WEBVTT 1 00:00:09.640 --> 00:00:13.759 A:middle L:90% my good fortune today too, introduced to dr Martha 2 00:00:13.769 --> 00:00:20.210 A:middle L:90% Henderson who started her Yes her scientific life with the 3 00:00:20.219 --> 00:00:26.170 A:middle L:90% doctrine and Astrophysics University of Minnesota in 1993. You 4 00:00:26.170 --> 00:00:29.160 A:middle L:90% can tell your tiny bit more about what happened next 5 00:00:29.539 --> 00:00:36.909 A:middle L:90% then something's happened. Uh crossed paths have been remote 6 00:00:36.909 --> 00:00:42.679 A:middle L:90% sensing and she after a long stand research scientists moving 7 00:00:42.679 --> 00:00:47.880 A:middle L:90% into US agricultural research service and she is currently at 8 00:00:47.880 --> 00:00:51.500 A:middle L:90% the hydrology remote sensing lab in Beltsville Maryland. Her 9 00:00:51.500 --> 00:00:54.649 A:middle L:90% research interests focus on mapping, water, energy and 10 00:00:54.649 --> 00:00:58.759 A:middle L:90% carbon laid surface flux is feel the continental scales using 11 00:00:59.240 --> 00:01:03.180 A:middle L:90% and or remote sensing with applications throughout monitoring soil moisture 12 00:01:03.189 --> 00:01:07.400 A:middle L:90% estimation. We have been on lands that science team 13 00:01:07.400 --> 00:01:10.799 A:middle L:90% together since 2006, known each other a very long 14 00:01:10.799 --> 00:01:12.670 A:middle L:90% time before that. And so we're very very pleased 15 00:01:12.670 --> 00:01:19.950 A:middle L:90% today to Dr now they came with the lights the 16 00:01:19.950 --> 00:01:25.310 A:middle L:90% way they are. Okay, well thank you thanks 17 00:01:25.310 --> 00:01:27.459 A:middle L:90% so much for having me here. It's a pleasure 18 00:01:27.459 --> 00:01:30.920 A:middle L:90% to speak with you in the seminar. So as 19 00:01:30.920 --> 00:01:34.019 A:middle L:90% randy mentioned, I work for the Agricultural Research Service 20 00:01:34.019 --> 00:01:34.819 A:middle L:90% which is the research arm of the U. S 21 00:01:34.819 --> 00:01:38.189 A:middle L:90% . D. A. And I'm in this hydrology 22 00:01:38.189 --> 00:01:42.329 A:middle L:90% remote sensing lab which is located in Beltsville Maryland. 23 00:01:42.340 --> 00:01:45.260 A:middle L:90% It's right outside the Washington D. C. Area 24 00:01:45.640 --> 00:01:47.930 A:middle L:90% . And I work with a group of people primarily 25 00:01:47.930 --> 00:01:52.959 A:middle L:90% trying to develop techniques for routine monitoring of evaporate transpiration 26 00:01:52.969 --> 00:01:55.629 A:middle L:90% E. T. For short crop water use if 27 00:01:55.629 --> 00:02:00.269 A:middle L:90% we're talking about agricultural lands and drought using multi scale 28 00:02:00.310 --> 00:02:04.000 A:middle L:90% satellite remote sensing data. And in particular what we've 29 00:02:04.000 --> 00:02:06.319 A:middle L:90% been working on most recently as part of the Lance 30 00:02:06.319 --> 00:02:08.919 A:middle L:90% at science team is trying to find ways to fuse 31 00:02:08.919 --> 00:02:14.750 A:middle L:90% together information from multiple different satellite platforms to get towards 32 00:02:14.750 --> 00:02:17.590 A:middle L:90% the ultimate goal of mapping water use at daily time 33 00:02:17.590 --> 00:02:22.560 A:middle L:90% steps and at very fine spatial scales at sub field 34 00:02:22.560 --> 00:02:24.969 A:middle L:90% scales to support agricultural applications. So I'll be talking 35 00:02:24.969 --> 00:02:29.000 A:middle L:90% to you about these data. Fusion techniques were working 36 00:02:29.000 --> 00:02:30.719 A:middle L:90% on today. First of all, why do we 37 00:02:30.719 --> 00:02:34.680 A:middle L:90% care about evaporate transpiration? This may be obvious to 38 00:02:34.689 --> 00:02:38.560 A:middle L:90% everybody and some groups, it's not so clear. 39 00:02:38.039 --> 00:02:42.969 A:middle L:90% There are just a plethora of applications and demands for 40 00:02:42.969 --> 00:02:45.860 A:middle L:90% this kind of information, both within the United States 41 00:02:45.860 --> 00:02:49.360 A:middle L:90% and globally. We can use these satellite derived heat 42 00:02:49.360 --> 00:02:53.699 A:middle L:90% maps to assess our current spatial patterns in water use 43 00:02:53.759 --> 00:02:58.250 A:middle L:90% at different spatial scales. And in terms of agriculture 44 00:02:58.250 --> 00:03:00.990 A:middle L:90% , we want to know how effectively our agricultural systems 45 00:03:00.990 --> 00:03:04.460 A:middle L:90% are using the water that's available to them. This 46 00:03:04.460 --> 00:03:07.400 A:middle L:90% can be used globally to pinpoint areas of potential food 47 00:03:07.400 --> 00:03:12.110 A:middle L:90% and water insecurity. We can also build up time 48 00:03:12.110 --> 00:03:15.400 A:middle L:90% series maps of of water use and study how water 49 00:03:15.400 --> 00:03:21.009 A:middle L:90% use is changing spatially and temporally in different areas due 50 00:03:21.009 --> 00:03:24.020 A:middle L:90% to different kind of drivers. Great changing climate drivers 51 00:03:24.030 --> 00:03:28.650 A:middle L:90% , changing precipitation patterns, changing air temperature patterns, 52 00:03:29.139 --> 00:03:32.639 A:middle L:90% changes in land use is we're converting forests into agricultural 53 00:03:32.639 --> 00:03:37.759 A:middle L:90% lands in some areas and then agricultural lands into no 54 00:03:38.539 --> 00:03:42.360 A:middle L:90% no oh come on. Make changes to your system 55 00:03:42.740 --> 00:03:46.860 A:middle L:90% um into urban settings. How is this impacting the 56 00:03:46.860 --> 00:03:50.719 A:middle L:90% regional landscape, water use and as populations grow in 57 00:03:50.719 --> 00:03:53.750 A:middle L:90% some areas and there's competing demands on water resources in 58 00:03:53.750 --> 00:03:54.949 A:middle L:90% areas. We need a way to account that the 59 00:03:54.949 --> 00:03:59.710 A:middle L:90% changes in water use due to these factors. E 60 00:03:59.710 --> 00:04:00.719 A:middle L:90% . T. Is an important input to a lot 61 00:04:00.719 --> 00:04:04.180 A:middle L:90% of other types of modeling systems. It's very important 62 00:04:04.189 --> 00:04:08.680 A:middle L:90% to accurately quantify the exchange of water vapor between the 63 00:04:08.680 --> 00:04:12.300 A:middle L:90% land and the atmosphere in our new numerical weather prediction 64 00:04:12.300 --> 00:04:15.860 A:middle L:90% systems to improve the weather forecast. So satellite remote 65 00:04:15.860 --> 00:04:18.879 A:middle L:90% sensing could be an input to improve forecasting systems and 66 00:04:18.889 --> 00:04:25.709 A:middle L:90% hydrologic modeling systems to predict early early detection of flooding 67 00:04:25.720 --> 00:04:28.879 A:middle L:90% , drought, high runoff events. And finally we're 68 00:04:28.879 --> 00:04:31.250 A:middle L:90% also using these et maps as an indicator of vegetation 69 00:04:31.259 --> 00:04:34.769 A:middle L:90% health. As the crops become stressed, they shut 70 00:04:34.769 --> 00:04:38.899 A:middle L:90% down their stomach to their transpiration flux is our lower 71 00:04:38.899 --> 00:04:41.800 A:middle L:90% we can detect those those changes and transpiration from a 72 00:04:41.800 --> 00:04:46.860 A:middle L:90% satellite platform, early potential signal of crop stress and 73 00:04:46.860 --> 00:04:48.680 A:middle L:90% maybe impacts on yield. The bottom line is we 74 00:04:48.680 --> 00:04:55.019 A:middle L:90% really can't hope to uh manage our freshwater resources either 75 00:04:55.019 --> 00:04:58.620 A:middle L:90% in the United States or globally if we cannot accurately 76 00:04:58.629 --> 00:05:02.730 A:middle L:90% measure how these resources are being used. So this 77 00:05:02.730 --> 00:05:05.670 A:middle L:90% is just kind of a schematic outline of the talk 78 00:05:05.670 --> 00:05:08.920 A:middle L:90% I'm gonna be giving today. I'll talk about some 79 00:05:08.920 --> 00:05:12.860 A:middle L:90% of the modeling tools that we use in this work 80 00:05:12.920 --> 00:05:15.250 A:middle L:90% . There's a multi scale E. T. Modeling 81 00:05:15.250 --> 00:05:19.410 A:middle L:90% system. We call Alexey Dis Alexey primarily uses remotely 82 00:05:19.410 --> 00:05:23.930 A:middle L:90% sensed maps of land surface temperature acquired in the thermal 83 00:05:23.930 --> 00:05:27.470 A:middle L:90% infrared wave bands. We also have a thermal image 84 00:05:27.470 --> 00:05:29.600 A:middle L:90% sharpener tool, the D. M. S. 85 00:05:29.610 --> 00:05:31.329 A:middle L:90% Data mining sharpener tool that we use to improve the 86 00:05:31.329 --> 00:05:34.819 A:middle L:90% resolution of our input thermal imagery. And I'll be 87 00:05:34.819 --> 00:05:40.310 A:middle L:90% talking about the star FM data fusion algorithm that fuses 88 00:05:40.310 --> 00:05:44.779 A:middle L:90% together individual data streams from different satellite sensors. We 89 00:05:44.779 --> 00:05:46.920 A:middle L:90% apply these tools to a number of different satellite assets 90 00:05:47.439 --> 00:05:50.160 A:middle L:90% . We use the geo stationary satellites that are usually 91 00:05:50.160 --> 00:05:55.170 A:middle L:90% used just for for the meteorological Services. For weather 92 00:05:55.170 --> 00:05:59.810 A:middle L:90% forecasting provides excellent land surface information as well. Really 93 00:05:59.810 --> 00:06:01.149 A:middle L:90% , really good temporal information. We get imagery every 94 00:06:01.149 --> 00:06:04.579 A:middle L:90% 15 minutes but at pretty coarse spatial resolution. So 95 00:06:04.579 --> 00:06:08.589 A:middle L:90% we also want to combine information from other satellite systems 96 00:06:09.040 --> 00:06:11.649 A:middle L:90% with finer details. Some of the polar orbiting systems 97 00:06:11.649 --> 00:06:15.310 A:middle L:90% like modus and land set. And I'll talk about 98 00:06:15.310 --> 00:06:17.410 A:middle L:90% how we're applying these tools in the satellite assets to 99 00:06:17.410 --> 00:06:23.040 A:middle L:90% some applications in agriculture, in mapping daily crop water 100 00:06:23.040 --> 00:06:27.720 A:middle L:90% use at field scale, also monitoring crop technology at 101 00:06:27.720 --> 00:06:30.170 A:middle L:90% the same field scale, combining those two data streams 102 00:06:30.170 --> 00:06:36.569 A:middle L:90% to try to predict impact of stress on yields and 103 00:06:36.569 --> 00:06:39.449 A:middle L:90% as a drought early warning tools. So this is 104 00:06:39.449 --> 00:06:42.290 A:middle L:90% what I'll be covering today now. I'm just gonna 105 00:06:42.290 --> 00:06:45.089 A:middle L:90% keep talking randy when I hit my limit. Just 106 00:06:45.089 --> 00:06:46.810 A:middle L:90% kind of make some wrap it up signs and it 107 00:06:46.810 --> 00:06:50.579 A:middle L:90% will stop wherever I am. So first of all 108 00:06:50.579 --> 00:06:55.160 A:middle L:90% multi scale E. T. Retrieval algorithm that we 109 00:06:55.160 --> 00:06:57.920 A:middle L:90% use in our work is based on principles of service 110 00:06:57.930 --> 00:07:01.899 A:middle L:90% energy balance. There are a lot of different ways 111 00:07:01.910 --> 00:07:06.389 A:middle L:90% to map evaporative flux is over landscapes and I'm just 112 00:07:06.389 --> 00:07:11.050 A:middle L:90% going to kind of categories them in two general categories 113 00:07:11.050 --> 00:07:14.290 A:middle L:90% . The first category is a water balance approach used 114 00:07:14.290 --> 00:07:15.370 A:middle L:90% in a lot of land surface modeling systems. A 115 00:07:15.370 --> 00:07:18.410 A:middle L:90% lot of hydrologic modeling systems. And in these types 116 00:07:18.410 --> 00:07:20.980 A:middle L:90% of models, the key input is precipitation. You 117 00:07:20.980 --> 00:07:24.790 A:middle L:90% have to know the precipitation rainfall very accurately over your 118 00:07:24.790 --> 00:07:29.240 A:middle L:90% modeling domain and then that precipitation is partitioned into all 119 00:07:29.240 --> 00:07:32.389 A:middle L:90% these different flux is and stores using typically a system 120 00:07:32.389 --> 00:07:36.699 A:middle L:90% of prognostic physically based equations. Really useful approach for 121 00:07:36.699 --> 00:07:39.220 A:middle L:90% a lot of applications because you get a lot of 122 00:07:39.220 --> 00:07:43.129 A:middle L:90% information about the hydrologic status of your land surface system 123 00:07:43.129 --> 00:07:45.899 A:middle L:90% . But the challenges, it just requires a whole 124 00:07:45.899 --> 00:07:47.360 A:middle L:90% lot of surface parameters to get this to run with 125 00:07:47.509 --> 00:07:49.699 A:middle L:90% correctly. We need to know a lot about the 126 00:07:49.699 --> 00:07:55.779 A:middle L:90% subsurface soil properties, the soil moisture holding capacity in 127 00:07:55.779 --> 00:07:59.620 A:middle L:90% the soil hydraulic conductivity to get the infiltration and the 128 00:07:59.629 --> 00:08:03.560 A:middle L:90% apple soil evaporation component of VT right. And we 129 00:08:03.560 --> 00:08:05.329 A:middle L:90% also have to know a lot about the vegetation. 