This repository has the implementation and tests of benchmarked bio-optic models that evaluates some water quality indexes by analyzing satellite images.
In attempt to analyze the water quality of reservoirs and lakes by remote sensing methods, such as satellites images, bio-optical models were used. Those models are mathematical and statistical algorithms which can be used to predict different water quality indexes by analyzing the water-leaving radiance measured at different bands of electromagnetic spectrum by sensors onboard satellites.
According to the literature there are different approaches used in bio-optical modeling since simple models, based on empirical and semi-empirical relations, until most complexes models based on radiative transfer theory.
Currently, this project implements a library of empirical and semi-empirical models which can be used to predict the concentration of chlorophyll-a, total suspended solids, water transparency, turbidity, phycocyanin and the detection of macrophytes in aquatic environments.
All the original equations are adapted in order to be applied in images collected by MSI (Multispectral Instrument) sensor onboard Sentinel-2A and Sentinel-2B platforms.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
To execute this project, you'll need the following technologies:
- 32- or 64-bit computer
- Python 3
This repository can be used as a complementary library for a main project and its modules can be used whenever they are necessary.
To install the package and dependencies use the following command:
pip install qda_modelos
In order to use qda_modelos in your project it's required to install the rasterio library:
pip install rasterio
You can install dependencies directly in your machine or in a virtual environment of your choice, such as VirtualEnv or Conda.
The following example implements the water quality index: turbidity.
The chosen method is miller_mckee_2004which expects satellite images of 659nm wavelength.
This example utilizes Sentinel-2 imagery of which the band 4 has the central wavelength (665 nm) closest to the models requirement.
- First, import the required packages and desired methods:
import rasterio as rio
from qda_modelos.total_suspended_solids_turbidity import miller_mckee_2004In this case, we chose the rasterio package to read and write .tif data files.
- Set and open the respective satellite images required to analyze the indexes:
reflectance_659nm_wavelength = rio.open("tests/assets/20m/B4_20m_20181224.tif").read()- Open the band image file and choose one or more methods to analyze the desired index:
meta = rio.open("tests/assets/20m/B4_20m_20181224.tif").meta
meta.update(driver="GTiff")
meta.update(dtype=rio.float32)
miller_mckee_2004 = miller_mckee_2004(reflectance_659nm_wavelength)- Create and save the new image generated by the respectives bands of the chosen method:
with rio.open("miller_mckee_2004.tif", "w", **meta) as dist:
dist.write(miller_mckee_2004.astype(rio.float32))- The output is a .tif file containing the processed image by the chosen method:
This repository implementations can be tested by running pytest command.
python3 -m pytest
Contributions are always welcome! To fix a bug or enhance an existing module, follow these steps:
- Fork the repo
- Create a new branch (
git checkout -b improve-feature) - Make the appropriate changes in the files
- Add changes to reflect the changes made
- Commit your changes (
git commit -am 'Improve feature') - Push to the branch (
git push origin improve-feature) - Create a Pull Request
While contributing, remember to add tests to the new developed methods.
- A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques
- Bio-optical Modeling and Remote Sensing of Inland Waters
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QUALIDADE DE ÁGUA EM RESERVATÓRIOS (IQAR)
Developed by CERTI Foundation.
This research was supported by FOZ DO CHAPECÓ ENERGIA S.A research and technological development program,
through the PD-02949-2405/2019 project, regulated by Brazilian Electricity Regulatory Agency (ANEEL).
This repository is licensed under the terms of the BSD-style license.