The Google Cloud Platform (GCP) is a popular choice for companies seeking a comprehensive cloud computing solution because it provides everything from essential computing resources to powerful data analytics and machi...The Google Cloud Platform (GCP) is a popular choice for companies seeking a comprehensive cloud computing solution because it provides everything from essential computing resources to powerful data analytics and machine learning capabilities. Saviynt is a cloud-based Identity and Access Management (IAM) system that integrates with Google Cloud Platform (GCP) and other services for additional functionality. However, other problems are associated with the transition, such as the requirement to correctly integrate IAM Saviynt into current IT infrastructures and provide comprehensive training to users on the new system. The paper will give a detailed review of the advantages, disadvantages, and best practices related to this transition.展开更多
Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another ...Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine(GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.展开更多
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
文摘The Google Cloud Platform (GCP) is a popular choice for companies seeking a comprehensive cloud computing solution because it provides everything from essential computing resources to powerful data analytics and machine learning capabilities. Saviynt is a cloud-based Identity and Access Management (IAM) system that integrates with Google Cloud Platform (GCP) and other services for additional functionality. However, other problems are associated with the transition, such as the requirement to correctly integrate IAM Saviynt into current IT infrastructures and provide comprehensive training to users on the new system. The paper will give a detailed review of the advantages, disadvantages, and best practices related to this transition.
基金Under the auspices of National Key Research and Development Program of China(No.2016YFA0600304)
文摘Large amounts of data at various temporal and spatial scales require terabyte(TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine(GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.
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.