130 00:08:05.329 --> 00:08:09.060 A:middle L:90% We need to know about its roots, dense routing 131 00:08:09.189 --> 00:08:13.519 A:middle L:90% distribution in the soil sensitivities of different plant types to 132 00:08:13.529 --> 00:08:16.290 A:middle L:90% moisture deficiencies in the root zone. To get the 133 00:08:16.290 --> 00:08:18.480 A:middle L:90% transpiration component right and very importantly, you need to 134 00:08:18.480 --> 00:08:22.040 A:middle L:90% know your precipitation very accurately. And that's not such 135 00:08:22.040 --> 00:08:24.350 A:middle L:90% a challenge here in the United States. We've got 136 00:08:24.350 --> 00:08:26.009 A:middle L:90% a lot of re engages in the US, we 137 00:08:26.009 --> 00:08:30.879 A:middle L:90% got a lot of Doppler uh radar stations to correct 138 00:08:30.879 --> 00:08:33.720 A:middle L:90% our satellite based estimates of precipitation more of a challenge 139 00:08:33.720 --> 00:08:35.820 A:middle L:90% in some other parts of the world where that the 140 00:08:35.830 --> 00:08:39.259 A:middle L:90% media logical infrastructure isn't so dense. Also you need 141 00:08:39.259 --> 00:08:43.409 A:middle L:90% to know ancillary sources of moisture into the system that 142 00:08:43.409 --> 00:08:45.899 A:middle L:90% aren't related to precept and I'll talk about that in 143 00:08:45.899 --> 00:08:50.120 A:middle L:90% a moment. So in contrast in this energy balance 144 00:08:50.120 --> 00:08:52.570 A:middle L:90% approach two et modeling that I'll be talking about, 145 00:08:52.679 --> 00:08:54.789 A:middle L:90% we don't need to know quite as much about the 146 00:08:54.789 --> 00:08:58.169 A:middle L:90% subsurface properties and we don't need to know as much 147 00:08:58.169 --> 00:09:01.419 A:middle L:90% about the plant response. And very importantly, we're 148 00:09:01.419 --> 00:09:05.149 A:middle L:90% not using precipitation as an input to our model, 149 00:09:05.740 --> 00:09:07.700 A:middle L:90% in this case the primary input our maps of land 150 00:09:07.700 --> 00:09:11.490 A:middle L:90% surface temperature. Again, we can derive, we 151 00:09:11.490 --> 00:09:15.899 A:middle L:90% can retrieve using satellite platforms equipped with thermal infrared imaging 152 00:09:15.899 --> 00:09:18.850 A:middle L:90% systems. We know there's a strong physical connection between 153 00:09:18.850 --> 00:09:24.149 A:middle L:90% evaporative cooling and the temperature of an object. So 154 00:09:24.149 --> 00:09:28.200 A:middle L:90% in this kind of modeling system, if we know 155 00:09:28.200 --> 00:09:30.490 A:middle L:90% how much energy is being put into the land surface 156 00:09:30.490 --> 00:09:33.340 A:middle L:90% system into a pixel on the land surface system down 157 00:09:33.340 --> 00:09:35.240 A:middle L:90% , welling radiation from the sun from the atmosphere minus 158 00:09:35.240 --> 00:09:39.769 A:middle L:90% reflected and emitted radiation, the net radiation at the 159 00:09:39.769 --> 00:09:43.840 A:middle L:90% land surface, we can estimate how much evaporative cooling 160 00:09:43.929 --> 00:09:46.230 A:middle L:90% must be occurring under that kind of radiation load to 161 00:09:46.230 --> 00:09:50.840 A:middle L:90% keep the soil and the temperature at the soil in 162 00:09:50.840 --> 00:09:52.409 A:middle L:90% the canopy at the temperatures we observe from the satellite 163 00:09:52.409 --> 00:09:56.110 A:middle L:90% platform. And we can answer that question using a 164 00:09:56.110 --> 00:10:00.759 A:middle L:90% model based on surface energy balance. So again, 165 00:10:01.340 --> 00:10:03.120 A:middle L:90% advantages here, we don't need to know as much 166 00:10:03.120 --> 00:10:05.769 A:middle L:90% about the system and were independent of precipitation information. 167 00:10:05.879 --> 00:10:09.570 A:middle L:90% We can map surface temperature much more accurately than we 168 00:10:09.570 --> 00:10:13.149 A:middle L:90% can map precipitation. But we're really getting only a 169 00:10:13.149 --> 00:10:15.919 A:middle L:90% handle on the evaporative component of the hydrologic budget. 170 00:10:15.919 --> 00:10:18.549 A:middle L:90% You're getting a lot more information with water balance models 171 00:10:18.549 --> 00:10:20.399 A:middle L:90% . So there's really good synergy here. We found 172 00:10:20.399 --> 00:10:24.539 A:middle L:90% that there's a lot of value in comparing estimates from 173 00:10:24.539 --> 00:10:26.490 A:middle L:90% these two types of modeling systems. We learn a 174 00:10:26.490 --> 00:10:30.480 A:middle L:90% lot about the models, we learn about the systems 175 00:10:30.480 --> 00:10:33.059 A:middle L:90% that were trying to model. And this is just 176 00:10:33.059 --> 00:10:35.379 A:middle L:90% an example of this over the Continental United States were 177 00:10:35.379 --> 00:10:39.700 A:middle L:90% comparing E. T. Estimates from a land surface 178 00:10:39.700 --> 00:10:43.750 A:middle L:90% model with our satellite based thermal retrievals of ET. 179 00:10:43.639 --> 00:10:46.940 A:middle L:90% And what we're finding is we get good agreement over 180 00:10:46.940 --> 00:10:48.559 A:middle L:90% a lot of the United States and areas that are 181 00:10:50.039 --> 00:10:52.840 A:middle L:90% these are kind of indicating some differences. The white 182 00:10:52.840 --> 00:10:56.529 A:middle L:90% regions here models agree fairly well, but there are 183 00:10:56.529 --> 00:11:00.990 A:middle L:90% specific areas where the satellite retrieval is consistently predicting higher 184 00:11:00.990 --> 00:11:05.129 A:middle L:90% evaporative flexes than the water balance model. And these 185 00:11:05.139 --> 00:11:09.700 A:middle L:90% areas can be kind of related to three different factors 186 00:11:09.700 --> 00:11:11.879 A:middle L:90% in general areas of high irrigation, we're getting more 187 00:11:11.879 --> 00:11:16.480 A:middle L:90% eating out of the satellite retrieval in the central valley 188 00:11:16.480 --> 00:11:22.110 A:middle L:90% of California and the Snake River Plain in Idaho along 189 00:11:22.110 --> 00:11:24.860 A:middle L:90% the Mississippi River basin. A lot of irrigation going 190 00:11:24.860 --> 00:11:26.370 A:middle L:90% on there, growing a lot of rice in the 191 00:11:26.370 --> 00:11:28.899 A:middle L:90% Mississippi basin. Um These are areas where there's kind 192 00:11:28.899 --> 00:11:33.940 A:middle L:90% of additional water added to the system not necessarily correlated 193 00:11:33.940 --> 00:11:37.200 A:middle L:90% with the local precipitation rates. We also find enhanced 194 00:11:37.200 --> 00:11:41.659 A:middle L:90% flux is in areas of relatively shallow water table. 195 00:11:41.840 --> 00:11:45.429 A:middle L:90% Again, along the Mississippi Basin, in the florida 196 00:11:45.429 --> 00:11:48.529 A:middle L:90% , Everglades and some of the coastal areas. These 197 00:11:48.529 --> 00:11:52.129 A:middle L:90% are areas where there may be direct extraction of additional 198 00:11:52.129 --> 00:11:56.159 A:middle L:90% moisture from the groundwater table by free apathetic vegetation. 199 00:11:56.480 --> 00:12:00.559 A:middle L:90% Maybe some direct evaporation. Again difficult to capture in 200 00:12:00.559 --> 00:12:03.399 A:middle L:90% a water balance model unless you know a priority what 201 00:12:03.399 --> 00:12:07.440 A:middle L:90% the groundwater table distribution is and another source is you 202 00:12:07.440 --> 00:12:11.350 A:middle L:90% can see up here all these little peaks, high 203 00:12:11.350 --> 00:12:13.740 A:middle L:90% density of subpixel water bodies is another area where we're 204 00:12:13.740 --> 00:12:16.360 A:middle L:90% seeing these enhancements. So we've got the prairie pothole 205 00:12:16.360 --> 00:12:20.519 A:middle L:90% regions in the Dakotas, the prairie kyoto, the 206 00:12:20.519 --> 00:12:22.730 A:middle L:90% Missouri Coteau, a lot of little potholes just holding 207 00:12:22.730 --> 00:12:26.070 A:middle L:90% up the moisture, but they're very small pixel skills 208 00:12:26.070 --> 00:12:28.980 A:middle L:90% . You've got the land of 10,000 lakes, Minnesota 209 00:12:28.980 --> 00:12:33.250 A:middle L:90% , a lot of little lakes, little wetlands contributing 210 00:12:33.250 --> 00:12:35.820 A:middle L:90% to the remote sensing et flux. But not captured 211 00:12:35.820 --> 00:12:37.070 A:middle L:90% by these water balance models. That doesn't know where 212 00:12:37.070 --> 00:12:41.309 A:middle L:90% all these little potholes are are lying on the ground 213 00:12:41.419 --> 00:12:43.200 A:middle L:90% . These are all hydrologic features that are of great 214 00:12:43.200 --> 00:12:46.460 A:middle L:90% interest to water resource managers. They need to know 215 00:12:46.460 --> 00:12:50.159 A:middle L:90% about how irrigation water is being used. They need 216 00:12:50.159 --> 00:12:52.750 A:middle L:90% to know about the hydrology of the the florida, 217 00:12:52.750 --> 00:12:56.090 A:middle L:90% Everglades. So this is one of the reasons why 218 00:12:56.090 --> 00:12:58.830 A:middle L:90% water resource managers have really kind of pinpointed thermal based 219 00:12:58.840 --> 00:13:01.799 A:middle L:90% E. T. Retrieval as an important tool for 220 00:13:01.799 --> 00:13:05.649 A:middle L:90% their water management decisions. So I'm not going to 221 00:13:05.649 --> 00:13:09.059 A:middle L:90% get into a lot of details about the modeling system 222 00:13:09.639 --> 00:13:11.740 A:middle L:90% . There's a lot of even thermal based ET models 223 00:13:11.750 --> 00:13:15.309 A:middle L:90% . The model we used is based on a two 224 00:13:15.309 --> 00:13:18.389 A:middle L:90% source representation of the land surface system. We partition 225 00:13:18.389 --> 00:13:22.039 A:middle L:90% surface temperature and flux is between the soil component of 226 00:13:22.039 --> 00:13:26.639 A:middle L:90% the scene and the vegetation component of the scene based 227 00:13:26.639 --> 00:13:28.769 A:middle L:90% on the local fraction of vegetation cover. And that's 228 00:13:28.769 --> 00:13:33.570 A:middle L:90% what's going on here partitioning up the bulk surface temperature 229 00:13:33.649 --> 00:13:37.850 A:middle L:90% . We also solve the energy budget for the combined 230 00:13:37.850 --> 00:13:41.379 A:middle L:90% system and the soil and the canopy independently. And 231 00:13:41.379 --> 00:13:43.210 A:middle L:90% we do this because soil and canopy components, the 232 00:13:43.210 --> 00:13:48.000 A:middle L:90% scene are differently coupled with the atmosphere and their their 233 00:13:48.009 --> 00:13:50.830 A:middle L:90% contribution to the flux. This is not necessarily in 234 00:13:50.830 --> 00:13:56.090 A:middle L:90% proportion to their their component temperatures. So we found 235 00:13:56.090 --> 00:13:58.370 A:middle L:90% this kind of system to be much more robust over 236 00:13:58.370 --> 00:14:01.830 A:middle L:90% a broad range of vegetation cover conditions more so than 237 00:14:01.830 --> 00:14:05.620 A:middle L:90% a very simple single source model. So surface temperature 238 00:14:05.620 --> 00:14:09.669 A:middle L:90% is very important in constraining the sensible heat flux off 239 00:14:09.679 --> 00:14:13.269 A:middle L:90% the land surface. Has anybody heard of sensible heat 240 00:14:13.269 --> 00:14:16.950 A:middle L:90% flux before you've heard of this term before? It's 241 00:14:16.960 --> 00:14:22.019 A:middle L:90% just the convection of heat off the um the ground 242 00:14:22.019 --> 00:14:26.909 A:middle L:90% that goes into heating the atmosphere. Uh So driven 243 00:14:26.909 --> 00:14:31.409 A:middle L:90% by temperature gradients temperature is also important for partitioning the 244 00:14:31.409 --> 00:14:33.350 A:middle L:90% net radiation between the soil and the canopy and for 245 00:14:33.350 --> 00:14:37.559 A:middle L:90% estimating that component of the energy balance that's conducted into 246 00:14:37.559 --> 00:14:41.259 A:middle L:90% the soil that goes into heating the soil profile. 