BING - Bayesian INference with Gordon coefficients

Python Version License

Welcome to the BING documentation. BING is a comprehensive Python package for ocean color remote sensing analysis, specializing in bio-optical modeling, spectral fitting, and satellite data processing with a focus on NASA’s PACE (Plankton, Aerosol, Cloud, ocean Ecosystem) mission data.

Features

  • Ocean Color Analysis: Process and analyze remote sensing reflectance (Rrs) data

  • Model Fitting: Bayesian and least-squares fitting of bio-optical models

  • PACE Integration: Native support for PACE OCI data processing

  • Uncertainty Quantification: Comprehensive error propagation and uncertainty analysis

  • Inelastic Processes: Support for both Raman scattering and chlorophyll fluorescence contributions to the forward radiative transfer model

Contents:

Radiative Transfer:

Quick Start

import numpy as np
from bing import evaluate
from bing.parameters import standard
from bing.models import utils as model_utils
from bing.fitting import chisq_fit, inference

# Load standard parameters
params = standard.expb_pow(satellite='PACE')

# Initialize models
models = model_utils.init(params.model_names, wavelengths)

# Perform fitting
result = chisq_fit.fit(Rrs_data, Rrs_uncertainty, models)

Indices and tables

Citation

If you use BING in your research, please cite:

@software{bing2024,
  title={BING: Biogeochemical Index Network Generator},
  author={Your Name},
  year={2024},
  url={https://github.com/yourusername/bing}
}