Live from Las Vegas, Nevada, it's theCUBE. Covering IBM World of Watson 2016, brought to you by IBM. Now, here are your hosts, John Furrier and Dave Vellante. Okay, welcome back everyone. We're here live in Las Vegas for IBM's World of Watson at the Mandalay Bay here. This is theCUBE's SiliconANGLE Media Flagship Program. We go out to the events and extract the signal from the noise. I'm John Furrier with SiliconANGLE. I'm here with Dave Vellante, my co-host, chief researcher at wikibon.com and our next guest is Inderpal Bandari, who's the global chief data officer for IBM. Welcome to theCUBE, welcome back. Thank you, thank you for having me. Nice to meet you. It's a pleasure to be here. You've had a great conversation with Dave at the last event. Ten years, Bob Pichino was just on us. We're just talking about the 10 year anniversary of IOD. Information on demand. And Dave's joke, what I thought was telling, we'll set it up this way, is that 10 years ago, different data conversation. How'd he get rid of it? I don't want the compliance and the liability. Now, it's shifted to a much more organic, innovative. Exciting. Yeah, I mean, value add. What's the shift? What's the big change in 10 years? What, besides the obvious of the Watson vision, why did it move so fast or too slow? What's your take on this? Yeah, no, so data used to be viewed as exhaust, right? Detritus, something to get rid of, like you pointed out. And now it's much more to an asset. And in fact, you know, people are even talking about quantifying it as an asset, so that you can reflect it on the balance sheet and stuff like that. So it certainly moved a long, long way. And I think part of it has to do with the fact that we are inundated with data, and data does contain valuable information. And to the extent that you're able to glean it and act on it efficiently and quickly and accurately, it leads to a competitive advantage. What's the landscape for architects out there? Because a lot of things that we hear is that, okay, I buy the data, I got a digital transformation, okay, but now I got to get, put the data to work. So I need to have it all categorized. What's the setup? Is there a general architecture philosophy that you could share with companies that are trying to set themselves up for some baseline foundational sets of building blocks? I mean, I think they buy the Watson dream, that's a little headroom. They just want to start in kindergarten or in literally whatever metaphor you want to use. They need a baseline. What's the building blocks approach? The building blocks approach, I mean, from a, if you're talking about a pure technical, architectural, that kind of approach, that's one thing. If you're really going after a methodology that's going to allow you to create value from data, I would back you up further. I would say that you want to start with the business itself and gaining an understanding of how the business is going to go about monetizing itself. Not its data, but you know, what is the business's monetization strategy? How does the business plan to make money over the next few years? Not how it makes money today, but over the next few years, how it plans to make money. That's the right starting point. Once you've understood that, then it's basically reflecting on how data is best used in service of that. And then that leads you down to the architecture, the technologies, the people you need, the skills mix, the processes. The counterintuitive, the way it used to be. It used to be the ivory tower, where it would convene and dictate policy and schemas on databases and say, this is how you do it. You're saying the opposite. Business union's got to go in and own the roadmap, if you will, for the business. It's a business roadmap. And then figure it out, then go back. Then go backwards. That's really the better way to address it, in my view. So the framework that we talked about in Boston, and just, you're like the professor, I'm a student. So, and I've been out speaking to other chief data officers about it. It's spot on in this framework. So let me briefly summarize it, and then we can drill into it. I hope you're attributing it to me, Dave. I'm saying this is in the files framework. I've stolen it, with no shame. No, I'm kidding. So, we're doing a live TV. It's, you know. He can source you. I will give him credit. And so, but you have said there are two parallel and three sequential activities that have to take place for chief data officer. The two parallel are partnership with the line of business and get the skill sets right. The three sequential are the thing you just mentioned. How are you going to monetize data, access to data, data sources, and trust. You're going to trust the data. Okay, so great framework. And I'd say I've tested it. Some CDOs have said to me, well, geez, that's actually better than the framework I had. So they've sort of evolved it. I said, you're welcome. Okay, but now, so let's drill into that a little bit. Maybe starting with the monetization piece. In the early days, John, and when people were talking about big data, it was the mistake people made was, I got to sell the data, monetize the data itself. Not necessarily is what you're saying. Yes, yes. I mean, that's the common pitfall with that. When you start thinking about monetization and you're the chief data officer, your brain naturally goes to, well, how do I monetize the data? That's the wrong question. The question really is, how is the business planning to monetize itself? What is the monetization strategy for the overall business? And once you understand that, then you kind of back into what data is needed to support it. And that's really kind of the strategy in place. And then the next two steps of, well, then how do you govern that data so it's fit for the purpose of that business need that you just identified. And finally, what data is so critical that you want to centralize it and make sure that it's completely trusted? So you back into those three steps. So thinking about data sources, people always say, well, should you start with internal? Should you start with external? And the answer presumably is it depends. It depends on the business. So how do you actually go through that decision tree? What's that process like?  So for example, I'll use IBM. I mean, in the case of IBM, took me the first few months to understand that our monetization strategy was around cognitive business, specifically making enterprises into cognitive businesses. And so then the strategy that we have internally for IBM's data is to enable cognition within IBM, the enterprise, and move forward with that. And then that becomes a showcase for our customers because it is after all such a good example of a complex enterprise. And so backing in from that strategy, it becomes clear what are some of the critical data elements that you need to master, that you need to trust, that you need to centralize, and you need to govern very, very rigorously. So that's basically how I approach it. Did I answer your question, Dave, or do you? Yeah, so you touched on the second part. I want to drill into the third sequential activity, which is- The sources, right? You mentioned some of them. Well, so we've just talked about this. Well, the sources, I mean, if you have something to add to that. Yes, in terms of, I think you mentioned the internal versus the external. So one thing I'll mention, especially if you kind of take that 10-year outlook that we were talking about. 10 years ago, CDOs had very internal outlooks in terms of the data was all internal business data. Today, it's much more external as well. There's a lot more exogenous data that we have to handle and drill into. And that's because we're making use of a lot more unstructured data. So things like news feeds, press releases, articles that have just been written all our fair game to amplify the view that you have about some entity. So for example, if we're dealing with a new supplier, previously we might gather some information by talking with them. Now we'd also be able to look at essentially everything that's out there about them and factor that in. So there's an element of the exogenous data that's brought to bear, and then that obviously becomes part of the realm of the CDO as well to make sure that that data is available and usable by the business. Jeff Jones said something. Go ahead, sorry. Oh, Jeff Jones would say that's the observation space that you want to have. The news feeds is extra metadata that could change the alchemy, if you will, of whatever the mix of the data is. Is that kind of what you're getting at? Yeah, I would say, I'd even go further than just metadata. I would say that in some sense it's part of your intrinsic data set. Because it gives you additional information about the entities that you're collecting data on and that you're measuring. But John Kelly in the keynote this morning, he made two statements. He said one is in three to five years, every healthcare practitioner is going to want to consult Watson. And then he also said, same thing for M&A, because Watson's going to know every public piece of data about every single company. So it would seem that within the three to five year timeframe, that the shift is going to be increasingly toward external data sources. Not necessarily the value in the lever points, but in terms of the volume, certainly of data. Is that fair assumption? I think it's a fair statement. I mean, I think if you think of it in the healthcare context, if a patient comes in and there's a doctor or a practitioner that's examining the patient right there, they're generating some data based on their interaction. But then if you think about the exogenous data that's relevant and pertinent to that case, that could involve thousands of journals and articles. And so your example of essentially saying that the external data could be far greater than the internal data, I would say we are already there. Okay, and then the third sequential piece is trust. You've got to be able to trust the data. We talk a lot about, we were down at big data NYC, the same week you guys made your big announcement of data work. Everybody talks about data lakes. We joke it's a data swamp and you can't really trust the data. You're further away from a single version of the truth than we ever were. So how are you dealing with that