247 00:14:41.940 --> 00:14:46.120 A:middle L:90% We have some different techniques for estimating the canopy component 248 00:14:46.250 --> 00:14:50.360 A:middle L:90% , evaporation component, basically the canopy transpiration rate. 249 00:14:50.039 --> 00:14:56.389 A:middle L:90% One method is a canopy resistance methodology that also allows 250 00:14:56.389 --> 00:14:58.679 A:middle L:90% us to estimate couple of carbon assimilation flux is along 251 00:14:58.679 --> 00:15:01.450 A:middle L:90% with the water flexes, and then the soil evaporation 252 00:15:01.450 --> 00:15:05.470 A:middle L:90% component is computed as a residual to the overall energy 253 00:15:05.470 --> 00:15:11.700 A:middle L:90% balanced model. So we've tested this model regionally in 254 00:15:11.700 --> 00:15:16.769 A:middle L:90% the form of it's kind of the land surface representation 255 00:15:16.769 --> 00:15:20.259 A:middle L:90% in this atmosphere land exchange inverse model, or Alexey 256 00:15:20.139 --> 00:15:22.870 A:middle L:90% . If we want to robust remote sensing retrieval, 257 00:15:22.870 --> 00:15:26.789 A:middle L:90% we have to make the model as insensitive to expected 258 00:15:26.789 --> 00:15:30.500 A:middle L:90% errors in the remote sensing data as possible. And 259 00:15:30.500 --> 00:15:33.259 A:middle L:90% we know we can never map land surface temperature perfectly 260 00:15:33.740 --> 00:15:35.529 A:middle L:90% . In an absolute sense, there's always atmospheric corrections 261 00:15:35.539 --> 00:15:39.759 A:middle L:90% emissivity corrections that we may not get exactly right. 262 00:15:39.440 --> 00:15:43.409 A:middle L:90% So this Alexey model actually uses time changes in surface 263 00:15:43.409 --> 00:15:48.629 A:middle L:90% temperature rather than absolute temperature. We can always measure 264 00:15:48.629 --> 00:15:52.259 A:middle L:90% time changes in any quantity much more accurately than we 265 00:15:52.259 --> 00:15:56.200 A:middle L:90% can measure absolutely quantities. So we run this, 266 00:15:56.200 --> 00:15:58.830 A:middle L:90% we run a time change model over the morning hours 267 00:15:58.830 --> 00:16:00.559 A:middle L:90% as the boundary layer is developing to do this, 268 00:16:00.559 --> 00:16:04.500 A:middle L:90% we need a satellite that gives us surface temperature at 269 00:16:04.509 --> 00:16:07.399 A:middle L:90% multiple times during the morning. So we have to 270 00:16:07.399 --> 00:16:11.500 A:middle L:90% run this model using geo stationary satellites That are sampling 271 00:16:11.500 --> 00:16:15.029 A:middle L:90% the land surface temperature every 15 minutes. But we 272 00:16:15.029 --> 00:16:18.879 A:middle L:90% may want to map flux is at higher spatial resolution 273 00:16:18.889 --> 00:16:21.279 A:middle L:90% than is supported by the geo stationary satellites. So 274 00:16:21.289 --> 00:16:23.440 A:middle L:90% that's why we have this flux desegregation algorithm which we 275 00:16:23.440 --> 00:16:27.710 A:middle L:90% just call this Alexey that uses higher temperature or higher 276 00:16:27.710 --> 00:16:33.039 A:middle L:90% resolution temperature information from satellites like land set and motives 277 00:16:33.269 --> 00:16:37.960 A:middle L:90% to get these finer scale maps and the net. 278 00:16:37.740 --> 00:16:41.139 A:middle L:90% I think it is a kind of a multi scale 279 00:16:41.139 --> 00:16:47.480 A:middle L:90% water use monitoring or mapping system going from global scales 280 00:16:47.480 --> 00:16:51.320 A:middle L:90% all the way down to sub field scales. So 281 00:16:51.320 --> 00:16:52.980 A:middle L:90% at the continental scale of the United States, again 282 00:16:52.980 --> 00:16:56.500 A:middle L:90% , we're using the geo stationary satellite data. We 283 00:16:56.500 --> 00:17:02.220 A:middle L:90% can get hourly flux is 5-10 km resolution. For 284 00:17:02.220 --> 00:17:06.529 A:middle L:90% higher resolution applications, we can disaggregate down to the 285 00:17:06.529 --> 00:17:10.829 A:middle L:90% modus scale. That's a kilometer resolution in the thermal 286 00:17:10.829 --> 00:17:11.630 A:middle L:90% wave bands. And you can see you can start 287 00:17:11.630 --> 00:17:15.819 A:middle L:90% to see some hydrologic patterns in the landscape at this 288 00:17:15.819 --> 00:17:18.549 A:middle L:90% scale, resuming into a region in central Iowa here 289 00:17:19.539 --> 00:17:22.470 A:middle L:90% . But most of our demand for information is at 290 00:17:22.470 --> 00:17:26.640 A:middle L:90% this highest spatial resolution resolution that's supported by the land 291 00:17:26.640 --> 00:17:32.460 A:middle L:90% set satellite. The field the field level. This 292 00:17:32.460 --> 00:17:34.859 A:middle L:90% is the level at which water is being actively managed 293 00:17:36.339 --> 00:17:37.539 A:middle L:90% over much of our global land surface. And this 294 00:17:37.539 --> 00:17:44.019 A:middle L:90% is where people want the information. Unfortunately the temporal 295 00:17:44.019 --> 00:17:47.460 A:middle L:90% support at that highest spatial resolution is not great. 296 00:17:48.039 --> 00:17:52.569 A:middle L:90% So with a single active land set satellite we might 297 00:17:52.569 --> 00:17:55.190 A:middle L:90% get an image every 16 days. If it's a 298 00:17:55.200 --> 00:17:57.599 A:middle L:90% perfectly clear area all the time. With two land 299 00:17:57.599 --> 00:18:00.710 A:middle L:90% sets, we'll get an image every eight days. 300 00:18:00.240 --> 00:18:03.200 A:middle L:90% If you factor in cloud cover, you might get 301 00:18:03.200 --> 00:18:04.579 A:middle L:90% an image every month, every couple months, maybe 302 00:18:04.579 --> 00:18:07.740 A:middle L:90% once per year if you're lucky, Very difficult to 303 00:18:07.750 --> 00:18:11.670 A:middle L:90% monitor seasonal water use based on one satellite image. 304 00:18:12.140 --> 00:18:15.670 A:middle L:90% So this is why we're working towards these data fusion 305 00:18:15.670 --> 00:18:18.269 A:middle L:90% algorithms. We're trying to combine information from all these 306 00:18:18.269 --> 00:18:22.029 A:middle L:90% different satellite platforms to get at the ultimate goal of 307 00:18:22.039 --> 00:18:26.670 A:middle L:90% mapping the daily water use at this finest spatial resolution 308 00:18:26.940 --> 00:18:32.529 A:middle L:90% . Yes, this is just a schematic diagram of 309 00:18:32.529 --> 00:18:34.660 A:middle L:90% how we apply our data. Fusion too. The 310 00:18:34.660 --> 00:18:37.940 A:middle L:90% problem of E. T. Mapping. And we're 311 00:18:37.940 --> 00:18:41.650 A:middle L:90% using an algorithm developed by Single, who's now a 312 00:18:41.650 --> 00:18:45.599 A:middle L:90% scientist in our lab called the spatial temporal adaptive reflect 313 00:18:45.599 --> 00:18:48.619 A:middle L:90% its fusion model. Originally developed this refusing reflect inst 314 00:18:48.619 --> 00:18:52.730 A:middle L:90% data but we're applying it now to ET data as 315 00:18:52.730 --> 00:18:56.630 A:middle L:90% well and it seems to work fairly well. So 316 00:18:56.630 --> 00:18:59.660 A:middle L:90% we can map daily flux is at the continental scale 317 00:18:59.660 --> 00:19:03.650 A:middle L:90% . Using the geo stationary satellites using Alexi, we 318 00:19:03.650 --> 00:19:06.920 A:middle L:90% can disaggregate down to a kilometer resolution more or less 319 00:19:06.920 --> 00:19:11.269 A:middle L:90% once per day with the motor satellites kilometer scale. 320 00:19:11.740 --> 00:19:12.420 A:middle L:90% We have to do a little gap filling here due 321 00:19:12.420 --> 00:19:15.819 A:middle L:90% to cloud cover but it's not too bad. And 322 00:19:15.819 --> 00:19:18.849 A:middle L:90% then periodically whenever we have a clear land SAT scene 323 00:19:18.339 --> 00:19:22.549 A:middle L:90% , We further disaggregate down to the 30 m scale 324 00:19:22.440 --> 00:19:26.789 A:middle L:90% of land set. So this was back in 2007 325 00:19:26.789 --> 00:19:29.660 A:middle L:90% . We had a fully functional lance at five and 326 00:19:29.660 --> 00:19:32.329 A:middle L:90% 7. So we had an eight day gap in 327 00:19:32.329 --> 00:19:33.329 A:middle L:90% this case. And we're trying to fill in the 328 00:19:33.329 --> 00:19:38.549 A:middle L:90% flux maps for these days. Star FM compares modus 329 00:19:38.559 --> 00:19:41.130 A:middle L:90% and land sat image pairs on days when they're both 330 00:19:41.130 --> 00:19:45.339 A:middle L:90% available and develop some kind of desegregation statistics based on 331 00:19:45.339 --> 00:19:48.720 A:middle L:90% the spatial and spectral similarity between those maps. And 332 00:19:48.720 --> 00:19:52.990 A:middle L:90% then applies those statistics to all the most scenes in 333 00:19:52.990 --> 00:19:56.099 A:middle L:90% the intervening time period to reconstruct the high resolution flux 334 00:19:56.359 --> 00:20:00.930 A:middle L:90% distribution at that land set scale. So we're getting 335 00:20:00.950 --> 00:20:04.710 A:middle L:90% temporal information from goes and modus. We're getting the 336 00:20:04.710 --> 00:20:08.589 A:middle L:90% spatial information from the land sat satellite. We want 337 00:20:08.589 --> 00:20:11.740 A:middle L:90% to know how well it looks okay, but we 338 00:20:11.740 --> 00:20:15.599 A:middle L:90% want to know quantitatively how well this This fusion works 339 00:20:15.650 --> 00:20:17.670 A:middle L:90% . One way to test it is just to use 340 00:20:17.670 --> 00:20:21.369 A:middle L:90% one pair of images as input to starve them and 341 00:20:21.369 --> 00:20:23.900 A:middle L:90% let it predict the flux field at the second date 342 00:20:23.910 --> 00:20:27.140 A:middle L:90% . Lancet date compared to a direct retrieval. Using 343 00:20:27.140 --> 00:20:30.880 A:middle L:90% landside imagery. We've done this in a limited sense 344 00:20:30.880 --> 00:20:33.869 A:middle L:90% , we need to do this more thoroughly at more 345 00:20:33.869 --> 00:20:37.779 A:middle L:90% validation sites, but it seems to retrieve the the 346 00:20:37.789 --> 00:20:41.549 A:middle L:90% spatial distribution fairly well. But the more direct way 347 00:20:41.630 --> 00:20:47.049 A:middle L:90% to test these algorithms is in direct comparison with ground 348 00:20:47.049 --> 00:20:51.460 A:middle L:90% based measurements of surface evaporative flux is and energy balance 349 00:20:51.460 --> 00:20:53.049 A:middle L:90% flux is and this is some work that was done 350 00:20:53.049 --> 00:20:56.690 A:middle L:90% by post doc, was in our lab for about 351 00:20:56.690 --> 00:20:59.269 A:middle L:90% a year and a half from Italy carmelo Camilleri. 