##Originally from README that was added into this file The Day 1 Lesson (Jupyter Notebooks) were designed in the following order:

1. intro_neon_aop_hyperspectral_python.ipynb
2. hdf5_hyperspectral_functions.ipynb
3. plot_spectral_signatures.ipynb
4. calculate_ndvi_extract_spectra_with_masks.ipynb

Data, once uploaded should be stored under the Remote-Sensing-Python folder, or the paths to access data will need to be modified in the notebook scripts. I am still updating and adding functions to the neon_aop.py module that is loaded at the start of lessons 2-4, but all functions required to run the notebooks should already be uploaded.

This is a development repository for the NEON Data Skills portal. Here the NEON Data Skills team develops and builds new content. Content is transferred to the NEONScience/NEON-Data-Skills repository to be published.

This directory will contain all the code related to the Remote Sensing Python lessons originally created for Data Institute 2017.

*images/Remote-Sensing-Python/...: each series/lesson will have a subdirectory with the same naming/file path as in the _posts directory. This allows for organized and automated creation of files and embedding the images in the files.

• code/Remote-Sensing-Python/...: this directory (same file paths inside) will contain any code that should be downloadable with the lesson via the "Download code" button at the bottom of each tutorial page.