Quickstart
Leaf Quickstart
This quickstart tutorial is written help you quickly experience and start building with Leaf. First, please register on our website to receive your credentials. We've included sample data in your account so you can begin querying data via Leaf immediately.
Leaf's Postman collection
Below you'll find instructions using curl and python, and we've also built a Postman collection to accompany this tutorial. Please find it here: Leaf Postman collection
Authentication
After creating and confirming your account, the first step is to authenticate with your email and password to retrieve a Leaf token. This token is what we'll use in subsequent steps to talk to the API. Be sure to keep a copy of this token for the remaining steps.
- cURL
- Python
- JavaScript
note: Your Leaf Token lasts for 30 days with "rememberMe": "true" in the
json. If you want it to last only 24h, feel free to set "rememberMe" to "false".
After expiration, just make the same request to the same endpoint and you will
get a new access token.
Get sample Field with attached operations
Inside your account, we've created a sample LeafUser and populated it with data. Let's take a look at the fields endpoint to see the sample field:
- cURL
- Python
It returns a JSON object representing the Fields and you can see that there are operations ids attached. Next section will explain how to get the operations by those ids.
Get all sample operation files
Next, let's look at operations data. 'Operations' refers to the data collected when a machine and any implements performed an operation on a farm. This command will list operations files available across your whole account:
- cURL
- Python
Get specific sample operation file
Now that you have a file ID, you can query for the operations data itself and a summary as well.
- cURL
- Python
Get sample operation file summary
- cURL
- Python
Get sample operation file image
You may have already noticed that we're seeing a lot of operations files for this one field and the timestamps span just a few days. This is because we're looking at multiple files output from a machine that effectively represent one large operation - a harvest. Before we talk about merging these pieces into a single operation, let's look at an image from one of the pieces:
- cURL
- Python
Get sample merged operation file
A single strip appears in that image which is no mistake. It's also not as useful as seeing the whole operation across the whole field! To solve this, we allow you to request a merge of operations data and produce images for data of the same type and timeframe. The sample data already includes a merged file.
Let's take a look at that merged harvesting image which is colored to indicate the range of wet mass collected. First, let's find the merged file by simply adding a parameter to filter to origin=merged
- cURL
- Python
And finally, use that file ID to retrieve a list of images, one for each operation property. From this response, you can load one of the sample images:
- cURL
- Python
Get sample satellite imagery
Finally, let's take a look at some satellite imagery. Using data from Sentinel-2, we produce NDVI images for fields you define to be monitored. First we'll list the fields being monitored from the satellite service:
- cURL
- Python
And then we can pull up the sample NDVI image of the sample field like this:
- cURL
- Python