 Hello everyone, welcome to NPTEL course on rural water resource management. This is week 12 lecture 2. In this series of lectures in week 12, we are looking at the final data that is needed for putting a water budget for rural water resource management. Along this course, we looked at different water data that is needed, why it is needed. We looked at conservation techniques and we also looked at important aspects of water management. Now we will look at why this is important in terms of water management and rural livelihood sustainability options. In the last lecture, we looked at the hydro-metrological data that included your rainfall and other aspect groundwater levels etc. In this week, we will be looking at the other aspects of the groundwater and rural water hydrology and the most important is your soil moisture driven by remote sensing data. Why is it driven by remote sensing data? Normally, you will have to have soil moisture measured at different depths. In the soil class, we understood that there is a particular volume of water that means to be present inside the pore spaces of the soil per plant to grow. So which means you have a depth of soil and the volume of the water changes. So it increases with depth if you go from 0 to 100 or is it decreasing with depth if you go from 0 to 100 centimeters. All these are both are possible in a soil profile. So there is a need of putting meters at regular intervals and measuring soil moisture. However, it has become very expensive and it is not especially representing the whole area and for that reason there has been an introduction of using remote sensing for soil moisture. It is driving these estimates for soil moisture. Basically, remote sensing is the process of collecting data from an object without touching it. So here we go. We are not going to go under the ground. However, the satellite or the remote sensing object can penetrate through the soil and estimate properties of the soil water column. It is driven by radar principle, but let's not get into the depth of how the data is collected. Right now it has been used worldwide for assessing the soil moisture in the soil profile. Now, if we know the soil moisture, the soil profile, we are at a very healthy stage to either irrigate or not irrigate. Take decisions basically. And that is where this WRIS soil moisture data housed in the WRIS website can happen. There are multiple other ways that you could get this data, satellite driven data, observation data, etc. However, there is no one single database that stores all the data. And for that reason, we are keeping this as an option for water management. So where we can go to this database update. So I'm only going through the database within the WRIS website. But as I said, there are multiple other resources that can be the same data or even better resolution temporal etc. Right now the one we have is pretty good enough. And from there, we can take a lot of understanding. We'll go through the data profile right now. Okay, so it is read by the NRSE through the ISRO. So ISRO has multiple application centers for satellite remote sensing application. There's AC, NRSE, RRSE, where NRSE stands for National Remote Sensing Center. SRC, SAC stands for Space Application Center and RRSE stands for Regional Remote Sensing Center. So these are centers within ISRO where they take remote sensing data. They apply it to the ground and get estimates of properties, parameters. There has to be some validation which they should have done in the modeling stage or data acquisition stage. So the NRSE has used a variable infiltration capacity model in short. So what it does is it takes satellite data. It's like a model, like a box you can assume. It takes the satellite data, different hyperspectral images and also the data from radar, and then estimates the soil moisture property and gives it as an output. This is how the soil moisture profile looks like in the WRIS website. You will get through it and see how this website is done. Sometimes there is some changes, so you won't see the entire website similarly. For example, last days I was trying to get this website, but there were some issues. Luckily it is working now, so I will be happy to show it live in class so that you can also estimate these properties at district level and also sometimes block level. What you can see here is at every district you have some soil moisture values and soil moisture can be anywhere from 0 to 100%, which is the pore space. The pore space that we discussed in the soil can be filled with air or water. If it is 100% water, then it is saturation. If it is 0% water, then it is unsaturated, totally unsaturated. If it is in between, we still call it unsaturated, depending on the percentage. There are some percentages that the plant likes depending on the soil. For example, if it is 100% water, then the plant will suffocate. It cannot breathe, you are put so much water. Same like this, we like water to drink, but if we are put in a swimming pool, we can survive for some time, but then after that we need to come out. The similar things are there, if it is too much water, it cannot breathe. The plant cannot breathe and the roots will decay and suffocate. So it is necessary to understand if you have to drain the soil or water the soil. And this data set helps you in managing that. Now, if you have this information, you can plan ahead on your irrigation schedule or if you need to buy brown water as a pump water out and then put it, or how much water is needed for your crops, because you have the current soil moisture and you have to increase it by a percentage for your plant to grow. So with this, let me open the webpage so that we could look at it. It is the WRIS website and as I said, it is somewhat a little slow today, but let's hope it works for data. So how I went here is I went to home, then water data and under the water data, you go to hydro-metrological, we already saw rainfall. We will see evapotranspiration in the next class and then we can do soil. When you click soil moisture, this page will come up. Normally, the India map as I showed in my presentation should have the data already there. For some reason, the data is like for example, it's black in color, but trust me, it's still populating. You can see slowly it's populating. And