problem internally at IBM? And what's the focus? Is it more on reporting? Is it more on supporting lines of business? The product? Yeah, the focus internal within IBM is in terms of driving cognition at, the way I would describe it is at points where today we have significant human judgment being exercised to make decisions. And that's thousands of points in our enterprise, complicated enterprise like IBMs. And each of those decision points is actually an opportunity to inject cognitive technology in play and then bring to bear an augmented intelligence to those decisions. That, you know, A factors in the exogenous data. So leaving a much better informed decision, but also then a much more accurate decision. Okay, the two parallel activities. Let's start with the first one, line of business, you know, relationships. Sounds like bromide. Why is it not just sort of a trite throwaway statement? What, where's the detail behind that? So the detail behind that, if you go back to the very first and the most important step in this whole thing with regard to the monetization strategy of the company, understanding that, if you don't have those deep relationships with the lines of business, there's no way that you'll be able to understand the monetization strategy of the business. So that's why that's a concurrent activity that has to start on day one. Otherwise, you won't even get past the, you know, that very first base in terms of understanding what the monetization strategies are for the business. And that can only really come by working directly with the business units, meeting with their leadership, understanding their business. So you have to do that due diligence and that's where that partnership becomes critical. Then as you move on, as you progress through that sequence, you need them again. So for instance, once you've understood the strategy and now you've understood what data you need to further that strategy and to govern it, you need their help in governing the business. Because in many cases, the business is maybe the ones collecting the data or at least controlling the source systems for that data. So that partnership then just gets deeper and deeper and deeper as you move forward in that progression. I love the conversation I'm monetizing earlier and there's some tweets going around, you know, what's holding it back, cost of building it obviously and manageability. But I want to bring that back and bring a developer perspective here because a lot of emphasis is on developing apps where the data is now part of the development process. I wrote a blog post in 2008 saying that data's a new development kit. It was radical at the time, but in reality, it came out to be true in that they're looking at data as a library of value to tap into. So stuff's in a daily, they could be sitting there for years but I could pull something out and be very relevant in context in real time and change the game on some insight and the insight economy as Bob was saying. So what is your strategy for IBM? Two, one, on board more developer goodness and two, how do you talk to customers who are really trying to figure out a developer strategy so they can build apps and not to go back and rewrite it and make it certainly mobile first, et cetera. But how does a data first app get built and how should developers be programming with data? I'll give you a way to think about it, right? I mean, and going back again to that 10 year paradigm shift, right? So 10 years ago, if somebody wanted to write an application and put it on the internet and it was based on data, the hardest part was getting hold of the data because it was just very, very difficult for them to get hold of the data, to access the data. And then those who did manage to get hold of the data, they were very successful in being able to utilize it. So now with the paradigm shift that's happened now is the approach is that you make the data available to developers and so they don't have to go through that work both in terms of accessing, collecting, finding that data, then cleaning it, it's all so significant and so time consuming that it could put back their whole process of eventually getting to the app. So to the extent that you have large stores of data that are ready to go and you can then make that available to a body of developers, it just unleashes an away. You're having a library of code available because it's all the hard work. I think that's a good way to look at it. I think that's a very good way to look at it because you've also got technologies like the deep learning technologies where you can essentially train them with data. So you don't need to write the code, they get trained through the data. So I think that's a very good way. It's DevOps of data, I mean it's like Agile meets, I mean you're right, a lot of the cleaning and this is where more noise, we all know that problem, more data creates more noise, better cleaning tools, so however you can automate that, seems to be the secret differentiator. It's an accelerator, it's a major accelerator for development if you have good sets of data that are available for them to use. So I want to round out my little framework here, your framework and my learnings, for the fifth one being skills. Yes. So this is complicated because it involves organization, skills changes, there's Pepper going through the lobby here. We're trying to get her on the cube. Dave has to think for Pepper, okay, should I take over? You want to go see Pepper? I want to see Pepper on the cube, hey sorry, I digress. But so a lot of issues there, there's reporting structures, so what do you mean when you talk about sort of the skill sets and re-skilling? So I'll describe to you a little bit about the organization that I have at IBM as an example. Some of that carries over and some of that doesn't. The reason I say that is again, I mean the skills piece, there are some generic skill sets that you need for to be a Chief Data Officer, to be a successful Chief Data Officer in an enterprise. There is one pillar that I have in my organization is around data science, data engineering, DevOps, deep learning and these are the folks who are adept at those technologies and approaches and methodologies and they can take those and apply them to the enterprise. So in a sense, these are the more technical people. Then another pillar that's again pretty generic and you have to have it, is the information and data governance pillar. So that anything that's flowing, any data that's flowing through the data platform that I spoke of in the first pillar, that data is governed and fit for purpose. So they have to worry about that. As soon as any data is, you even think of introducing that into the platform, these folks have to be on that and they're essentially governing it, making sure that people have the right access, security, the quality is good, it's improving, there's a path to improving it and so forth. I think those are some fairly generic skill sets that you have to get. In the case of the first pillar, what's difficult is that there aren't that many people with those skills and so it's hard to find that talent and so the sooner you get on it, so that's the biggest barrier. In the case of the second pillar, what's the most difficult piece there is you need people who can walk the balance between monetization and governance.  Handcuff, right? And your handcuff. And then if it's too much monetization, you might run aground because you've ignored some major regulation. So walking that line is really- Awesome market value. Yes, that's exactly right. You can really get ahead of your skis, as they say, and have a face plan. If you try too hard to- And you might be skiing backwards down the floor. Well, we see this with web startups like Twitter, that's a big conversation with Twitter, Facebook. If you try to monetize too early, you lose the flywheel effect of the value. Absolutely. So walking that balance is critical. So that's really finding the skill set to be able to do that. That's what's at play in that second part. The third one is if you are applying it to an enterprise, you have to integrate this platform into the workflow of the enterprise itself. Otherwise, you're not going to create any impact because that's where the impact gets created, right? That's basically where the data is at the tip of the spear, so to speak. So it's going to create value. And in a large enterprise which has legacy systems, which has silos, which has acquiring companies, and so on and so forth, that's in of itself a significant job. And that skill set is also interesting. That's a handicap because if you have that kind of siloed mentality, you don't get the benefits of the data sharing, right? So what's that? What's that? How much effort would it take? I mean, just kind of painting that picture kind of like out there, like massively hard. Yeah, no, that's, you know, a lot of people think that data mining is all about my data.  