neutron radiography system,a point-spread-function computing model was introduced,and the simulation code was developed. The results of calculation show that fast-neutron radiographs vary with the size of fast neutron sources,the size of fiber cross-section and the imaging geometry. The results suggest that the following qualifications are helpful for a good point spread function: The cross-section of scintillating fibers not greater than 200 μm×200 μm,the size of neutron source as small as a few millimeters,the distance between the source and the scintillating fiber array greater than 1 m,and inspected samples placed as close as possible to the array. The results give suggestions not only to experiment considerations but also to the estimation of spatial resolution for a specific system.展开更多
基金Under the auspices of National Key Research and Development Project of China(No.2021YFD1500103)Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDA28100500)+2 种基金National Natural Science Foundation of China(No.4197132)Science and Technology Development Plan Project of Jilin Province(No.20210201044GX)Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project(No.CASPLOS-CCSI)。
文摘Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.
基金Science and Technology Program of Guangzhou,China(2014A050503060).
文摘Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this study,a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer(MODIS)characterized by coarse spatial resolution and high temporal resolution and Gaofen-1(GF-1)with fine spatial resolution and coarse temporal resolution.This method was applied to the Huangpu district of Guangzhou,Guangdong,China.The results showed that compared to field observation,the predicted values of LAI had an acceptable accuracy of 73.12%.Using Moran’s I index and Kolmogorov-Smirnov tests,it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions.Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38%compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging.Moreover,the regression models that accounts for the relationship between NDVI(Normalized Difference Vegetation Index)and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870.Finally,the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps,which showed reasonably spatial and temporal variability.The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products.
基金Supported by the National Key Research and Development Program of China(2018YFC1506601)National Natural Science Foundation of China(91437220)China Meteorological Administration Special Public Welfare Research Fund(GYHY201306045 and GYHY201506002).
文摘A real-time,long-term surface meteorological blended forcing dataset(SMBFD)has been developed based on station observations,satellite retrievals,and reanalysis products in China.The observations are collected at national and regional automatic weather stations,satellite data are obtained from the Fengyun(FY)series satellites retrievals,and the reanalysis products are obtained from the ECMWF.The 90-m resolution digital terrain elevation data in China are obtained from the Shuttle Radar Topographic Mission(SRTM)for temperature and humidity elevation adjustment.The dataset includes 2-m air temperature and humidity,10-m zonal and meridional winds,downward shortwave radiation,surface pressure,and precipitation.The spatial resolution is 1 km,and the temporal resolution is 1 h.During the data processing procedure,various data fusion techniques including the space–time multiscale variational analysis,the discrete ordinates radiative transfer(DISORT)model,the hybrid radiation estimation model,and a terrain correction algorithm are employed.Dependent and independent evaluations of the dataset are performed against observations.The SMBFD dataset is also compared with similar datasets produced in other major meteorological operational centers in the world.The results are as follows.(1)All variables show reasonable geographic distribution features and realistic spatial and temporal variations.(2)Dependent and independent evaluations both indicate that the gridded SMBFD dataset is close to the observations,while the dependent evaluation yields better results than the independent evaluation.(3)Compared with similar datasets produced in other meteorological operational centers,the real-time and retrospective surface meteorological fusion data obviously have higher quality.The dataset introduced in the present study is in general stable and accurate,and can be applied in various practice such as meteorology,agriculture,ecology,environmental protection,etc.Meanwhile,this dataset has been used as the atmospheric forcing data to drive the operational High-resolution Land Data Assimilation System of China Meteorological Administration.The dataset with the network Common Data Form(NETCDF)can be decoded by various programming languages,and it is freely available to non-commercial users.
基金Supported by the Foundation of Double-Hundred Talents of China Academy of Engineering Physics (Grant No. 2004R0301)
文摘For a scintillating-fiber array fast-neutron radiography system,a point-spread-function computing model was introduced,and the simulation code was developed. The results of calculation show that fast-neutron radiographs vary with the size of fast neutron sources,the size of fiber cross-section and the imaging geometry. The results suggest that the following qualifications are helpful for a good point spread function: The cross-section of scintillating fibers not greater than 200 μm×200 μm,the size of neutron source as small as a few millimeters,the distance between the source and the scintillating fiber array greater than 1 m,and inspected samples placed as close as possible to the array. The results give suggestions not only to experiment considerations but also to the estimation of spatial resolution for a specific system.
