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Vegetation Index Maps of Asia Temporally Splined for Consistency Through a High Performance and Grid System

Vegetation Index Map provides the crop density information over a precise region. Remote Sensing (RS) images are at the basis of creating such map, while the decision-maker requirement stands for Vegetation Index Maps at various in-country administrative levels. However, RS image includes data noises due to influence of haze or cloud especially in the rainy season. Temporally Splined procedure such as Local Maximum Fitting (LMF) can be applied on RS images for ensuring the data consistency. Running the LMF procedure with single computer takes impractical amount of processing time (approx. 150 days) for Asia regional RS image (46 bands/dates, 3932 rows, 11652 columns). Importing the LMF on High Performance Computing (HPC) platforms provides with a time optimization mechanism, and LMF has been implemented in cluster computers for this very purpose. A single cluster LMF processing timing still did not perform within an acceptable time range. In this paper, the LMF processing methodology to reduce processing time by combining the parallelization of data and task together on multi-cluster Grids is presented.