1 00:00:00,667 --> 00:00:03,337 [rousing music] 2 00:00:03,370 --> 00:00:06,373 [bright music] 3 00:00:07,975 --> 00:00:09,409 [laughter] 4 00:00:10,744 --> 00:00:12,446 - Did you hear that? 5 00:00:12,479 --> 00:00:15,215 I think it's almost lunchtime. 6 00:00:15,249 --> 00:00:19,152 But have you ever wondered where food comes from? 7 00:00:19,186 --> 00:00:21,255 Why do some of the best oranges 8 00:00:21,288 --> 00:00:25,559 come from Florida, and peaches come from Georgia? 9 00:00:25,592 --> 00:00:27,761 Does it matter where food grows? 10 00:00:27,794 --> 00:00:30,531 - Where food grows is really important because we want 11 00:00:30,564 --> 00:00:33,800 to make sure that everybody in the world has enough food, 12 00:00:33,834 --> 00:00:35,702 and nobody is hungry. 13 00:00:35,736 --> 00:00:38,639 My job at NASA Harvest is to help people around the world 14 00:00:38,672 --> 00:00:42,342 become more food secure, which means that they have 15 00:00:42,376 --> 00:00:44,144 enough water, enough nutrients 16 00:00:44,178 --> 00:00:46,847 to ensure that they can survive in case 17 00:00:46,880 --> 00:00:50,350 of a drought and have access to food. 18 00:00:50,384 --> 00:00:52,819 We can see farms all around our world 19 00:00:52,853 --> 00:00:54,821 using satellite imagery. 20 00:00:54,855 --> 00:00:56,924 They can look extremely different depending 21 00:00:56,957 --> 00:00:59,626 on the rainfall, temperature and soils, 22 00:00:59,660 --> 00:01:01,728 and their shapes and sizes. 23 00:01:01,762 --> 00:01:04,431 So you can find really large farms in places 24 00:01:04,464 --> 00:01:07,334 like Iowa and Illinois that are going on. 25 00:01:07,367 --> 00:01:09,369 And then you might find irregular, 26 00:01:09,403 --> 00:01:12,706 very small fields in Africa where there's a dependency 27 00:01:12,739 --> 00:01:15,275 on smallholder agriculture. 28 00:01:15,309 --> 00:01:17,811 The environment and certain geographic features 29 00:01:17,845 --> 00:01:19,780 affect how well plants grow. 30 00:01:19,813 --> 00:01:21,281 Certain geographic features 31 00:01:21,315 --> 00:01:23,283 are important for specific crops. 32 00:01:23,317 --> 00:01:26,153 For example, in states like Iowa and Illinois, 33 00:01:26,186 --> 00:01:28,322 farms are very flat, expansive, 34 00:01:28,355 --> 00:01:29,857 and the weather and environment 35 00:01:29,890 --> 00:01:31,925 are great for growing corn. 36 00:01:31,959 --> 00:01:33,894 Likewise, in China and in India, 37 00:01:33,927 --> 00:01:36,496 the environment is suitable for production of rice. 38 00:01:36,530 --> 00:01:39,132 Rice needs hot and humid climates, 39 00:01:39,166 --> 00:01:43,370 and is best suited for regions that have high humidity, 40 00:01:43,403 --> 00:01:47,841 prolonged sunshine, and a sure supply of water. 41 00:01:49,676 --> 00:01:51,545 - "Orange" you glad you asked? 42 00:01:51,578 --> 00:01:54,615 Having an eye in the sky is beneficial to farmers. 43 00:01:54,648 --> 00:01:57,117 But what does that data mean? 44 00:01:57,150 --> 00:01:59,586 Ground and climate conditions play an important role 45 00:01:59,620 --> 00:02:03,090 in determining where and when certain crops are grown. 46 00:02:03,123 --> 00:02:06,493 But how do we know what grows better where? 47 00:02:06,527 --> 00:02:08,962 What does the satellite data tell us? 48 00:02:08,996 --> 00:02:11,598 - So we use the images that come from satellites 49 00:02:11,632 --> 00:02:14,168 to map and monitor farms on Earth 50 00:02:14,201 --> 00:02:16,003 by using the information contained 51 00:02:16,036 --> 00:02:18,572 in every single pixel. 52 00:02:18,605 --> 00:02:21,508 A pixel is the smallest dot that makes up an optical 53 00:02:21,542 --> 00:02:25,145 satellite image, and basically determines how detailed 54 00:02:25,179 --> 00:02:28,782 picture of Earth is from that satellite. 55 00:02:28,815 --> 00:02:32,719 So the size of a pixel is really important. 56 00:02:32,753 --> 00:02:36,223 A high resolution satellite image enables us 57 00:02:36,256 --> 00:02:38,959 to see different features on the ground. 