Cody did some magic folder copying to make it work..
He created a new env in miniforge
copied the context of the arcpy default env folder into it
D:\Programs\miniforge\envs\arcgispro-py3-clone
and in ArcPro (after a restart) added the new environment by going to the folder location
and adding it this way.

PyCharm recommendation:
To update the existing environment see the conda proup subcommand
        > conda proup --help

To manually create a new environment for use with the current ArcGIS application:
 1. Generate a list of additional packages installed in your current environment,
    with the conda command:
        > conda env export > env.yaml
 2. (Optional) If you have additional dependencies installed through pip,
    find those with pip freeze:
        > pip freeze > requirements.txt
 3. Create a new environment by cloning arcgispro-py3, and activate it:
        > conda create --clone arcgispro-py3 --name my-env --pinned
        > activate my-env
 4. Add back missing conda packages from your current environment:
        > conda env update -n my-env -f env.yaml
 5. (Optional) Add back missing packages from pip:
        > pip install -r requirements.txt
 6. (Optional) Make it the default for the ArcGIS application and the
     "Python Command Prompt":
        > proswap my-env


In Python Command Prompt (pinned to start menu):

conda create --clone arcgispro-py3 --name arc-rasterio --offline
conda activate arc-rasterio





The Python Command Prompt in ArcGIS Pro is a tool that allows you to work with Python scripts and manage Python environments effectively. Here's a quick overview to help you get started:
Accessing the Python Command Prompt:
- Open ArcGIS Pro.
- Navigate to the Start Menu on your computer.
- Search for Python Command Prompt (installed with ArcGIS Pro).
- Launch it to access the Python environment associated with ArcGIS Pro.
Key Features
Run Python Scripts: You can execute Python scripts directly within the ArcGIS Pro environment.
Manage Conda Environments: Use Conda commands to manage Python packages and environments.
Access ArcPy: The Python Command Prompt is pre-configured to work with ArcPy, the Python library for ArcGIS.
Common Commands
Check Python Version: python --version
List Installed Packages: conda list
Install a New Package: conda install <package_name>
Update ArcGIS Python Environment: conda update -n arcgispro-py3 --all
Tips
Always use the Python Command Prompt provided by ArcGIS Pro to ensure compatibility with its Python environment.
Avoid modifying the default environment directly; instead, create a clone if you need customizations
(conda create --clone arcgispro-py3 --name myenv).
This tool is essential for automating workflows, performing geospatial analysis, and managing Python dependencies in ArcGIS Pro.
Let me know if you'd like further guidance!


Option 2: Use conda on the Command Line
1. Locate your Python CLI

Open the ArcGIS Pro Python Command Prompt (search Windows for “Python Command Prompt (ArcGIS Pro)”).

2. Activate your environment (if you cloned it):

bash
conda activate arcgispro-custom
Install Rasterio (with dependencies) from conda-forge:

conda install -c esri -c conda-forge rasterio
-c esri ensures Esri’s packages stay pinned.

-c conda-forge pulls in GDAL, PROJ, Fiona, etc., all needed by Rasterio.

Verify installation in Python:

python
>>> import rasterio, arcpy
>>> print(rasterio.__version__, arcpy.__version__)
Restart any IDE or ArcGIS Pro to load the updated env.

Troubleshooting & Tips
Always clone the default arcgispro-py3 before installing third-party packages to avoid breaking out-of-the-box functionality.

If you run into version conflicts, you can try specifying versions:

bash
conda install -c esri -c conda-forge rasterio=1.3.6 gdal=3.7.2
In some cases, mixing channels can lead to dependency issues. If you hit errors, create a fresh conda environment (conda create -n myenv -c esri -c conda-forge python=3.9 arcpy rasterio).

Once installed, you’ll be free to write scripts like:

python
import arcpy
import rasterio

# ArcPy workflow
arcpy.management.Clip(...)

# Rasterio workflow
with rasterio.open("clipped.tif") as src:
    arr = src.read(1)
—and have both libraries available in the same interpreter.