One of the functions of the Chief Data Office is to change that mindset. And to start making use of the data in a broader context than just a departmental siloed type of approach. And now some data can legitimately be used only departmentally, but the moment you need two or more departments start using that data, I mean, it's essentially corporate data. So are those roles a shared service? What do you see that works? It maybe varies, but is it a shared service that reports into the Chief Data Officer or is it embedded into the business? Those skill sets that you talked about? I think those skill sets are definitely part of the Chief Data Officer, you know, organization. Now, it's interesting you mentioned that about embedding them in the business units. Now, in a large enterprise, a complicated enterprise like IBM, the different business units, and that potentially have different business objectives and so forth, you know, you do need a Chief Data Officer role for each of these business units. And that's something that I've been advocating. That's my fourth pillar. And we're setting that up within the context of IBM. So that they serve the business unit, but they essentially report it to me so that they can make use of the overall corporate structure that you want. You do their performance review. The performance review is done by the business unit. It is, okay. But the functional direction is given by me. Okay. So I'll get back to this data. And I can't go either way, I'm sure. That can go, yes, there's a balance there that you have to specify. So we're going to do a lot of time first, but I'll get back to this data mining thing, because you bring up a good point. We can maybe continue on our next time we talk, but data mining is where all the cutting edge kind of best practices are, were, are, were, were, they're still there technically if you're, but the dynamic of data mining is that you're assuming no new data. So with, if you have a lot of data coming in, most of the best data mining techniques are like a corpus, you attack it, and then, but if the pile of data is getting bigger faster than you could data mine it, what good is, again, we're in this circular hole. I'm going to again, you know, just take you back 10 years from now and now, right? And the differences between the two. So it's very interesting points that you bring up. I'll give you an example from 10 years ago, this data mining example, not 10 years ago, actually my first go around at IBM, so it's like 94. One of the things I'd done was we had a program, a computer program that every team in the National Basketball Association started using. And this was a classic data mining program. I would look at the data and find insights and present them. And one of the insights that it came up with, and this was for a critical playoff game, it told the coach, you got to play, you're back up point guard and you're back up forward. Now think about that. Which same coach would actually go with that? So it's very hard for them to believe that. They don't know if it's right or wrong. It's a moment of truth. And the way we got around that was we essentially pointed back to the snippets of video where those circumstances occurred. And now the coach could see what was going on and make an informed decision. Flash forward to now. The systems we have now can actually look at all that context, all at once. What's happening in the video, what's happening in the audio. Also the data can piece together the context. So data mining is very different today than what it was then. Now it's all about weaving the context and the story together and serving it up. Yeah, what happened, what's happening and what's going to happen kind of is the theaters of insight, right? I mean, what happened is easy. Just look at the data and spit out some insight. What's happening now is a little bit harder in memory. I think that's the difference between cognition as it is today versus data mining as we understood it a few years ago. Great conversation, we can go for another hour but we're getting a lot of love to follow up on some of the deep learning. Maybe come down to our monk next time we're in this. Certainly on the sports data, we have a whole program on sports data. So we love the sports with ESPN of tech and bringing you all the action here. Yes, I did that work before Moneyball. My mistake was I didn't ride the bike. Yeah, you could have been. You're too busy riding the next algorithm. But that's okay. We put a little footmark in the cube notes for that. Thank you very much. Thank you. Okay, live in Mandalay Bay. We'll be right back with more of our live coverage. I'm John Furrier, Dave Vellante. We'll be right back. Today, I am helping people.


Here's a game that's extinct. Mariah, a recent game. Has anyone played this game? Whoa, first scene. Well, you were very lucky because Mariah was, quote, one of the best, one of the PC's most disturbing games, and now it's gone forever. That article was published in 2017. I don't have time to talk about what the deal was with this game, but it was a unique game. That this was something cool and it is actually extinct and probably could not be made again for a bunch of reasons and it's just gone.


to build up the series of industry studies from Levin, later on, or otherwise, the Silicon Valley Route 120 argument is a lot more about open circulation through not-to-be. I'm just a little curious about a particular someone who's as sensitive as you to the limitations of software madness, not also carrying over the skepticism to the degree to which what is critical is appropriation to an exclusion element as opposed to a full process of intersection. Yeah, go ahead. Very fast. Yeah, yeah. I'm teaching these studies that you mentioned. So it's a, well, first, it's very much that we still are in the IT business and with a lot of software components. They never challenge the quality of these patents, but I agree with you that in some industries, the role of patents can be challenged, and I'm open to that. But I don't agree with the idea that consists in saying, oh, entrepreneurs do not consider that as a key driver of their success or the success of a new venture. You have first mover advantage. And of course, you have other mechanisms, and they are complementary with the patent system. But it's like if you would ask the question to Nike what will be the success factor of its next show on the market. Do you think they will ever mention trademark regulation? No, because they take it as granted. That's exactly my point. One, if you have a good patent system, it will not become the key factor of success and entrepreneurship. It's like you have a road, and you can drive on it. So it's not the key factor, but it's part of the framework condition. In some very specific technologies, we can talk about strategic behavior and how it may hinder innovation. I fully agree with you. But we must think about that carefully. We could continue this debate for a long time. Patent clearly excites people and provokes very different reactions. We're gonna take a break now. We're gonna have a cup of coffee and we'll come back for our next panelists in 15 minutes. Thank you.


The UN is deploying a rapid reaction unit in the eastern part of the Democratic Republic of Congo to help contain armed groups. Under Secretary General for Peace Operations Jean-Pierre Lacras says the decision came after President Felice Chisikedi announced security measures, including declaring a state of siege in North Kivu and Ituri provinces bordering Uganda to contain armed groups during the 30-day state of siege that entered into effect last Thursday. The provincial governments of Ituri and North Kivu are now led by the military governor with the police chief as vice governor according to a presidential order. Lacras said UN forces would continue to collaborate with the new military authorities appointed in the two provinces. So you get notified about fresh news updates.


ThinkTek Hawaii, civil engagement lives here. Okay, research in Minoa on a given Monday at the one o'clock block. We like, you know, week after week, we continue this great discussion about SOAS, the School of Ocean Earth Science and Technology, and HIGP, the Hawaii Institute of Physics and Planetology, and we keep going. And one of the names that keeps popping up is Scott Rowland. And now, today, he's here with us. I am. Scott is in the faculty of SOAS. He's in the Department of Geophysics, what is it, Geology and Geophysics at SOAS. Welcome to the show. Thank you very much. Thank you. So we want to talk to you about what's going on in the eruption, and we want to divide the show in two parts. The first is, what's been going on, how is it now? And the second part is, what's going to happen? We like, we know how much geophysicists like to predict things. Right. Well, I'm a geologist, not a geophysicist. Sorry. So I can't, maybe I won't predict. We know the difference anyway. All right. Certainly. I will do my best to catch you up and give you a sense of what can happen, and I'm not going to predict what will happen. OK. So let's talk about what has happened and where we are now. And by the way, we have this great background, this background, USGS, the United States Geological Survey. That's correct. And like NASA, NASA, they give these great pictures. They're the original of the USGS picture, and we're using it as a background today.  OK. I won't touch that comment. All the information that I know about the eruption comes from the USGS. They have done an excellent job of putting photos, maps, graphs, diagrams, discussions online in near real time. Sometimes they will post their photos from their early morning helicopter flight probably within half an hour of the helicopter landing. So if I go up there, I'm likely to meet somebody from USGS. I don't know about that. They're out in the field, and regular folks aren't allowed in there. If you were a resident of the area, you certainly would meet the USGS folks. But just wandering around outside, probably not. You might see them fly by on a helicopter.  I went over a couple weeks ago, almost three weeks ago now, to go on a helicopter tour and got rained out. Oh, no! Yes. So the weather doesn't help sometimes. For the last couple weeks, the eruption has been relatively stable, as in the same thing is going on. It's still very dramatic, but there have not been big changes. And it's still essentially a story of two eruptions, one down in Puna, within Leilani estates. That's where the vent is. And the lava flow is about 13 kilometers long, about eight miles long, heads off to the northeast and east and enters the ocean at what used to be Capojo. And it was just about two weeks ago that those homes, a pretty horrible event of all those homes being covered by lava as the lava entered the ocean. But that flow has been very, very stable, and it's maintained the same channel since then, with minimal overflows. So it's a very stable channel, and the lava has been pouring into the ocean ever since at a relatively high rate for Kilauea, that the USGS posted a notice on their Facebook page that they've estimated that the output is something like 100 cubic meters per second. Wow. And then it builds the land. It builds what's behind us on the back, on the back right back. That's correct. It's entering the ocean and building out the land. It hasn't built out a lot of land yet, but the water is relatively, the offshore slopes there are relatively gradual. What's this picture? This is an image that one of my colleagues took from one of these helicopter tours, and underneath that steam plume is what used to be Capojo and Capojo vacation lands, the subdivisions, and Capojo Bay, which is now completely filled. All