.png){kind=icon}
.png){kind=icon}
Return
Hyperspectral Aquatic Radiative Transfer Modeling Using a High-Performance Cluster Computing-Based Approach
.png){kind=icon}
PRE
AI
DOI：10.2747/1548-1603.49.2.275.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 similar to 100 hours until termination, whereas a parallel approach required only similar to 2.5 hours (42 compute nodes)-a 40x speed-up. Tools developed for this parallel execution are discussed.
Keywords:
NEURAL-NETWORK
SUSPENDED SEDIMENT
ACCURATE MODEL
WATER-QUALITY
ALGORITHM
IRRADIANCE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.9
Papers:
1.1K
Citations:
4.5K