Satellite or drone image: which one to choose?
Blogpost by Nicos Keable-Vézina, Director, Precision Agriculture
A matter of resolution and application
For nearly a year, I’ve been asked a lot of questions about imagery. True to the spirit of independence and transparency that drives the team behind FieldApex, I chose to go through the different types of imagery on the market and have a closer look at their respective applications and limitations in precision farming.
The big question is: “Is resolution what matters most”?
The straight answer is “no”. But resolution does influence the application that can be made from an image. In agriculture, images are mainly used to analyze plants. So for the sake of this post, I will focus on sensors that can generate images of this type.
Satelitte imagery: Balancing resolution versus analysed area
Commercial satellites offer resolutions ranging from 30 cm to several meters. In general, the higher the resolution offered by a satellite, the lesser territory its images will cover. To successfully capture areas of such magnitude as farming land without compromising resolution, the strategy is to use a satellite constellation featuring relatively high resolution, rather than a single satellite with a broad reach but lesser resolution.
1-10 m: the optimal resolution for fertilizer prescription
It is generally accepted that satellites with resolutions ranging from 1 m to 6 m are the most suitable for fertilizer prescription.
Below 1 m, images are relatively expensive, except when looking at images from previous seasons which are not very helpful for analysing crops of the current season. In addition, there are fewer satellites offering images with a resolution of less than 1 m than there are offering images of a greater resolution. Considering the limited image sources and the high demand for this type of sensor, the risk is great that images captured at the time when the agronomist or the farmer need it will be covered by clouds.
Identifying pest using drone images
Interestingly, it is possible to achieve much finer resolutions (from cm to mm) using specialized cameras mounted on drones. Such resolutions are especially interesting for very specific applications such as pest detection and identification.
Satellite imagery or drone imagery: which one to choose?
In fact, these sensors are complementary. Here is an overview.
- Generation of crop health indices (NDVI, SAVI);
- Generation of variable rate application maps;
- Identification of soil texture;
- Identification of management zones;
- Identification of stress zones (water, diseases).
- Field crops (corn, wheat, soybeans, canola, etc.);
- Presence detection and, in some cases, pest identification;
- Detection and, in some cases, identification of diseases and weeds;
- Identification of certain nutrient deficiencies;
- Topometry for leveling purposes.
- Wine grape;
- Fruit trees;
- Other value-added crops (e.g. vegetables.).
- Low cost, often payable on use;
- No travel needed;
- More simple calibration.
- Dependent on atmospheric conditions (clouds, veil) at the time of passage of the satellite;
- Insufficient resolution to detect pest at the individual level.
- Possibility of choosing the precise moment for the passage of a drone;
- Images of way higher resolution.
- Costly in time and money;
- Many regulatory barriers.
The future of imagery
In the future, drones and unmanned planes and gliders could muddy the waters by making imagery even more accessible. It will become more and more common to use both satellite and drone imagery within a same precision farming platform. At FieldApex, we are already working on it. Watch for our upcoming post on this topic. Although several barriers need to be overcome in terms of regulation and technology before these unmanned aircrafts come to the market, it seems more and more realistic to see them over our fields in a near future.
What’s the bottom line? Satellites or drones?
In my opinion, the question is not so much which imagery to choose, but what application to make of it. I believe that agronomists and farmers should target platforms whose primary purpose is to provide recommendations rather than just show images. It could be prescribing fertilizer rates, generating variable rate application shapefiles, identifying areas for pest control treatment or identifying soil textures, to name a few. Depending on the intended application, it will be up to the platform provider to use the images featuring the most adequate resolution. When and if combined to the power of artificial intelligence, the data extracted from these images is invaluable in agriculture. This is yet another topic to be continued in another post!