This example extracts data over agricultural areas in the "Corn Belt" of the USA to show how we can monitor crop growth. We use the Normalised Difference Vegetation Index (NDVI), which compares the NIR channel to the red channel in each image pixel to highlight the presence of vegetation. The period covers one year from Jan-Dec. The NDVI peaks in about August, just when the crop starts to dry out.

How to monitor crop growth

• Step 1: Import the area
• Step 2: Set up the optical satellite data block
• Step 3: The time series block
• Step 4: Interpret the output

Step 1: Import the area

• Attached to this article is a file called US-corn-belt.geojson. Download it.
• This is a file that describes a rectangular area covering an agricultural area in Iowa. 
• Use the Upload Area button on the right hand side of the map to upload this file into Earth Blox, which will automatically give it an Area Number (which will be Area 1, unless you have another area already on the map).
• It should look like this (lying between Des Moines, Omaha and Kansas City):

Step 2: Set up the optical satellite data block

• From the toolbox, go to INPUT->SATELLITE IMAGERY and choose the optical satellite block. It will already have the cloud filtering and visualisation block in place.
• Drag out the visualisation block and drop it in the bin.
• Select Sentinel 2.  (We use Sentinel 2 because is can see finer detail than Landsat, but you are free to change it to Landsat to compare the difference. You will also notice there are fewer months that are cloud free for Landsat).
• Make sure the Area selection is Area 1. 
• Choose dates from 1st January 2020 to 31st December 2020. 
• Leave the cloud filtering block on the default settings. 
• It should now look like this:

• The red warning triangle tells you that this block is not ready to run. If you click on it, it will tell you that you need an output block. That is what we will do next.

Step 3: The time series block

• From the ANALYSIS toolbox, bring in the TIME SERIES block into the workspace. 
• Then drag it into the space beneath the Cloud filtering block. When you drag in the time series block make sure it “snaps” in place. 
• We want to look at a monthly time series of NDVI, as this will give us the best indication of healthy vegetation, so select Monthly on the time series block.
• From the ANALYSIS toolbox, go to INDICES, and bring in the NDVI block into the workspace.
• Click the NDVI block into the TIME SERIES block.
• And finally, add an Other data visualisation block from the OUTPUT menu, and give the layer a name.
• It should look like this:

Step 4: Interpret the output

• Once you click the green RUN WORKFLOW button it will take some time to analyse all the data from the 12 months selected, and then map them as a time series.
• Once complete, click the “play” button on the map interface below the area of interest to view the
  animation.
• Each step in the animation is a month, going from January to December.
• Click the legend icon to see how the range of NDVI is mapped to levels of blue, when negative (no vegetation) and levels of green, when positive (increasing vegetation cover or health). (In this example there are no areas of blue, but if you look elsewhere you might see some.)
• As you watch the animation, note how the landscape goes through different levels of NDVI, beginning low, reaching a peak in August, then dropping off in October as the fields are harvested.
• Now click the “Layers” icon and use the slider to make the NDVI image semitransparent. It is also worthwhile zooming right in so that individual fields are clearly visible. Then switch from “Map” to “Satellite” view so that you can see the highly detailed image of the fields in the background.
• By using your mouse to drag the animation, notice how individual fields exhibit changes in NDVI at different points through the year, indicating that these are different crops that are reaching maturity at various times of the year. 
• Areas that maintain a high NDVI throughout the year will be forests or hedgerows.

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