352 00:21:00.039 --> 00:21:03.250 A:middle L:90% And he really looked at three major field experiments sites 353 00:21:03.039 --> 00:21:06.950 A:middle L:90% in the United States conducted over the past decade. 354 00:21:07.640 --> 00:21:08.329 A:middle L:90% We picked these sites because we like to have a 355 00:21:08.329 --> 00:21:11.380 A:middle L:90% lot of flux towers within a single lands that scene 356 00:21:11.390 --> 00:21:14.809 A:middle L:90% , because we could get the most points for uh 357 00:21:14.819 --> 00:21:18.230 A:middle L:90% the amount of effort we put into this uh this 358 00:21:18.509 --> 00:21:22.220 A:middle L:90% enterprise. So we looked at the soil moisture experiment 359 00:21:22.220 --> 00:21:26.470 A:middle L:90% of 2002, this is ames Iowa bunch of flux 360 00:21:26.470 --> 00:21:29.109 A:middle L:90% towers during this growing season, in rain fat, 361 00:21:29.119 --> 00:21:32.839 A:middle L:90% corn and soybean fields. We also looked at the 362 00:21:32.839 --> 00:21:37.450 A:middle L:90% Bear XO eight experiment which was conducted in bushland texas 363 00:21:37.519 --> 00:21:40.420 A:middle L:90% . It's in the texas panhandle, right outside of 364 00:21:40.420 --> 00:21:44.019 A:middle L:90% Amarillo texas doing a lot of irrigation. They're here 365 00:21:44.019 --> 00:21:45.710 A:middle L:90% , we're looking at irrigated and rain fed cotton fields 366 00:21:47.339 --> 00:21:49.859 A:middle L:90% . These are sitting on the Ogallala aquifer. They 367 00:21:49.859 --> 00:21:56.289 A:middle L:90% are depleting the Ogallala aquifer currently at an unsustainable rate 368 00:21:56.299 --> 00:21:59.029 A:middle L:90% , mostly in support of irrigated agriculture. So there's 369 00:21:59.029 --> 00:22:00.829 A:middle L:90% a lot of interest in, in water use efficiency 370 00:22:00.829 --> 00:22:04.769 A:middle L:90% in this area. And as a contrasting state. 371 00:22:06.220 --> 00:22:08.750 A:middle L:90% Looking at the meat amara flux site, outside of 372 00:22:08.750 --> 00:22:14.380 A:middle L:90% Lincoln Nebraska where they have corn and corn soybean rotations 373 00:22:14.380 --> 00:22:18.059 A:middle L:90% growing growing under irrigated management and rain fed management. 374 00:22:21.940 --> 00:22:23.349 A:middle L:90% If we look at the model results at these three 375 00:22:23.349 --> 00:22:26.589 A:middle L:90% sites on land, sad dates. When we actually 376 00:22:26.589 --> 00:22:30.950 A:middle L:90% have land set thermal uh imagery, we can see 377 00:22:30.950 --> 00:22:33.329 A:middle L:90% the model does a pretty good job at partitioning the 378 00:22:33.329 --> 00:22:36.930 A:middle L:90% surface energy balance at these three different very different kind 379 00:22:36.930 --> 00:22:41.240 A:middle L:90% of climatic regimes with different crops. So the red 380 00:22:41.240 --> 00:22:45.039 A:middle L:90% is the net radiation, the blue are the evaporative 381 00:22:45.049 --> 00:22:47.950 A:middle L:90% flux is the latent heat. If you're talking about 382 00:22:47.950 --> 00:22:49.480 A:middle L:90% energy energy terms E. T. If you're talking 383 00:22:49.480 --> 00:22:55.680 A:middle L:90% about mass flux and sensible heat flux. So we're 384 00:22:55.680 --> 00:22:56.980 A:middle L:90% getting errors in our et flux is on the order 385 00:22:56.980 --> 00:23:00.309 A:middle L:90% of 10 on a daily time step. Which is 386 00:23:00.309 --> 00:23:03.359 A:middle L:90% pretty good for any kind of satellite based retrieval. 387 00:23:03.140 --> 00:23:07.250 A:middle L:90% But the question is how well can we estimate what 388 00:23:07.250 --> 00:23:08.960 A:middle L:90% the evaporative flux is? Our between these labs land 389 00:23:08.960 --> 00:23:15.799 A:middle L:90% set dates using our data fusion algorithm. And this 390 00:23:15.799 --> 00:23:18.109 A:middle L:90% is just an example over one rain fed soybean field 391 00:23:18.119 --> 00:23:22.029 A:middle L:90% . From this Mexico to experiment in Central Iowa to 392 00:23:22.039 --> 00:23:26.549 A:middle L:90% demonstrate how we do this. In this case the 393 00:23:26.549 --> 00:23:30.490 A:middle L:90% red squares are E. T. Retrievals on land 394 00:23:30.490 --> 00:23:33.849 A:middle L:90% set dates. The from the dis Alexey model, 395 00:23:34.839 --> 00:23:38.609 A:middle L:90% the blue circles are observed. Daily flux is the 396 00:23:38.609 --> 00:23:42.680 A:middle L:90% gray line. Is the reference ET. That we 397 00:23:42.680 --> 00:23:47.289 A:middle L:90% can compute on a daily basis using local just meteorological 398 00:23:47.289 --> 00:23:49.490 A:middle L:90% data. So the potential the maximum ET we would 399 00:23:49.490 --> 00:23:53.210 A:middle L:90% expect on a given date if we were going to 400 00:23:53.210 --> 00:23:56.930 A:middle L:90% try to estimate daily et on the basis of land 401 00:23:56.930 --> 00:23:59.400 A:middle L:90% sad alone. And we do try to do this 402 00:23:59.410 --> 00:24:02.390 A:middle L:90% even with the Spar city of sampling, what we 403 00:24:02.390 --> 00:24:04.009 A:middle L:90% would do is compute the ratio of actual to reference 404 00:24:04.009 --> 00:24:07.710 A:middle L:90% ET On each of these lands that dates. So 405 00:24:07.710 --> 00:24:14.130 A:middle L:90% it's a normalized et like 0-1. Yeah. One 406 00:24:14.130 --> 00:24:17.049 A:middle L:90% being maximally T. And assume that that's kind of 407 00:24:17.049 --> 00:24:19.109 A:middle L:90% an indicator of the surface moisture status, something that's 408 00:24:19.109 --> 00:24:25.299 A:middle L:90% relatively conserved in time. We would interpolate those ratios 409 00:24:25.309 --> 00:24:29.740 A:middle L:90% between the land set dates and retrieve the actually t 410 00:24:29.740 --> 00:24:32.500 A:middle L:90% from the reference et on non land set dates. 411 00:24:32.500 --> 00:24:33.880 A:middle L:90% And if we do that, this is what we 412 00:24:33.880 --> 00:24:37.940 A:middle L:90% get an estimate of daily ET just based on land 413 00:24:37.940 --> 00:24:40.769 A:middle L:90% sad. And it's not it's not too bad. 414 00:24:41.539 --> 00:24:42.160 A:middle L:90% It does pretty well. You know, at the 415 00:24:42.160 --> 00:24:45.730 A:middle L:90% beginning of the season, doesn't do too poorly at 416 00:24:45.730 --> 00:24:48.250 A:middle L:90% the end of the season. But you can see 417 00:24:48.250 --> 00:24:52.359 A:middle L:90% we're really underestimating the water use in the mid part 418 00:24:52.359 --> 00:24:55.109 A:middle L:90% of the season right here. So what happened There 419 00:24:55.109 --> 00:24:56.630 A:middle L:90% was a rainfall event that occurred close to the 4th 420 00:24:56.630 --> 00:25:00.410 A:middle L:90% of July during sex. So to so it kind 421 00:25:00.410 --> 00:25:03.980 A:middle L:90% of makes sense this land said image didn't know about 422 00:25:03.990 --> 00:25:07.289 A:middle L:90% the moisture enhancements associated with this rainfall event and its 423 00:25:07.299 --> 00:25:11.349 A:middle L:90% effects that kind of worn off by the time of 424 00:25:11.349 --> 00:25:15.710 A:middle L:90% this second lands at date. we really can't hope 425 00:25:15.710 --> 00:25:18.740 A:middle L:90% to recover that using Lance at alone and this is 426 00:25:18.740 --> 00:25:21.619 A:middle L:90% the time period where the data fusion has added the 427 00:25:21.619 --> 00:25:26.210 A:middle L:90% most value in this particular case. This is the 428 00:25:26.220 --> 00:25:27.329 A:middle L:90% fuse time series. And you can see it's done 429 00:25:27.329 --> 00:25:30.130 A:middle L:90% a much better job at capturing those enhanced flux is 430 00:25:30.130 --> 00:25:33.890 A:middle L:90% due to that rainfall event. So what's going on 431 00:25:33.890 --> 00:25:36.579 A:middle L:90% here, motus? It's kilometer resolution, it's pretty 432 00:25:36.579 --> 00:25:38.079 A:middle L:90% coast of course, but it is giving us some 433 00:25:38.079 --> 00:25:41.779 A:middle L:90% temporal information about this kind of larger scale rainfall system 434 00:25:41.779 --> 00:25:45.359 A:middle L:90% that moved through the the system, the area, 435 00:25:47.039 --> 00:25:49.000 A:middle L:90% giving us some indication that the moisture was a little 436 00:25:49.000 --> 00:25:53.849 A:middle L:90% higher during this time period we saw. Excuse me 437 00:25:55.539 --> 00:25:59.569 A:middle L:90% , this is the cumulative water use curve that we 438 00:25:59.569 --> 00:26:02.509 A:middle L:90% retrieve. The dotted line is a land set only 439 00:26:02.509 --> 00:26:07.190 A:middle L:90% retrieval the red line is the lancet motus Fused results 440 00:26:07.190 --> 00:26:11.569 A:middle L:90% and it agrees pretty well with the observed water use 441 00:26:11.569 --> 00:26:14.269 A:middle L:90% curve much better than land said. Only. We 442 00:26:14.269 --> 00:26:15.859 A:middle L:90% saw that kind of value added at most of the 443 00:26:15.859 --> 00:26:22.289 A:middle L:90% soybean sites in this soil moisture experiment of 2002 and 444 00:26:22.289 --> 00:26:25.960 A:middle L:90% that kind of a sickly cornfield site. We saw 445 00:26:25.970 --> 00:26:27.779 A:middle L:90% improvements due to the fusion. We saw less improvement 446 00:26:27.789 --> 00:26:32.220 A:middle L:90% due to fusion at the the healthy corn sites because 447 00:26:32.220 --> 00:26:34.710 A:middle L:90% these sites had already kind of Fully emerged by four 448 00:26:34.720 --> 00:26:40.759 A:middle L:90% July um and they weren't quite as responsive as a 449 00:26:40.759 --> 00:26:42.759 A:middle L:90% system to this rainfall event as the soybean fields. 450 00:26:42.940 --> 00:26:47.509 A:middle L:90% But overall, the improvement in the seasonal cumulative water 451 00:26:47.509 --> 00:26:52.210 A:middle L:90% use Went from about seven relative error using land said 452 00:26:52.210 --> 00:26:56.170 A:middle L:90% only to about four relative error with very little bias 453 00:26:56.940 --> 00:26:59.869 A:middle L:90% at the seasonal time steps. So that's that's useful 454 00:26:59.869 --> 00:27:03.509 A:middle L:90% information. And just a summary of the results of 455 00:27:03.509 --> 00:27:07.059 A:middle L:90% the other two sites barracks mead. So here we're 456 00:27:07.059 --> 00:27:10.750 A:middle L:90% looking at a new irrigated cotton water use irrigated cotton 457 00:27:10.750 --> 00:27:14.799 A:middle L:90% water use the green are observed. The black line 458 00:27:14.799 --> 00:27:18.740 A:middle L:90% is the fused model estimates and then here are the 459 00:27:18.740 --> 00:27:22.619 A:middle L:90% cumulative water use curves for the rain fed and irrigated 460 00:27:22.619 --> 00:27:26.150 A:middle L:90% cotton, both modelled and measured and at the more 461 00:27:26.150 --> 00:27:27.869 A:middle L:90% humid need site, we're looking at an irrigated irrigated 462 00:27:27.869 --> 00:27:32.849 A:middle L:90% corn and the water cumulative water use curves at those 463 00:27:32.849 --> 00:27:33.339 A:middle L:90% two sites. The model is doing a pretty good 464 00:27:33.339 --> 00:27:37.619 A:middle L:90% job. It's maybe not capturing every little irrigation event 465 00:27:37.619 --> 00:27:41.130 A:middle L:90% in these cotton fields, which is kind of reasonable 466 00:27:41.130 --> 00:27:44.329 A:middle L:90% . The fields are fairly small scale, but we're 467 00:27:44.329 --> 00:27:48.440 A:middle L:90% recovering the seasonal water use fairly well. It's interesting 468 00:27:48.440 --> 00:27:51.039 A:middle L:90% to note the difference in the water use in these 469 00:27:51.039 --> 00:27:53.529 A:middle L:90% two climatic systems. So, Barracks, that's in 470 00:27:53.529 --> 00:27:56.930 