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
The approach to estimate the length of extended targets by using the bistatic high resolution range profile（ H RRP） is analyzed in this paper. The relationship between the bistatic H RRP and the monostatic H RRP of ...The approach to estimate the length of extended targets by using the bistatic high resolution range profile（ H RRP） is analyzed in this paper. The relationship between the bistatic H RRP and the monostatic H RRP of extended targets are investigated. It is demonstrated by simulations that the target length measured by the bistatic H RRP is more meaningful and accurate than that by the monostatic HRRP,though the monostatic H RRP has been well developed and widely used in target recognizing and classification. The estimation results of a cone shaped target are present and compared at the end of the paper. To assure the reliability of the simulation,the bistatic H RRP is obtained through the scattering field data calculated by a fullwave numerical method,FE-BI-MLFMA.展开更多
In this paper, Bayesian technique of direction finding based on two different priorities is described. Some useful formulas are deduced. The performance of the method and the influence of the priors on direction findi...In this paper, Bayesian technique of direction finding based on two different priorities is described. Some useful formulas are deduced. The performance of the method and the influence of the priors on direction finding are demonstrated by computer simulations.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Supported by the National Natural Science Fundation of China(61001192)
文摘The approach to estimate the length of extended targets by using the bistatic high resolution range profile（ H RRP） is analyzed in this paper. The relationship between the bistatic H RRP and the monostatic H RRP of extended targets are investigated. It is demonstrated by simulations that the target length measured by the bistatic H RRP is more meaningful and accurate than that by the monostatic HRRP,though the monostatic H RRP has been well developed and widely used in target recognizing and classification. The estimation results of a cone shaped target are present and compared at the end of the paper. To assure the reliability of the simulation,the bistatic H RRP is obtained through the scattering field data calculated by a fullwave numerical method,FE-BI-MLFMA.
文摘In this paper, Bayesian technique of direction finding based on two different priorities is described. Some useful formulas are deduced. The performance of the method and the influence of the priors on direction finding are demonstrated by computer simulations.