that steam comes from the interaction of the hot lava entering the ocean. And it's built out some land. You see that very jagged peninsula of dark rock just beyond the steam plume. That's new land, as of a couple of weeks ago. And the USGS reports, in most of their morning reports, that they can see a big area of upwelling water offshore, which is an indication that the lava flow is flowing underwater, and it's not too far down. So if this continues, it's going to certainly be building out. Is there a drop-off out there? There is, but unlike where lava went into the ocean for many years from the Pueblo oil flows, it's much less steep here at Capojo. And so as long as it keeps flowing into the ocean at the same place, it's going to be much more likely to build out that land. It does drop off well offshore eventually. So yes. So that land would be, right at the ocean, relatively, it would be a slight incline going up toward the mountain as the lava comes down. Is it ready? Is that land that's been created? Is it ready for me to build my new house? Build your house on? Well, I think it's going to belong to the state if it's new. If it buries someone's land, someone's private land, they still own that lot. That's still theirs. But anything new belongs to the state. I mean, like the accretion part, the part that's out in the ocean. That wouldn't belong to them if they had the coastal land to start with. As I understand it, that's correct. That new land belongs to the state. If it enters the ocean in the National Park, it belongs to the National Park. Very interesting. That's right. So, I mean, at this rate of flow, we are actually increasing the land mass at a significant rate, then. Certainly, although when we take a look at some of the photos in a moment, we'll see where this is kind of coming from. If you go to the next slide from the one that was just shown. So, this is a USGS photo that they posted yesterday. And kind of off in the distance, you can barely make out the active channel. So, if you look from where the big steamplume is, follow back to the right. You can make out a little bit of an orange color. That's the active lava channel entering the ocean.  But clearly, this all has to come from somewhere. And that's the second part of the story, namely, up at the summit of Kilauea. As magma leaves the summit storage chamber, it is, the summit is deflating and collapsing. Hale Maumau has increased in size by almost two times and gotten much deeper than it was just a month and a half ago. And so, all of that magma that's traveling down the East Drift Zone, erupting into the ocean to make new land, that had to come from somewhere. And basically, we're losing volume up at the summit in order to gain it down here at the coast. You sound so matter-of-fact about it. All your geology people sound so matter-of-fact about it, because you're so excited about the science of it. Well, yeah. But in fact, you know, this is doing violence to our land, isn't it? Well, it's, you're absolutely right. We always have to keep in mind that some 500 homes have been destroyed by this eruption since a month and a half or so ago, which weighs on all of us heavily. I don't think anybody would deny the fact we'd much rather none of this happened and all those folks had their homes back. So that excitement of what's going on, being able to watch these processes, it's tainted heavily by knowing there's that loss of property. It's a mixed bag. Absolutely. Oh, that's absolutely the case. That's right. So let's see another picture. Okay. Here's one of the maps that the USGS posts almost daily. The pink color is the new lava as of May 4th or May 3rd, I think. The purple flows are older lava flows. And since about three weeks ago, a little over three weeks ago, vent number eight, just below the words Leilani Estates, has been the source of all the lava flows down in Puna. And that dashed line shows the line of the lava channel and it goes to the northeast, then it swings over to the east and makes kind of a right angle turn to the south and then another right angle turn to the east and enters the ocean were over where it says active ocean entries. And the map actually hasn't changed a whole lot in the last couple of weeks. Let's go to the next one. So if you knew where the underground passed the plumbing, the volcanic plumbing was, say five years ago, then I suppose, at least to some extent, you could predict where the magma would go in that plumbing and where it would pop up in that plumbing and what the flow would be now. So you're partly correct. We certainly know where the plumbing system is, because we know where eruptions have taken place in the past. That's why we go out and map volcanoes to see where previous eruptions have taken place. Using sonar or something. No, just using our eyes, walking around, walking around. So there are very good geologic maps of lower Puna that were produced back in the early 90s by Frank Trusdell and Richard Moore at USGS. And there were maps prior to that as well. And so by looking at those maps, you can tell that eruptions have occurred along the east rift zone hundreds of times probably in the geologic past. And you can see where the lava flows go. So we know where the plumbing system is. We also know that from tracking earthquakes, both in the past and in the early part of this event, the thing that we really don't know until it happens is at what point is the magma going to stop propagating down rift and erupt at the surface? That's the big unknown. It's going down underneath. It's migrating through the rift zone plumbing system. You can't see that. Correct. You can trace the earthquakes. So every time as the magma moves, it has to push the rocks out of the way and sometimes break them. That's an earthquake. And that causes earthquakes. There's one this morning. So those earthquakes that are going on now are up at the summit. And that's the other part of this eruption, the holly moe moe increasing in size and parts of the caldera floor dropping and some explosions taking place up at the summit. So that's the other part of this eruption. And it produces these magnitude five to five and a half or so. It's pretty serious. They're pretty serious. They're kind of funny. They're reported in the press all the time as earthquakes. They are more explosions than earthquakes. And what the USGS is reporting is the equivalent earthquake size for that amount of explosive energy. It's certainly they shake the ground, but it's not as if there are big faults moving each time there is one of these explosions. So when they say it's a magnitude 5.5 or 5.3 or whatever, what they're mainly saying is that that particular explosion released the amount of energy equivalent to a 5.5 or 5.3. So if you don't know exactly when or where it's going to pop up from the plumbing. Correct. How do we determine a sort of a boundary where the average show should not be walking? Once the magma is migrating down the rift zone, such as it was the very first week of May, as it starts to approach the surface, it needs to push the rocks out of the way. And what you start noticing are cracks on the ground. And certainly May 2nd, 3rd and 4th in Leilani Estates, ground cracks were reported. Fissure. Fissures. Little ones. Yes. Yes. Well, some of them were pretty good size. Good to fall in. If you were being very careless, you could.  In the forest, where it's a lot harder to see things, you could conceivably fall into one of them. Once they get wide.  And it's a pretty good bet that if there is going to be an eruption, it's going to be in that region where the ground cracks are. So, as somebody doing that, it's like USGS? Oh, certainly. Once the earthquakes became, the other thing that happens is that the earthquakes start to get much more frequent in that same locality. And so very early in May, the earthquakes became really numerous in Lailania States. They were really shallow. People could actually hear them. The ground started to crack. And then earthquakes weren't really getting much farther downrift from that. And so it seemed as if the magma was starting to decide, for whatever reason, that this is the place where it's going to start accumulating. There have been cases in the past where magma has migrated down the rift zone and not erupted at all. Either just stayed there or continued on down to perhaps erupt underwater. So until the magma actually starts coming out of the ground, you're not 100% certain that there's even going to be an eruption. But certainly once you start getting a bunch of ground cracks, that's a pretty good sign that likely there will be one. So I know this is a very broad question, but I'd like to ask it and see what you say. Why is this eruption different? I think from the geologist's perspective, the reason this eruption is different is that we've got simultaneous flank activity down on the rift zone and summit activity up at Kilauea Caldera. We've seen each of those separately in the recent geologic past. Flank activity in 1955 and 1960. Summit activity 1924 and to a little extent 1960. But to have them going on at the same time, not that anyone thought that could never happen. It's just in our geologic career lifetimes, we have not seen those two events going on simultaneously. I think that's what's been interesting. It's a broader involvement. Yes, more things happen. You really get to see the connection between where the magma is coming from and where it's going. One more thing before we go to our break is I know Pune geothermal venture, not far. I don't know how far it is. And I wonder if you could connect up what you've been talking about with what happened, what did happen. I'm not sure what did happen at Pune geothermal venture. Pune geothermal shut down their production very soon after the earthquake started to localize in Laelani Estates. They're about a mile away from the center of that subdivision. It's pretty close. That's pretty close. I mean, the whole reason why Pune geothermal is there is because it's on the rift zone. That's where the hot geothermal waters are. So if you're going to build a geothermal plant, it totally makes sense to put it there. You want it there. Sure. It makes it much more efficient. That's right. And so they moved off-site a whole bunch of pentane, which is part of their energy production process. They