 Welcome to NPTEL course on remote sensing and GIS for rural development. This is week three, lecture five. In this week, we have been looking at different remote sensing products, both Indian and overseas like US NASA data sets that can be used for Indian regions. In the last lecture, we had looked at NASA's GIS-DIC data set, whereas you also have data Geovonny Earth Explorer. Every single link has its own tutorials online. I would recommend you to take those because it gets updated by NASA often. Since you're using the data, it's always good to take the tutorials from their resource also. Here, I'm introducing that for Indian regions. In the last slide, we have the last lecture. We had looked at the data that is available for water resources. And we looked specifically on rainfall. And then we looked at the resolution of rainfall, soil and climate. Here, you don't get soil type in this data set. You will not get a soil type, but more different depths of soil moisture. See, in the Indian data set, the Govan driven data set, it was one value of soil moisture. The units were meter cube by meter cube. Here, the units will be kg meter square, kg meter square. Either way, this unit less because the top and the bottom numerator denominator will cancel each other. So the point here is you will find different data sets at different resolutions. More importantly, you will find at different depths. This was not readily available for the Indian data sets. One. And number two is maybe you will not be getting the recent data sets. Here, you can even get yesterday's data and then you can work on some algorithms, models, or you can go to the village and then look at some areas of interest. Whereas the Govan data set, it is having a time lag, which means there's a time difference from when you can access the data. The other part we looked at is the resolution. The resolution is much, much higher, better in this data set because they have more sophisticated instruments and data processing systems. The data is at a higher spatial and temporal resolution compared to the Govan ones. Again, the Govan ones are not purely driven by India data sets. It does have a collaboration of different NASA, Sentinel, and other data. So let's jump into the accessing the data set again. I will share the website we already had looked at in the previous class, which is the GES DIC. Here, I also wanted to give you some input on the different tools they have. So I will click the Giovanni GIS and the link that we have, Earth Data USGS, Earth Explorer. So these I didn't cover in the previous one. So let's look at Earth Explorer. Again, the login is the same, but you could just log in once again. One login is enough for all the login systems. Excuse me. So now it is getting logged in. It says multiple logins have been there. So I have to first go through this one. So it's already here. So launch Giovanni. You come to Giovanni tool. It is a tool that helps you simple intuitive way to visualize, analyze, and access the data. So let's look at it. And you can make these plots ready, time average map, seasonal time series, and then scatter plot correlation map, etc. So as I said, we need a seasonal time series or a map correlation comparison between maps we can do. Yeah. So here I have a scatter plot time average interactive. We will be doing a seasonal map, which is not available into an animation also. Let's do an animation. 365 time steps. So daily you can do. So let's do 2022, then one to December end. So if you're making a presentation for an entity, etc., you can do this quickly. We have the bounding boxes, but again, just click it, hold on to the mouse and draw the box. You can close this box. And then let's say we want evapotranspiration. So one of the parameters in the model that we need to use for rule, water resource management and rule development is controlling the evapotranspiration. It is a loss out of the system. So you need to make sure you reduce it. So the US is kg per meter square, very similar to millimeters. So let's say that we are going to do an hourly, monthly, daily. I think daily would be better. So let's do the daily one here. But the date is not available. So yeah. So this has end dates to 2022 from 2000. And then totally evapotranspiration daily. And then there's a monthly. So let's do the monthly and then go to results. There you are. So it has plotted the results. So for the box that we have made, it has downloaded the data, you can go to back to data selection and do the animation. So it says it has no data for this. So you can select this bounding box, which is good. Plot data. So it doesn't do 2022. So let's stop here. But I've shown you how to take data at different time steps and then do an animation. Or we can do a time average map. We don't need a map. We need a time series. So you can say a time area average differences or just a time series. Recurring averages for Indian region and for that particular date. It does struggle a little bit. Okay. That is good. Now the start date must be 2016, 12, 13 or earlier for the given data set. Okay. It's not letting us do it. So that was this repression. Then you can also select between model or observation data. So you have here model and observation data. Observation data is also good. We can definitely look into multiple, multiple factors. Okay. So let's not do evapotranspiration. We can do rainfall. So rainfall does take up. So I'll just take these two out. And then precipitation rate or you want a preservation millimeters per day or millimeters inches per day you can select on those. Let's do millimeters per day. But we don't have the data that long. So let's come down to the data set that comes in 2022. This one has. So you can always look at the model and what models they have used. FLDAS monthly. Okay. So it is now running the data. Evapotranspiration was tricky. So we will just ignore that. But we are just looking at a time series of average area average. So for a particular area, what is the time series of the data? So that is what it is calculating now. And I hope we did India part. So let it come. Yes. So now we have a time series of rainfall data. And the units you can double check at what units you want. You just have to double change the units into your particular unit. But the dates are correct. So Jan to Jan, the user selected this a bounding box. And for that bounding box, you have the time series of data. So it has the date. And for that reason, what is per day? What is the average? It plots plots plots. Here it is monthly Jan, March, you can see the monthly averaged rainfall. It could be average, it would be total rainfall also time series of area average rainfall flux monthly. So all these data you could download. Again, this is not a per point. So you can only download as an image and put it