58 00:02:38,992 --> 00:02:41,161 For example, we can distinguish between 59 00:02:41,195 --> 00:02:44,231 a building and a tree in close proximity. 60 00:02:44,264 --> 00:02:46,033 So if there's a tree right next to a building, 61 00:02:46,066 --> 00:02:47,634 you can distinguish it. 62 00:02:47,668 --> 00:02:51,705 As the resolution gets lower, it becomes more difficult 63 00:02:51,738 --> 00:02:54,441 to distinguish between these different features, 64 00:02:54,474 --> 00:02:56,310 especially when they're close to each other. 65 00:02:56,343 --> 00:02:59,713 So for example, we wouldn't be able to tell the difference 66 00:02:59,746 --> 00:03:04,084 between a crop field, a park, and a forest, 67 00:03:04,117 --> 00:03:06,186 It would all just be vegetation. 68 00:03:06,220 --> 00:03:08,555 So when we use satellite imagery, 69 00:03:08,589 --> 00:03:10,524 we have to translate it into information 70 00:03:10,557 --> 00:03:12,226 that others can understand. 71 00:03:12,259 --> 00:03:14,661 And sometimes this requires using a map. 72 00:03:14,695 --> 00:03:17,097 A map has a key that has symbols 73 00:03:17,130 --> 00:03:19,032 that tell us what is in the map. 74 00:03:19,066 --> 00:03:22,603 So when we develop a map from the imagery 75 00:03:22,636 --> 00:03:24,037 that we've collected, 76 00:03:24,071 --> 00:03:25,706 we create these color-coded maps 77 00:03:25,739 --> 00:03:29,643 giving us ideas of what places might be experiencing drought, 78 00:03:29,676 --> 00:03:34,214 like red areas, and areas that are borderline drought 79 00:03:34,248 --> 00:03:36,316 that would be represented with orange 80 00:03:36,350 --> 00:03:38,085 and areas that are flourishing 81 00:03:38,118 --> 00:03:40,621 that it could be green, for example. 82 00:03:40,654 --> 00:03:43,590 So satellite data are actually really important 83 00:03:43,624 --> 00:03:46,460 for understanding how well crops are doing. 84 00:03:46,493 --> 00:03:49,596 And so we can compare like this season 85 00:03:49,630 --> 00:03:53,200 with previous seasons, as well as give a forecast 86 00:03:53,233 --> 00:03:56,303 and a prediction for what might happen. 87 00:03:56,336 --> 00:03:58,238 At the end of the season, like, will we have enough, 88 00:03:58,272 --> 00:03:59,806 will we have the same as usual, 89 00:03:59,840 --> 00:04:01,708 will we have less than usual, 90 00:04:01,742 --> 00:04:03,610 or will we have more than enough 91 00:04:03,644 --> 00:04:06,346 that then could be exported and support 92 00:04:06,380 --> 00:04:09,516 other countries that might not have enough. 93 00:04:09,550 --> 00:04:12,352 So we do get a lot of data from satellites. 94 00:04:12,386 --> 00:04:15,255 It is really important because we can see 95 00:04:15,289 --> 00:04:17,057 and understand what's happening in large areas. 96 00:04:17,090 --> 00:04:18,759 But what makes that possible 97 00:04:18,792 --> 00:04:21,228 is a lot of ground information. 98 00:04:21,261 --> 00:04:23,564 We need to know what is on the ground 99 00:04:23,597 --> 00:04:26,433 in order to understand what's in the satellite data. 100 00:04:26,466 --> 00:04:29,002 And so we go to the field a lot 101 00:04:29,036 --> 00:04:32,239 with phones and GPSes. 102 00:04:32,272 --> 00:04:34,107 There's other ground information 103 00:04:34,141 --> 00:04:36,577 like from weather stations that are on the ground 104 00:04:36,610 --> 00:04:40,447 that are used so we can evaluate, validate, 105 00:04:40,480 --> 00:04:44,952 and make sure that what satellite data is telling us 106 00:04:44,985 --> 00:04:49,089 is in fact true by looking at vegetation conditions. 107 00:04:49,122 --> 00:04:52,459 - So it's important to make sure that the satellite data 108 00:04:52,492 --> 00:04:55,729 "pears" nicely with the data that's on the ground. 109 00:04:55,762 --> 00:04:57,998 And you can help NASA do that 110 00:04:58,031 --> 00:05:01,101 by using the Land Cover Adopt a Pixel tool 111 00:05:01,134 --> 00:05:03,470 on the GLOBE Observer app. 112 00:05:03,504 --> 00:05:06,874 Now you know how NASA helps us get food in our world. 113 00:05:06,907 --> 00:05:09,076 I'm going to go see what's growing in my garden. 114 00:05:09,109 --> 00:05:10,777 See you next time.