States that remote sensing is the acquisition of information about an object, area and phenomenon without making physical contact with the object vicinity or phenomenon under investigation). In different word, it is possible to collect information about any object or target from a range of distance by remote sensing that is pretty significant for geographers who have get admission to it. Therefore remote sensing can be used as one of the most effective tool which is used with problems related to environmental issues and any changes that happened from observing the earth for extended time, and according to (Alonso and Valladares, 2008) it provide an exceptional quantity of statistics about distinctive problems related to the environment, for instance, remote sensing has been used in some field like in climatology and meteorological research and turn out to be a powerful device in these field This is due to the fact remote sensing capable to supply exquisite amounts of multispectral and temporal information for scientific scholars and researchers. The first meteorological satellite was launched with the identify of television Infrared Observation Satellite (TIROS-1) in the 1960s.
So, the first use of RS for meteorological purposes started out from this date. (Sobrino et al., 2004) mentions that nowadays, remote sensing plays a necessary function in climatology studies, particularly in estimating LST. There are notable benefits in the usage of remote sensing information and strategies over typical methods in terms of climatology. Such as, traditionally, a range of methods have been developed to calculate and retrieve air temperature inside a city area, such as from a land-based observation station or a hooked up temperature sensor. However, all these techniques have some boundaries according to (Mallick et al., 2008).
First of all, these typical methods are very steeply-priced and, due to acquire metrological information for a large area, it is not possible to set up a giant quantity of meteorological stations with anticipated density. Secondly, the amount of time needed is more than the use of remotely sensing data. In contrast, remote sensing is capable to provide meteorological data for a grate scale and in a short time by imparting high-resolution and multi-temporal data, with the capability of measuring one of a kind surface situation stated by (Owen et al., 1998). Thirdly, in order to cover considerable areas the traditional technique of accumulating metrological data, analysts have to disseminate the consequences. However, remote sensing lets the analyst to achieve statistics for a big area and for the unique time. Fourthly, remote sensing is able, in a short time, to accumulate information about the urban climate, and produce correct outcomes to solve problems In contrast, a ground-based observation station is only capable to get thermal information for the facets round the station. Finally, remote sensing is in a position to extract some physical characteristics of the land surface, primarily based on measuring radiance reflected and emitted from surfaces such as the physical characteristics of roofs of buildings, pavements, vegetation and simple floor by (Gartland, 2008
The Advantages of the use of remote sensing statistics with climatic studies. The picture on the right suggests an example of a weather station detect climatic issues, such as temperature-rain ratio, and so on. It is hard to set up many similar stations in a massive area. However, the image on the left indicates a platform remark of a scale of land surface which is in a position to supply biophysical data, consisting of x, y location, z elevation, or depth, biomass and land surface temperature, soil moisture etc. by (Jensen, 2000). Thermal infrared band to calculate land surface temperature for different scales. Basically, thermal sensors are the most broadly used for the reason of retrieving LST, and the satellite thermal band is the most and best waveband which is used in the study on LST. According to the International Workshop on the Retrieval and Use of LST (2008), which identified the satellite thermal band as the most excellent band to use for studying LST, and this, was dependent upon three reasons. Firstly, the land surface emitted approximately 80% of the thermal energy, and the sensors acquired this energy between the 10.4 and 12.5μm wavelength regions. Secondly, the whole thing above absolute zero (0K or -73.15°C) emits radiation in the infrared range of the electromagnetic spectrum. Thirdly, the earth’s atmosphere lets in only a component of thermal energy to come through and be transmitted from the destination to the platform or detectors states by (Chuvieco and Huete, 2010; Jensen, 2000). As in accordingly, the incoming radiation signal, obtained by using the platform in this region, is formed by means of the earth’s emitted power and not from the sun’s reflected energy.
Consequently, this spectrum place is regarded as the thermal infrared because it permits warmness variation to be detected in an objective surface. See Figure (3) Figure (3): Shows that the solar radiation decreases and the thermal radiation transmittance produced with the aid of earth’s surface is increased, based on the one-of-a-kind wavelengths which appears round 15–30% of the spectral radiance produced via earth transmitted in the thermal region, as represented by means of the blue coloration in the above picture.
