Update README.md

README.md

@@ -508,6 +508,7 @@ A conceptually simple and scalable approach to serving up deep learning model in
 * [rioxarray](https://github.com/corteva/rioxarray) -> geospatial xarray extension powered by rasterio
 * [aws-lambda-docker-rasterio](https://github.com/addresscloud/aws-lambda-docker-rasterio) -> AWS Lambda Container Image with Python Rasterio for querying Cloud Optimised GeoTiffs. See [this presentation](https://blog.addresscloud.com/rasters-revealed-2021/)
 * [godal](https://github.com/airbusgeo/godal) -> golang wrapper for GDAL
+* [Write rasterio to xarray](https://github.com/robintw/XArrayAndRasterio/blob/master/rasterio_to_xarray.py)
 
 ## Python low level numerical & data manipulation
 * [Dask](https://docs.dask.org/en/latest/) works with your favorite PyData libraries to provide performance at scale for the tools you love -> checkout [Read and manipulate tiled GeoTIFF datasets](https://examples.dask.org/applications/satellite-imagery-geotiff.html#) and [accelerating-science-dask](https://coiled.io/blog/accelerating-science-dask-gentemann/). [Coiled](https://coiled.io) is a managed Dask service.
@@ -539,6 +540,8 @@ A conceptually simple and scalable approach to serving up deep learning model in
 * [folium](https://python-visualization.github.io/folium/) -> a python wrapper to the excellent [leaflet.js](https://leafletjs.com/) which makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. Also checkout the [streamlit-folium](https://github.com/randyzwitch/streamlit-folium) component for adding folium maps to your streamlit apps
 * [GEOS](https://geos.readthedocs.io/en/latest/index.html) -> Google Earth Overlay Server (GEOS) is a python-based server for creating Google Earth overlays of tiled maps. Your can also display maps in the web browser, measure distances and print maps as high-quality PDF’s.
 * [GeoDjango](https://docs.djangoproject.com/en/3.1/ref/contrib/gis/) intends to be a world-class geographic Web framework. Its goal is to make it as easy as possible to build GIS Web applications and harness the power of spatially enabled data. [Some features of GDAL are supported.](https://docs.djangoproject.com/en/3.1/ref/contrib/gis/gdal/)
+* [tifffile](https://github.com/cgohlke/tifffile) -> Read and write TIFF files
+* [xtiff](https://github.com/BodenmillerGroup/xtiff) -> A small Python 3 library for writing multi-channel TIFF stacks
 
 ## Python graphing and visualisation
 * [hvplot](https://hvplot.holoviz.org/) -> A high-level plotting API for the PyData ecosystem built on HoloViews. Allows overlaying data on map tiles, see [Exploring USGS Terrain Data in COG format using hvPlot](https://discourse.holoviz.org/t/exploring-usgs-terrain-data-in-cog-format-using-hvplot/1727)