 Hello everyone, welcome to the next lecture, we are discussing the topic of application of remote sensing in water resource management. In the last lecture, we just briefly defined what water resource management is and we also saw some of the tasks that or some of the sub domains that is encompassed within the term water resource management and also we have seen what all the different variables required in water resource management that can be retrieved from remote sensing. We started discussing about evapotranspiration and we defined what evapotranspiration is. We saw vegetation index based models, Penman-Mortetor-Pricetailer based models for it and we also discussed the surface energy balance equation. So today we will continue with the topic, we will see how the surface energy balance equation is solved using remote sensing data sets. So this is what we have seen in the last lecture like what surface energy balance equation is. So to simply write Rn like to simply write what normally we will do in remote sensing is, we are interested in getting this e term, e term is the evapotranspiration that is what we need to get it. So we will try to estimate each of the radiation components separately, we call it as Rn. So this Rn minus this Q, some people will call it Q, some people will call it G. So Rn minus Q is equal to H plus E. So this implies E is equal to Rn minus Q minus H. So this is like the generic representation of evapotranspiration using the surface energy balance equation. We try to estimate all other variables that are involved in the surface energy balance equation and estimate ET as a residual. This is one such way, we will see this in the next slide. Say as I told here the evapotranspiration ET is estimated as residual of the surface energy balance equation, lambda E is nothing but the ET term or E term whatever we can call is equal to Rn minus H minus G where G is nothing but the Q we have seen in the previous slide. Other than this we can also estimate evapotranspiration as a fraction of Rn minus G that is ET is equal to some fraction of Rn minus G. This is also possible to directly estimate. So if we can just get only this and if we can estimate this fraction somehow then ET can be estimated as a product of these two. Here we would not be worried about H, we would not be trying to calculate H in this method but we will try to estimate this the fraction of energy that is going out as evapotranspiration into Rn minus G. So we call this as EF evaporative fraction. If we estimate this EF using this Rn minus G we can estimate ET. So this is one way or the most common way is estimate ET as the residual of the surface energy balance equation. So this simple equation is now being expanded here. So that is this Rn term is now being expanded into it is like full-fledged thing. Here you can see that albedo term is there LST that is like the surface temperature what we get from thermal infrared remote sensing that is there and surface emissivity. So all these things are that can be or the variables that can be retrieved from remote sensing. We estimate LST or we retrieve LST, we retrieve albedo, we estimate surface emissivity and so on. Using this thing in addition to incoming solar radiation we can estimate this Rn. So this Rn is equal to this. Similarly H also can be estimated using like the heat transfer equation. So I am not going to expand in detail how this equation came in, what all the different assumptions behind it, how to estimate it and all that will be like that those things are out of the scope of this particular course. But there also to estimate H the land surface temperature that we estimate or that we retrieve from the thermal infrared remote sensing data sets is needed. G heat conduction again we will be needing LST NDVA like vegetation indices and so on. So effectively combining the surface temperature, albedo, vegetation indices, surface emissivity and all the variables we try to estimate each and every component of the surface energy balance equation and estimate it. So this is the basic principle of how remote sensing is useful for getting evapotranspiration using the surface energy balance principle. Again this is like a equation, physical equation that is present. We try to solve this equation by getting different inputs from remote sensing. So some of the common models that a few of you might have heard is Siebel. This is based on surface energy balance based model. Another close model is called metric, they are related with each other. There is another model called alexi-dyslexi family, there are like another model called sparse. There are plenty of different remote sensing based models, they are like even very good review papers on the topic. So basically this itself is like a very big field on its own to cover. But just as kind of like a summary, essentially surface energy balance based models require land surface temperature data, without that particular data we will not be in a position to solve surface energy balance based equation. So that is one thing, primary thing. So this is the primary backbone of solving this equation from remote sensing is thermal infrared remote sensing. In addition to this, we will also be needing vegetation variables like vegetation indices, emissivity, albedo and so on. So that is to deal with the basic concepts about how to retrieve aeroportranspiration from remote sensing data sets. So there are like plenty of global data sets available. Say MODIS provides its own aeroportranspiration product, there is another product called GLEAN which is which again provides like global aeroportranspiration, then the PTGPL product is there. So there are like plenty of available ET products at global scale which we can download and use for our applications, even like a simple internet based search will take us to the product page of these different products. So next we are going to talk about soil moisture and how remote sensing is helpful in retrieving soil moisture. So to simply define soil moisture is the amount of water contained within a soil whether it is a volume. Say you have a box, it contains some amount of soil. Say what is the amount of water like the volume based, what is the volume of the soil and what is the volume of water, if you calculate it that is one way of representing soil moisture or if you calculate this box contains this much mass of soil and this much mass of water, if you calculate the mass that is another way of representing soil moisture. That is we can these 2 definitions are basically called volumetric definition or gravimetric definition. Volumetric definition is volume of water divided by volume of soil. So gravimetric is mass of water divided by mass of soil that is essentially if you take like a small box filled with soil it will be kind of like a, it would not fill all the entire thing, there will be some gaps in between