 Welcome to today's class. So we are in module 4 and we are trying to learn about the different applications to which radar remote sensing can be helpful in the field of hydrology. So we are presently in the 4th lecture of the 4th module. So as part of today's class let us try to understand about radar ultimetry. I am giving you a new term here, ultimetry. India has 4% of the world's freshwater resources, ranking it among the top 10 water rich countries. You know despite such abundant water resources, there are recent research studies which have reported India as one of the most water stressed countries of the world. And the conventional method of representing river discharges is by indirect measurements in the form of a rating curve which is nothing but a calibrated relationship between water levels and corresponding discharge. Rating curve, calibrated relationship between water levels and corresponding discharge. But then due to low gauge density, monitoring the surface water resources with in situ gauges becomes slightly challenging especially when we are considering global surface water resources. This is also partly caused by the inability to access isolated locations. As a result, majority of rivers, lakes and reservoirs remain unmeasured, un-gaged. As a result of this, the primary constraint for hydrological studies related to reservoir monitoring, climate change impact and ecological assessment is the lack of data from these un-gaged water resources of India, lack of data. And this is where radar ultimetry comes into picture because radar altimeters, they are the most common type of radar system which are used on aircrafts and satellites. And the importance is that radar altimeters help us to give precise measurements of average height of a water surface even up to centimeter scale accuracy. And a satellite ultimetry, they provide us with complementary information about the river discharge by measuring the hydraulic variables such as the width of a river or slope of a river, water surface elevation and so on. And before we move into the details of radar ultimetry, let us first try to understand the basic concept. The fundamental principle of satellite ultimetry is to estimate the water surface elevation by measuring the distance from the satellite to the surface of the water. Remember that the sensors that are onboard the satellite, they are capable of transmitting signals at the rate of even around say 2000 pulses per second for the high precision altimeters. So, these pulses of microwave radiation, they travel towards the earth surface whenever I am explaining, you can look at the screen and then relate to the diagrams shown. So, the sensors onboard satellite, they transmit signals of microwave radiation which travel through the atmosphere towards the earth surface and the earth surface in turn are scattering these radiations. So, the earth surface receives these echoes from the target surface. Once again the sensors onboard satellites, they transmit signals even at the rate of around 2000 pulses per second and these microwave radiation travel towards the earth surface. And finally, the sensor is measuring the two-way travel time taken by the signal in travelling from the sensor to the target surface on the earth and then back to the sensor. There is a two-way travel time which is being measured and this two-way travel time is used to estimate something known as a range R. When we were discussing the concepts in module 2, we have covered what is range in terms of a radar. But now in the case of radar ultimately, think of it in this way. There is a sensor onboard satellite and the two-way travel time of microwave pulses are used to estimate the distance of the sensor from the target, target being the water level. So, time is converted to one-way distance or range here. Now remember, the height of reflecting surface above the reference ellipsoid is used here because any horizontal surface cannot be considered as a reference surface for measuring vertical elevations, isn't it? This is where the geoid comes into picture. Geoid is nothing but the surface of equal potential energy. So, imagine the earth surface is something like this what you see on the screen. We usually consider mean sea level as the datum. We define mean sea level as the geoid and we use it as a reference surface from which all the vertical elevations are calculated. Remember, any horizontal surface cannot be considered as reference. For us, the mean sea level is used as a reference surface from which the vertical elevations are calculated. Remember, geoid is not a mathematically well-defined reference surface. It is not possible for us to mathematically define a geoid therefore, we have an ellipsoid which is why the height from the ellipsoid was estimated in the previous slide. Once again, mathematically well-defined reference surface which are created to provide a common reference surface and this is also used to form the basis of map of projections. And ellipsoids, they approximate the shape of geoid which is why they are preferred to be used as reference surface. Now, let us move ahead to understand the history of satellite altimetry. Towards your left side, you see a series of missions, their names are being displayed. The first altimeter sensor was onboard Skylab 3, satellite launched in 1973. This was followed by