How Satellite Images Are Converted Into NDVI
- NDVI is a measure of a plant’s health and density in a given area based on how it reflects light and energy.
- Combining NDVI with other vegetation indices like EVI and NDRE ensures accurate data is collected.
- Drone-based NDVI can be used alongside satellite NDVI for more precise data.
One of the key aspects of farming involves constant monitoring of fields to detect problems and address them quickly. As such, we have witnessed the growth of many monitoring technologies and the use of numerous metrics, including agricultural indices like the Normalized Difference Vegetation Index (NDVI), which uses satellite and drone mapping to monitor crop health.
This index uses satellite and drone mapping so that farmers, researchers, and other relevant parties can keep an eye out on their fields. Crop monitoring through NDVI imagery facilitates the making of informed decisions.
Below, we take a look at what NDVI is, how it’s measured, and suggest best practices when using it for better results.
What Is NDVI?
NDVI is a measure of a plant’s vigor or health in a particular area depending on how it reflects light. From a human eye, we tend to judge a plant’s health by how green the vegetation is. However, that’s not all. Underneath the greenness, plants typically absorb red light and reflect near-infrared (NIR).
NDVI measures the difference between visible red light and near-infrared light reflected from plants. If a plant is healthy, it absorbs red light and reflects NIR light. On the flip side, a sickly plant will reflect more red light and absorb less NIR.
Since NDVI relies on satellite images from space, it’s advisable to use agribusiness cameras like Cropler to verify NDVI trends by cross-checking with actual plant conditions on the ground.
Let’s now take a look at how satellite images are converted into NDVI and how this helps in making agricultural decisions.
How Satellites Capture and Process NDVI Data
Earth observation satellites like Sentinel-2, Landsat, and MODIS revolve around the Earth and capture multi-spectral images.
To ensure accurate NDVI values, we need clean images. However, that isn’t always possible. From time to time, it’s cloudy, and the satellites capture distorted images. In addition, the ground may be covered by snow, dust, and other debris, which leads to undesirable picture quality. That doesn’t mean these images are useless. They need to undergo cleaning or processing to eliminate these distortions and allow accurate NDVI calculations.
As mentioned, healthy vegetation absorbs Red Light for photosynthesis and reflects a significant portion of NIR. So, after the images are processed, next is calculating NDVI satellite imagery through the following formula:
Turning Raw Satellite Data Into Actionable Insights
Once farmers have the raw satellite data, they use specialized software to read NDVI imagery. A good example is Geographic Information System (GIS) software which analyzes NDVI values, helping farmers identify areas that may need intervention like more watering.
Farmers also use Precision Agriculture Platforms, like Cropler’s web platform, which integrate NDVI with real-time data from the field. This allows for the monitoring of these values over time, allowing farmers to detect any changes timely and act appropriately. For instance, if in one section of the field, the NDVI values have been declining, it could indicate crop stress, pest infestations, or nutrient deficiencies.
By using NDVI visualization tools, farmers can practice precision agriculture, which means making targeted decisions instead of applying the same treatment to an entire field. This approach helps reduce costs, improve yields, and minimize environmental impact.
Key Agricultural Applications of NDVI
NDVI images in agriculture can be used in multiple applications, including the following.
Variable Rate Application (VRA)
This is a smart farming technology that allows farmers to apply inputs like pesticides or fertilizers only in areas where they’re needed. Drone NDVI imagery allows this by giving farmers an overview of what’s happening in every corner of their fields.
For instance, if they notice one area has high NDVI values, then it may mean it requires less fertilizer. On the other hand, if another area has low NDVI values, then they can specifically investigate the crops in that area and determine whether they need pesticides or more irrigation.
This is beneficial for farmers because it encourages precision farming. Instead of increasing irrigation or applying pesticides in the whole field, farmers only apply measures in the needed areas, saving on resources and reducing environmental impact.
Early Stress Detection
Through NDVI values, farmers can identify issues affecting their crops and act quickly. For example, if plants don’t have enough water, they’ll have less chlorophyll activity, resulting in low NDVI values. Also, if plants are affected by diseases, NDVI values will reduce even before the human eye detects discoloration. With such data, farmers can detect early plant stressors and take action.
Crop Rotation and Soil Management
Healthy soil contributes to healthy plants. NDVI trends help reveal areas where soil fertility is declining, and crop rotations are planned to replenish nutrients. This also helps avoid over-farming in certain regions and, therefore, prevent soil degradation.
Yield Forecasting
Finally, NDVI values indicate vegetation density in a field, which is a good predictor of expected yields. High values indicate strong yield potential and vice versa, allowing farmers to adjust logistics like market planning and supply chain management.
Challenges and Best Practices for NDVI Use
While NDVI is a great crop monitoring tool, it has several downsides, for example:
- Cloud cover affects accuracy.
- Soil background reflectance can affect NDVI readings, particularly in areas with sparse populations.
- Different crop types and growth stages require careful interpretation, as NDVI values can fluctuate naturally.
How can farmers navigate the above challenges to ensure they make good use of NDVI and get accurate data? By following the practices described below.
Combine NDVI With Other Vegetation Indices
These indices include the Enhanced Vegetation Index (EVI), Normalized Difference Red Edge (NDRE), and soil moisture data.
- Just like NDVI, EVI also uses satellite images. However, the latter is more useful as it reduces atmospheric interference like haze and minimizes soil background noise, leading to a more accurate reading.
- NDRE is specifically used to measure chlorophyll content in vegetation. It comes in handy in monitoring crops sensitive to nitrogen deficiency and water stress.
- Soil moisture data helps identify the availability of water in the soil, which can help farmers react quickly.
Use Drone-Based NDVI
Drone-based NDVI provides images with higher resolution, allowing farmers to monitor individual rows of crops or even single plants. In addition, while satellites will only pass over a field every few days or weeks, drones can be used anytime, allowing for frequent data collection. Once a farmer notices issues through satellites, they can use NDVI drone mapping to zoom in on the problem areas and detect issues like diseases before they spread.
When you have questions, we'll help you find the answers you need to take control of your fields and the harvest you care about. Contact Cropler to discover more about our products and the ways we help you proactively protect your operations from the effects of crop diseases. Learn more
All Your Data on One Platform
The cropler web platform allows farmers to gather and analyze NDVI photos over a period of time, hence monitoring growth history and specifics. With this data, farmers can identify crops in distress and act quickly. To learn more about how Cropler’s agri-cameras and web platform work, check out some of our frequently asked questions. You can also contact us for more information on how Cropler can make your field monitoring easier.