in your values. So that for that reason, we don't normally take time series data. But you can always take a map. And then from the map, you can extract the data. So all these can be done, correlation maps can also be done. Earth Explorer is also quick that you can do analysis and stuff. You can actually look at the data set here. So it's the same thing, data sets such as search for rainfall, or you can actually click these images and see where you want. Let's do Landsat. Landsat is the land use land cover of radar is for the soil moisture. But we can do the Landsat. Landsat Diamize the surface water extent. Then you can like similarly to the previous exercise, you can actually give a date range and a location where you want the data to be mapped. Okay. It is searching. So let me search while we come back here. Okay. So that is one. We are going to go into now the browse by category and measurement. And we want soil moisture index. Let's see what all the soil we have. Who's on soil moisture. And we also have soil moisture. So let's just click at soil moisture. So there is one data set, 36 years time resolution, just a whole from 1979 to 2550. Let's click at, excuse me, at the data set, but it doesn't have India. We could see that it's only for US. So we won't be able to use this data set. So we can go back here and first let's put the bounding box, click on this square, rectangle, draw the box, hold on the mouse, click, and then hold on the mouse and release. So maybe I'll do it again. You can clear, we mark this, press escape or redo it again. So press this box, cancel, press and then draw, hold, just click your left in the pointer, left arrow key and then draw a box and then leave it. The new box will be overlapped or you can put the numbers here, which is also correct. Available range is full. So let's do that. Time series. Again, let's just pick one year. We don't want such a big year. You can click the years. The years will come. We want 20, 22. Let's just use 20, 22. Let's do Jan, Jan 1, 2, December 31st. Here there's no enter button. So don't worry about it. Just click somewhere outside, it will come. To make sure you can click the data again. The data has been stored, the map data has been stored, and then click back. You can click back or click back here, it will remove the image. Now, what you need to do is we are going to look at climate variables, which are your precipitation, your snow cover, evapotranspiration, etc. So you can actually take the measurements here and say I need soil moisture or you can have root zone moisture. These are properties, soil porosity are properties. So you can have root zone soil moisture and close. So automatically it populates it. And there is a data range that is given up to March is good. Groundwater and soil water conditions for grace follow on, etc. You might see only the US regions in some, but it does have all the regions because you are given the condition like that. Then you click search. We didn't specify it here. So let's double check the date. It is fine. Area is fine. So soil moisture. Okay. Normally I use this one. Let's use NOVA is the model. Okay. So GLDS NOVA is a land driven model. And it has all the parameters that are required for your hydrological water balance. So I'll just show you by clicking this and then see what data is available. Data citation, documentation references, data calendar from which year to which year it has been taken and what type of format the data is stored. Next CDF is a particular format where multiple images are kept in one file. You will have to physically click and open each file. But now don't worry about it because you will also be able to download single file by just putting a single time series. Since we have put a multiple time series, all the data will be put in one file. But for you normally how I do it is just take one data and then link it up. Okay. So you can read here about the data and what are the products it gives. You can go to the online archive. You can go to web services. You can open data. These require some coding. So don't worry about it if you don't know. But a data search is good and G1E is also good. Okay. So here I have clicked the desktop based data acquisition and you can click the year folder. Always a year, month and date. Okay. So year, month and date and the version is given. So you can see how all the data is being stored for December and only available until 22nd. But here you have the data for 2016, 0102. So the first date is missing, but the other dates are there. Oh, these are monthly. So you can get the monthly 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12. So monthly estimates are there. Okay. So in the third data, you can click here and say export data as CSV or JSON. You can also view the data as a list or table. You can click the data to understand more about the data. You will see like a lot of collections. So this is what I said each one is each month. So this is 2020 to nine month. And this is the eighth month, seven months. So you can only download this if you want. Or you can add the data to your whole download. So for example, if I click here, only that single granule or single image will be downloaded. Okay. So let's close this for a minute. And you can see here behind it is being populated, the data, all the data that we took for the entire globe has been populated for GLDS NOAA. And you can actually edit it for just your data time frame. So here again, you'll have to put the time frame. We have set 2022 Jan 1st up to 2022 December 31st. Okay. Apply. So now it'll get updated. And you can also create an area of interest and you can compare, compare the features between two two, two images. Okay. Good. So what we need is you can download it as a month. So this is your data. You have downloaded or you've asked Giovanni or Earth data to say, okay, I want these data. Right. You can actually play with the slider and say, okay, I don't I want only this month, then it gets updated. So each month you can look at the data using the slider. You can click the months, you can click the days, if you want a day particular day you want, you can take and you can take it off if it is too much taking your time. So you can also use all these tools, but don't, as I said, don't get distracted by all the tools. All you want is to download the data so that we can export it in the QGIS. In one of the experiments coming soon, we will teach you how to use QGIS, download a data, a particular data. So we won't go searching like how we did searching here. This is just for you to introduce the buttons where you can click what you can do. Okay. Let me close it just for the internet. And you can also download the data. So all this we have seen here. It's the same data as I said, and you can have in multiple marketable formats. Okay. So