V.S. Sunderam et al. (Eds.): ICCS 2005, LNCS 3516, pp. 496 – 503, 2005. © Springer-Verlag Berlin Heidelberg 2005 Java-Based Grid Service Spread and Implementation in Remote Sensing Applications Yanguang Wang 1 , Yong Xue 1,2,* , Jianqin Wang 1 , Chaolin Wu 1 , Yincui Hu 1 , Ying Luo 1 , Shaobo Zhong 1 , Jiakui Tang 1 , and Guoyin Cai 1 1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Institute of Remote Sensing Applications, Chinese Academy of Sciences, P.O. Box 9718, Beijing 100101, China 2 Department of Computing, London Metropolitan University, 166-220 Holloway Road, London N7 8DB, UK {wyg_nju@hotmail.com, y.xue@londonmet.ac.uk} Abstract. Remote sensing applications often concern very large volumes of spatio-temporal data, the emerging Grid computing technologies bring an effective solution to this problem. The Open Grid Services Architecture (OGSA) treats Grid as the aggregate of Grid service, which is extension of Web Service. It defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. It is not effective used in data-intensive computing such as remote sensing applications because its foundation, Web Service, is not efficient in scientific computing. How to increase the efficiency of the grid services for a scientific computing? This paper proposes a mechanism Grid service spread (GSS), which dynamically replant a Grid service from a Grid node to the others. We have more computers to provide the same function, so less time can be spent completing a problem than original Grid system. This paper also provides the solution how to adept the service duplicate for the destination node’s Grid environment; how each service duplicate communicates with each other; how to manage the lifecycle of services spread etc. The efficiency of this solution through a remote sensing application of NDVI computing is demonstrated. It shows that this method is more efficient for processing huge amount of remotely sensed data. 1 Introduction With the development of modern space remote sensing technology, the sensors have got a great increment in spatio-resolution and spectrum-resolution, and have made huge volumes of data for our remote sensing applications. While today’s PC is faster than the Cray supercomputer of 10 years ago, it is still often inadequate to provide a * Corresponding author.