A:middle L:90% the texas panhandle, very hot, dry site, 471 00:27:56.930 --> 00:28:02.099 A:middle L:90% lot of dryer infection can really drive some strong evaporative 472 00:28:02.099 --> 00:28:04.509 A:middle L:90% losses on these very windy hot days. So you 473 00:28:04.509 --> 00:28:07.829 A:middle L:90% can see the water use between these two management is 474 00:28:07.829 --> 00:28:10.940 A:middle L:90% peeling peeling off pretty early in the system. Whereas 475 00:28:11.299 --> 00:28:15.190 A:middle L:90% in this more humid Nebraska site, the water use 476 00:28:15.190 --> 00:28:18.079 A:middle L:90% between irrigated and non irrigated corn is pretty similar until 477 00:28:18.079 --> 00:28:21.140 A:middle L:90% the very end of the season. This kind of 478 00:28:21.140 --> 00:28:25.579 A:middle L:90% water use information, spatially detailed, temporal detailed is 479 00:28:25.589 --> 00:28:27.319 A:middle L:90% just gold for water resource managers. If we can 480 00:28:27.319 --> 00:28:30.299 A:middle L:90% do this accurately over large areas, they need to 481 00:28:30.299 --> 00:28:33.599 A:middle L:90% know how to allocate water in advance between different competing 482 00:28:33.599 --> 00:28:40.359 A:middle L:90% uses between irrigation demands, ecosystem services, urban uses 483 00:28:40.369 --> 00:28:42.390 A:middle L:90% , if they can do this reliably using satellite data 484 00:28:42.390 --> 00:28:45.230 A:middle L:90% at this spatial and temporal scale, it's gonna be 485 00:28:45.230 --> 00:28:51.519 A:middle L:90% it's gonna be a a great asset. So this 486 00:28:51.519 --> 00:28:53.539 A:middle L:90% is just a picture of one of our experimental sites 487 00:28:53.549 --> 00:28:56.200 A:middle L:90% . Because give you an idea of how we run 488 00:28:56.200 --> 00:28:59.450 A:middle L:90% these experiments. This is at the barracks site. 489 00:29:00.039 --> 00:29:02.039 A:middle L:90% So here we have the irrigated cotton field in the 490 00:29:02.039 --> 00:29:03.880 A:middle L:90% background and this is the in irrigated cotton field. 491 00:29:03.890 --> 00:29:06.589 A:middle L:90% You can really see why they have to irrigate in 492 00:29:06.589 --> 00:29:08.460 A:middle L:90% this area. Because this this cotton field did nothing 493 00:29:10.140 --> 00:29:12.250 A:middle L:90% produce nothing during this growing season. This is an 494 00:29:12.259 --> 00:29:17.410 A:middle L:90% eddy co variant system that's measuring the turbulent flux is 495 00:29:17.410 --> 00:29:19.069 A:middle L:90% off the land surface, The sensible heat, the 496 00:29:19.069 --> 00:29:22.759 A:middle L:90% latent heat Off a patch that's maybe 100 m in 497 00:29:22.759 --> 00:29:26.789 A:middle L:90% dimension up wind of that sensor. We have a 498 00:29:26.789 --> 00:29:27.849 A:middle L:90% rain gauge. We have some radiation sensors. We 499 00:29:27.849 --> 00:29:32.529 A:middle L:90% also collected some high resolution aircraft data to help bridge 500 00:29:32.529 --> 00:29:36.079 A:middle L:90% the gap between the observation scale and the satellite pixel 501 00:29:36.079 --> 00:29:38.089 A:middle L:90% scale. And then this is a fused map of 502 00:29:38.089 --> 00:29:41.430 A:middle L:90% ET over the whole experimental area. We can make 503 00:29:41.430 --> 00:29:44.859 A:middle L:90% those maps for every day during the growing season and 504 00:29:44.859 --> 00:29:47.980 A:middle L:90% kind of make an animation or a movie of changing 505 00:29:47.980 --> 00:29:52.529 A:middle L:90% water. Use using this this fusion technique. And 506 00:29:52.529 --> 00:29:57.119 A:middle L:90% let's see if this movie plays here. Okay, 507 00:29:57.119 --> 00:30:00.690 A:middle L:90% so early in the season you can see some pulsing 508 00:30:00.690 --> 00:30:03.849 A:middle L:90% between some more muted tones. Those are cloudy days 509 00:30:03.849 --> 00:30:04.950 A:middle L:90% where there's not a lot of ev apple transpiration. 510 00:30:06.339 --> 00:30:10.109 A:middle L:90% The dark green indicates high eV apple transpiration on clear 511 00:30:10.109 --> 00:30:11.789 A:middle L:90% sky days. You can see some changes in the 512 00:30:11.789 --> 00:30:15.710 A:middle L:90% water management going on. So the southern part of 513 00:30:15.710 --> 00:30:18.450 A:middle L:90% this big pivot is turning on. We're going to 514 00:30:18.450 --> 00:30:19.740 A:middle L:90% see the southern part of this pivot turning on a 515 00:30:19.740 --> 00:30:22.529 A:middle L:90% little later in the season. This is the irrigated 516 00:30:22.529 --> 00:30:26.160 A:middle L:90% cotton field next to the un irrigated cotton field. 517 00:30:26.809 --> 00:30:29.269 A:middle L:90% This is our reference et site. Little grass plot 518 00:30:29.269 --> 00:30:33.329 A:middle L:90% . Well watered. This is a concentrated animal feeding 519 00:30:33.329 --> 00:30:36.839 A:middle L:90% operation. Big manure lagoons in the center of the 520 00:30:36.839 --> 00:30:40.329 A:middle L:90% operation evaporating a lot of water. And over here 521 00:30:40.329 --> 00:30:41.460 A:middle L:90% is the city of bushland texas. A lot of 522 00:30:41.460 --> 00:30:45.309 A:middle L:90% residential land, probably irrigating their lawns using a lot 523 00:30:45.309 --> 00:30:49.150 A:middle L:90% of water. You can see some strange experimental plots 524 00:30:49.150 --> 00:30:52.119 A:middle L:90% here. This this is why I love land stats 525 00:30:52.119 --> 00:30:55.859 A:middle L:90% so much. I can look at this map and 526 00:30:55.869 --> 00:30:57.970 A:middle L:90% figure out what's going on. I can make connections 527 00:30:57.970 --> 00:31:02.190 A:middle L:90% to what humans are doing on the landscape to cause 528 00:31:02.190 --> 00:31:03.369 A:middle L:90% these moisture patterns. I can't do that with motive 529 00:31:03.369 --> 00:31:07.269 A:middle L:90% scale data at a kilometer resolution. So this is 530 00:31:07.269 --> 00:31:10.890 A:middle L:90% why why Lance has become such a, such a 531 00:31:10.890 --> 00:31:15.690 A:middle L:90% big deal for water resource management. Now I'm coming 532 00:31:15.690 --> 00:31:17.859 A:middle L:90% to the Forestry department so I thought I'd better put 533 00:31:17.859 --> 00:31:19.349 A:middle L:90% a forest slide in here as well as some corn 534 00:31:19.349 --> 00:31:25.289 A:middle L:90% and soybean sides. What's it? It is also 535 00:31:25.299 --> 00:31:29.160 A:middle L:90% Okay. Okay. Forrest. Okay. But I 536 00:31:29.160 --> 00:31:30.839 A:middle L:90% wanted to have at least something over a forest system 537 00:31:30.849 --> 00:31:33.420 A:middle L:90% and this is some early work I did with dr 538 00:31:33.420 --> 00:31:37.359 A:middle L:90% win and trying to validate some of these models over 539 00:31:37.359 --> 00:31:41.930 A:middle L:90% forested. Manager's managed forested plots. And this is 540 00:31:41.930 --> 00:31:45.279 A:middle L:90% using our carbon enabled version of the model. So 541 00:31:45.279 --> 00:31:48.990 A:middle L:90% we're uh huh. Kind of evaluating carbon assimilation patterns 542 00:31:48.990 --> 00:31:52.980 A:middle L:90% , latent heat flux in correlation with L. A 543 00:31:52.980 --> 00:31:56.329 A:middle L:90% . And we were just talking about how to reinvigorate 544 00:31:56.329 --> 00:32:01.579 A:middle L:90% this this particular course of research and create some dense 545 00:32:01.579 --> 00:32:07.589 A:middle L:90% time series over some sites in north Carolina. This 546 00:32:07.589 --> 00:32:09.130 A:middle L:90% is an example of this data mining sharpener that I 547 00:32:09.130 --> 00:32:13.289 A:middle L:90% mentioned. It's a very nice tool, thermal imagery 548 00:32:13.289 --> 00:32:16.119 A:middle L:90% is typically collected at course or resolution than shortwave bands 549 00:32:16.119 --> 00:32:21.240 A:middle L:90% on the same satellite platform just because of the wavelength 550 00:32:21.240 --> 00:32:23.640 A:middle L:90% effect on the sensors. So we thermal people were 551 00:32:23.640 --> 00:32:27.069 A:middle L:90% always stuck with kind of crappy looking images like this 552 00:32:27.069 --> 00:32:30.549 A:middle L:90% . Whereas the shortwave guys uh like these nice crisp 553 00:32:30.549 --> 00:32:31.579 A:middle L:90% images and we get jealous. So we want to 554 00:32:31.579 --> 00:32:35.579 A:middle L:90% find a way to improve the resolution of this to 555 00:32:35.579 --> 00:32:37.819 A:middle L:90% match that of the shortwave bands. Doctor go in 556 00:32:37.819 --> 00:32:39.970 A:middle L:90% . Our lab has has come up with this regression 557 00:32:39.970 --> 00:32:43.640 A:middle L:90% tree approach where you throw all the band data into 558 00:32:43.640 --> 00:32:46.109 A:middle L:90% a regression tree uses the short wave data to sharpen 559 00:32:46.109 --> 00:32:50.130 A:middle L:90% up the The thermal image. So we're doing everything 560 00:32:50.130 --> 00:32:52.460 A:middle L:90% at 30 m resolution. And we've tested over a 561 00:32:52.460 --> 00:32:54.660 A:middle L:90% number of sites. We talked earlier about testing this 562 00:32:54.660 --> 00:32:59.440 A:middle L:90% over some urban sites today but it does a good 563 00:32:59.440 --> 00:33:02.470 A:middle L:90% job at recovering shadows and field boundaries. So that's 564 00:33:02.470 --> 00:33:06.119 A:middle L:90% that's just kind of a data enhancement tool. So 565 00:33:06.119 --> 00:33:08.880 A:middle L:90% what I've talked about so far is applying these different 566 00:33:08.880 --> 00:33:12.640 A:middle L:90% modeling tools, the data fusion, the sharpening tool 567 00:33:12.740 --> 00:33:14.980 A:middle L:90% , the et modeling to a number of different satellite 568 00:33:14.980 --> 00:33:19.630 A:middle L:90% as assets To make these maps of water use at 569 00:33:19.640 --> 00:33:23.609 A:middle L:90% 30 m resolution. We're also doing work and this 570 00:33:23.609 --> 00:33:27.029 A:middle L:90% is more work done by Fungal in my lab on 571 00:33:27.029 --> 00:33:31.059 A:middle L:90% mapping crop technology at this same spatial resolution crowd phrenology 572 00:33:31.059 --> 00:33:34.880 A:middle L:90% is a really useful way to interpret some of the 573 00:33:34.890 --> 00:33:36.960 A:middle L:90% water use curves. We're seeing, we want to 574 00:33:36.960 --> 00:33:39.319 A:middle L:90% see the water use turn on as the crops uh 575 00:33:39.329 --> 00:33:42.250 A:middle L:90% emerge. And we want to know when the most 576 00:33:42.250 --> 00:33:45.900 A:middle L:90% sensitive stage of crop growth is to moisture deficiencies. 577 00:33:45.910 --> 00:33:47.109 A:middle L:90% If we want to do good yield estimates. So 578 00:33:47.109 --> 00:33:50.750 A:middle L:90% I'm just gonna go through this quickly. We use 579 00:33:50.750 --> 00:33:53.089 A:middle L:90% the same star FM algorithm but applied to the shortwave 580 00:33:53.089 --> 00:33:57.000 A:middle L:90% reflect INce's and get time series of some kind of 581 00:33:57.000 --> 00:34:00.059 A:middle L:90% vegetation index to which we can kind of fit uh 582 00:34:00.440 --> 00:34:04.579 A:middle L:90% a curve. And in our case we're using a 583 00:34:04.589 --> 00:34:07.050 A:middle L:90% time set algorithm. So here's star FM. Applied 584 00:34:07.050 --> 00:34:12.030 A:middle L:90% to reflect ince's same thing, we use modus lands 585 00:34:12.030 --> 00:34:15.010 A:middle L:90% that image pairs then apply star FM two Modis imagery 586 00:34:15.010 --> 00:34:19.050 A:middle L:90% . When we don't have Lance at data to predict 587 00:34:19.050 --> 00:34:22.320 A:middle L:90% the full time series Of the reflect ince's at 30 588 00:34:22.320 --> 00:34:27.280 A:middle L:90% m resolution. So this is just a time series 589 00:34:27.280 --> 00:34:30.010 A:middle L:90% of tv. Using landscape modus fusion over this area 590 00:34:30.010 --> 00:34:34.650 A:middle L:90% . In central Iowa, we can pick out some 591 00:34:34.650 --> 00:34:37.150 A:middle L:90% different land use classes and look at the difference in 592 00:34:37.150 --> 00:34:40.750 A:middle L:90% the phonological evolution in these different classes, comparing. 