Additional Resources

NEON

We recommend the following tutorial series and tutorials for working with NEON AOP data. These provide example workflows at a range of levels from beginner to intermediate/advanced, in various programming languages including Python, R, and Google Earth Engine (JavaScript and Python).

Python

• Introduction to Hyperspectral Remote Sensing Data in Python

• Notebooks for working with NEON and EMIT Hyperspectral Data

R

• Introduction to Hyperspectral Remote Sensing Data in R

• Introduction to Light Detection and Ranging (LiDAR) in R

Bilingual (Python/R)

• Download and Explore NEON Data

• Using an API Token when Accessing NEON Data with neonUtilities

• Compare tree height measured from the ground to a Lidar-based Canopy Height Model

Google Earth Engine (GEE)

NEON GEE Publisher Catalog

GEE JavaScript

• Intro to AOP Data in Google Earth Engine (GEE) Tutorial Series

• Random Forest Species Classification using AOP and TOS data in GEE

• Principal Component Analysis of AOP Hyperspectral Data in GEE

GEE Python

• Intro to AOP Datasets in Google Earth Engine (GEE) using Python

• Intro to AOP Hyperspectral Data in Google Earth Engine (GEE) using Python geemap

• Exploring NEON AOP remote sensing and GBIF occurrence data in Google Earth Engine Python (geemap) to assess the impacts of a wildfire

NASA Airborne

• Airborne and Field Data Resource Center

• NASA Airborne Science Data Tutorials

• BioSCape

• SHIFT

• VSWIR Imaging and Thermal Applications, Learning, and Science Repository