Sproles: We’re in the prairie central heartland of Montana of the headwaters of the Missouri River, and we’re at the Central Agricultural Research Center, which is Montana State University’s working agricultural research lab here in the center part of the state. Part of NASA’s SnowEx is to better understand what and what we cannot measure from the air or from space to try to advance satellite remote sensing of snow. Polamacki: Out here as you can see looking around the snow that we’re studying out here is hugely spatially variable. Sproles: You can have anywhere from, you know, as you can see behind me, a lot of snow to no snow over very short distances. And so how we can better quantify that from space is important. Polamacki: Some really interesting questions that I think we’re getting at up here that we wouldn’t be able to up in the mountains where we are normally working. Feduschak: Today I have dug a few snow pits, taken some measurements, been wrestling with technology as always seems to be the case. Sproles: We’re using a range of techniques. We’re going from old school techniques, like digging snow pits, where we can really get detailed measurements of the snow and the snow properties as we go with depth.

[natural sound] Sproles: We’re doing simple transects where we’re measuring snow depth going across the whole landscape here and then we’re using some pretty sophisticated techniques as well. We’re using UAV’s, uncrewed aerial vehicles, or drones. Polamacki: When I flying the drone earlier today, I was basically just taking many, many photos of the ground from up high to get really high resolution measurements with our camera of the patchiness of the snow. We can throw all of those photos together in a piece of photogrammetry software, stitch them together into a three-dimensional model. We can basically select any point in this giant field and determine what the snow depth was. It’s a way for us to cover a lot of ground pretty quickly. Mullen: Today I’m collecting albedo measurements from a UAV. Albedo is essentially how reflective the surface is. It tells us both how much energy coming from the Sun the surface is reflecting, but more importantly with regards to snow, it tells us how much energy that snow is absorbing, which allows us to kind of determine how fast it’s going to melt and allows us to better predict runoff quantities for water resource modeling. Rizza: We’re looking to use the lidar data to map the snow surfaces. and ultimately be able to calculate snow volumes and water content. Lidar is an active sensing technology and so it’s a laser that gets shot out of the sensor. It bounces off a surface, whether that be the ground or, in this case, the snow surfaces, bounces back to the sensor and that measurement is recorded very precisely to give us very accurate distance measurement. The biggest challenge I’d say is the cold and the wind. The wind is always a challenge for us with a drone in particular. Sproles: It’s an extremely hard environment to work in, it’s harsh, it’s windy, things blow around. But that’s just the nature of where we’re working. Mitchell: Most of the agriculture on this landform and in the surrounding area is dry land agriculture, meaning they don’t use flood or pivot irrigation. So we’re curious how significant this snow is to the soil water, which then turns into crop water. Sproles: Prairies and grasslands occupy about ten percent of the Earth’s land surface, so that’s a lot of land, right? And of that land surface, about 30 percent of it has seasonal snow, meaning that it doesn’t snow once a year, but you have accumulation and melt periods throughout. Feduschak: As kind of biomes are moving north, these prairies and the water that they hold are going to become increasingly important for human habitation and food production. And so getting an idea of how much snow is on the landscape, how that snow is changing, when it’s melting, where it’s going is really important for us to understand, and it’s definitely one of the big gaps in our understanding of snow as we stand today.