593 00:34:40.760 --> 00:34:44.969 A:middle L:90% So these little these are land set and these little 594 00:34:44.980 --> 00:34:47.420 A:middle L:90% stars are lancet modest fusion to really kind of fill 595 00:34:47.420 --> 00:34:51.690 A:middle L:90% out the the time series, fit our curve for 596 00:34:51.690 --> 00:34:55.090 A:middle L:90% the different classes and pick out these Fiona logical metrics 597 00:34:57.940 --> 00:35:00.000 A:middle L:90% that will be useful for other analyses. If we 598 00:35:00.000 --> 00:35:05.329 A:middle L:90% do that spatially over central Iowa, we can see 599 00:35:05.329 --> 00:35:07.900 A:middle L:90% the forests are emerging first. They're usually occurring in 600 00:35:07.900 --> 00:35:13.039 A:middle L:90% right period areas along the river systems in this landscape 601 00:35:13.050 --> 00:35:15.400 A:middle L:90% , corn is planted, first emerges first, soybean 602 00:35:15.400 --> 00:35:17.530 A:middle L:90% emerges a little bit later and we can map out 603 00:35:17.539 --> 00:35:22.420 A:middle L:90% for example, start of season. The next, 604 00:35:22.429 --> 00:35:24.690 A:middle L:90% the next part of this talk is how do we 605 00:35:24.690 --> 00:35:30.690 A:middle L:90% combine this water use information with this phrenology information two 606 00:35:30.699 --> 00:35:37.989 A:middle L:90% for agricultural applications including yield estimation. Oh that's what 607 00:35:37.989 --> 00:35:40.789 A:middle L:90% this slide is showing. We're combining these two to 608 00:35:40.789 --> 00:35:45.570 A:middle L:90% eat data streams. Um to look at the stress 609 00:35:45.570 --> 00:35:50.710 A:middle L:90% on yield. Now this is a drought index that 610 00:35:50.710 --> 00:35:53.409 A:middle L:90% we've created in our lab. It's called the evaporative 611 00:35:53.409 --> 00:35:58.829 A:middle L:90% stress index. Or see and there are drought in 612 00:35:58.829 --> 00:36:00.829 A:middle L:90% this. I don't know how many different drought indices 613 00:36:00.829 --> 00:36:02.650 A:middle L:90% . There are hundreds of different drought indices there for 614 00:36:02.650 --> 00:36:06.530 A:middle L:90% you to choose from associated with every component of the 615 00:36:06.530 --> 00:36:09.099 A:middle L:90% hydrologic budget you can think of. There are precipitation 616 00:36:09.099 --> 00:36:13.150 A:middle L:90% anomalies, the standardized precipitation in disease. Those are 617 00:36:13.150 --> 00:36:15.550 A:middle L:90% the most commonly used. There are anomalies and soil 618 00:36:15.550 --> 00:36:21.250 A:middle L:90% moisture anomalies in stream flow anomalies and groundwater table. 619 00:36:22.130 --> 00:36:24.550 A:middle L:90% This is based on anomalies in E. T. 620 00:36:25.030 --> 00:36:28.079 A:middle L:90% And we think that this is a pretty good indicator 621 00:36:28.079 --> 00:36:31.070 A:middle L:90% of vegetation health because it's really directly sampling changes in 622 00:36:31.070 --> 00:36:37.019 A:middle L:90% the transpiration flux uh from this year to prior years 623 00:36:37.030 --> 00:36:39.349 A:middle L:90% , a really good indicator of physiological functioning of the 624 00:36:39.360 --> 00:36:44.670 A:middle L:90% vegetative canopy, even more directly related than anomalies in 625 00:36:44.670 --> 00:36:47.969 A:middle L:90% the precipitation inputs itself. So here we're looking at 626 00:36:47.969 --> 00:36:53.420 A:middle L:90% anomalies in this normalized et ratio et over pt the 627 00:36:53.420 --> 00:36:58.699 A:middle L:90% red indicates areas where it was anomalous li lo in 628 00:36:58.699 --> 00:37:00.219 A:middle L:90% this case for a three month period ending in july 629 00:37:00.219 --> 00:37:04.989 A:middle L:90% of uh huh 2012. And this was in fact 630 00:37:05.000 --> 00:37:07.960 A:middle L:90% the heart Of the area in the corn belt that 631 00:37:07.960 --> 00:37:12.369 A:middle L:90% was affected by a flash drought in 2012. So 632 00:37:12.380 --> 00:37:15.579 A:middle L:90% significant depletion of the soil moisture reserves in this area 633 00:37:15.579 --> 00:37:16.929 A:middle L:90% . And this drought came on very very rapidly. 634 00:37:16.940 --> 00:37:21.699 A:middle L:90% So they call it a flash drought event. And 635 00:37:21.699 --> 00:37:23.449 A:middle L:90% this is happening more and more frequently in different parts 636 00:37:23.449 --> 00:37:25.949 A:middle L:90% of the world. So people really want advanced notice 637 00:37:25.949 --> 00:37:29.579 A:middle L:90% of these flash drought events as early as possible. 638 00:37:29.579 --> 00:37:31.539 A:middle L:90% So you can adjust your decision making process. So 639 00:37:31.539 --> 00:37:34.400 A:middle L:90% this is how it played out in 2012. This 640 00:37:34.400 --> 00:37:36.250 A:middle L:90% is the U. S. Drought monitor. I'm 641 00:37:36.250 --> 00:37:37.590 A:middle L:90% sure you've all seen the U. S. Drought 642 00:37:37.590 --> 00:37:42.050 A:middle L:90% monitor maps. Its well publicized their updated every seven 643 00:37:42.050 --> 00:37:46.329 A:middle L:90% days. It's kind of a subjective uh combination of 644 00:37:46.329 --> 00:37:51.849 A:middle L:90% a number of different drought indicators indicating drought severity. 645 00:37:52.630 --> 00:37:54.840 A:middle L:90% This is our C. E. Based anomaly map 646 00:37:55.329 --> 00:37:58.820 A:middle L:90% and this is veg dry. This is a drought 647 00:37:58.820 --> 00:38:01.630 A:middle L:90% index that's widely used. It's based mostly on anomalies 648 00:38:01.630 --> 00:38:06.969 A:middle L:90% and ndvi I so it's tracking anomalies in the green 649 00:38:06.969 --> 00:38:09.550 A:middle L:90% vegetation cover amount. So we're going through 2012. 650 00:38:09.550 --> 00:38:13.500 A:middle L:90% We saw something going on in in the Central US 651 00:38:13.510 --> 00:38:15.130 A:middle L:90% back in May and R. E. S. 652 00:38:15.130 --> 00:38:16.550 A:middle L:90% I. That wasn't really being reported in other drought 653 00:38:16.550 --> 00:38:19.730 A:middle L:90% and disease. As little concern that we had an 654 00:38:19.730 --> 00:38:23.949 A:middle L:90% algorithm problem in our our model but as time evolved 655 00:38:24.320 --> 00:38:28.510 A:middle L:90% we saw that same area just getting stronger and stronger 656 00:38:28.860 --> 00:38:32.139 A:middle L:90% and finally in july and in august the other indicators 657 00:38:32.139 --> 00:38:35.809 A:middle L:90% kind of caught up. This is a drought that 658 00:38:35.809 --> 00:38:37.500 A:middle L:90% was driven not only by lower than normal precept but 659 00:38:37.500 --> 00:38:44.670 A:middle L:90% also a lingering heat waves, very hot temperatures baking 660 00:38:44.670 --> 00:38:46.539 A:middle L:90% the moisture out of the soil more rapidly than usual 661 00:38:46.920 --> 00:38:51.559 A:middle L:90% . And some kind of windy periods also exacerbating the 662 00:38:51.559 --> 00:38:54.409 A:middle L:90% evaporative losses. So we were sensitive that sensitive to 663 00:38:54.409 --> 00:38:57.329 A:middle L:90% that fairly early on in this E. T. 664 00:38:57.329 --> 00:39:01.500 A:middle L:90% Based index. Um So the theory here is you 665 00:39:01.500 --> 00:39:06.449 A:middle L:90% may see a signal of impending crop stress first in 666 00:39:06.449 --> 00:39:09.360 A:middle L:90% one of these thermal based retrievals. The canopy temperatures 667 00:39:09.360 --> 00:39:12.929 A:middle L:90% are elevating. You're seeing the stress and thermal ban 668 00:39:12.940 --> 00:39:17.110 A:middle L:90% before you see physical degradation of the green canopy that 669 00:39:17.110 --> 00:39:21.809 A:middle L:90% might be conveyed by NdVI I So we're thinking there 670 00:39:21.809 --> 00:39:24.170 A:middle L:90% may be some early warning potential associated with its index 671 00:39:24.480 --> 00:39:28.300 A:middle L:90% . These are observations that were collected by observers on 672 00:39:28.300 --> 00:39:31.860 A:middle L:90% the ground in every county, qualitatively assessing what the 673 00:39:31.860 --> 00:39:35.460 A:middle L:90% moisture status was in every county. And and those 674 00:39:35.460 --> 00:39:37.130 A:middle L:90% observers were seeing something going on in May, but 675 00:39:37.130 --> 00:39:39.739 A:middle L:90% it wasn't being reflected in the precept or the vegetation 676 00:39:39.739 --> 00:39:43.889 A:middle L:90% index. And that's exactly where the hit. The 677 00:39:43.889 --> 00:39:45.619 A:middle L:90% biggest hits in the corn yields occurred that year. 678 00:39:45.619 --> 00:39:49.909 A:middle L:90% So we've been working with Dave johnson, he's another 679 00:39:49.920 --> 00:39:51.869 A:middle L:90% member of the science team, Lance, that science 680 00:39:51.869 --> 00:39:54.800 A:middle L:90% team works with the National Egg Statistics Service. He 681 00:39:54.800 --> 00:39:59.440 A:middle L:90% has to put out estimates of corn yield starting in 682 00:39:59.440 --> 00:40:01.699 A:middle L:90% august every year. And then he updates that every 683 00:40:01.699 --> 00:40:07.929 A:middle L:90% couple uh weeks as as more information comes in, 684 00:40:07.969 --> 00:40:09.909 A:middle L:90% we're thinking we may have some early warning potential of 685 00:40:09.909 --> 00:40:13.599 A:middle L:90% yield hits in this thermal based ET index. So 686 00:40:13.599 --> 00:40:15.559 A:middle L:90% we're looking at correlations for different corn growing states in 687 00:40:15.559 --> 00:40:21.780 A:middle L:90% this case with c distributions in day 105 for example 688 00:40:21.780 --> 00:40:24.820 A:middle L:90% . And how that correlated with at harvest yield anomalies 689 00:40:25.309 --> 00:40:28.380 A:middle L:90% and for all the different corn growing states. And 690 00:40:28.380 --> 00:40:31.159 A:middle L:90% we find that the maximum correlations are occurring about when 691 00:40:31.159 --> 00:40:34.340 A:middle L:90% he needs to put his first yield estimates out. 692 00:40:34.349 --> 00:40:36.179 A:middle L:90% So that's a good thing. There are some states 693 00:40:36.179 --> 00:40:39.300 A:middle L:90% that don't behave nicely North Dakota Minnesota. Well those 694 00:40:39.300 --> 00:40:42.110 A:middle L:90% were some of those states where we have all these 695 00:40:42.110 --> 00:40:46.139 A:middle L:90% little wetlands sitting around corrupting our course resolution stress signal 696 00:40:46.610 --> 00:40:49.739 A:middle L:90% . So this was all done with a 10 km 697 00:40:49.739 --> 00:40:52.599 A:middle L:90% map. We're hoping once we can get this data 698 00:40:52.599 --> 00:40:54.619 A:middle L:90% fusion algorithm running more operationally, we can use it 699 00:40:54.630 --> 00:40:59.489 A:middle L:90% to spatially desegregate these core stress signals down to the 700 00:40:59.489 --> 00:41:04.000 A:middle L:90% field scale where we can really isolate corn and soybeans 701 00:41:04.010 --> 00:41:07.260 A:middle L:90% from, from these water bodies are forest uh sites 702 00:41:08.409 --> 00:41:13.010 A:middle L:90% . And make these okay, make these maps at 703 00:41:13.010 --> 00:41:15.960 A:middle L:90% much finer spatial resolution. So this is where we're 704 00:41:15.960 --> 00:41:16.960 A:middle L:90% going with this right now. I'll just skip over 705 00:41:16.960 --> 00:41:22.010 A:middle L:90% this. So this map is being put out operationally 706 00:41:22.019 --> 00:41:28.420 A:middle L:90% over the Continental United States at our website right now 707 00:41:28.429 --> 00:41:30.769 A:middle L:90% . I just turned it on for this season last 708 00:41:30.769 --> 00:41:34.630 A:middle L:90% week but we're trying to add