Most digital agriculture solutions have been reluctant to include satellite imagery because of reliability reasons; If it’s cloudy during the satellite overpass, you won’t get an image. And in addition to being more reliable, they want more information out of satellite imagery than vegetation indices; information on soil, on carbon sequestration, and high value solutions that could give them “more bang for the buck”. Well that it is exactly what radar satellite imagery can offer.
What is SAR and why is SAR interesting ?
Active radar based satellite imagery, also known as SAR (Synthetic Aperture Radar) is not a new technology, but has gained some popularity over the last 4 years with the launch of the Sentinel-1 satellites, which provide high-resolution SAR images of the earth, every few days. The technology is radically different from “traditional” satellite imagery which uses optical sensors, making it quite difficult to use and understand.
However, the advantages of this technology are huge! SAR imagery can provide information even during cloudy weather, in the middle of the night and is extremely good at detecting materials and the topography. It opens new avenues of innovation for agriculture that can propel satellite technology at the top of the must-have list of every digital agriculture company. It can provide soil composition information, soil moisture data, information about farming practices like tilling and much more!
Let’s have a look at how it works.
How does SAR work ?
First the basics: Radar is a detection system that uses radio waves to determine the range, angle, or velocity of objects. Active radar sensors send radio waves from a transmitter, the waves reflect off an object and return to the receiver, giving information about the object's location and speed.
So SAR imagery is the signal received from the reflected radio waves, and in the case of Sentinel-1, the receiver is a satellite orbiting the earth about 700km away from the ground. Pretty cool right?
This explains why SAR imagery is not affected by cloud coverage or night. The waves penetrate through the clouds, bounce on the ground and back to the satellite. No light needed.
The second concept very important to understand with SAR is the backscatter. It describes the way the reflected waves get back to the receiver. While in traditional optical imagery, wavelengths like green or near-infrared are recorded, for SAR the recorded signal is backscatter. Depending on the surface on which it bounces, it can vary greatly.
It is definitely a challenge to interpret the information, but it is also a great asset. The SAR sensors are sensitive to moisture, which means that it is possible to detect humidity variations in the soil based on the backscatter signal from the satellite. It also means that you can detect information on the soil texture from the backscatter, and should be able to make the difference between a sandy soil and a silty one, right? Well, with the proper calibration, it is!
The next logical questions are: What about crops in the field? They are going to affect the backscatter, no? And for moisture I need underground information, not surface. How is that handled?
To answer these questions, we need to introduce a third concept (it’s the last one, promised!): The type of radio wave band. The four bands most commonly utilized for SAR applications are referred to as: P, L, C and X.
Depending on the band, the wavelength will have varying abilities to penetrate through different materials at different depths to provide information. For the case of Sentinel-1 which utilizes the C-band, wavelengths are able to penetrate the uppermost layers of the soil or vegetation canopy.. This means that it can also provide underground soil information. This opens up a number of possibilities that we could only dream of with optical satellite imagery.
What's next ?
The SAR technology applied to agriculture is still in its infancy, but we can already perceive all the opportunities it can bring. Additionally, it relies heavily on the inclusion of artificial intelligence methods, which amplify the potential of the technology (to understand how AI helps satellite imagery in agriculture, you can read this article). We can already create solutions that revolutionize the way we perceive Agriculture monitoring, and we expect dozens of new use cases to appear in the coming years!
At SpaceSense, we made SAR imagery one of our specialties. So if you want to know more about our SAR-based solutions reach out to us below!
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