Human eyes are excellent remote sensors of, say, the nicely saturated green color of a healthy plant. They are also good at detecting the not-so-healthy yellow hues. What they are bad at, however, is seeing in the deep red, or NIR (near infrared), and on the edge between the two.
Enter multispectral imagery from space! Superman’s vision, basically, aimed at determining the vegetation density, growth rate and by extension, general health. It all boils down to the absorption and reflection of different wavelengths of light. A healthy plant reflects a lot of near-infrared light, which a human eye can’t see at all.
Put simply, a vegetation index tells you exactly what you don’t know but need to. It informs you about the issues that are invisible to your eyes. Farming software, then, translates these color variations into the familiar visible wavelengths.
There are several widely accepted measurement scales for vegetation indices, the most popular being -1 to 1 (NDVI, NDRE, MSAVI, etc.).
EOSDA Crop Monitoring
We offer not one but four vegetation indices at your service! Why so many? The answer is simple, slightly different specializations. Basically, the more indices you use, the wider your scope beyond the visible range becomes. We recommend monitoring the health of your crops from four different angles, depending on the stages of their growth.
Good Old NDVI
The Normalized Difference Vegetation Index has been in use since the 1970s. It is reliable throughout most seasons, with a few important exceptions. We at EOS, highly recommend referring to NDVI during the active stages of crop growth, for maximum accuracy.
To check the NDVI data, simply navigate the general chart below the map in the Fields tab. Note: this is a default tab and the chart loads automatically for any given field. Similarly, NDVI is the default index.
To see precise NDVI value figures, click on the expand-icon.
NDRE – On The Edge Of Red
The first two letters suggest that this index is similar to NDVI, while the latter two stand for Red Edge. This index is more sensitive to the edge between the red and near-infrared bands of light, which makes it a more accurate nitrogen-in-the-air detector than NDVI. Which is why we recommend using them both at the same time.
On the chart, you won’t be able to see more than one vegetation index at a time, but it is really easy to switch between them.
To see the NDRE figures on the chart, simply select it from the drop-down menu list on the top left, or from the one just above the chart, on the right.
MSAVI – Crops Are About To Start Growing
The Modified Soil Adjusted Vegetation Index works best at the early growth stages, since it accounts for the bare soil on the field that is not due to poor growth but simply awaiting the crops to pop out. NDVI, on the other hand, under such conditions, reads as lack of vegetation, which is interpreted as a bad omen. Just look at your field with MSAVI -eyes and give your crops some time for the Normalized Difference index to detect them.
Select MSAVI in the drop-down menu, to see its five annual/seasonal curves.
ReCl – Check Your Chlorophyll Levels
Re – stands for Red edge, Cl – for chlorophyll. This index has been designed to detect the chlorophyll content and is most relevant during the active stages of growth. It is measured differently from NDVI, NDRE, and MSAVI, using a scale of all natural numbers starting from 0.
To see the representation of this index on the chart, select it from one of the two drop-down menus.
NDMI – Moisture Level Detection
NDMI is all about water stress detection, since it is a “moisture” index. The scale is set between 1 (no water stress) and 0 (high water stress). The value, however, will vary depending on the crop’s variety and its current stage of development. NDMI will also show you which areas in the farm or field have higher/lower moist levels.
Tip: abnormally lower than average NDVI may indicate a water stress. Use NDMI to confirm.
To apply NDMI to your farm or field, select it from a drop down list of indices.
Generally speaking, green is good, red is not good. We have taken this simple concept and put it to work in our visual interface layout. To have a look at your field from a vegetation index point of view, go to the Zoning tab, select a date, a number of zones (variations on the field, 2-7 available), and click calculate.
This is how the Zoning tab looks from the inside, with a list of fields that you have already added.
And this is the field which you have selected from the list to break it into a number of zones (2-7), according to vegetation index data.
After you have clicked “Calculate,” you should see something interesting, as follows:
This is your field with three different areas (zones) of vegetation growth according to NDVI, red signifies either a lack of vegetation due to various issues that can be fixed, or because there simply isn’t any vegetation there at the moment. Read more about Zoning on our website.
Finally, you can download the vegetation index data either as a raster .tiff file or a shape file. Just locate the transparent bar with mini data on the vegetation index in the bottom right corner of the map. It is right above the field analytics window, in the Fields tab.
Healthy Crops – Make It So
To monitor your crops, you need to get the full picture of their state at any stage, such as health, density, and growth. Out of hundreds of vegetation indices existing today, we have chosen four:
- NDVI, especially relevant during the active stage;
- NDRE, can be used alongside NDVI, and/or during later growth stages;
- MSAVI, useful at a very early stage of plant development, when there is a lot of bare soil in the field;
- ReCl, to monitor the chlorophyll content, the tell-tale sign of the crop’s health;
- NDMI, to monitor the crop’s water stress level.
You have our recommendations on how to use them to your maximum benefit now. Keep your crops alive and well!
Natalia Ivanchuk holds a Master’s degree in Applied Mathematics (2013) from the National University of Water and Environmental Engineering. She is an author of 60+ scientific publications, monographs, and other scientific works.
In 2019, she successfully defended a thesis, “Mathematical modeling of filtration processes in soil environments taking into account the influence of elements of engineering structures” and earned the Candidate of Technical Sciences degree.
Currently, Natalia is an Associate Professor at the Department of Computer Sciences and Applied Mathematics at the National University of Water and Environmental Engineering in Rivne, Ukraine. She teaches different programming languages (C++, C#, JS, among others) at the said university.
Natalia is actively engaged in scientific research related to the practical application of new programming algorithms. Programming is also something she likes to do in her spare time.
Her expertise and constant desire to learn and perfect her programming skills, especially in working with Python, has been most beneficial for EOS Data Analytics.