it and when you pour water over it the gaps will be filled. So we can either estimate the amount of water with respect to the volume or with respect to the mass content. So calculation of or estimation of soil moisture is of paramount importance to agricultural applications, drought monitoring, meteorological applications and so on. It is one of like the major, we call it a state variable. So it is like one of the important variables in the earth system which is required for several applications. So estimating the soil moisture again is not like a straightforward task. Normally we would not observe soil moisture also directly, we have to do some other measurements and get soil moisture out of it. So the soil moisture can actually be defined as a surface or skin soil moisture like only at the surface. Say you are just touching the surface without going in, how wet you feel? It is an indicator of surface soil moisture like the skin soil moisture you can call or you can estimate soil moisture at the near surface with respect to say 0 to 5 centimeter or 0 to 10 centimeter depth. Some small depth of soil say this much 5 centimeter depth, 10 centimeter depth and so on. So that we call near surface soil moisture or we can also estimate root zone soil moisture. Say some vegetation, they will extend their roots under the soil. It may extend to some distance say half a meter, 1 meter or some large trees will have tens of meters of roots. So those vegetation can actually extract water from soil up to the depth to which they have the roots basically. So we can define the soil moisture present within the root zone or even we can define soil moisture all the way up to the groundwater table. Say this is like the groundwater table. So after this all the pores or all the gaps in the solid earth will be filled with water, we call it a saturated zone. Above this zone there will be a mix of soil air gap plus water filling within the gap. So we can also somehow estimate soil available, water available in the soil column all the way up to this saturated zone. So that is also possible. So for different applications people will normally define soil moisture in different sense. Surface soil moisture, near surface soil moisture or root zone, battle zone etc. So the depth of measurement basically will vary and as the depth changes the estimates that we make also changes because the surface may appear really wet. But the while water may not have percolated down say after say 15 centimeters or 20 centimeters. So if someone else calculates the roots on soil moisture up to 1 meter depth the estimate will be totally different. Say to conceptually still let us say this is depth, this is the surface ok. Let us say this is soil moisture. Let us say in the surface you have just watered the soil. So the soil moisture will be very high. Normally soil moisture with respect to 0 to 1 ok, some sense. So as soon as you wetted the soil using some water the soil, the soil moisture will suddenly increase in the surface then slowly it will go down. Say this is like the root zone say 1 meter depth. If I calculate the soil moisture within the first 5 centimeters I may calculate it say 0.9 very close to 1. It is just watered. If someone else calculates soil moisture up to 1 meter depth it can be something like this. It can be kind of like an average which will tell the person may calculate let us say 0.5. So there is like a huge difference. The depth of measurement is going to change the way how soil is distributed within the, how moisture is distributed within the soil column. So this is really important to understand. So how remote sensing is helpful to retrieve soil moisture? Almost the different kinds of remote sensing that we have seen optical, thermal, active microwave, passive microwave, combination of any of these are all helpful to retrieve soil moisture in some sense we will quickly see certain examples of how these different kinds of remote sensing is helpful to retrieve soil moisture. First we will start with the optical remote sensing for soil moisture. Optical here I mean using surface reflectance in the visible NIR and STI air bands. So we have already seen that whenever water is added to soil its reflectance will go down and the reflectance is more pronounced in SWIR bands. So this we have discussed it in detail when we discussed about the spectral reflectance characteristics of different land surface features. So reflectance is a indicator or soil moisture is one of the primary control of reflectance as soil moisture increases reflectance will decrease. Using this property people have estimated the moisture content of the surface in some studies. But almost all the studies that try to relate the change in surface reflectance with soil moisture or empirical in nature, empirical in the sense they are either done in like lab controlled lab conditions or like in field conditions which are well known normally. Because reflectance does not only depend on soil moisture but also depend on several other factors say for soil itself if you take the soil organic content will reduce a reflectance. So when there is organic soil and there is like a normal soil inorganic soil let us assume both of them have same moisture content but organic soil may still appear darker because of its organic content. So these are all some other variables which will change the reflectance of the soil. So there is no directly we know that soil moisture will influence reflectance but there is no direct relationship that we can establish and that we can apply to various regions to estimate soil moisture. But there are some studies which used this reflectance with soil moisture to do it but not often used. However recently there was like a model developed called Optram optical crepezoidal model which showed that with using the reflectance data it is possible to retrieve soil moisture. So there the authors have used what is known as the STR that is SWER transform reflectance that is we have the reflectance surface reflectance data in SWER band typically like lying between say 1.4 to say 2.5 micrometer within this range if we measure the surface reflectance then transform the surface reflectance into like some sort of like a scaled variable say 1 minus reflectance the whole square divided by 2 times the reflectance. So this is the STR the transform reflectance. So for each pixel within an image for each pixel we will have this STR value and correspondingly we will have NDVI value for that particular pixel right we can estimate it NDVI can be easily obtained from red and NAR bands. So just for a given steady region try to plot the values of NDVI and this STR for all the pixels. For one pixel let us say the NDVI is 0.2 STR is let us say it is like 0.5. So this is like a coordinate so the x coordinate is 0.2 y coordinate is 0.5. So somehow plot it. So like this if we plot it