One of my most difficult and rewarding projects has been receiving and decoding weather satellite imagery. So far, I’ve decoded two different types of satellite data: geostationary and polar-orbiting.
Geostationary satellites orbit earth at a distance and orbit speed that makes them relatively stationary in the sky in comparison to earth. This results in satellites that can always be found above the same area on earth; if a person is under this area, they can point an antenna towards the satellites and always receive their transmissions. At my location, the NOAA operated geostationary weather satellite GOES 16 is always above me in the sky. This satellite has many instruments, but I was primarily interested in the Advanced Baseline Imager (ABI). The ABI is an imaging instrument that takes photos in 16 different wavelength bands. It’s camera takes very high quality “full disk” images, making it possible to receive a photo of the entire earth instead of just a “local” close-up image. The satellite transmits the photo data every 30 minutes at a frequency of 1.7 GHz. To receive the photos, I pointed a 2.4ghz directional WiFi grid antenna, modified for 1.7 GHz reception, at GOES 16. I purchased this grid antenna off Craigslist, which required me to fabricate a replacement part out of an aluminum can. To filter the signal, I connected a low noise amplifier between the antenna and a Software Defined Radio, which transmitted the signal to a Raspberry Pi. The Raspberry Pi ran an open-source software, GOES Tools, to decode the data received from the antenna. Unfortunately, this specialty software was plagued with issues, requiring me to wade through a web of driver updates, file edits, and customizations. The final program ran on a Raspberry Pi single-board computer located in the field. I created a hard-wired local area network between the Raspberry Pi and a laptop, which gave me the ability to control the GOES Tools software and check my satellite alignment without an internet connection. After lots of calibrations, the images started to be received from GOES 16’s downlink.
When I saw my first downloaded image of the entire earth, I was in awe. I could not believe that I had built a system capable of gathering such breathtaking images of our planet from 22,236 miles away. I could see large winter storms before they hit and make time-lapses of the “terminator” line (the junction between night and day) cresting across the earth's surface. This project inspired more space radio signal experiments, such as receiving Slow-Scan Television (SSTV) images from the International Space Station (ISS) and locating signs of life in “dead” satellites launched in 1964.
Geostationary satellites orbit earth at a distance and orbit speed that makes them relatively stationary in the sky in comparison to earth. This results in satellites that can always be found above the same area on earth; if a person is under this area, they can point an antenna towards the satellites and always receive their transmissions. At my location, the NOAA operated geostationary weather satellite GOES 16 is always above me in the sky. This satellite has many instruments, but I was primarily interested in the Advanced Baseline Imager (ABI). The ABI is an imaging instrument that takes photos in 16 different wavelength bands. It’s camera takes very high quality “full disk” images, making it possible to receive a photo of the entire earth instead of just a “local” close-up image. The satellite transmits the photo data every 30 minutes at a frequency of 1.7 GHz. To receive the photos, I pointed a 2.4ghz directional WiFi grid antenna, modified for 1.7 GHz reception, at GOES 16. I purchased this grid antenna off Craigslist, which required me to fabricate a replacement part out of an aluminum can. To filter the signal, I connected a low noise amplifier between the antenna and a Software Defined Radio, which transmitted the signal to a Raspberry Pi. The Raspberry Pi ran an open-source software, GOES Tools, to decode the data received from the antenna. Unfortunately, this specialty software was plagued with issues, requiring me to wade through a web of driver updates, file edits, and customizations. The final program ran on a Raspberry Pi single-board computer located in the field. I created a hard-wired local area network between the Raspberry Pi and a laptop, which gave me the ability to control the GOES Tools software and check my satellite alignment without an internet connection. After lots of calibrations, the images started to be received from GOES 16’s downlink.
When I saw my first downloaded image of the entire earth, I was in awe. I could not believe that I had built a system capable of gathering such breathtaking images of our planet from 22,236 miles away. I could see large winter storms before they hit and make time-lapses of the “terminator” line (the junction between night and day) cresting across the earth's surface. This project inspired more space radio signal experiments, such as receiving Slow-Scan Television (SSTV) images from the International Space Station (ISS) and locating signs of life in “dead” satellites launched in 1964.
Polar-orbiting satellites pass over earth, only appearing above the horizon every few hours. This makes it important to precisely time receiving their data. There are two different types of polar-orbiting satellites I was able to receive images from, Analog NOAA Satellites and Digital Russian Meteor Satellites. The Digital Meteor Satellites images are much higher quality, but more difficult and less forgiving to receive and decode.
To increase the chance of receiving the transmission from these satellites, I built and installed a 15 ft antenna mast out of PVC on top of my garage. I then tuned a dipole antenna to 134 MHz and positioned it to receive the right hand circularly polarized satellite signal downlinks. This antenna was placed on the top of the mast with a coaxial cable connecting to my Software Defined Radio (SDR). The SDR takes the radio signals and turns them into a digital signal that my computer can interpret. I used the software SRD# to tune to the 137MHz signal and then piped the audio to the NOAA satellite decoding software, WXtoImg, which takes the signal from the satellite and converts it to final imagery. These weather satellites produce additional data, such as heat maps and storm data. Below are some photos of my receiver setup and a few of the photos I received.
To increase the chance of receiving the transmission from these satellites, I built and installed a 15 ft antenna mast out of PVC on top of my garage. I then tuned a dipole antenna to 134 MHz and positioned it to receive the right hand circularly polarized satellite signal downlinks. This antenna was placed on the top of the mast with a coaxial cable connecting to my Software Defined Radio (SDR). The SDR takes the radio signals and turns them into a digital signal that my computer can interpret. I used the software SRD# to tune to the 137MHz signal and then piped the audio to the NOAA satellite decoding software, WXtoImg, which takes the signal from the satellite and converts it to final imagery. These weather satellites produce additional data, such as heat maps and storm data. Below are some photos of my receiver setup and a few of the photos I received.