here you could see the value, soil moisture values going up and down, correct? And you could see that this is for all the states in together. So the x-axis is your soil sample location, which is your states, okay? Whereas your y-axis has a soil moisture. So you have soil moisture and then these states. And then the average of that is taken as a volumetric soil moisture, which is 25.56%. If you ask as a hydrologist, is it high? No, it is not. Because right now we are in the March period. Look at the date. It's 2023-29, up to 2020-0-3-30. So just one week before this class recording, this data has come up. So the data comes and then they do these models. So the satellite data is taken in and other parameters are taken in. They run these models, then they populate it on the website. So technically a week they take for this. And that is where it's kind of, we can say near real time. Because if you know the soil moisture week before, then you could definitely do some irrigation planning to save the water. So you could see here the legend is a high soil moisture percentage higher than 45 as blue as the color suggests. And because our blue is water and you have higher water in high port spaces. And then you have the red color to show it is alarming. It is dangerous kind of soil moisture in the low places. Okay. So here we have on the right hand side, you can see that it is done for India. The whole data you see here, this average volumetric soil moisture. And it is taken as a daily volumetric soil moisture content in 15 centimeters depth. So it doesn't go beyond 15 centimeters. Why is the magic number 15 is because that is where most of the agricultural plants have root depth. If you have, this is the 15 centimeters root depth and then some root go down, then you need better estimates. But if your plant is only taking water from 15 centimeters, then this data is enough. Okay. So the water should be anywhere brain and also soil moisture held in the soil within this 15 centimeters. Okay. So when you apply water, water moves down the soil profile in the 15 centimeters, it gets relocated and then the plants can take it up. If it is a well-brained soil, like gravelly soil, then water will just flush through. And so soil moisture is not going to be kept. Right. We saw these concepts in soil attention specifically in back. So coming back for this date, you could see that the soil moisture is around 25.56 for the entire country. And just here within this range, I could see that Kerala has high soil moisture content 45.49%. And because it is on the western that region, Kerala is blessed with a good soil, soil moisture and soil formation properties also a lot of organic matter, a lot of, you know, degrading weathering of soil is happening. And those would have fresh soil with water holding capacity. So let me see if there are other states in the region. So I'm going to pull down. There is no other states given, but you could see it here coming down. So this is the daily volumetric content of soil moisture as a number. So the percentage 25.56, 25.45 average is that. So this is for the entire India of the two days it is taken an average. Then you have the other states when you see zero doesn't mean that there is zero soil moisture. Maybe there's no data because I'm the man and it might have some data but it's not driven. So please understand that these are satellite driven products. So sometimes the satellite. The location is small. As in it doesn't, it doesn't take the entire pixel. So at the end of the day you won't have data for them on areas and luxury areas, you might have to go and put physical sensors. Okay, let me try here. You can see luxury is also zero as we thought because the satellite data is too big to capture the amount has to be small so that you can capture the data. Otherwise, you can't slide searching for, you know, sand particles using your just plain eye. You cannot do it. You have to zoom in. And for that zooming in like a microscope you need a better higher resolution satellite. Okay, so but for the entire India it is still good. You could see that the entire is mapped. I'm going to zoom out for that we could see it clearly. And you could see that as I said here along the western dark region, this part along the Konkan region, there is good soil moisture. Central India has some good soil moisture and always the northeast has good soil moisture because of the high altitude rainfall situations. Whereas the western regions have really drastic rainfall patterns and right now the summer is kicking in and a lot of groundwater is used because of that. So if you look at it soil moisture has good correlations with your groundwater because if your soil moisture goes down, you can use groundwater to put water into the system. Okay, so we're going to look at the I'm just going to remove the access for now and then let's take one step ahead on looking at what does it mean. Okay, so you have the data you have the statewide data and then the average data as a point data, and you can download. Okay, you can also have all the states here so I'm just going to click it off, you can download all the states data for that particular average so this is the average data for that particular the two dates we have it and you can also download these metrics as per statewide. Okay, so now what I'm going to do is I'm going to show you on the left side what we have on the left side we have a unit wise section. Okay, so which means, do you want it as a basin or a state as I again, the basin can be your water basin, whatever basins include in the Ganges, the Indus, the Karey basin, etc. But since all these administrations are done using state boundaries, we'll keep it as a state boundary. As I said, there is one model that drives the satellite data, and that is the NRSC VIC model so NRSC is the data provider, the person who's doing sampling and all those things whereas your VIC is the model. So you put NRSC because it is driving the VIC model. Let's see Maharashtra. There's a reason we do Maharashtra because there is a lot of sugar cane which grows here and sugar cane is more than a year clock. Okay, so it needs water throughout so when you see a soil moisture going down, then it is alarming that you have to go back and water the crop. So that is what we have