GOS-3, CSAT, Geosat, ERS-1 and it was in the year 1992 that the era of high precision altimetry started and this was started after the launch of Top X Poseidon followed by Jason-1, Jason-2, Jason-3, Searle-Altica, Sentinel-3 and so on. Remember, all these are names of satellite altimetry missions from different countries, from different space agencies. So, the continuous observations from satellite altimetry ensure the availability of a consistent time series of data in a fully operational manner. At this point, let me introduce you to Searle-Altica. See, it was jointly developed by ISRO and CNES, that is the French Space Agency. And Searle mission provides altimetric measurements to study the ocean circulation and the sea surface elevations using Ka-band altimeter, that is 35.75 GHz altimeter. Again, please feel free to visit the MOSDAC website for more details. Now, please note that a space-bond altimeter shall have a footprint, the area that the satellite sees at an instant of time, which we discussed in module 2 as having an elliptical shape, having a major axis and minor axis footprint. So, a space-bond altimeter shall have a footprint which is very large, very large of the order of kilometers. But then altimeters, they have an advantage because the first part of the echo coming from an altimeter shall be from the nadir, unlike synthetic aperture radars which have side-looking geometry. Remember, we covered this as part of module 2. So, the size of footprint can be defined by something known as the pulse length. In the diagram in front of you, assume H denotes the flying height of an altimeter, C is nothing but the velocity of light, FB is the beam surface footprint, beam surface footprint and FB is the pulse surface footprint, FB. Let us try to understand more about altimeters by a simple derivation. So, at flying height of an altimeter which is somewhere here, we have defined the pulse surface footprint that is FB, the beam surface footprint that is FB and of course, C we use to denote the velocity of light. So, what I will do is, I will try to apply the Pythagoras theorem and write H plus C tau P whole square equal to H square plus X square, I can rewrite this relationship as C tau P is nothing but H square plus X square under a root minus H and just re-written the relationship. So, I am going to call it as equation 1. Remember, we know what is C velocity of light tau P, we know what is H flying height and we know what is X that is FB by 2. So, let us try to focus on H square plus X square. Can we expand it using some means? H square plus X square under a root, I can write it as H 1 plus X square by H square, simple expansion, which means I can write H square plus X square under a root as nearly equal to H plus X square by 2 H and I am going to call it as equation 2. So, what I will do is I will try to substitute 2 in 1 and rewrite 1. I can write C tau P nearly equal to X square by 2 H. So, what did I do? I first used the Pythagoras theorem, I defined C tau P, I called it as equation 1 and then one of the terms of C tau P that is H square plus X square under a root, I have expanded it and I have substituted the value in equation 1 to rewrite C tau P as X square by 2 H. So, hold that thought because now I want to use X as F P by 2 from the diagram, remember? Which means we can further use F P as which means I can use the relationship and write F P is equal to 2 into 2 C tau P. Again, why am I showing you all this? Because I want to discuss about pulse limited, altimeter limited, altimeter. Let me write that down. In pulse limited altimeter, F B is going to be greater than F P. F B is going to be greater than F P and here the pulse size is going to determine the footprint size. Always remember the pulse size is going to determine the footprint size. So, in pulse limited altimeter the pulse is going to determine footprint size but what about beam limited altimeter? Here, the scenario is F B is less than F P and this altimeter that is the beam limited altimeter is going to have a very large antenna. So, most of the altimeters shall be pulse limited owing to restrictions on the size of antenna. Keep that thought in mind and let me show you how a satellite altimeter collects data. Remember, in the nadir direction the pulses are being sent and you see the data collection from an altimeter which means if I download the data from an altimeter and then try to visualize the data samples are going to look something like this. Pulsars are being sent in the nadir direction and what you see here is the data visualization for JSON2, one satellite altimeter emission JSON2 and here you see some red dots as well as some green dots, is not it? So, the red dots refer to 20 hertz data and the green dots refer to 1 hertz data. More details are also shared here and here whenever you read a research article on radar altimetry you will come across a term known as virtual stations not actual station, virtual station. It is nothing but the intersection of an orbit ground track and water body. Intersection of orbital track and water body we call it as virtual stations. The details about the satellite based and model based modes are given here. Turn your attention a little bit on ultrasonic sensors now because you know there are many ways by which we can measure the water surface elevation from rivers and lakes and reservoirs and