moved that closer to Kilo. And they filled, as I understand it, they filled all their wells, their geothermal wells, with cold water, and then put big metal caps on top of them. What's the point of that? I think the worry was, and this is speculation, the worry was that lava might somehow get down into the bottom of those wells, interact with the geothermal waters, and release hydrogen sulfide. Like an explosion. I don't know if it would be an explosion or not. That's beyond my expertise. OK. And so the lava flows have covered a couple of those well heads. Maybe about three, four weeks ago, and then flows haven't entered that area where the wells are ever since ever since then. Much of the geothermal plant, their facilities, are on a little hill, and that hill has protected them from being buried by lava. They're kind of surrounded, but they're not being buried. This may be beyond where you've looked, but I wonder if the steps taken, and the natural, you know, natural processes that have happened around Poonergy and Thermal Venture make it really difficult to start it up again. That is beyond my expertise. I don't know if... My guess is it's going to be more a political question than an engineering question. I mean, in order to drill those wells, they drilled through lava flows in the first place. They know where the wells are, and it seems to me, even if they get more of them buried, if they wanted to, they could drill through the new lava and find the same one. I'm guessing. I don't know if that's sure, but I think it's going to be more a political question or an economic question than a engineering feasibility one. Right. Okay, that's Scott Rowland. We're talking about the eruption in Kilauea and the Lailania states down below, and we've talked about where it is now, what has happened so far. When we come back from this break, we're going to take the opportunity to talk about what's going to happen later on this thing and how this will integrate with the natural seismic process in that area. Oh, I'm so interested, Scott. We'll be right back after this short. Hey, that's you. I want to know will you watch my show? I hope you do. It's on Tuesdays at one o'clock, and it's out of the comfort zone, and I'll be your host, R.E.B. Kelly. See you there. Hey, Aloha. Stan Energyman here on Think Tech Hawaii, where community matters. This is the place to come to think about all things energy. We talk about energy for the grid, energy for vehicles, energy and transportation, energy and maritime, energy and aviation. We have all kinds of things on our show, but we always focus on hydrogen here in Hawaii because it's my favorite thing. That's what I like to do. But we talk about things that make a difference here in Hawaii, things that should be a big changer for Hawaii. And we hope that you'll join us every Friday at noon on Stan Energyman, and take a look with us at new technologies and new thoughts on how we can get clean and green in Hawaii. Aloha. Okay, we're back. We're live with Scott Rowland. He's a professor of geology and geophysics in the Geology and Geophysics Department at SOES, the School of Ocean and Earth Science at UH Minoa. And he's here to talk about the eruption, the continuing eruption. Oh, one killer whale. That's really important. So you had more pictures. Let's look at them before we get to the question of what's going to happen now. All right. This is a thermal image, and the USGS have been posting knees almost daily as well. The background, the color part is a satellite image, such as one would get from Google Earth. And then superimposed on that, that black and white part is a thermal image collected from a number of photographs, thermal images from a helicopter.  And you can see the active lava channel very, very distinctly in that portion of the image. And if you go to the next slide, it's just a close-up. And these thermal images are remarkable. This is the first time that one of these big lava channels feeding on lava flow has been imaged, at least in Hawaii, from one of these thermal cameras. And there's so much information here about the shapes of the channel, the shapes of the lobes, where heat's being lost, and so on. I find it interesting that it's so thin compared to the larger depiction of the lava. Sure. That has a lot to do with how it was controlled by topography when the flow was first in place about two and a half weeks ago. What are the black spots in the middle of the flow? Those are little islands of non-moving lava. And so if not an island, you could stand on, but there are definitely cooler islands inside that channel. Yes. Okay. Let's move to the next so we can see the summit. All right. So the other part of the story is the enlargement of Halimahu, which most people will remember, is the crater within the caldera of Kilauea. And the USGS posted this pair of photographs, one from mid-May and then one from the second week of June. And if you go to the next slide, what I've done is add a couple of lines. The yellow lines start at the far left and far right edges of Halimahu in the May 19th image, and I drop them straight down to the June 13th image. And you can see how much the crater has enlarged off to the right and off to the left. And in fact, it's even bigger than that now. So as magma leaves the summit storage system in Kilauea, basically support is lost and the floor of Halimahu is dropping because there's material being lost. Is that finished? I don't think so. It's still going on. If you go to the next slide, this is a comparison of, these are what are called shaded relief images. So it's the topography of the Halimahu Moe. And the image on the left was collected in the early 2000s, 2005 actually, or 2003, I think, by Pete McGinnis-Mark, who usually hosts this show. That's sort of pre all of this activity going on. And then over on the right is a June 8th image showing, and these are at the same scale, showing how big Halimahu Moe had become by June 8th. And again, this is outdated, the northeast part, where it looks relatively flat outside of Halimahu Moe. Much of that has collapsed in since June 8th. So it's much, much deeper and it's much bigger in diameter now. Estimate, if I stood on the rim, the new rim, the rim today in the right-hand side, and I looked down into that shadowed area, how far would the drop be before I got to the bottom of the depressed area? I believe it's almost 300 meters. 900 feet. 900 feet, 1,000 feet. That's a long way. That's a big hole. It really is. And prior to that, it was about 85 meters to the bottom. Wow, what a change. So it's three times as deep as it was just a month and a half ago. And it could be more before we're done. Yes, if you go to the next picture, I think it's a photo from a helicopter showing, you know, they flew by and collected just photographs and video of this. And over on the left, you can barely make out kind of a striped pattern, kind of running from lower left to upper right and disappearing under some steam. That's the old Halimahu Moe parking lot. And if you had gone to Halimahu Moe 11 years ago, you could have parked right there and hiked on a small trail, which has now largely fallen into the crater and looked down into Halimahu Moe. So things have changed enormously up there at the summit. So where is the volcano house with respect to all of this? Off to the, actually behind us in this view. So it would be behind us. Is it at risk? There's a long way from the edge of Halimahu Moe, even the new one, to the volcano house. So at the moment, it's fine. And it's closed, and it gets a good shake every time there's an explosion. But I have not heard of any reports of damage at the volcano house. That would be a loss if that crashed into the crater. I agree. It would, I agree. Franklin Delano Roosevelt used to go up there. There's a lot of history. Yeah, a lot of history, yeah. Okay, what else we got? Okay, and then this is a photo from the Volcano Observatory, from HVO, looking out the window. Now we're looking more or less south across to the right is the main pit of Halimahu Moe. And you can sort of see a shelf. It drops from the level of the caldera off to the left, down a big step. And then at the edge of that steam over on the right, it drops down into the main pit. And that step that joins the far left to the far right formed in about a week or so. And those of us who look at these webcam images all the time, could more or less follow this thing happening. And the USGS posted, they've got a live video feed and they managed, they found the footage that caught this, not forming the whole thing, but increasing in size a little bit. And you can watch the camera shake a little bit. There's an earthquake. And then you can see off in the distance. It's a little hard to tell because of all the steam. You can see that whole shelf just dropped down a little bit. So people are going to be using these film clips and the data for years to try to analyze what really happened. You know, huge things happen. That's exactly right. And as someone who in the past has used reports like this to study older eruptions, the careful day-to-day monitoring of events at the summit, how fast lava flows are erupting, when they cross this road or this road or this road, that stuff is so valuable for trying to piece back together an eruption, a past eruption. So, you know, we owe a lot to the USGS folks who are putting in long hours to make all of these measurements. Well, they're geologists.  