additional tabs onto this 709 00:41:34.630 --> 00:41:37.840 A:middle L:90% website covering some different geographic locations. And I'll just 710 00:41:37.840 --> 00:41:39.550 A:middle L:90% briefly talk about some of the areas other areas we're 711 00:41:39.550 --> 00:41:44.440 A:middle L:90% working on. So this is the coverage of goes 712 00:41:44.440 --> 00:41:47.429 A:middle L:90% east and goes west, our noah satellites, weather 713 00:41:47.429 --> 00:41:52.340 A:middle L:90% satellites. So we're stitching together east and west over 714 00:41:52.650 --> 00:41:55.090 A:middle L:90% Coronas. We're also expanding to north America. We 715 00:41:55.090 --> 00:41:59.369 A:middle L:90% hope to turn on that tab this season. And 716 00:41:59.369 --> 00:42:00.230 A:middle L:90% also we got a very, very nice view of 717 00:42:00.230 --> 00:42:02.710 A:middle L:90% south America with goes east. So these were some 718 00:42:02.710 --> 00:42:06.369 A:middle L:90% of our first areas. So that's an example of 719 00:42:06.369 --> 00:42:08.849 A:middle L:90% the product will be putting out over north America because 720 00:42:08.849 --> 00:42:12.679 A:middle L:90% droughts don't end at the border, they continue into 721 00:42:12.690 --> 00:42:17.849 A:middle L:90% Canada into Mexico. And this is an example of 722 00:42:17.849 --> 00:42:21.360 A:middle L:90% a first E. T. Map. This is 723 00:42:21.360 --> 00:42:24.099 A:middle L:90% clear sky et over south America generated at 10 kilometers 724 00:42:24.099 --> 00:42:28.199 A:middle L:90% resolution. I had a scientist visiting our lab for 725 00:42:28.199 --> 00:42:31.920 A:middle L:90% a year from Embrapa, the Brazilian agricultural research System 726 00:42:31.920 --> 00:42:35.800 A:middle L:90% and he's been helping me kind of evaluate these maps 727 00:42:35.800 --> 00:42:38.469 A:middle L:90% over brazil and South America. We can make the 728 00:42:38.469 --> 00:42:42.820 A:middle L:90% same time series of maps here and look and see 729 00:42:42.820 --> 00:42:44.489 A:middle L:90% if we see some of the there was a major 730 00:42:44.489 --> 00:42:47.920 A:middle L:90% drought in the amazon In the western Amazon in 2005 731 00:42:49.400 --> 00:42:53.960 A:middle L:90% That's showing up here even bigger one in 2010 and 732 00:42:53.969 --> 00:42:58.619 A:middle L:90% a very lingering drought in northeast brazil that was really 733 00:42:58.619 --> 00:43:00.409 A:middle L:90% impacting the local communities. They had to bring in 734 00:43:00.409 --> 00:43:02.909 A:middle L:90% some water trucks to supply the water needs. It 735 00:43:02.909 --> 00:43:05.880 A:middle L:90% lasted for a long time. There's another one turning 736 00:43:05.880 --> 00:43:08.130 A:middle L:90% on down here in Southeast brazil right now. We 737 00:43:08.130 --> 00:43:12.639 A:middle L:90% also see some flooding impacts, big floods in the 738 00:43:12.639 --> 00:43:15.760 A:middle L:90% Amazon in 2009 And one in 2012. So we're 739 00:43:15.760 --> 00:43:21.190 A:middle L:90% writing some papers an evaluation of this Brazilian, uh 740 00:43:21.190 --> 00:43:23.420 A:middle L:90% huh drought map. We've done some yield estimation, 741 00:43:23.420 --> 00:43:27.940 A:middle L:90% correlation studies and some of the the crop growing states 742 00:43:27.949 --> 00:43:30.300 A:middle L:90% in brazil, we have to be very careful of 743 00:43:30.309 --> 00:43:34.400 A:middle L:90% trying to mask out forested lands. I'm sorry, 744 00:43:34.400 --> 00:43:36.130 A:middle L:90% but we got to get rid of those forest pixels 745 00:43:36.130 --> 00:43:37.989 A:middle L:90% in this particular application because especially in the amazon, 746 00:43:38.170 --> 00:43:42.079 A:middle L:90% those trees are really deep rooted and they're very resilient 747 00:43:42.090 --> 00:43:45.989 A:middle L:90% to drought impacts compared to the shorter rooted agricultural crops 748 00:43:46.050 --> 00:43:49.070 A:middle L:90% . So it's going to just ruin our signal. 749 00:43:49.070 --> 00:43:50.829 A:middle L:90% If we have all those forested pixels in there, 750 00:43:50.829 --> 00:43:52.239 A:middle L:90% we've done the best we can. We need to 751 00:43:52.250 --> 00:43:55.449 A:middle L:90% do better job at doing this mask and we need 752 00:43:55.449 --> 00:43:59.500 A:middle L:90% to do this at high spatial resolution as well because 753 00:43:59.510 --> 00:44:01.679 A:middle L:90% our initial results, are there okay. Work in 754 00:44:01.679 --> 00:44:04.730 A:middle L:90% some states, not so well in other states were 755 00:44:04.730 --> 00:44:07.179 A:middle L:90% tracking down what the causes of this. This problem 756 00:44:07.179 --> 00:44:09.670 A:middle L:90% is, but I think land use masking and going 757 00:44:09.670 --> 00:44:12.869 A:middle L:90% to higher spatial resolution is going to be the key 758 00:44:12.869 --> 00:44:15.230 A:middle L:90% here. So soybean, clearly a big export from 759 00:44:15.230 --> 00:44:17.500 A:middle L:90% brazil. We looked at some other crops as well 760 00:44:20.590 --> 00:44:22.750 A:middle L:90% , other parts of the world. So these are 761 00:44:22.750 --> 00:44:25.119 A:middle L:90% geo stationary satellites operated by the european Union called the 762 00:44:25.119 --> 00:44:29.690 A:middle L:90% Media's satellites. And with the operational satellite, we 763 00:44:29.690 --> 00:44:31.429 A:middle L:90% get a really good view of the full African continent 764 00:44:31.440 --> 00:44:35.599 A:middle L:90% and we get a more oblique view of europe. 765 00:44:35.610 --> 00:44:37.900 A:middle L:90% We've been looking at both these areas. This is 766 00:44:37.900 --> 00:44:42.639 A:middle L:90% a three kilometer resolution maps supported by mediaset over North 767 00:44:42.639 --> 00:44:46.920 A:middle L:90% africa. Lots of nice spatial detail in this map 768 00:44:47.489 --> 00:44:51.010 A:middle L:90% . We've done a lot of research over the Nile 769 00:44:51.010 --> 00:44:54.090 A:middle L:90% River basin. I don't know if this is going 770 00:44:54.090 --> 00:44:57.489 A:middle L:90% to work. So this is kind of a promo 771 00:44:57.489 --> 00:45:00.050 A:middle L:90% video that Nasa put together for this project comparing a 772 00:45:00.050 --> 00:45:05.630 A:middle L:90% lot of different data sources, trim precipitation distribution over 773 00:45:05.630 --> 00:45:12.550 A:middle L:90% the Nile basin. So satellite dr precipitation maps over 774 00:45:12.550 --> 00:45:15.250 A:middle L:90% the course of a year and you can see the 775 00:45:15.250 --> 00:45:19.500 A:middle L:90% pulsing of the the rains during the dry season. 776 00:45:19.500 --> 00:45:22.360 A:middle L:90% So this is Alexey. Drived evaporate transpiration over the 777 00:45:22.360 --> 00:45:24.340 A:middle L:90% same time period. You can see the greening moving 778 00:45:24.340 --> 00:45:30.090 A:middle L:90% through the Ethiopian Highlands and then receding southward. This 779 00:45:30.090 --> 00:45:31.980 A:middle L:90% is a retrieve soil moisture. This is from a 780 00:45:31.980 --> 00:45:35.250 A:middle L:90% hydrologic model. You can see some of the problems 781 00:45:35.250 --> 00:45:37.349 A:middle L:90% in their inputs. This is a different soil type 782 00:45:37.349 --> 00:45:40.519 A:middle L:90% down here which is putting this weird discontinuity and V 783 00:45:40.670 --> 00:45:43.809 A:middle L:90% . C. The green up in this map. 784 00:45:45.889 --> 00:45:49.579 A:middle L:90% And there may be one other thing. So this 785 00:45:49.579 --> 00:45:52.000 A:middle L:90% is the grace. This is groundwater storage retrieved from 786 00:45:52.000 --> 00:45:57.030 A:middle L:90% a satellite that measures gravitational anomalies. We're trying to 787 00:45:57.030 --> 00:46:00.690 A:middle L:90% piece all these different satellite based indicators together to see 788 00:46:00.690 --> 00:46:02.610 A:middle L:90% if we can do what water balance just using satellite 789 00:46:02.619 --> 00:46:07.139 A:middle L:90% data. So this is the focus area of of 790 00:46:07.139 --> 00:46:09.570 A:middle L:90% the Nile study. This is the comparison. That's 791 00:46:09.570 --> 00:46:14.179 A:middle L:90% another place where we've compared energy balance versus water balance 792 00:46:14.179 --> 00:46:15.849 A:middle L:90% estimates. So on the left is an energy balance 793 00:46:15.849 --> 00:46:19.690 A:middle L:90% model on the right as the water balance model. 794 00:46:19.690 --> 00:46:22.260 A:middle L:90% And if you look from a distance, the broad 795 00:46:22.260 --> 00:46:24.900 A:middle L:90% patterns are very similar. But there are some very 796 00:46:24.900 --> 00:46:30.010 A:middle L:90% important differences were capturing the intense irrigation in the Nile 797 00:46:30.010 --> 00:46:32.340 A:middle L:90% river delta that is not captured in this water balance 798 00:46:32.340 --> 00:46:35.869 A:middle L:90% . That doesn't really know where the irrigation is occurring 799 00:46:35.880 --> 00:46:37.639 A:middle L:90% . And this is another irrigation scheme in Sudan. 800 00:46:37.670 --> 00:46:40.300 A:middle L:90% The Gezira scheme that we're seeing enhanced flux is missed 801 00:46:40.300 --> 00:46:45.260 A:middle L:90% here. This is the sued wetland in which is 802 00:46:45.260 --> 00:46:47.849 A:middle L:90% a really major sink of water along the white Nile 803 00:46:47.860 --> 00:46:52.769 A:middle L:90% before it reaches up with the blue Nile. Really 804 00:46:52.769 --> 00:46:55.369 A:middle L:90% important to quantify on a basin scale. All these 805 00:46:55.369 --> 00:47:00.219 A:middle L:90% different extractions and water use, very limited information available 806 00:47:00.219 --> 00:47:02.320 A:middle L:90% about this on the ground. We can get kind 807 00:47:02.320 --> 00:47:06.480 A:middle L:90% of an objective use of this water use situation using 808 00:47:06.480 --> 00:47:07.400 A:middle L:90% the satellite data, but we really have to be 809 00:47:07.469 --> 00:47:13.500 A:middle L:90% sensitive to some of these non standard moisture sources and 810 00:47:13.500 --> 00:47:16.849 A:middle L:90% sinks that are difficult to capture a priority. A 811 00:47:16.849 --> 00:47:22.000 A:middle L:90% map of the famine, drought impacted area in the 812 00:47:22.000 --> 00:47:27.179 A:middle L:90% Horn of Africa in 2011 shows up in the fetal 813 00:47:27.179 --> 00:47:30.489 A:middle L:90% anomalies and I just gave this presentation in the Czech 814 00:47:30.500 --> 00:47:32.239 A:middle L:90% Republic. So I had to throw in some stuff 815 00:47:32.239 --> 00:47:34.960 A:middle L:90% about europe. I'm not going to show you this 816 00:47:34.969 --> 00:47:38.739 A:middle L:90% this uh movie. So I was in the Czech 817 00:47:38.739 --> 00:47:43.960 A:middle L:90% Republic. This is a really interesting example of why 818 00:47:43.960 --> 00:47:46.840 A:middle L:90% we need land set in some, some kinds of 819 00:47:46.840 --> 00:47:51.289 A:middle L:90% landscapes. So this is the Czech Austrian border here 820 00:47:52.480 --> 00:47:53.920 A:middle L:90% and this is a glance at pixel size and this 821 00:47:53.920 --> 00:47:57.869 A:middle L:90% is the modus pixel size. So you can see 822 00:47:57.869 --> 00:48:01.590 A:middle L:90% that there's a big kind of change in land use 823 00:48:01.590 --> 00:48:06.170 A:middle L:90% patterns right at that border. Well this, the 824 00:48:06.179 --> 00:48:07.690 A:middle L:90% Czech Republic was under a kind of a socialized farming 825 00:48:08.070 --> 00:48:10.849 A:middle L:90% regime For a number of years, the field sizes 826 00:48:10.849 --> 00:48:15.469 A:middle L:90% have been consolidated into bigger fields, whereas in Austria 827 00:48:15.469 --> 00:48:16.789 A:middle L:90% , the fields are just progressively chopped into smaller and 828 00:48:16.789 --> 00:48:22.159 A:middle L:90% smaller parts through inheritance. So we certainly need land 829 00:48:22.159 --> 00:48:24.909 A:middle L:90% set down in Austria. We may get away with 830 00:48:24.909 --> 00:48:32.010 A:middle L:90% motive scale evaluations in in the Czech Republic. And 831 00:48:32.010 --> 00:48:36.010 A:middle L:90% this is just a demonstration of our first drought maps 832 00:48:36.010 --> 00:48:37.489 A:middle L:90% that we just produced about a month ago over europe 833 00:48:37.489 --> 00:48:42.210 A:middle L:90% using the media set satellites. The next step of 834 00:48:42.210 --> 00:48:44.360 A:middle L:90% course, we've got to got to tackle the whole 835 00:48:44.360 --> 00:48:47.389 A:middle L:90% globe. We're doing this with a modus satellite. 