Mohammad Tavosi; Mehdi Vafakhah; Vahdi Moosavi
Abstract
Due to the importance of meteorological data and limitations of data gathering from ground stations, remote sensing can play an important role in the preparation of these data. The purpose of this study was to quantitatively evaluate the Land Surface Temperature (LST) obtained from Moderate Resolution ...
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Due to the importance of meteorological data and limitations of data gathering from ground stations, remote sensing can play an important role in the preparation of these data. The purpose of this study was to quantitatively evaluate the Land Surface Temperature (LST) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images for estimating the maximum and minimum daily air temperature in the Taleghan watershed. For this purpose, the maximum and minimum daily air temperature data of three existing ground stations for the period 2009 to 2015 were obtained. Day and night LST and Normalized Difference Vegetation Index (NDVI) values of MODIS were also prepared. The relationships between each of the effective variables and maximum and minimum daily air temperature in ground stations have been extracted using multiple linear regression method. The results showed that there was a significant correlation between maximum and minimum daily temperature of ground stations with day and night LST and NDVI from MODIS sensor. Therefore, these variables were used in regression relationships. The results of validation showed that the established relationships with all effective variables had the most accuracy. Therefore, the best model for estimating the maximum daily air temperature had , NSE and RMSE values of 0.74, 0.74, and +4.7, respectively and for estimating the minimum daily air temperature had 0.71, 0.72 and +2.9, respectively. Therefore, by converting the surface temperature obtained from MODIS sensor images, the air temperature can be estimated with high accuracy on a daily and monthly scales for various studies.
zohreh hashemi; Hamid Soodaei zadeh; Mohammad Hossein Mokhtari
Abstract
Land surface temperature is considered a key parameter in the physic processes of land surface at all scales of local to global. In this study, the relationship between land surface temperature with vegetation and soil surface moisture in land uses of Zahak plain of Sistan area was investigated. In order ...
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Land surface temperature is considered a key parameter in the physic processes of land surface at all scales of local to global. In this study, the relationship between land surface temperature with vegetation and soil surface moisture in land uses of Zahak plain of Sistan area was investigated. In order to, Landsat TM (1987), TM (2001) and OLI (2018) satellite imagery were used. After the preprocessing and image processing steps, the extraction of land use maps was performed based on the monitored classification method and through maximum probability algorithm for a period of 30 years. Also, land surface temperature was evaluated statistically by separate window method and the relationship between land surface temperature with vegetation and soil moisture. The results showed that the accuracy of classification by maximum probability method through geomorphic facts data, TM and OLI images in terms of kappa coefficient of 0.89, 0.95 and 0.84, respectively, based on the overall accuracy of 91.8, 96.45 and 87.89% was obtained. During 1987, 2001 and 2018, average of the land surface temperature indices were 38.13, 45.73 and 41.14 ° C, the normalized difference vegetation index was -0.11, -0.13 and -0.16, and the normalized difference moisture index was estimated 0.64, 0.63 and 0.58. The relationship between land surface temperature and normalized difference of vegetation index was no correlative. The correlation between land surface temperature and the normalized difference of humidity index was also inverted and negative. Plant regeneration and growth was decreased owing to factors including hydrological drought and Climatic conditions due to reduced rainfall, rising air temperature and Dust storms. Therefore, due to the lack of suitable vegetation, vegetation is not effective in reducing the surface temperature of the study area.
Moslem Torky; Seyed Abolfazl Masoodian
Abstract
The expansion of urbanization and the increase of population in metropolises and the growth ofindustrial activities of cities, It has caused changes in urban area climate. One result of these changesis the city's heat islands. The city of Mashhad has also grown rapidly in recent years. This studyinvestigates ...
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The expansion of urbanization and the increase of population in metropolises and the growth ofindustrial activities of cities, It has caused changes in urban area climate. One result of these changesis the city's heat islands. The city of Mashhad has also grown rapidly in recent years. This studyinvestigates the heat/cold island of Mashhad metropolis based on the background climate in order toidentify its spatiotemporal behavior. For this purpose The MODIS Terra and Aqua land surfacetemperature (LST) data were obtained and the heat island was examined accordingly. A new wasused to measure the heat island. In this method, Modis land use data was used to determine the urbanand suburban boundaries as well as to determine the land use type of the study area. The backgroundclimate was determined based on Far-side temperature and the representative non urban area wasselected based on the most frequent temperature and the heat island was calculated. Survey ofheat/cold island in the daily period showed that during the day the average temperature of city islower than non urbun temperature and at night is higher. Also the seasonal survey of heat island/couldisland of Mashhad metropolitan shows that daily cold island is the highest during the warm seasonsand lowest in the cold seasons and the seasonal variability of nightly heat island is less than the dailycold island. The core of the daily cold island is located between the Haram and the Shahid FehmidahSquare towards the western area of Mashhad. The day time cold island matches the areas of the citywith high vegetation coverage. The core of the nightly heat island is consistent with the old textureand dense area around the Haram towards the northwest of the city. The heat/cold island intensity isalso directly related to the wind speed. The role of land use in intensifying or reducing the intensity ofthe heat island of Mashhad is well seen. In the development of the city, more attention can be paid tothe use of urban land use in order to moderate the temperature of the city.
alireza bazrgar; morteza tayebi
Abstract
Land surface temperature (LST) monitoring has been widely used as one of the most important environmental parameters by the high temporal resolution sensors such as the MODIS sensor (daily temporal resolution capability and spatial resolution of one kilometer). One of the main problems of these sensors ...