They are. They are fascinated with what's going on. They are indeed. That's right. We've got one more picture. Let's take a look at that. I think there's, okay. These are radar images collected from an Italian satellite, just sort of showing a time series of how Halimaumau has increased in size and how new fractures are forming in and around the vicinity of Halimaumau. And again, these are online and they have them arranged as sort of a time series, like a time-lapse sequence. And the link is down there. It's worth clicking on that to look at that. And there's, again, a huge amount of information here that's available. Well, that's, you know, I wanted to get to that. So, we learned a lot, because we have instruments now. We didn't have a few years ago. Sure. This is a really big eruption for Hawaii. And I wonder what we have learned that would take us forward to understand, you know, when this is going to end and when it's going to begin again. Right. Well, when it's going to end is the question that I've been asked many times and the folks at the big island have been asked many times as well. I don't have an answer. One can look at past eruptions and compare the volume that's been erupted so far today to eruptions in the past. And I'm afraid I don't have those numbers in my head, but I believe the eruption at Kapoho in 1960 produced a slightly more lava that then has been erupted to date. The current one may have passed it just in the last few days. When I checked previously it had, you know, it had not gone that far. So that's one thing. You can just say, okay, we can look at all the past eruptions in this area, how what their volume is. How does this one compare? We have somewhat of an idea of how much magma can be stored in the summit magma chamber. And that's based on geophysical measurements over the decades. It's based on some geochemical arguments of looking at the magma compositions and how much they evolve. And if you know how much they evolve, you have kind of a sense of how big the volume must be. And so we can compare the total amount that's been erupted to that total storage amount. And when is it going to quote unquote empty out? As far as I know, the USGS thinks that there is still magma coming into the volcano from the hotspot. So it's not as if it's just emptying. There is, I believe, there is still supply coming in. So that's the kind of, I don't want to call them games, but that's the type of thinking that people are doing to try and understand when this may stop. You can look just at photographs of the lava fountain. Two weeks ago, the lava fountain down in Laelani Estates was pretty consistently 200, 250 feet high. Now it's more 150, 180 or so. So it's dropped down a little bit. I don't know how the effusion rate has changed, but it's definitely not quite as high of a fountain. That doesn't necessarily mean there's less coming out. It just means it's not being, the gas is not expanding it quite as high as it was before. Putting all of those things together is what people are trying to do to get some sense of what the future of this is going to be. I don't think anyone is going to stick their neck out and say a day and an hour and a minute or maybe even a week for when it's going to stop. Because I just don't think we have enough ability to know that very, very well. I'll stick my neck out. Okay. And I'll say yes, but it will stop, won't it? It will stop eventually. That is correct. So you can get someone to take that bet. No bet there. Well, does this mean your classes are filling up? Everybody wants to hear from you about this? I hope so. I hope that the eruption in the news is something that will make people interested in what's going on. I mean, we really want people, not just on the Big Island, but everywhere. We want regular folks when they're outside at the beach or hiking or whatever to look at the rocks, look at the soil, look at the cliffs, look at the coastline and learn that kind of stuff. This is part of being in touch with the environment. Absolutely. Absolutely. And it's part of being an informed citizen. So that if a law gets passed that has some effect on where you can or cannot build or things like that, the more people who understand whether such a law is a good idea or not such a good idea, the better off we are. We want regular folks to understand the natural world, biological as well. Better because it makes for a more informed citizenry. The whole world. That's right. That's right. And so that's our job at the university is to encourage that, give them that background knowledge and then send them out there with that knowledge so they can start making their own observation. Thank you, Scott Rowland. You're very welcome. Great to have you on the show. My pleasure.


Hello everyone, welcome to the next lecture in the course. We are discussing about the applications of remote sensing in water resource management where we discussed about the different ways of estimating ET that is evapotranspiration and also we started discussing about soil moisture. So, we discussed what soil moisture is like the different depths of soil moisture measurement, how it will play a role and optical and thermal methods of estimating soil moisture. Today, we are going to cover how microwave remote sensing is useful in soil moisture estimation. While discussing microwave remote sensing both active and passive, I told that microwave signals react to the electrical and physical properties of the features on the earth surface. That means, whenever water is added to the soil, then it will change the electrical property of the soil which will be picked up by the microwave signals. Both active as well as passive microwave can be used. We have seen some examples while dealing with those particular topics itself. So, here we will see like the basic principles behind the estimation of soil moisture from passive and active microwave remote sensing. First, we will start discussing about the passive microwave radiometry for soil moisture estimation. So, we have already seen that passive microwave radiometers measures or observes brightness temperature that is what we will get from the data. So, the brightness temperature depends on the actual temperature of the object and the emissivity of the object. So, that Tb is equal to emissivity into T in microwave wavelengths that is like a direct relationship. So, whenever a soil is wet, the emissivity will change and actually it will go down. For dry soils in microwave wavelengths, emissivity will be higher. When the soil gets wet, the emissivity will go down, due to which the brightness temperature of wet soils will be lower than the brightness temperature of dry soils. Observing this particular difference is helpful in retrieval of soil moisture estimation from passive microwave radiometry. So, the retrieval of soil moisture is kind a two-step process. So, first, the brightness temperature observed by passive microwave radiometers has to be inverted to get the dielectric constant of the surface. That is based on the dielectric constant like dielectric constant is a property of the object which relates to its like electrical nature. So, the first relationship is to from Tb or brightness temperature scientists will try to retrieve the dielectric constant through what is known as using radiative transfer models. That is if this is like the basic land conditions including the dielectric constant, how the brightness temperature will be. So, the radiative transfer models will be able to simulate this. So, they will be trying to use this brightness temperature to retrieve to retrieve the dielectric constant. And once it is done, then using physical relationships, physical or empirical relationships, people will try to connect this dielectric constant with the soil moisture. So, this is done using what are known as dielectric mixing models. So, first from the brightness temperature we get the dielectric constant and from this dielectric constant we go to soil moisture. It is kind of a two step process. So, apart from soil moisture, the brightness temperature is also influenced by vegetation, surface roughness, atmospheric and cosmic background emission. So, basically when we get brightness temperature, these effects the atmosphere and cosmic background emission effects will be removed or at least reduced to the great extent by the data providers. So, the brightness temperature what we get after processing by the data providing agencies, we can assume it to be free from these effects. But still we have to account for the effects of vegetation and surface roughness while we are trying to retrieve soil moisture. So, how vegetation will influence we have already seen this. So, presence of vegetation above the soil will attenuate the signals from the soil basically. So, whenever a microwave radiometer sees a land surface containing vegetation and soil together, then essentially the microwave emission from the surface is going to be attenuated as it passes through the canopy. And we have also seen that longer wavelengths or shorter frequencies are capable of penetrating the canopy to a better extent than shorter wavelengths. And actually studies have proven that L band is like kind of suitable for soil moisture estimation that is why we have two dedicated missions smalls and smalls for soil moisture estimation operating in the L band. So, this vegetation effect is typically characterized as vegetation optical depth. So, that is you can think it off in terms of like water content present in kind of a the volume present above that particular pixel. See there is like a pixel. So, whatever present over there like vegetation like whatever vegetation present over there it will have water content in different parts of its like the parts of plant rights whether it is shrunks, stems or leaves vegetation will present. Even in the case of like recent rainfall the leaves on the top will contain rain droplets that is again like water present within the in the vegetation system. All these things combined will affect the signals from the soil. So, the vegetation optical depth typically characterizes the vegetation water content. So, if you have like a 2D pixel imagine it kind of like a 3D volume. So, whatever the water water contained within that particular volume will actually affect the signals from the soil. So, it will be modeled or it will be obtained through some other ancillary measurements in order to remove the effect of vegetation. So, the basic equation what we call the radiometric transfer equation, radiative transfer equation is the brightness temperature observed by the satellite sensor majorly comprises of 4 different components. The first thing is temperature of the land surface that is the brightness temperature of the land surface attenuated by the atmosphere. This is component number 1, the downwelling atmospheric emission that is if there is like a land surface some part of like the particular land surface will receive emission from the atmosphere that is component number 2. Similarly, we have also seen the cosmic emission that is the emission from the outer sky may influence their measurements. So, that is component number 3 and component number 4 is upwelling atmospheric