this, you know, Maharashtra state selected. Okay, so Maharashtra state is selected, you can see that it's now showing up and then I'm going down to the district. Sometimes you do get the district but sometimes you don't but let's do things that will go on. Okay, slowly, slowly you will see these names populate, so India, Maharashtra, Zalgowan or also Pune, we can take Pune because it has a lot of dams. It means like if the soil moisture is low, then the person sitting on the dams can release the water. So we have Pune and then we have daily tap step. Yes, monthly doesn't make sense, yearly is also not that helpful for farmers for people to manage water and release water we need to. So just because of the time it takes for the data to come in, I'm just going to do a couple of days in last week of March. Let's see until when we do have data so let's take from 20 until April there's no data so let's take the last data in March, which is 30. As I said still one week data is okay and with populate you can see that as we can click it it's populating here, but just the analysis is running behind to do some measurement. It's 10 days of data daily volumetric soil moisture content 15 centimeters, you could see. Right, and then the average volumetric soil moisture is 23.16%, which is very less, we need more water for the crops. You could see that right next to that is Tane and Mumbai region has higher soil moisture that is because of also the climatic factors around that area. But most importantly Pune has a lot of agricultural activities compared to Tane and Mumbai so there's a lot of water demand for the crops. As you can see here now it has populated 23.16 and I'm coming down to show the daily changes in the value that you could see there is a steady decline of the soil moisture. This is correct because of the summer is peaking also so March and slowly the summer is coming out and then you would see the temperatures rising suddenly in the data which is this dry is evaporation transpiration and then the soil moisture is lost. And then let's come down. You could see the date wise what is the value so that you can download and the excelsure. There's 11 entries, including the dates so it's 11 days we have modeled, and then we could see how the, you know, soil moisture has changed. You can download this as a chart, print chart, download as image CSV, CSV will give you the data in Excel format. Also you can download and then use it here there's no station. Okay, so in like the other data that we use, we don't have a station name, we have a district so we can say for district, we have this data. If you look at Pune here, there's no, you know, final resolution in Pune. Okay, so you can only have it until Pune there's no blocks in Pune that have the data. So you have to be careful saying that I can I do the village level analysis using this know you cannot. So within Pune, if one part of Pune has a lot of agricultural activity, and then there's a Pune city. So I'm going to say that the water consumption is the same on both sides. No, it's not. So, this is kind of an average. This is sometimes a limitation of using remote sensing data. But it is the best data we have for now. Okay, and a lot of advisories on on water management are being given using this data. But as I said, does we have to take it cautiously because of the spatial resolution, but temporarily it's one of the best every day you get data. So every day the satellites are taking images, it runs these models, which are based on temperature rainfall and other attributes, and then you get the output as a net soil moisture. So just while we're here, I'm just going to try if we could get a higher resolution, you know, date resolution on this model. I'm just going to go from one to 13 of March. Because I know for sure, in the March period, the temperature has picked up quite drastically. The soil moisture also picks up slightly and then just keeps on going down because of the temperature rising temperature. And if you do February, clearly see that February is cooler, and you had more soil moisture and then it starts to come down. Okay, so soil moisture is a very important property for crop management. I'm going to do that once in check. January for sure was a very cold and cold weather was there you can see beautifully how from the soil moisture 35, it has come down to 23. It has crossed more than 12% soil moisture. And this is not purely because of the crops. It is the changing climate also. And if you go to the rainfall season, you will see around 100% or 80% the rain soil moisture, because the rainfall was giving the body. Let me show you that. So for example, we had good rainfall in August. To show you the data. So from August now this is seasonal, you could see how it goes up and down. And that up and down is because of the rainfall coming and because of the crops because in the current season, the current is the rainfall season. There is a plant sowing and then harvesting going on new crops to be being put. And that is where you see this up and down motion soil moisture has taken up, and then rainfall happens it recharge. The rainwater taken up rainfall happened recharge. And there's a sudden dip. And this dip is because of the plants plants the curry plants is take the water and then it just keeps on defeating. Once it depletes, you can also see it rises suddenly because of some rainfall one or two rainfall events or irrigation those kind of things and then keeps on declining. So if you progress in March, April, May, you'll see it's still going down to very, very dry conditions. Okay. And this is how you could get some data for your location. And also get an average seed average doesn't make sense here because for the overall period, how do you prepare for it you have 62, which is almost double the average, and then you have 23 which is very low compared to the average. So, average doesn't make a lot of sense, but it does help you to understand the range of which the soil moisture can go up and down. In this class, you could download the data for your village level analysis water for soil and other aspects. This is not only for agriculture, because it also supports the recharge process for groundwater drinking water. With this, I would like to conclude today's lecture on soil moisture data connection.