836 00:48:47.869 --> 00:48:51.429 A:middle L:90% We're using modus in a geo stationary mode. In 837 00:48:51.429 --> 00:48:53.340 A:middle L:90% this case modus gives a stay night temperature differences. 838 00:48:53.340 --> 00:48:57.679 A:middle L:90% Were using kind of a time differential temperature measurement from 839 00:48:57.679 --> 00:49:00.679 A:middle L:90% a single satellite in this case to map the evaporative 840 00:49:00.679 --> 00:49:02.480 A:middle L:90% flux is over the whole globe. And we want 841 00:49:02.480 --> 00:49:06.789 A:middle L:90% to put out these drought indices at the global scale 842 00:49:06.789 --> 00:49:08.630 A:middle L:90% as well, which we can compare back to geo 843 00:49:08.630 --> 00:49:12.739 A:middle L:90% stationary satellites. So I just have time sequences of 844 00:49:12.750 --> 00:49:19.730 A:middle L:90% these two different satellite retrievals and randy. I'm I'm 845 00:49:19.739 --> 00:49:22.570 A:middle L:90% going to wrap up with these closing thoughts about the 846 00:49:22.570 --> 00:49:29.079 A:middle L:90% utility of satellite based E. T. Retrievals and 847 00:49:29.079 --> 00:49:32.039 A:middle L:90% the importance of land surface temperature in in making these 848 00:49:32.039 --> 00:49:36.699 A:middle L:90% estimations over certain types of landscapes and for certain types 849 00:49:36.699 --> 00:49:39.079 A:middle L:90% of applications. So thank you for for listening so 850 00:49:39.079 --> 00:49:45.699 A:middle L:90% well and staying away and I appreciate that. Mhm 851 00:49:47.070 --> 00:49:50.320 A:middle L:90% . We don't have time for but we'll take a 852 00:49:50.489 --> 00:49:55.820 A:middle L:90% question yes about story friend who is not working as 853 00:49:55.820 --> 00:50:00.030 A:middle L:90% a host of Mhm martin. And I remember that 854 00:50:00.039 --> 00:50:04.519 A:middle L:90% this a family physical methods that fuses now if we're 855 00:50:04.519 --> 00:50:07.309 A:middle L:90% talking and which is pretty regular for talking about the 856 00:50:07.630 --> 00:50:08.980 A:middle L:90% fact that we're talking about irrigation, which we don't 857 00:50:09.760 --> 00:50:13.579 A:middle L:90% you to say cloud doesn't that mean? For example 858 00:50:13.960 --> 00:50:16.110 A:middle L:90% you'll also be seeing it down the process of reducing 859 00:50:16.289 --> 00:50:21.199 A:middle L:90% administration because of the house. We can't do a 860 00:50:21.199 --> 00:50:23.849 A:middle L:90% direct retrieval using under cloud covered areas. So we're 861 00:50:23.849 --> 00:50:30.099 A:middle L:90% only using estimates of the clear sky evaporative flux is 862 00:50:31.760 --> 00:50:34.840 A:middle L:90% so we need to do more testing of this under 863 00:50:34.840 --> 00:50:37.329 A:middle L:90% kind of mixed sky uh conditions. But where we're 864 00:50:37.329 --> 00:50:40.400 A:middle L:90% getting the actual Lance that data and the actual motive 865 00:50:40.409 --> 00:50:44.099 A:middle L:90% retrieval is under clear sky conditions. And then we 866 00:50:44.099 --> 00:50:45.670 A:middle L:90% have kind of a cloudy sky gap filling algorithm that 867 00:50:45.670 --> 00:50:52.280 A:middle L:90% we have to superimpose on this. So great. 868 00:50:52.659 --> 00:50:54.320 A:middle L:90% Well, yes, it would it would the gap 869 00:50:54.320 --> 00:50:58.650 A:middle L:90% filling algorithm. We have to estimate evaporate transpiration under 870 00:50:58.650 --> 00:51:01.389 A:middle L:90% cloudy conditions. So we would we would we know 871 00:51:01.389 --> 00:51:05.389 A:middle L:90% that the reduced we know we can quantify the reduced 872 00:51:05.389 --> 00:51:07.820 A:middle L:90% radiation load in those cases. So we can we 873 00:51:07.820 --> 00:51:13.900 A:middle L:90% can retrieve that. And the center pivot irrigation here 874 00:51:16.159 --> 00:51:22.969 A:middle L:90% . You got moisture. Oh, it can it 875 00:51:22.969 --> 00:51:25.860 A:middle L:90% can affect your potential et estimates. So if you're 876 00:51:25.860 --> 00:51:30.849 A:middle L:90% trying to estimate the potentially t over center irrigated pivot 877 00:51:30.860 --> 00:51:32.349 A:middle L:90% with meteorological data, that's not inside the pivot, 878 00:51:32.360 --> 00:51:37.239 A:middle L:90% it's going to estimate uh drier conditions in a larger 879 00:51:37.239 --> 00:51:40.960 A:middle L:90% pt than you actually realize in that field because of 880 00:51:40.960 --> 00:51:45.809 A:middle L:90% this uh self humidifier thing situation. So that is 881 00:51:45.820 --> 00:51:50.179 A:middle L:90% that is something that we have to take into account 882 00:51:50.179 --> 00:51:53.579 A:middle L:90% if we have extended irrigation areas irrigated areas. If 883 00:51:53.579 --> 00:51:57.119 A:middle L:90% we have little isolated pivots here or there, it's 884 00:51:57.119 --> 00:51:58.550 A:middle L:90% not going to make such a big difference. But 885 00:51:58.550 --> 00:52:00.019 A:middle L:90% if you have a whole huge irrigation district, then 886 00:52:00.019 --> 00:52:02.150 A:middle L:90% you have to modify your P. T. Estimates 887 00:52:02.150 --> 00:52:05.960 A:middle L:90% over those areas. But it won't affect the direct 888 00:52:05.960 --> 00:52:09.329 A:middle L:90% retrieval of the evaporative flux which is obtained through energy 889 00:52:09.329 --> 00:52:15.880 A:middle L:90% balance rather than atmospheric kind of moisture exchange principles. 890 00:52:16.449 --> 00:52:22.480 A:middle L:90% Yes. About water sources. There's never come. 891 00:52:22.050 --> 00:52:27.340 A:middle L:90% Who is using? Okay. I will comment on 892 00:52:27.340 --> 00:52:30.000 A:middle L:90% who's using. Not my model estimates, but somebody 893 00:52:30.000 --> 00:52:32.929 A:middle L:90% else's model estimates who's who's working in Idaho. And 894 00:52:32.929 --> 00:52:36.420 A:middle L:90% it's uh one big use in the United States is 895 00:52:36.420 --> 00:52:39.699 A:middle L:90% water rights management. So in the Western U. 896 00:52:39.699 --> 00:52:43.360 A:middle L:90% S. You have your water rights, you can 897 00:52:43.369 --> 00:52:45.530 A:middle L:90% pump up into those water rights, you can't exceed 898 00:52:45.530 --> 00:52:47.659 A:middle L:90% the water rights and you have to pump a certain 899 00:52:47.659 --> 00:52:50.829 A:middle L:90% amount of water every year. Otherwise you forfeit your 900 00:52:50.829 --> 00:52:54.059 A:middle L:90% right. So there's just a huge pressure to monitor 901 00:52:54.650 --> 00:52:57.909 A:middle L:90% water use at scales of individual fields. And they 902 00:52:57.909 --> 00:53:00.869 A:middle L:90% used to do this based on electrical records, the 903 00:53:00.869 --> 00:53:02.769 A:middle L:90% pump electricity. And they would empirically relate that to 904 00:53:02.769 --> 00:53:06.659 A:middle L:90% water pumps. Now they're doing it with satellite E 905 00:53:06.659 --> 00:53:10.449 A:middle L:90% . T. So you can identify users who are 906 00:53:10.449 --> 00:53:15.710 A:middle L:90% pumping more than their allocated allotment and go after those 907 00:53:15.710 --> 00:53:21.469 A:middle L:90% people. And you can also use this information to 908 00:53:21.469 --> 00:53:25.960 A:middle L:90% negotiate water rights, trades and transfers interstate water compacts 909 00:53:25.969 --> 00:53:29.869 A:middle L:90% . It's being used. Satellite T retrieval is being 910 00:53:29.869 --> 00:53:32.289 A:middle L:90% used in the courts right now to negotiate interstate water 911 00:53:32.300 --> 00:53:37.389 A:middle L:90% compacts and some disputes about water uses. Or if 912 00:53:37.389 --> 00:53:42.460 A:middle L:90% you need to establish a curtailment of irrigation in Syria 913 00:53:42.469 --> 00:53:45.219 A:middle L:90% . So if it's in a drought time, you 914 00:53:45.230 --> 00:53:47.469 A:middle L:90% need to supply the senior water rights, the junior 915 00:53:47.469 --> 00:53:51.590 A:middle L:90% water rights holders may be curtailed and they use this 916 00:53:51.590 --> 00:53:53.739 A:middle L:90% information to make those decisions as well. So those 917 00:53:53.739 --> 00:53:58.219 A:middle L:90% are some kind of legalistic applications. Were using it 918 00:53:58.219 --> 00:54:02.449 A:middle L:90% more in terms of crop stress identification and yield yield 919 00:54:02.449 --> 00:54:09.570 A:middle L:90% estimations, drought monitoring notice in in Arkansas, there 920 00:54:09.570 --> 00:54:12.920 A:middle L:90% was some difference in terms of agreement. You mentioned 921 00:54:12.920 --> 00:54:15.559 A:middle L:90% Minnesota and some of these areas of career. Yeah 922 00:54:15.940 --> 00:54:17.599 A:middle L:90% . How does that take positive model? Deal with 923 00:54:17.610 --> 00:54:20.550 A:middle L:90% the underlying substrate? Say if you have a shallow 924 00:54:20.559 --> 00:54:27.500 A:middle L:90% parcel quick. Yeah, in Arkansas is a special 925 00:54:27.500 --> 00:54:29.880 A:middle L:90% case because Arkansas, most the corn is grown under 926 00:54:29.880 --> 00:54:31.539 A:middle L:90% irrigation. And so we're not just, we're just 927 00:54:31.539 --> 00:54:35.420 A:middle L:90% not seeing a lot of variability in Arkansas and it's 928 00:54:35.420 --> 00:54:37.369 A:middle L:90% not really related to the climate. So I think 929 00:54:37.369 --> 00:54:42.170 A:middle L:90% that's what's going in Arkansas, but hopefully this isn't 930 00:54:42.170 --> 00:54:44.920 A:middle L:90% , this is another area where these techniques might be 931 00:54:44.920 --> 00:54:47.409 A:middle L:90% useful if there is some hidden moisture source that we 932 00:54:47.409 --> 00:54:50.969 A:middle L:90% don't know about a priority. The land surface temperature 933 00:54:50.969 --> 00:54:53.000 A:middle L:90% should be giving us a clue that that moisture is 934 00:54:53.000 --> 00:54:55.559 A:middle L:90% available. So I think I think we should be 935 00:54:55.559 --> 00:54:59.039 A:middle L:90% able to pick this up. Now. My my 936 00:54:59.050 --> 00:55:01.429 A:middle L:90% husband is a forest hydrologist for the Forest Service. 937 00:55:01.429 --> 00:55:05.880 A:middle L:90% He's working on groundwater impacts on forestry. He's really 938 00:55:05.889 --> 00:55:09.380 A:middle L:90% interested in identifying forest patches that are being influenced by 939 00:55:09.389 --> 00:55:14.429 A:middle L:90% by groundwater systems. So I think we want to 940 00:55:14.429 --> 00:55:16.659 A:middle L:90% look spatially and see here's a strangely behaving patch of 941 00:55:16.659 --> 00:55:21.719 A:middle L:90% forest or something sitting over a cursed substrate, it's 942 00:55:21.719 --> 00:55:23.579 A:middle L:90% wetter than we expect. And, and uh, 943 00:55:23.590 --> 00:55:27.230 A:middle L:90% and see if we can explain why that is. 944 00:55:27.230 --> 00:55:29.659 A:middle L:90% And that's why I think doing these comparisons is so 945 00:55:29.670 --> 00:55:32.170 A:middle L:90% , so useful because these strange areas just pop out 946 00:55:34.239 --> 00:55:37.630 A:middle L:90% regionally. Thanks for together. Thank you. Thank 947 00:55:37.630 --> 00:55:40.659 A:middle L:90% you all