Grid Services for SST Measures

The effects of the increase in the average temperature of the Earth's near-surface air and oceans in recent decades are dramatically manifest: desertification, glacier retreat, increased intensity and frequency of hurricanes and extreme weather events. Monitoring the ecosystem is currently the only way we have to assist Governments in making sound decisions concerning the reduction of these dramatic effects and the protection of our environment. Satellite remote sensing data, offering the possibility of covering large spatial area with a high temporal frequency, represents the ideal solution to monitoring, but the huge data volume to process, calibrate and validate by in-situ dataset, cannot be operated effectively by traditional database and computational resources. Grid technology, easily providing powerful computational resources and efficient distributed data management, is an excellent solution for remote sensed data processing and management system. In this paper we present a prototype of a remote sensed data processing system on Grid technology that allows, by a graphical interface, data selection and processing to validate SST measure particularly in costal area.

[1]  L. McMillin,et al.  Theory and validation of the multiple window sea surface temperature technique , 1984 .

[2]  Peter J. Minnett,et al.  Satellite multichannel infrared measurements of sea surface temperature of the N.E. Atlantic Ocean using AVHRR/2 , 1984 .

[3]  William J. Emery,et al.  Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation , 1994 .

[4]  William G. Pichel,et al.  Comparative performance of AVHRR‐based multichannel sea surface temperatures , 1985 .

[5]  Roger Saunders,et al.  Theoretical algorithms for satellite‐derived sea surface temperatures , 1989 .

[6]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[7]  John Sapper,et al.  The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar‐orbiting environmental satellites , 1998 .

[8]  Peter J. Minnett,et al.  The regional optimization of infrared measurements of sea surface temperature from space , 1990 .

[9]  Yong Xue,et al.  Grid service spread applied to remote sensing processing , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[10]  I. J. Barton,et al.  Dual channel satellite measurements of sea surface temperature , 1983 .

[11]  Xiaofeng Li,et al.  Validation of coastal sea and lake surface temperature measurements derived from NOAA/AVHRR data , 2001 .

[12]  Larry M. McMillin,et al.  Estimation of sea surface temperatures from two infrared window measurements with different absorption , 1975 .

[13]  Steven Tuecke,et al.  The Open Grid Services Architecture , 2004, The Grid 2, 2nd Edition.

[14]  George A. Maul,et al.  Estimation of sea surface temperature from space , 1971 .

[15]  C. Walton,et al.  Nonlinear Multichannel Algorithms for Estimating Sea Surface Temperature with AVHRR Satellite Data , 1988 .