For the past couple years, I've been receiving images from weather satellites with an antenna in the back yard. I wrote about it here. These images are relatively easy to receive, but don't provide very good resolution of the earth. In my experience, image resolution and ease of reception are inversely related, meaning the higher resolution image, the harder it is to actually receive. The satellites I've received previously are actually equipped with very powerful sensors, but the data must be compressed to be received with a simple antenna. To receive their full resolution images, a satellite dish is required in addition to a higher quality radio (compared to the RTL-SDR). For me, a satellite dish was easy to get my hands on as my neighbor had one he no longer needed. Dish size is important because the larger the area of the reflective surface, the "louder" the signal will be. The dish I got was a bit on the small side, but it worked. A dish also requires a feed, which is the part that actually receives the signal from the satellite. You can think of the feed as the antenna, and the dish is focusing the signal on the antenna. I first tried making a very crude feed, but it did not work very well because it's dimensions weren't accurate, so I used my university's 3d printer to create a holder for the feed, which worked much better. Next, I needed a better radio as the bandwidth of the signal was too big for my RTL-SDR to handle. I chose to get an Airspy Mini, which is the next step up in terms of performance. The next challenge for receiving these higher resolution images is putting it all together. I need to connect my computer to the radio, which is then connected to the feed, which is attached to the dish's arm. Then, when a satellite is passing overhead, I need to track it with the dish as it transmits its image back to earth. If I'm not pointing the dish at the right angle and elevation for a moment, it will result in a loss of data in the image, which shows as white noise. The process of receiving and image is much more involved, so harder to automate, but more rewarding when you get a clean image.