Published February 13, 2026 | Version v1
Dataset Open

A Synthetic Bio-Optical Dataset for Danjiangkou Reservoir: Hyperspectral Reflectance and Inherent Optical Properties

  • 1. ROR icon Nanjing Institute of Geography and Limnology
  • 2. Changjiang Basin Ecology and Environment Monitoring and Scientific Research Center, Changjiang Basin Ecology and Environment Administration, Ministry of Ecology and Environment
  • 3. Hubei Key Laboratory of Intelligent Monitoring, Early Warning and Protection for Watershed Aquatic Ecology

Description

Summary

This dataset contains 10,000 samples of synthetic bio-optical data generated specifically for the optical characteristics of Danjiangkou Reservoir (China). It was created using the Danjiangkou Bio-Optical Model (DBOM) forward simulation framework.

The dataset links biogeochemical concentrations (Chlorophyll-a, Inorganic Suspended Matter, CDOM) and phytoplankton community structure (Cyanobacteria, Green algae, Brown algae) to Inherent Optical Properties (IOPs) and hyperspectral Remote Sensing Reflectance ($R_{rs}$). The dataset contains the stochastic perturbation of bio-optical parametrization coefficients (e.g., spectral slopes, specific absorption coefficients), which were randomized by $\pm 30\%$ around their local default values to simulate natural variability and support algorithm uncertainty analysis.

Data Generation Methodology

  1. Concentration Generation: Input concentrations were generated using a multivariate log-normal distribution to mimic natural covariance. Phytoplankton community fractions were generated using a Dirichlet distribution.

  2. Forward Modeling:

    • IOP Model: Localized bio-optical models for Danjiangkou Reservoir were used to compute absorption ($a$) and backscattering ($b_b$) coefficients.

    • Parameter Perturbation: Key bio-optical parameters (including $S_{cdom}$, $S_{nap}$, $a^*_{ph}$, etc.) were independently perturbed using a uniform distribution $U(0.7, 1.3)$ for each sample to introduce realistic optical complexity.

    • Radiative Transfer: Remote sensing reflectance ($R_{rs}$) was derived from IOPs using the semi-analytical model by Lee et al. (2011).

  3. Spectral Resolution: Hyperspectral data covering 350 nm to 900 nm with a 2 nm interval.

File Structure

The data is stored in a single NetCDF file (.nc) compatible with Xarray and other multidimensional array libraries.

  • Dimensions:

    • sample_id: 10,000 (Number of samples)

    • wavelen: 276 (Spectral bands from 350 to 900 nm)

  • Data Variables (Outputs):

    • Rrs: Remote Sensing Reflectance [$sr^{-1}$]

    • aph: Absorption coefficient of phytoplankton [$m^{-1}$]

    • ad: Absorption coefficient of detritus/non-algal particles [$m^{-1}$]

    • acdom: Absorption coefficient of CDOM [$m^{-1}$]

    • bbp: Particulate backscattering coefficient [$m^{-1}$]

    • bp: Particulate total scattering coefficient [$m^{-1}$]

  • Coordinates (Inputs & Truth Data):

    • Chla: Chlorophyll-a concentration [$mg/m^3$]

    • ISM: Inorganic Suspended Matter concentration [$g/m^3$]

    • ag440: CDOM absorption at 440 nm [$m^{-1}$]

    • frac_cyano, frac_green, frac_brown: Fractional composition of phytoplankton groups (0-1).

    • Perturbed Parameters: The file also records the specific randomized parameters used for each sample (e.g., S_cdom, A_ph, gamma_nap) to allow for sensitivity analysis.

Potential Applications

  • Development and validation of semi-analytical algorithms for inland waters.

  • Training machine learning models for water quality retrieval.

  • Phytoplankton functional type (PFT) discrimination studies.

  • Sensitivity analysis of bio-optical inversion algorithms.

Technical Info

  • Format: NetCDF4

  • Compression: zlib (level 9)

  • Code Author: Shun Bi (bishun@niglas.ac.cn)

Files

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Additional details

Funding

National Natural Science Foundation of China
42501484
National Natural Science Foundation of China
U25A20753
National Natural Science Foundation of China
42425102
National Natural Science Foundation of China
42501511