Introduction to AOP Data in Google Earth Engine (GEE)

Google Earth Engine (GEE) is a free and powerful cloud-computing platform for carrying out remote sensing and geospatial data analysis. In this tutorial, we introduce you to the NEON AOP datasets that have been uploaded to GEE and demonstrate how to find more information about them.

AOP GEE Data Availability & Access

AOP has published a subset of AOP Level 3 (mosaicked) data products at 5 NEON sites (as of Spring 2022) on GEE. This data has been converted to Cloud Optimized GeoTIFF (COG) format. NEON L3 lidar and derived spectral indices are available in geotiff raster format, so are relatively straightforward to add to GEE, however the hyperspectral data is available in hdf5 (hierarchical data) format, and have been converted to the COG format prior to being added to GEE.

The NEON data products that have been made available on GEE can be accessed through the projects/neon folder with an appended prefix of the Data Product ID, matching the NEON data portal. The table below summarizes the prefixes to use for each data product, and is a useful reference for reading in AOP GEE datasets. You will see how to access and read in these data products in the next part of this lesson.

The table below summarizes the sites, products, and years of NEON AOP data that can currently be accessed in GEE.

Get Started with Google Earth Engine

Once you have set up your Google Earth Engine account you can navigate to the Earth Engine Code Editor. The diagram below, from the Earth-Engine Playground, shows the main components of the code editor. If you have used other programming languages such as R, Python, or Matlab, this should look fairly similar to other Integrated Development Environments (IDEs) you may have worked with. The main difference is that this has an interactive map at the bottom, similar to Google Maps and Google Earth. This editor is fairly intuitive. We encourage you to play around with the interactive map, or explore the ee documentation, linked above, to gain familiarity with the various features.

Read AOP Data Collections into GEE using ee.ImageCollection

AOP data can be accessed through GEE through the projects/neon folder. In the remainder of this lesson, we will look at the three AOP datasets, or ImageCollections in this folder.

An ImageCollection is simply a group of images. To find publicly available datasets (primarily satellite data), you can explore the Earth Engine Data Catalog. Currently, NEON AOP data cannot be discovered in the main GEE data catalog, so the following steps will walk you through how to find available AOP data.

In your code editor, copy and run the following lines of code to create 3 ImageCollection variables containing the Surface Directional Reflectance (SDR), Camera Imagery (RGB) and Digital Surface and Terrain Model (DEM) raster data sets.

//read in the AOP image collections as variables

var aopSDR = ee.ImageCollection('projects/neon/DP3-30006-001_SDR')

var aopRGB = ee.ImageCollection('projects/neon/DP3-30010-001_RGB') 

var aopDEM = ee.ImageCollection('projects/neon/DP3-30024-001_DEM')

A few tips for the Code Editor:

• In the left panel of the code editor, there is a Docs tab which includes API documentation on built in functions, showing the expected input arguments. We encourage you to refer to this documentation, as well as the GEE JavaScript Tutorial to familiarize yourself with GEE and the JavaScript programming language.
• If you have an error in your code, a red error message will show up in the Console (in the right panel), which tells you the line that failed.
• Save your code frequently! If you try to leave your code while it is unsaved, you will be prompted that there are unsaved changes in the editor.

When you Run the code above (by clicking on the Run above the code editor), you will notice that the lines of code become underlined in red, the same as you would see for a spelling error in most text editors. If you hover over each of the lines of codes, you will see a message pop up that says: <variable> can be converted to an import record. Convert Ignore.

If you click Convert, the line of code will disappear and the variable will be imported into your session directly, and will show up at the top of the code editor. Go ahead and convert the variables for all three lines of code, so you should see the following. Tip: if you type Ctrl-z, you can re-generate the line of code, and the variable will still show up in the imported variables at the top of the editor. It is a good idea to retain the original code that reads in the variable, for reproducibility. If you don't do this, and wish to share this code with someone else, or run the code outside of your own code editor, the imported variables will not be saved.

Note that each of these imported variables can now be expanded, using the arrow to the left of each. These variables now show associated information including type, id, and properties, which if you expand, shows a description. This provides more detailed information about the data product.

Information about the image collections can also be found in a slightly more user-friendly format if you click on the blue projects/neon/DP3-30006-001_SDR, as well as DP3-30010-001_RGB andDP3-30024-001_DEM, respectively. Below we'll show the window that pops-up when you click on SDR, but we encourage you to look at all three datasets.

This allows you to read the full description in a more user-friendly format. Note that the images imported into GEE may have some slight differences from the data downloaded from the data portal. For example, note that the reflectance data in GEE is scaled by 100. We highly encourage you to explore the description and associated documentation for the data products on the NEON data portal as well (eg. DP3.30006.001) for relevant information about the data products, how they are generated, and other pertinent details.

You can also click on the IMAGES tab to explore all the available NEON images for that data product. Some of the text may be cut off in the default view, but if you click in one of the table values the table will expand. This table summarizes individual sites and years that are available for the SDR Image Collection. The ImageID provides the path to read in an individual image. In the next step, we will show how to use this path to pull in a single file.

Read AOP Data into GEE using ee.Image

As a last step, we will go ahead and use the path specified in the SDR Asset Details Images table to read in a single image. Pulling in a single image uses almost identical syntax as an image collection, see below:

var TALL_2017_SDR = ee.Image('projects/neon/DP3-30006-001_SDR/DP3-30006-001_D08_TALL_SDR_2017')

Import this variable, and you can see that it pulls in to the Imports at the top, and shows (426 bands) at the right. To the right of that you will see blue eye and target icons. If you hover over the eye it displays "Show on Map". Click this eye icon to place a footprint of this data set in the Map display. If you hover over the target icon, you will see the option "Center Map on Record". Click this to center your map on this TALL SDR dataset. You should now see the footprint of the data as a layer in the Google Map.

A Quick Recap

You did it! You should now have a basic understanding of the GEE code editor and it's different components. You have also learned how to read a NEON AOP ImageCollection into a variable, import the variable into your code editor session, and navigate through the ImageCollection Asset details to find the path to an individual Image. Lastly, you learned to read in an individual SDR Image, pull the footprint of the data into a Map Layer, and center on that region.

It doesn't look like we've done much so far, but this is a already great achievement! With just a few lines of code, you can import an entire AOP hyperspectral data set, which in most other coding environments, is not simple. One of the barriers to working with AOP data (and reflectance data in particular) is it's large data volume, which requires high-performance computing environments to carry out analysis. There are also limited open-source tools for working with the data; many of the software suites for working with hyperspectral data require licenses which can be expensive. In this lesson, we have loaded spectral data covering an entire site, and are ready for data exploration and analysis, in a free geospatial cloud-computing platform.

In the next tutorials, we will pull in spectral data, visualize RGB and false color image composites, interactively plot spectral signatures of pixels in the image, and carry out some more advanced analysis that is highly simplified by the built in GEE functions.

Get Lesson Code

Into to AOP GEE Assets