1 00:00:00,000 --> 00:00:04,000 [airplane] 2 00:00:04,000 --> 00:00:08,000 [airplane] 3 00:00:08,000 --> 00:00:12,000 Sproles: We’re in the prairie central heartland of Montana of the 4 00:00:12,000 --> 00:00:16,000 headwaters of the Missouri River, and we’re at the Central Agricultural Research 5 00:00:16,000 --> 00:00:20,000 Center, which is Montana State University’s working agricultural research lab 6 00:00:20,000 --> 00:00:24,000 here in the center part of the state. Part of NASA’s SnowEx is 7 00:00:24,000 --> 00:00:28,000 to better understand what and what we cannot measure from the air 8 00:00:28,000 --> 00:00:32,000 or from space to try to advance satellite remote sensing 9 00:00:32,000 --> 00:00:36,000 of snow. Polamacki: Out here as you can see looking around 10 00:00:36,000 --> 00:00:40,000 the snow that we’re studying out here is 11 00:00:40,000 --> 00:00:44,000 hugely spatially variable. Sproles: You can have anywhere from, you know, as you can see 12 00:00:44,000 --> 00:00:48,000 behind me, a lot of snow to no snow over very short distances. 13 00:00:48,000 --> 00:00:52,000 And so how we can better quantify that from space is important. 14 00:00:52,000 --> 00:00:56,000 Polamacki: Some really interesting questions that I think we’re getting at up here that we 15 00:00:56,000 --> 00:01:00,000 wouldn’t be able to up in the mountains where we are normally working. 16 00:01:00,000 --> 00:01:04,000 Feduschak: Today I have dug a few snow pits, taken some 17 00:01:04,000 --> 00:01:08,000 measurements, been wrestling with technology as 18 00:01:08,000 --> 00:01:12,000 always seems to be the case. Sproles: We’re using a range of techniques. We’re going 19 00:01:12,000 --> 00:01:16,000 from old school techniques, like digging snow pits, where 20 00:01:16,000 --> 00:01:20,000 we can really get detailed measurements of the snow and the snow properties 21 00:01:20,000 --> 00:01:24,000 as we go with depth. 22 00:01:24,000 --> 00:01:28,000 [natural sound] Sproles: We’re doing simple transects where we’re 23 00:01:28,000 --> 00:01:32,000 measuring snow depth going across the whole landscape here 24 00:01:32,000 --> 00:01:36,000 and then we’re using some pretty sophisticated techniques as well. 25 00:01:36,000 --> 00:01:40,000 We’re using UAV’s, uncrewed aerial vehicles, or drones. 26 00:01:40,000 --> 00:01:44,000 Polamacki: When I flying the drone earlier today, I was basically just 27 00:01:44,000 --> 00:01:48,000 taking many, many photos of the ground from up high 28 00:01:48,000 --> 00:01:52,000 to get really high resolution measurements with our 29 00:01:52,000 --> 00:01:56,000 camera of the patchiness of the snow. We can throw 30 00:01:56,000 --> 00:02:00,000 all of those photos together in a piece of photogrammetry 31 00:02:00,000 --> 00:02:04,000 software, stitch them together into a three-dimensional model. We can 32 00:02:04,000 --> 00:02:08,000 basically select any point in this giant field and 33 00:02:08,000 --> 00:02:12,000 determine what the snow depth was. It’s a way 34 00:02:12,000 --> 00:02:16,000 for us to cover a lot of ground pretty quickly. 35 00:02:16,000 --> 00:02:20,000 Mullen: Today I’m collecting albedo measurements from a UAV. 36 00:02:20,000 --> 00:02:24,000 Albedo is essentially how reflective 37 00:02:24,000 --> 00:02:28,000 the surface is. It tells us both 38 00:02:28,000 --> 00:02:32,000 how much energy coming from the Sun the surface is reflecting, but more 39 00:02:32,000 --> 00:02:36,000 importantly with regards to snow, it tells us how much energy that 40 00:02:36,000 --> 00:02:40,000 snow is absorbing, which allows us to kind of determine 41 00:02:40,000 --> 00:02:44,000 how fast it’s going to melt and allows us to better predict 42 00:02:44,000 --> 00:02:48,000 runoff quantities for water resource modeling. 43 00:02:48,000 --> 00:02:52,000 Rizza: We’re looking to use the lidar data to map the snow surfaces. 44 00:02:52,000 --> 00:02:56,000 and ultimately be able to calculate snow volumes and water 45 00:02:56,000 --> 00:03:00,000 content. Lidar is an active sensing technology 46 00:03:00,000 --> 00:03:04,000 and so it’s a laser that gets shot out of the sensor. It bounces off 47 00:03:04,000 --> 00:03:08,000 a surface, whether that be the ground or, in this case, the snow surfaces, 48 00:03:08,000 --> 00:03:12,000 bounces back to the sensor and that measurement is recorded very precisely to give us 49 00:03:12,000 --> 00:03:16,000 very accurate distance measurement. The biggest challenge 50 00:03:16,000 --> 00:03:20,000 I’d say is the cold and the wind. The wind is always a challenge 51 00:03:20,000 --> 00:03:24,000 for us with a drone in particular. Sproles: It’s an extremely 52 00:03:24,000 --> 00:03:28,000 hard environment to work in, it’s harsh, it’s windy, things blow around. But that’s just 53 00:03:28,000 --> 00:03:32,000 the nature of where we’re working. Mitchell: Most of the 54 00:03:32,000 --> 00:03:36,000 agriculture on this landform and in the surrounding area is dry land 55 00:03:36,000 --> 00:03:40,000 agriculture, meaning they don’t use flood or pivot irrigation. 56 00:03:40,000 --> 00:03:44,000 So we’re curious how significant this snow is to 57 00:03:44,000 --> 00:03:48,000 the soil water, which then turns into crop water. 58 00:03:48,000 --> 00:03:52,000 Sproles: Prairies and grasslands occupy about ten percent 59 00:03:52,000 --> 00:03:56,000 of the Earth’s land surface, so that’s a lot of land, right? 60 00:03:56,000 --> 00:04:00,000 And of that land surface, about 30 percent of it has seasonal snow, meaning that 61 00:04:00,000 --> 00:04:04,000 it doesn’t snow once a year, but you have accumulation and melt periods throughout. 62 00:04:04,000 --> 00:04:08,000 Feduschak: As kind of biomes are moving north, these prairies and 63 00:04:08,000 --> 00:04:12,000 the water that they hold are going to become increasingly important 64 00:04:12,000 --> 00:04:16,000 for human habitation and food production. 65 00:04:16,000 --> 00:04:20,000 And so getting an idea of how much snow is on the landscape, 66 00:04:20,000 --> 00:04:24,000 how that snow is changing, when it’s melting, where it’s going 67 00:04:24,000 --> 00:04:28,000 is really important for us to understand, and it’s definitely one 68 00:04:28,000 --> 00:04:32,000 of the big gaps in our understanding of snow 69 00:04:32,000 --> 00:04:39,915 as we stand today. 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