emission. So, the upwelling atmospheric emission is what is going towards the sensor directly from the atmosphere. So, among the brightness temperature components, components 2, 3, 4 are actually like atmospheric and outer space components which are kind of unwanted things to us. So, once we remove it then essentially we will be left with the brightness temperature from the land surface which we can use further in order to reduce soil moisture. And this brightness temperature from the land surface again comprises of different different components. So, even the land surface has several components within it, there can be like soil, there can be like vegetation standing over the soil or some other features. If we come if we assume that the land surface is comprised of vegetation and soil then the brightness temperature can be written as can be split as the first component is temperature of the soil which contains the signal of soil moisture. So, T s is the thermodynamic temperature of soil and epsilon is the emissivity. Then emission from the canopy because canopy also has its own temperature that will do some emission. Then the emission from the canopy towards the soil that gets reflected that is third part. So, in the three parts we have one the direct emission from the soil that is attenuated by the canopy it has to pass through the canopy if something is present. Then the direct emission from the canopy and the third part is the emission from the canopy in the downward direction reflected by the soil. So, among these things again if you look at it we have this parameter what is known as like the attenuation parameter by vegetation, then we have what is known as like the single scattering albedo and so on. So, there are like several variables involved, but for the sake of simplicity we will not discuss all these things in detail here. But just as an overview the brightness temperature of the land surface observed by a passive microwave radiometer again comprises this particular three components out of which only one component is of direct interest towards. So, the emission from the canopy component has to be removed from this and that is why like scientist will always work towards like this VOD like vegetation optical depth as one of the parameters which contains information about vegetation. So, the basic radiative transfer equation what we have just seen comprise of three methods can be solved in two ways either in forward modeling way or in inverse modeling way. In the forward modeling, so the soil moisture is assumed as given a particular value so that the brightness temperature will be simulated and it will be matched with the observed brightness temperature that is due to some effects of soil moisture and the land surface will have a particular brightness temperature right. So, this particular brightness temperature will be simulated by feeding the model with soil moisture and this will be simulated and whatever is observed by the satellite will be compared with this and the errors will be minimized by playing with the parameter. So, this kind of modeling is called forward modeling. Assume initial value of soil moisture and try to retrieve brightness temperature and match it. This is one way. Other way is satellite has already observed soil moisture sorry brightness temperature. So, the using that particular brightness temperature use the radiative transfer model in the reverse fashion that is now I know what is the emission from the land surface. So, what causes that emission? So, come in the reverse direction. So, that is called inverse modeling. So, radiative transfer model actually works like this okay this is the land surface property this will be the emission. If we do this that is forward modeling but if we observe the emission and come towards the land surface property that is inverse. So, soil moisture can be modeled in both the ways. Normally that brightness temperature will be fed into radiative transfer models to estimate soil moisture that is how normally the satellite base retrievals will work normally. So, again we need to account for vegetation parameters as well as surface toughness parameters. So, again there are like different classes of algorithms exist or based on the sensor characteristics. If a sensor has say a mono configuration, mono configuration means a single instance angle, single frequency or like a fixed polarization everything is like fixed without any change. Then the algorithms that are developed to retrieve soil moisture has to depend upon other ancillary data sets in order to estimate vegetation and surface toughness properties. Say like you can search in the internet for the what are known as like the ATBDs algorithm theoretical basis documents of say SMAP satellites, SMAP SM retrieval measurement or SMAS SM retrieval measurement. So, these are like publicly available documents. When you go through them documents it will have like all the technical information about what other ancillary data the scientists use how important they are all this information you can get. But if the sensors configuration is very simple without much options available for us then essentially the vegetation information or the surface toughness information has to be obtained independently. So, some ancillary data sets has to be developed. But if the sensor has multiple configuration say like a dual frequency or a dual polarization then the algorithms can be suitably modified in order to retrieve soil moisture, VOD and our surface toughness simultaneously. So, normally if you take like the SMAP SMAP retrieval algorithms we have both the single channel algorithm and dual channel algorithm. So, in the single channel algorithm the vegetation of properties or the VOD will be estimated using Ndva information available from optical data sets whereas in dual channel algorithm the vegetation optical depth will be retrieved simultaneously along with soil moisture because of the way the algorithm uses the data from the satellite. So, based on the satellite configuration and the data available to us there are again variety of algorithms available to us. We will not see the algorithms in detail but just like a generic overview is what we are getting from this particular lecture. So, this is with respect to passive. So, the basic principle is from the brightness temperature observed separating the unwanted effects like first removal of atmospheric effects then removal of canopy effects and surface toughness effects will lead us to the retrieval of soil moisture. This is like the basic principle and using this basic principle only algorithms are being developed and operational data products are also available for soil moisture estimation like we discussed about like several data sources. So, the SMAP data soil moisture data is available from what is known as the NSIDC National Snow and Ice Data Center which contains soil moisture data obtained from SMAP satellite. So, which is like freely developed it is available at 36 kilometer resolution 9 kilometer resolution at multiple temporal frequencies different levels and so on. So, interested users can download the data is again available in the HDF format which is again we just briefly saw how it is and use it for various applications. So, next we move on to the active microwave for soil moisture. So, similar concept addition of water to the soil will increase its back scattering coefficient. So, the soils or wet soils will appear brighter in active microwave images. But the problem with active microwave remote sensing is in comparison with passive microwave active microwave remote sensing is highly influenced by surface toughness parameters and vegetation parameters. So, they have to be perfectly modeled or ground measurements has to be taken care of in order to retrieve soil moisture from active remote sensing. So, in active remote sensing both the sensor configuration that is which frequency it is observing at what look angle it is collecting the data, what polarization it is collecting the data. So, all these kind of sensor configuration and the surface characteristics especially the presence of surface roughness and vegetation characteristics will influence soil moisture estimation. So, the advantage that active microwave remote sensing has over passive microwave is its spatial resolution. Say we have already seen that passive microwave radiometers will have very coarse spatial resolution in the order of few kilometers and the soil moisture observing missions has spatial resolution close to 30, 40 kilometers. So, which each pixel size will be roughly 30 kilometers or 40 kilometers or at best the disaggregated product itself will be available at say 9 kilometers and so on. Which is kind of extremely coarse for various applications for regional or local level applications. But active remote sensing can provide data in the order of hundreds of meters or sometimes like Sentinel normal provides data at 10 meters or 20 meter resolution, Sentinel 1 satellite. So, if we can collect all the information required for soil moisture estimation like surface roughness and all, theoretically it is possible to retrieve soil moisture at 10 meter or 20 meter pixel size which is large improvement when we compare this with the passive microwave radiometry. But as I told the surface roughness effect has to be removed and this surface roughness effect comes into picture to a larger extent when the satellite or the look angle changes like normally the lower angles are preferred rather than comparing with higher angles like look angle and instance angle these two are like relatively used or interchangeably used. So, the instance angle normally lower instance angles are preferred for soil moisture estimation because at higher instance angle surface roughness effect is more pronounced. So, the different classes of algorithms available for the retrieval of soil moisture can be classified as physical models, semi empirical models, empirical models and change reduction methods. So, physical models uses physics based equations relating back scatter coefficient to dielectric constant and then relating dielectric constant to soil moisture. So, these are physical equations. The problem with physical equations is they demand lot of data like if you want to remove surface roughness effect we need to know the surface