the schematic that you see in front of you shows how measurement of water levels can be done using an ultrasonic sensor. So, the ultrasonic sensor it transmits short high frequency sonic pulses at regular intervals that propagate at the velocity of sound and the ultrasound they exhibit the property of reflection with wave reflection being the change in direction of a wave front at an interface between two different media so that the wave front returns to medium from where it originated. So, the level of water is determined by the distance between the sensor and the level of liquid which is causing the reflection and distance is measured by measuring the total travel time. You see something here known as MCU is not it? It stands for microcontroller unit which is programmed to send continuous signals and to record the time of return of signals. So, the total time recorded includes travel time of the incident and reflected wave. Again the data that is recorded by sensors is usually transmitted to other sensors or machines for processing using internet of things technologies, IoT technologies which is made available at the user interface. Just a simplistic diagram to help us understand how ultrasonic sensors can also be used to measure water levels. Now, let me try to discuss a few case studies using radar ultimately that we have worked on. What you see here is part of a research study wherein you see different locations, isn't it? Different location of dams. I am just going to expand, zoom and show you the details pertaining to Bansagar lake and what you see in red and green are the tracks, the path covered by Saral satellite, Saral Altica in green and red shows the track covered by Jason. Now, to discuss in more detail, the maps of reservoirs and satellite passes are shown here for different regions. We have Srisailam, we have Rana Pratap Sagar and different regions are shown here wherever there are virtual stations that is wherever the satellite orbital track is crossing a water body. You can see the legend is mentioned here, Gage station, Saral Altica orbital track, Jason II or III orbital track and the reservoir. So, our trace with radar ultimately began when we were trying to estimate what is the accuracy of water levels that we get from radar ultimately. So, what we did is we tried to compare the water levels, what we get from satellite with respect to what is recorded by the CWC Institute stations on the ground and what you see here is a comparison between Saral Altica developed by ISRO and CNES, Saral Altica. So, the water levels from Saral Altica are compared with respect to the Institute stations here. You see the R square value for different reservoirs along with a scattered diagram. So, this is a direct comparison of how accuracy assessment can be done for the water levels that we get from radar ultimately. So, at this point, let me take the liberty of introducing some of the research based studies wherein radar ultimately have been used in hydrology. So, the surface water and ocean topography abbreviated as SWAT SWOT. So, the SWAT mission is scheduled to be launched in the year 2022 and it is committed to measuring surface water hydrology and it is developed by NASA and the French Space Agency CNES in association with the Canadian Space Agency and the UK Space Agency. So, this mission it shall be providing the surface water elevation as well as the water mask of water bodies using a 2D wide SWAT KA band interferometer. I am going to repeat that again 2D wide SWAT KA band interferometer and SWAT is going to have a temporal resolution of 21 days. So, at this point, let me try to show you the difference between ultimetry satellites and SWAT. Ultimetry satellites measures the footprint in the nadir direction which means there are footprints crossing a water body and what we need is the virtual stations wherein the orbital track is crossing a water body. We get to estimate the range once we know the travel time taken by the microwave pulses. The notations are given here along with the corrections of Cp known as propagation correction and Cg known as geophysical correction. This is for ultimetry mission. Now, when it comes to SWAT, it has a 2D wide SWAT KA band interferometer what you see on the screen. 2D wide SWAT KA band interferometer and when measurement is to be made over reservoirs, the data collection is as shown on the screen. Remember, the ellipsoidal height is getting converted into elevation by taking local undulation in account, ellipsoidal height converted into elevation by taking local undulation into account. So, to choose an appropriate case study region for examining the potential of SWAT mission, let me show you the Hirakut reservoir here which lies in the Mahanadi river basin of India. So, what I will do is I will try to zoom out a little bit and then overlap the repeat orbit that will occur during the calibration validation phase for SWAT mission. You can see that it directly passes over Hirakut reservoir, isn't it? For our study, we decided to use the Mahanadi river basin as it is one of the well documented river basins of India and also it is one of the gold sites. We call it a SWAT gold site which implies that more data is going to be collected at these sites during the calibration validation phase than anywhere else during the three year scientific phase. Remember, we