for all the pixels then it will appear in form of like a scatter plot like this and within this scatter plot we can define 2 edges one is called the dry edge and the top end is called the wet edge where the wet end signifies the soil that are full of water and the dry edge signifies that are like dry soils. So once we fit this envelope then for any pixel having a corresponding value of NDVI and this STR we can calculate the moisture content. So this is like a very simple expression I am not going to discuss in detail about how the model works what are the physical assumptions behind it and all but simply put reflectance based soil moisture earlier like many people were not able to successfully retry but in the reason past a study demonstrated few studies have demonstrated that retrieval of soil moisture is possible from optical remote sensing data sets but again there will be high influence of vegetation other variables which can change a reflectance. So all these things we have to keep in mind but it is possible to retry reflectance based soil moisture. Next we are going to see is using thermal infrared remote sensing for soil moisture estimation. So the surface temperature will change when a soil is dry or wet if the soil is dry most of the incoming radiation will be used up to heat up the surface. So the surface will be at a higher temperature whereas if we add water to the surface then the incoming radiation will be used up by the water to evaporate and then the energy will be used up by the water itself for this phase transformation. So the surface temperature would not have increased to a large extent. So wet soils will be cooler dry soils will be warmer. Using this technique people have estimated soil moisture. In addition to this the thermal inertia of the soil also will vary like wet soils have high thermal inertia like the temperature will not change much within a day whereas for dry soils the temperature will change drastically within a day. So observing multi temporal surface temperature measurements like what is possible from geostationary satellites it is possible to retry soil moisture. Say an example for thermal inertia is given here. So this is over like the a particular area or a field in Karnataka state this is from ground measurements basically. So this is obtained over like a parcel of like soil. Here you can see the surface temperature measurements during when the soil is dry is actually shows very large difference in diurnal cycle like it is low at nights then it suddenly increases and reaches like a peak value during daytime then it comes down whereas when the soil is wet the diurnal cycle is more sub cured the temperature difference between the maximum minimum is pretty low in the order of like few kelvins whereas it is almost in order of the 10 Kelvin when the soil is dry. So the thermal inertia property can be used when or to estimate soil moisture but again this is not straightforward task because soil moisture also can change within a day someone may irrigate or some high kind of like drying can happen anything can happen so. But some studies have utilized these properties of thermal infrared remote sensing data sets like that is the land surface temperature to estimate soil moisture. So the combination of optical remote sensing and thermal remote sensing is again useful for the estimation of soil moisture. So one model is called like the classical triangle model which is useful for estimation of soil moisture. So again we will just see like the basic principle of the working. Say we have already seen this OPTRAM model right. So similar to OPTRAM model where we will plot STR and NDVI here we plot LST and NDVI where LST is the land surface temperature that you get from thermal imagery. If you plot it it will form in kind of like a triangle like this like all the scatter will fall somewhere like this where this top edge is called like the dry edge which represents low soil moisture conditions and this bottom edge is called the wet edge which represents high soil moisture conditions ok. So over a region over a steady region if we can estimate this LST and NDVI if we can plot them together and from that scatter plot we will be defining this dry edge and wet edge after defining them we can estimate soil moisture. This is again one of the widely used model for estimating soil moisture from by combining thermal infrared and optical datasets that is NDVI. Here we are primarily using NDVI because NDVI is form is the x axis and LST is forming like the y axis. So these are all very simple models for estimation of soil moisture from optical or thermal or combination of optical and thermal remote sensing. But what all the pros and cons of this optical remote sensing basically say first thing reflectance based methods. We have very good spatial resolution nowadays we have data available at say 10 meter resolution freely available to us. So we can theoretically estimate soil moisture at 10 meter right multiple satellites are available and for very long time say from 70s onwards we have like a good time series of data. Hyperspectral sensors are promising yes. So all these things are advantages very high spatial resolution multiple satellites hyperspectral sensor can still provide very good information and so on. But the major disadvantages are the reflectance is weakly related to soil moisture content because the reflectance can change not only with respect to soil moisture content but also with other things. And also reflectance based models cannot be applied during cloudy conditions because during cloudy conditions we will not be in a position to observe the earth we can see only the clouds. At night time we cannot do it because we need so sunlight to calculate reflectance and sunlight will not be available at night. And this is we can like neglect poor temporal resolution now we are having data maybe once every 5 days or 6 days still which is okay. Then the thermal based methods again they provide good spatial resolution multiple satellites and all. But again here also cloudy conditions is a major thing we will not have thermal data during cloudy conditions earth's atmosphere will change the LST. Atmospheric effect plays a major role and also there are like in the models that use temperature and reflectance together or any way together there are like lot of assumptions involved if those assumptions are not satisfied the model may poorly perform. So effectively optical and thermal remote sensing can estimate soil moisture but soil moisture is not highly correlated with this with what we measure in optical remote sensing or thermal remote sensing. So this is not one of the widely used technique. So for estimating soil moisture microwave remote sensing is often used and we will discuss how microwave remote sensing is useful for soil moisture estimation in the next lecture. Thank you very much.