Because of this Thermal band became the most broadly used and has emerged as an indispensable parameter in the research of LST; evaporation; monitoring and detecting vegetation abundance and health; detecting and monitoring volcanic activity; determining burned land; coal furnace detection; water irrigation planning and monitoring; air temperature modeling; and so on (Allen et al., 2007; Chapman and Thornes, 2003; Cristobal et al., 2008; Moran, 1995 and Prakash, 2000). 2.2-Remote Sensing Platforms for Measuring LST. Nowadays, there are numerous structures such as NOAA, AVHRP, MODIS (Terra and Aqua) and Terra ASTER with thermal bands, which they use for reading LST, and estimating the kinetic temperature of the earth’s surface. However, amongst presently available satellites which have a thermal infrared band, Landsat-5 TM and 7 ETM+ are used most broadly in reading LST, with specific regard to city areas.
Landsat imagery consists of seven one of a kind bands which are placed on exclusive wavelengths. Six of them are placed inside visible, close to and brief infrared regions, with only one band placed inside a thermal band. This range of exclusive bands has massive conceivable for many environmental applications, especially research of LST, which has led to various aspects being detected simultaneously. Landsat statistics has huge chances for many applications concerning to LST. In another way, it is famous that the unique aspects of the earth’s surface emit a number of sorts of radiation on one of a kind wavelengths. Consequently, it is very important to use satellite data such as Landsat with a range of bands.
ASTER data has 4 thermal bands and collecting data during day and night time with 90m spectral resolution. In addition, it is obvious that these platforms are only fit when the work is at the macro-level, whenever it compares with other platforms such as NOAA/AVHRR which may cause the allowing of the identification of the different features, which are within digital pixels in the digital images (Gallo et al., 1993). On the other hand, realization different kind of land cover is one of the abilities of Landsat data. Thus, by the means of the use of Landsat data, it is feasble to learn about the relationship between the temperature of the earth’s surface and the different sorts of land cover, in order to assess the role of every land cover in phrases of its influnce on LST (walawner and Hajto, 2010). As Qin et al. (2001) mentioned that for analyzing the comprehensive spatial patterns of thermal variation on the Earth’s surface band 6 is excessive sufficient see table (1). Moreover, Landsat has one band, placed in the thermal region, (10.4–12.5μm) defined as a spectral range and it is resolution defined as (120 meter) for the thermal band which is Band 6. ASRER (Advanced Space Borne Thermal Emission and Reflection Radiometer) is one of the 5 units flying on Tarra satellite launched in 1991 as section of NASA’s Earth Observing System. It is used to acquire certain data on surface temperature, emissivity, reflectance and elevation at the quite excessive spatial resolution. ASTER is collects fact in a way that use four thermal bands. It has a limitation for studying rapidly retrieving surface conditions due to a revisit time of 16 days. Also, there might be prices related to the with acquiring some images.
Developed Algorithms in Retrieving Land Surface Temperature Complicated formula needs for the calculating and retrieving LST procedure, owing to the range of participating factors which is manipulate and affect LST LST (Dash et al., 2002; Jin, 2004; Kerr et al., 2005). The factors are listed below:
Nowadays, by developing quite numbers and techniques and different kind of satellite data such as: the split-window method projected by Sobrino et al., (1996); the temperature/emissivity separation method projected by Gillespie et al., (1998); the mono-window algorithm projected by Qin et al. (2001); and the single-channel method projected by Jimenez-Munoz and Sobrino (2003), as long as the data was used in this study was brought from land a Landsat-5 TM platform. In the thermal region it had one place for locating band. Subsequently, the relation to the procedure of atmospheric correction has been sign it as the only limitation of the Landsat data, which become as a challenging task when in contrast to the other sensors like ASTER, which the place for locating to the bands are more than one in the thermal region (Alsultan et al., 2005; Cristobal et al., 2009). Heretofore, the calculation of the surface temperature was derived normally from the atmospheric correction procedure by using the Landsat-5 TM, which used to be very complicated in theory and needs extra parameters (Zhang et al., 2007). On the other hand, suitable algorithms has been prepare and investigate by the attempting of the extraordinary researchers, based on the techniques of radiometric and atmospheric correction, in order to retrieve LST by using the usage of Landsat-5 TM statistics (Chavez, 1996; Qin et al., 2001).
