Hyperspectral Aquatic Radiative Transfer Modeling Using a High-Performance Cluster Computing-Based Approach Academic Article uri icon


  • For aquatic studies, radiative transfer (RT) modeling can be used to compute hyperspectral above-surface remote sensing reflectance that can be utilized for inverse model development. Inverse models can provide bathymetry and inherent-and bottom-optical property estimation. Because measured oceanic field/organic datasets are often spatio-temporally sparse, synthetic data generation is useful in yielding sufficiently large datasets for inversion model development; however, these forward-modeled data are computationally expensive and time-consuming to generate. This study establishes the magnitude of wall-clock-time savings achieved for performing large, aquatic RT batch-runs using parallel computing versus a sequential approach. Given 2,600 simulations and identical compute-node characteristics, sequential architecture required ~100 hours until termination, whereas a parallel approach required only 2.5 hours (42 compute nodes)-a 40x speed-up. Tools developed for this parallel execution are discussed.

published proceedings


author list (cited authors)

  • Filippi, A. M., Bhaduri, B. L., Naughton, T., King, A. L., Scott, S. L., & Gueneralp, I.

citation count

  • 4

complete list of authors

  • Filippi, Anthony M||Bhaduri, Budhendra L||Naughton, Thomas||King, Amy L||Scott, Stephen L||Gueneralp, Inci

publication date

  • January 2012