opticallyshallowdeep 1.2.3
Identify optically shallow and deep waters in satellite imagery
Requires Python: >=3.8

# Optically-Shallow-Deep 

This python tool delineates optically shallow and deep waters in Sentinel-2 imagery. The tool uses a deep neural network (DNN) that was trained on a diverse set of global images.

Supported input includes Level-1C (L1C) SAFE files and ACOLITE-processed L2R netCDF files. The output geotiff contains probabilities of water pixels being optically shallow and deep. 

**Home page:** <a href="https://github.com/yulunwu8/Optically-Shallow-Deep" target="_blank">https://github.com/yulunwu8/Optically-Shallow-Deep</a>

**Publication:** Richardson, G., Foreman, N., Knudby, A., Wu, Y., & Lin, Y. (2024). Global deep learning model for delineation of optically shallow and optically deep water in Sentinel-2 imagery. *Remote Sensing of Environment*, 311, 114302. <a href="https://doi.org/10.1016/j.rse.2024.114302" target="_blank">https://doi.org/10.1016/j.rse.2024.114302</a>

Originally coded by G. Richardson and A. Knudby, modified and packaged by Y. Wu

Models trained by G. Richardson and N. Foreman

 
## Installation 

**1 - Create a conda environment and activate it:**

```bash
conda create --name opticallyshallowdeep python=3.10
conda activate opticallyshallowdeep
```

**2 - Install tensorflow**

For mac OS: 

```bash
conda install -c apple tensorflow-deps
python -m pip install tensorflow-macos
```


For Windows and Linux:

```bash
pip3 install tensorflow==2.13.0
```


More on installing tensorflow: [https://www.tensorflow.org/install](https://www.tensorflow.org/install)


**3 - Install opticallyshallowdeep:**

```bash
pip3 install opticallyshallowdeep
```


## Quick Start

For L1C files: 

```python
import opticallyshallowdeep as osd

# Input file 
file_L1C = 'folder/S2.SAFE' 

# Output folder 
folder_out = 'folder/test_folder_out'

# Run the OSW/ODW classifier 
osd.run(file_L1C, folder_out)
```

For ACOLITE  L2R files: 

```python
import opticallyshallowdeep as osd

# Input files 
file_L1C = 'test_folder_in/S2.SAFE' 
file_L2R = 'test_folder_in/L2R.nc' 

# Output folder 
folder_out = 'folder/test_folder_out'

# Run the OSW/ODW classifier 
osd.run(file_L1C, folder_out, file_L2R=file_L2R)
```

The L1C file is always required as it contains a built-in cloud mask. Pixels within 8 pixels of the cloud mask are masked to reduce the impact of clouds. 


Output is a 1-band geotiff, with values of prediction probability of optically shallow water (OSW): 100 means most likely OSW, 0 means most likely optically deep water (ODW). Non-water pixels are masked. 

A log file, an intermediate multi-band geotiff, and a preview PNG are also generated in the output folder. They can be deleted after the processing. 


**Sample Sentinel-2 scene and output:**

<img src="images/TOA.jpeg"  height="500">

<img src="images/OSW.jpeg"  height="500">


## Tips 

It is recommended to treat pixels with values between 0 and 40 as ODW, and those between 60 and 100 as OSW (Richardson et al., 2024).

Users have reported that averaging results from multiple images acquired on different days can help reduce noise and improve the overall accuracy of classification results.


## Training, test, and validation data 

All annotated shapefiles used in training, testing, and validating the DNN model are in the annotated_shapefiles folder, grouped by Sentinel-2 Scene ID.