In the time of our research of the literature, the listed of studies has been done using different with Landsat data, like radioactive transfer, mono-window algorithm and single-channel method. Moreover, the split-window also counts as a method but it is almost impossible to be used with Landsat-5 TM data, the only reason is that it has only on thermal band (Sobrino et al., 2004). In contrast, every of these methods have a particular capability; consequently, the analyst should have a clear vision before determining to use any of them. For example, if the aim of the user is to changing radiative transfer, in this case for retrieving LST using Landsat-5 TM data, an equation needs to have situ parameters of atmospheric profile simultaneously, during satellite passing over the goal because without this equation it is almost impossible to retrieve LST using Landsat-5 TM data. Nonetheless, with that respect, the most broadly method has been used is mono-window algorithm because it mostly covers all the requirements, like atmospheric correction and land surface emissivity during the LST process which is one of the most important parameters in the study of LST according to (Prakash, 2000). Mono-window has been suggested in Qin et al.(2001) work. Moreover, the work is not only used brightness temperature; it also used spatial variability of atmospheric parameters to increase their work. In addition, Zhang et al.(2007) mentioned that, availability of mono-window algorithm is make it more important to the analysts who did possess situ atmospheric profile data.
Despites of, mono-window algorithm is in a position to mix standard atmospheric profile with local in-situ ground meteorological data, which in case necessities parameters will be provided such as emissivity, effective mean atmospheric temperature and atmospheric transmittance. In fact, the use of this algorithm is primarily based on three district parameters which makes a Profound impact on the retrieving LST, and give an accurate result. These parameters have been listed below: land surface emissivity, mean atmospheric transmittance, and near surface temperature (Air Temperature). Unlikeness, it is important to know that many great studies has been done without using all of these parameters.
Furthermore, a variety of algorithms and strategies has been used in different studies by the use of a Landsat thermal band to retrieve LST. 2.3 Calculating and Retrieving Land Surface Temperatures There are number of strategies has been mentioned in the literature section for recovering land surface temperatures from remote sensing instruments by using thermal infrared band. Moreover, the most extensively used techniques are the following there methods in accordance to the literature. First, the signal channel method: the usage of this technique is for those sensors which have one thermal band for instance Landsat TM/ETM+. Second, the two channel of split-window method: the usage of this technique is only for those sensors which have at least two thermal bands such as ASTER & MODIS. Third, the temperature and Emissivity Separation: this method has been innovated by Gillespie et al (1998).Therefor, the above study has been shown that the signal channel technique applied for retrieving land surface temperatures considering the fact that the Landsat TM/ETM+ sensor incorporates only one thermal band (B6). The equation of transferring thermal radiance is the core of this approach which is commonly focused on converting satellite digital values to a radiometric value by functioning the high gain values and satellite low (Markham and Barker, 1986).
In addition, the differences among using signal channel methods has appear in literature which should be noted significantly. The reason for that is, there are there three different methods can be implemented with signal channel methods which have been classified below, the Radiative Transfer Equation (RTE), the Mono-window algorithm of Qin et al.’s (2001) algorithm and Jimenez-Munoz and Sobrino’s (2003) algorithm. In this study, RTE, the first with technique related with single-channel methods which has been used. Nevertheless, the signal-channel method needs some atmospheric parameters which have generally derived from MODTRAN radiative transfer. A easy and rapid way to overcome this negative side is to implement on-line atmospheric correction parameter calculator which has been innovated via Barsi etal.(2005).
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