Add CLMS LST sharpening with Landsat notebook

[azlinszkysinergise]

Adds sentinelhub/clms_lst_landsat_sharpening.ipynb. This is an end to end use case, It sharpens the hourly 3 km CLMS LST V3 to 100 m with a Landsat 8/9 residual field, using the Sentinel Hub Process API on CDSE. It then evaluates the method:

• temporal stability of the residual pattern (Budapest, 2022–2024);
• portability of the Landsat–CLMS bias across ten cities in different climate zones;
• adequacy of the additive model;
• an error budget from a leave-one-date-out validation.

Notes

• Executed with outputs on the CDSE JupyterLab sentinelhub kernel; formatted with black 23.3.0; kernelspec sentinelhub.
• Credentials: uses SHConfig(), and asks with getpass only when no credentials are configured. None are stored.
• Not added to sentinelhub/.tests: a full run makes several hundred Process API requests and takes a long time.
• Size ~7.4 MB (embedded figures), within the range of existing notebooks.
• Archived on Zenodo: https://doi.org/10.5281/zenodo.22944002. This file is identical to the archived version.
• Companion custom script: sentinel-hub/custom-scripts, data-fusion/clms_lst_sharpening_with_landsat.

🤖 Generated with Claude Code - fully checked and creation guided by me

Description:

PR Checklist:

• Code is formatted and linted (Python linting action)
• Kernel spec metadata was correctly set (check_kernelspec action)
• Tests pass (test-samples action)
• If applicable, documentation has been updated to reflect changes

[azlinszkysinergise]

this is the pr for the custom script sentinel-hub/custom-scripts#375