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Land surface temperature (LST) monitoring has been widely used as one of the most important environmental parameters by the high temporal resolution sensors such as the MODIS sensor (daily temporal resolution capability and spatial resolution of one kilometer). One of the main problems of these sensors is their low spatial resolution, which limits the performance of these sensors for applications such as fire detection in forest areas and the study of urban thermal islands. In contrast, high spatial resolution sensors such as the ASTER sensor (90 meter spatial resolution and 16-day temporal resolution at the land surface temperature product), they have low temporal resolution, which results in application such as rapid change monitoring. In fact, due to technical limitations, there is no sensor that has a high resolution in spatial and temporal dimensions. To solve this problem, low-cost and efficient spatial-temporal fusion methods have been developed. The most important methods for fusion spatial-temporal methods are enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Spatial and Temporal Data Fusion Approach (STDFA). This work uses the ESTARFM and STDFA algorithms and a new method (SWT-STDFA) based on the STDFA method and the two-dimensional stationary wavelet transformation to fuse LST data spatially and temporally. The LST products of ASTER and MODIS sensors were fused for a part of Tehran city and finally, a virtual image was obtained with a spatial resolution equal to that of the ASTER sensor and a temporal resolution equal to that of the MODIS sensor. Also, based on the existence of a classification map prepared on the basis of normalized vegetation difference index (NDVI) in STDFA and SWT-STDFA algorithms, the effect of using normalized Green Difference Vegetation Indices (GNDVI) and soil adjusted vegetation Index (SAVI) on the accuracy of the synthetic image of the output is discussed. The results of the research indicate the high accuracy of the proposed method with the root mean square error of 3.03 Kelvin, standard deviation of 2. 21 Kelvin, mean absolute difference 1.72 Kelvin and correlation coefficient of 0.92 between the image of the actual land surface temperature and the predicted synthetic image Compared to the other two methods. Also, the increase of vegetation’s indices GNDVI and SAVI in the classification of both STDFA and SWT-STDFA methods did not have much effect on the accuracy of the synthetic image of the output.
Salman Ahmadi; Reza Soodmand
Abstract
The temperature of the Earth's surface is a very important parameter in environmental studies, climate change, soil moisture content, Evapotranspiration and urban thermal islands at different scales. Currently, there is no perfect method for accurately measuring the temperature of the surface of the ...
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The temperature of the Earth's surface is a very important parameter in environmental studies, climate change, soil moisture content, Evapotranspiration and urban thermal islands at different scales. Currently, there is no perfect method for accurately measuring the temperature of the surface of the earth, but since high spectral resolution sensors prevent the vapor spectral absorption in the infrared bands, this Increases computational accuracy in determining vegetation index. The purpose of this paper is to calculate the surface temperature using satellite images of OLI and TIRS sensors of Landsat 8. In this research, the separate window algorithm has been used to calculate ground temperature. The algorithm uses spectral radiance and emissivity to calculate the surface temperature. To estimate the spectral radiance in Landsat 8, the bands of 10 and 11 have been used. Emissivity is also obtained by using the NDVI threshold technique by using the OLI bands 2, 3, 4 and 5. Also, In this paper the temperature is calculated by The algorithm has been calibrated and corrected by a two-dimensional projective mathematical model, which tried to bring the calculated temperature closer to the actual ground temperature. In the present paper, the RMSE value is equal to 0.3678°C and the correlation between Meteorological data and temperature estimated by the model is equal to 0.9791. Also, the performance of the model that used to estimate the Earth's surface temperature is equal to 0.9751.
Alireza Ramezani Khojeen; Mir Masood Kheirkhah Zarkesh; Peyman Daneshkar Arasteh
Volume 7, Issue 3 , November 2015, , Pages 49-64
Abstract
Calculating the canopy temperature and land surface temperatureusing satellite imagery is very attractive to estimate actual evapotranspiration (ET) by energy balance algorithm. In studies, to evaluate ET, the accuracy of the calculated thermal gradient between surface and air, as well as temperature ...
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Calculating the canopy temperature and land surface temperatureusing satellite imagery is very attractive to estimate actual evapotranspiration (ET) by energy balance algorithm. In studies, to evaluate ET, the accuracy of the calculated thermal gradient between surface and air, as well as temperature difference between various land covers is very important. To calculate land surface temperature (LST) in Shahr-E-Kord plain, the study area, there were three principal challenges. First, the absence of enough studies about calculating LST using Landsat8 thermal bands, the second, lack of canopy temperature and land surface temperature observed data, and finally, the only available data for surface temperature was minimum daily surface temperature in the climatology and synoptic stations. In this study, in order to convert the surface brightness temperature to the LST, the split-window algorithm of NOAA-AVHRR was used. Also, the proposed SEBAL algorithm was applied to calculate the surface emissivity. Due to the lack of the reference weather stations, after calculating LST at the satellite overpass time in non-reference weather stations, the deviation error calculation method was used to calibrate satellite LST and to prepare daily LST layers. Results showed that all calculated correlation coefficients were more than 0.9. Also, all existing regression relations were significant at 95% and even 99% level of confidence. In different day-images, maximum difference of calculated deviation errors was less than 0.5 K and, the calculated RMSEs were between 1.9 to 2.2 K, acceptable comparing to similar studies.