roughness. So, what is known as like the RMS height we should measure. So, there are like some instruments called like micro profilometer or something which we should take to field, put it in the field and then measure how the topography varies very minutely all these things we have to do we have to calculate the RMS height and feed it or sometimes we may have to measure vegetation parameters carefully or we may have to measure even soil moisture at the time of satellite overpass all these things may be required for physics, physics based models. So, normally physics based models can retrieve high quality soil moisture provided we give all sort of data required by it. So, that is one class of algorithm then semi empirical models. So, they are kind of like a balance between physics equation and what we measure in field really say if we write all the relationships in using like physics based equation like this is the dielectric there are like plenty of equations which I am not giving here, but just like schematically speaking let us say there is an equation relating dielectric constant with the back scattering coefficient. So, if this equation is kind of like highly physical which involves lot of parameters to measure can we model it simply say can we directly measure soil moisture and can we relate back scattering coefficient to soil moisture something like that. So, doing some sort of simplified assumptions and using ground measurements to substitute what is there in the physics based equation that is semi empirical. So, it is a combination of field developed equations maybe vegetation parameters you could have developed simple equations based on NDVI or LAI that may be valid for that particular region which can be used in the physics based equations. So, these kind of models are semi empirical. Then comes empirical, empirical is directly relating the back scattering coefficient with field observed value say you have a region we observe soil moisture at certain plots at the time of satellite overpass and we somehow try to develop a relationship between the soil moisture and the satellite returned back scattering coefficient. So, then using the back scattering coefficient at all other plots and using this relationship developed we can estimate soil moisture. So, the empirical models are extremely simple, but at the same time they have to be applied or they can be applied only over the region where they are like where the relationships are developed and they are like non-transferable and even like nonscalable even if the satellite parameters changes then the equation will change and also like measurements has to be taken at the time of satellite overpass which again may complicate the data requirements. The final class of model is the change direction models. So, the change direction based models will not actually retrieve the true soil moisture value the actual soil moisture value, but they do some sort of like a relative level ok what is like the change between two time intervals. If we have some reference soil moisture with respect to that reference how the soil moisture changed. So, we can always get to this relative change. So, the change direction methods how it works like a simple equation is given here. So, the normally what they are like different different ways which it can be done. So, this is normalized radar backscattering moisture index which works based on the backscattering coefficients observed at time t1 and t2. This equation is developed based on having like a reference dry and wet backscattering coefficient values for one particular pixel like you need to have like a large time series of data say data something looks like this within the time series say this is like the minimum and this is like the maximum and if this is like the in between backscattering coefficient value how this changes. So, this is kind of like relative measures. So, basically change direction methods assumes the vegetation characteristic and the surface toughness characteristic changes in a much slower way in compared to the soil moisture that is you we always compare the backscattering coefficient in time t1 and t2. If we assume the change in backscattering is only due to change in soil moisture then obviously we are assuming that surface toughness parameter has not changed much and vegetation parameters has not changed much. So, these are like again simplified representations of real world. These kind of change direction techniques may be applicable over like crops which has like longer seasonality where the vegetation growth takes place lower and the surface toughness may not change once crops start growing farmer will not try to disturb the surface characteristics. So, under those conditions it can work or like under like a uniform vegetation canopy then these things may work under natural environments this may work. But again there are like there are limitations but still change direction algorithms one of like the most widely used algorithm for soil moisture estimation. So, as I told the different factors that affect soil moisture retrieval from microwave are the sensor characteristics the frequency polarization the instance angle plus the surface characteristics which includes surface toughness vegetation biomass and water content soil texture and topography because I have already discussed especially in active microwave remote sensing the overall backscattering coefficient can change based on the topography whether it is on a flat topography or the topography is phasing outwards or away from the radar and so on all these things will play a major role. So, essentially our aim is to remove all other effects apart from soil moisture for us to retrieve soil moisture from this particular method. So, on an overall like for overall comparison between active and passive microwave methods if we look at it then the passive microwave is actually highly related to soil moisture and hence the results are very much promising especially over like bare soils and also they are not affected by clouds or daytime conditions. They provide high temporal resolution in the order of like once every 2 days, once every 3 days or sometimes model based data comes every 3 hours once. So, passive microwave radiometers are like the best ones at least now to provide soil moisture. But the major disadvantage with them is their core spatial resolution which is available in order of kilometer scale and hence we need to develop some sort of like disaggregation or downscaling algorithms to bring this coarse resolution soil moisture to fine spatial resolution. So, active microwave again goes over this limitation it provides us fine spatial resolution again it is not affected by cloud covered but at the same time it is highly influenced by surface roughness and vegetation amount and coarse temporal resolution. Say active microwave data may be available say central 1 and 2 combined we may get data once every 6 to 8 days that is now but if you look at olden days data may be available once every 20 days and so on. So, the temporal resolution is bit coarser and the influence of surface roughness and vegetation cover is very high when we compare this with passive. So, in order to overcome the limitations of passive scientists are trying to combine optical data like thermal visible and air data with passive microwave data for improving the spatial resolution. And also both active and passive combinedly are being used say the SMAP mission has a SMAP Sentinel combined soil moisture product at 3 kilometer spatial resolution. So, that is combination of active and passive remotes and things. So, this is kind of like a trade off some characteristics of passive microwave will be taken some characteristics of active microwave will be taken. Similarly thermal data is often integrated with passive microwave radiometry data in order to retrieve or in order to disaggregate soil moisture. So, this is also being carried out especially from the context of improving the spatial resolution of soil moisture observations. So, bugging there are like some advantages and disadvantages for these kind of like synergistic methods especially when we discussed about the difference between passive microwave radiometry and thermal infrared we discussed one important thing that is like the depth of penetration. The thermal infrared remote sensing normally senses or what we observe is only from the top millimeter of a surface the skin temperature. So, essentially the soil moisture that we retrieve from it will have only the top surface soil moisture information be it canopy if it is canopy we will see only the canopy if it is bare soil we will see only like the top portion of soil. Whereas microwave especially longer wavelengths will have certain penetration capacity if there is a canopy standing over the soil some sort of penetration would have occurred or if it is bare soil the depth of measurement may be few centimeters at least say 4 or 5 centimeters at least. So, the depth of measurement of soil moisture observed by passive microwave radiometers and thermal infrared sensors are different. So, when we try to combine them we should keep in mind this difference. So, what we are measuring is because I already told you as the depth of measurement changes the soil moisture amount may change or what we actually want for our application will be different from what is actually measured. We may be wanting root zone soil moisture whereas thermal measurements will only provide the skin soil moisture. So, these kind of differences will always come and we should keep in mind when we try to combine different wavelengths or different technologies of remote sensing together for improving the spatial resolution. So, this is like a very broad overview of usage of microwave remote sensing for soil moisture estimation again this particular topic itself is very wide and very broad. So, normally we cover this particular topic in few weeks of lectures when we offer this as a part of course to the students here in IIT Bombay. But since this is like introductory remote sensing course I did not want to divulge more into the technical details and I provided like a broad overview. But in the slides there will be like plenty of references which the interested users can always refer to. So, with this we end this particular lecture. Thank you very much.