as I said, I need only soil moisture for a particular zone. You can click here. Go to results, it will plot. So it will be very carefully looking at how to download the data in different formats like here, download as a GOTF, KMZ, PNG, and FCDF. Let's see if we'll pull all of it together, whereas GOTF, KMZ, PNG, each date is a single file. Okay. So we have gone through the GLDS, GES, etc. So whatever you want in terms of data, you can type here. If it is there, it will come. Be very, very careful in accessing the resolution you want. You want to monthly do it here. Click, it will be three hours. Every three hours you get data. Spatial resolution will be bigger to course compared to a smaller resolution time. Okay. So that is the given take. If you have daily, you can have a very focused pixel. But if you have three hours, then the pixel is too big. This is 25 by 25 kilometers. So you have to understand, do I want a 25 by 25 kilometers or 10 meter resolution? If it is 10 meter, I will click this one. So this is kind of very, very high resolution, spatially. And you have one day. Okay. So one degree is 100 kilometers. Right. And 0.25 is 25 kilometers. Here it is 0.01. Okay. So you should be able to get high resolution imagery data with this and very, very new date. But the time, the time resolution is only once per day. It's not every day. Okay. So it's not every hour, three hours like that we can take data. Okay. So with this, I will stop with the NASA side, the data and other things. Now let us go into the remaining part of the presentation. So to analyze all this data, what do you need? You need a software and that is a GIS software. So I will be introducing GIS concepts in the next lecture onwards. What is GIS? Why do we need GIS? And within the GIS, we will be using only two formats. It's a vector format or a raster format, the data. And we need a platform, a software where we can put the data and analyze it. And that is a QGIS software that I'll be teaching. QGIS is the open source GIS software. Normally, GIS software is very, very expensive if you buy the proprietary software, but open source software is good. So I'll be introducing GIS in the next lecture series week. Today, I want to give you in the remaining five minutes, what is this software so that you can start downloading or I will have a session on downloading this. So you can go to this link to download the software. Let me take you to this link. So what you will find here is the project. First, let me open the project-based website. So just type QGIS, Google search it, you'll find this one or you can have this link. It does get updated, so that's why I kept it. So you have all these QGIS about and who runs the system. I'll give you the presentation also the details. But most importantly, you will find all the people that are using this software here. And it's even the space agencies of European nations use this. Because it's very, very expensive to buy the software and only some people are using it. If you make it open source and a lot of people take part in building the software, then everyone can use it and that is the model they use. It's a very nice model, a very futuristic model thinking of every student. For example, IIT courses are given. This is the same course I give for IIT students. So the same course is now available for every student through NPTEL. So similar to that, here it is open and free. They do get some donations or project money, but it is kept open for free for everyone. So the link I give is to download. So you can download now the new version. The new version is here. It always says the newest version is 3.28, but you will always go for the most stable version. So it is 3.22, which is lesser than 3.28, but it is more stable, which means all the errors, bugs, everything is taken care. Any software, if it is new, will have some issues and errors. And that is what a beta version we call. Here, the beta version is 3.28, the one which is the newest, which is rich in features, but still it is not stable. It may crash. So the 3.22 LTR is the best current presentation. And for different operating softwares, Mac, Linux, VSD, tablets, you have different softwares to download. Let me go back to the presentation. So what we did is, this is the logo of QJS and how it has evolved. Overview QJS, it was first started by Mr. Gary Sherman in early 2002, not too long ago. It was created under the public license. General public, anyone can use it. And very versatile, runs on Linux, Unix, Macs, OS, Office, Windows, Android. Any software, always this QJS has been propelled to be used for. As I said, when you talk about satellite launches and using supercomputers, you cannot put a proprietary software there because it is heavy. And each time we have to update it. Whereas this open source, you can run it and then take the software out, again upload it again, and then use it multiple systems. Supercomputer has multiple nodes. You cannot put the software in every node. So here, if it is open source, you can definitely populate every node with the software. It is a live environment community. Why is this important? Because you can post a question, ask answers, and they will communicate with you in a very lively fashion. So right now we have the chat GPT going on. It's like that you go there, you type a question and then come back a day or two later, someone will be answering those questions. And these are driven by volunteers. So no one gets paid for answering the questions. But they do it so that everyone learns this software. So many public and private agencies have initiated QJS, including the US National Security Agency, the Austrian State of Wollinburg, Swiss Regional Agencies in Glaros and Solothon, New Zealand's Land Information Public Service Department. So all of them are using QJS. And this is how the interface looks like. You have a good real estate. Real estate means an area for putting the maps, adding the maps, and then adding layers for analysis. Here's where the information of the layers come across. So here's where you put down the data. And here's where the data information is stored. And then all these are tools, the tools that help you to navigate and do the analysis, etc. I have been using the previous versions because with each version update, not many tools are getting updated. So I prefer to use a stable version. 3.28 is the newest, as I said, but 3.22 is the most stable version. So I hope the link I've given you, you'll be using for downloading and installing QJS. We will again try to have a hands-on session on how to download and install QJS because it is going to be very important for the later part of the course. I hope we will get you all using this QJS software also. With this, I conclude today's lecture. I will see you in lecture week 4. Thank you.