Recent developments in science and technology have made it possible to analyze data observed by satellites using optical properties. By monitoring changes in the ocean environment and ecosystem, we are currently conducting ocean environmental studies to identify abnormal weather phenomena. International aerospace laboratories such as NASA and ESA are publishing these observed data to ocean scientists around the world. Satellite sensing data accumulates day by day, but data volume for the global scale is so large that scientists usually divide the space for only the area of interest and perform time series analyses. Time series analysis is mainly applied to nonlinear distributions. However, studies of the ocean environment require analysis of the global ocean and ocean ecosystems. Data analysis in the global domain requires nonlinear data fitting for every cell of the satellite imagery data. However, commercial and open-source data analysis tools such as Matlab or R do not provide non-linear data fitting for multiple cells. Because of this, there is a difficulty for ocean scientists to directly implement the analysis of data and it is hard to guarantee distributed and parallelized computation performance. Therefore, in this paper, we propose an algorithm that can distribute and parallelize, in a multi-dimensional database environment, the Levenberg-Marquadt (LM) algorithm, which is well known as a non-linear data fitting algorithm. Our algorithm achieved about 7.5 times speed-up on average, compared to the MINPACK LM algorithm, which is based on MPI and written in FORTRAN. In addition, our algorithm improved 74.3 times speed-up when comparing to the maximum performance for each algorithm. As future research, we will utilize the developed algorithms in the ocean science field for data analysis of global scale satellite imagery data.