discussed about radar calibration. So, I am talking about Calval phase of SWAT mission here. Also interesting to note is how the Mahanadi river basin is getting covered by different ultimatary missions. So, towards the right side what you see is the SWAT of SWAT mission over Mahanadi river basin. So, the one day repeat phase of SWAT mission is shown here. Remember, it is one of the gold sites where SWAT Calval orbit is going to pass. So, studies are being conducted to evaluate the potential of SWAT over Mahanadi river basin in India. With this background, let me take your attention to another story. See, I am trying to give you different aspects of research which are being conducted using radar remote sensing pertaining to hydrology and in today's lecture, we have been focusing on radar altimetry that is how water levels are measured from space. So, in this context let me introduce you to a toolkit known as SIMS which is known as Sentinel-1 based inland water dynamics mapping system. It was developed in-house by my group at IIT Bombay. So, always as hydrologists we know that for generating discharge velocity relationship curves or flood inundation maps, one of the key inputs which hydrodynamic model needs is the time series of river stage data. So, to access this toolkit, one can visit this site which opens up a basic interface like this and first step is we get to define the study area for processing and for demonstration purposes to make it consistent with my earlier slides, I am going to zoom to the Hirakut reservoir and what I have done is I have specified the polygon, I have specified the time period of analysis and the remaining options are for processing reverse. So, once processing has begun, this tool is going to extract the Sentinel-1 data for the region of interest within the time period selected and you can see the tab that displays the progress. So, water pixels are identified using thresholding technique and we do have options to generate the maximum extent water mask shapefile, the time series of water surface area and the time series of surface water extent shapefiles. You can see it is showing processing started and the number of images identified 5. So, each shapefile or output can now be exported and the download link is going to be visible for us to export the same. You know, the reason I am showing you this is because monitoring changes in inland water, it is of paramount importance because they directly tend to interact with the oceans and atmosphere through horizontal and vertical mass fluxes. And in this context, since toolkit, it was developed using Python and Google Earth Engine, it is capable of automated extraction of temporal dynamics of surface water extents, particularly over rivers, lakes and reservoirs and just showing you for demonstration purposes what can be achieved by open source kits. So, once download is complete, we get to visualize the results in QGIS platform. We just need to extract the files and import them. So, in the interest of saving time, I have already opened the list of files in QGIS and corresponding to the study area, we have specified. You can see the time series of surface water extent shapefiles, they are displayed here and the time series of water surface area is also present. These are the outputs you get from Sims Toolkit. Let me shift your attention towards the plans for getting ready for SWOT after launch. Again, these details are being shared as a glimpse to discuss the projects which are relevant to hydrology using radar altimetry. So, temporarily speaking, from the perspective of SWOT mission, each place is going to be sampled at least twice every month. Now, the exact timing of course is going to depend mostly on the latitude and SWOT we know that it is going to rely on a very normal technology. But then, how do we know that the data we get from satellites is correct or not, as in it is accurate or not. SWOT is going to require an in-depth program of calibration and validation and the phase when the satellite is going to fly a specific orbit dedicated to the calval activities and finding gold sites like the one in India, finding gold sites in the tropical band where the biggest reverse flow was the aim of the project. So, before I end my today's lecture, let me also take your attention to an ongoing NASA earlier doctor project, which is titled Leveraging Citizen Science for the Monitoring of Lake Volumes Using SWOT. See, lakes, we know they are the key components of biogeochemical processes and often the lake water levels, they are used as proxies, proxies for indicating climate change. So, I am going to take your attention to a lake which is in Vineyard in Kerala, because it is also falling in the orbit of SWOT. So, with the help of center for water resources development and management at a CWRDM Kerala, an institute monitoring site has been established at Phukod Lake in Kerala and this is mainly for monitoring the water level in the lake because it also falls in the calval orbit of SWOT. So, next time when you visit Vineyard, please check out Phukod Lake and how you can contribute to Citizen Science. So, let me hope that you could understand the lectures today about radar ultimate tree and let me also hope that you could get a small glimpse of the research works which were being carried out. I shall meet you in the next class. Thank you.