S2Mosaic post

docs/_posts/2024-08-01-S2Mosaic-Creating-Cloud-Free-Sentinel-2-Mosaics.md

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+---
+layout: post
+title: "S2Mosaic: Crafting Cloud-Free Sentinel-2 Mosaics with Ease"
+---
+
+Clouds in satellite imagery: the persistent nemesis of remote sensing analysts everywhere. They obscure our view, skew our data, and generally make life difficult. But fear not! Today, we're introducing S2Mosaic, a Python package designed to create cloud-free mosaics from Sentinel-2 imagery. 🛰️☁️🌈
+
+## Why S2Mosaic?
+
+Picture this: you're trying to analyze water extent with S2 data, but half your images are covered in clouds. Frustrating, right? S2Mosaic resolves this by intelligently selecting and combining multiple Sentinel-2 scenes to create a single, truly cloud-free composite!
+
+## The Code Explained
+
+Let's dive into it with a simple example.
+
+{% highlight python %}
+from s2mosaic import mosaic
+from pathlib import Path
+{% endhighlight %}
+
+### Setting Up Parameters
+
+First, we define our mosaic parameters. We're targeting a specific S2 grid area and time range, and we want a visual (true color) composite.
+
+{% highlight python %}
+grid_id = "50HMH"
+start_year = 2022
+start_month = 1
+start_day = 1
+duration_months = 2
+output_dir = Path("output")
+required_bands = ['visual']
+{% endhighlight %}
+
+### Creating the Mosaic
+
+Now for the magic! We call the `mosaic` function with our parameters:
+
+{% highlight python %}
+result = mosaic(
+    grid_id=grid_id,
+    start_year=start_year,
+    start_month=start_month,
+    start_day=start_day,
+    duration_months=duration_months,
+    output_dir=output_dir,
+    required_bands=required_bands,
+
+)
+
+print(f"Mosaic saved to: {result}")
+{% endhighlight %}
+
+### What's Happening Under the Hood?
+
+1. **Scene Selection**: S2Mosaic searches for all available Sentinel-2 scenes within our specified time range and grid id.
+2. **Sorting**: Scenes are sorted based on the percentage of valid (cloud-free) pixels (or image date if specified).
+3. **Cloud Masking**: Each scene is processed using the state-of-the-art OmniCloudMask to identify and mask out clouds and shadows.
+4. **Compositing**: The selected scenes are combined using the mean of valid pixels (or first or last valid pixel if specified) to create a single, cloud-free mosaic.
+5. **Output**: The final mosaic is saved as a GeoTIFF file in our specified output directory (or returned as an array if no output directory is supplied).
+
+## Advanced Usage: Customizing Your Mosaic
+
+S2Mosaic is flexible! Let's look at a more advanced example where we specify particular bands and tweak some parameters:
+
+{% highlight python %}
+array, profile = mosaic(
+    grid_id="50HMH",
+    start_year=2022,
+    start_month=1,
+    start_day=1,
+    duration_months=2,
+    sort_method="oldest",
+    mosaic_method="first",
+    required_bands=["B04", "B03", "B02", "B08"],
+    no_data_threshold=0.001,
+    ocm_batch_size=4,
+    ocm_inference_dtype="bf16"
+)
+
+print(f"Mosaic array shape: {array.shape}")
+print(profile)
+{% endhighlight %}
+
+In this case, we're:
+- Selecting specific bands (Red, Green, Blue, and Near Infrared)
+- Adjusting the cloud masking parameters for optimal performance
+- Getting the mosaic as a NumPy array and rasterio profile instead of saving to disk
+
+## Example Output
+
+Below is an example output with images taken from the South coast of Western Australia, a notoriously cloudy area. The imagery dates from 01/06/2024-01/08/2024.
+
+The settings used to create this output
+{% highlight python %}
+mosaic(grid_id='50HNG',
+        start_year=2024,
+        start_month=6,
+        duration_months=2,
+        required_bands=['visual'],
+        no_data_threshold=0.001,
+        )
+{% endhighlight %}
+
+<img src="https://github.com/DPIRD-DMA/blog/blob/master/docs/images/blog_images/2024-08-01-S2Mosaic-Creating-Cloud-Free-Sentinel-2-Mosaics_image_1.png?raw=true" width="1850">
+
+Notice how the cloudy patchwork has been transformed into a clear, continuous view of the landscape.
+
+That's all folks! The next time clouds try to rain on your remote sensing parade, you'll have S2Mosaic in your toolkit to clear things up. Happy mosaicking! 🌍🛰️🧩
+
+### Acknowledgements
+A big thanks to the Microsoft Planetary Computer for making the data easily accessible and the entire open-source remote sensing community for their continuous innovations.