modis sensor

Heat capacity is a physical quantity of the surface that is directly related to the amount of heat energy required to change the surface temperature. Land surfaces with a high of thermal capacity are a sign of moderate climatic conditions and the presence of the low ones is known as a reason for the occurrence of desert climate conditions. When uniform heat energy absorption is occurred by different surfaces, their temperature changes can be inversely related to the heat capacity. However, due to obstacles and shadows, the sunlight as the most important factor in the reception of heat energy during the day does not receive uniformly to the surface of the earth. In this article, by adjusting the shadows effect the differences in sunlight energy received by different parts of land surfaces are modeled. Then, by calculating the day and night land surface temperatures a method has been proposed for estimation of relative heat capacity. In this method, the time series of MODIS images are used to reduce the destructive effects of atmospheric conditions in the estimation of land surface temperature. The percentage of shadow’s presence in each position is also estimated through the spatial analyses on digital elevation models. The proposed method has been calibrated through the ground truths identified with expert knowledge about the soil properties. The results demonstrate that the efficiency of the calibrated method reaches the overall accuracy of 93% in a relative assortment of land surfaces in terms of their heat capacities.

In this research, the spatiotemporal variations of snow-covered days (SCDs) in this region were analyzed using the data of the sixth version of MODIS Terra and MODIS Aqua sensors on a daily basis in the period of 2003-2020. In order to reduce the cloud cover effect, three algorithms were applied to the data. For the digital elevation model, the Digital Surface Model (DSM) of the Japan Space Exploration Agency was used. The relation between two snow-cover phenological components (SCAs and SCDs) and the relation between the SCDs and the altitude were investigated. The findings show an increase in SCDs in the months of November, December, and January. Maximum SCDs are observed in January in Sabalan Mountain and then Sahand. The reduction of SCDs in the spring and summer months is also affected by the two factors of latitude and altitude. The absolute maximum of SCDs in this region is observed at 160 days/ year in the mountain of Sabalan. Examining the changes in SCDs in March and April shows a decrease in SCDs in high-altitude classes. At the same time, it shows the increasing pattern of SCDs in November and December at many altitude levels. Analysis of the relation between SCA and SCDs in different months illustrated that SCAs has decreased in regions with more SCDs (heights) due to the reduction of topographic areas. The relation of SCDs and altitude also showed that the minimum of SCDs occurred in all altitude levels (even altitudes above 3500 m with 4 days) in August and the maximum occurred in December with 22 days at the altitude of 3500 m. SCDs decrease with increasing altitude in mountainous areas of 3500 to 4000 m, due to the increase of land slope and instability of SC in steep areas.

In recent years, the study of climate changes as well as their effects, has become a constant topic in the scientific fields of many countries. One of the main features of these changes is the increase in air temperature over the last 5 decades compared to the last 500 years. Statistics show an increase of one degree centigrade in air temperature over the last 5 decades. The land surface temperature means the radiant temperature of the earth's crust and the amount of pure energy that is balanced on the earth's surface under climatic conditions and depends on the reached the amount of energy, surface emissivity, humidity and atmospheric airflow. Land surface temperature is considered as one of the key variables in climate and environmental studies of the Earth’s surface. It is also one of the basic parameters in the physical features of  the earth's surface at all scales from local to global. Currently, the most important sources of climatic data are meteorological stations, and these stations provide climatic statistics for certain points, while the temperature may alter at different intervals stations and decrease or increase compared to the desired station. Therefore, it is necessary to have a technology that can eliminate the shortcomings of meteorological stations in calculating the temperature at sampling intervals and in impassable places where it is not possible to build a meteorological station. In recent years, new sciences such as remote sensing have provided new ways to monitor the environment and acquire, evaluate, and analyze environmental data, and can provide a wide range of parameters relating to the environment. This technology is considered as an important and increasing source of information for studying climate change that has a direct impact on global warming. Over the past two decades, 18 algorithms have been developed to calculate the land surface temperature. These algorithms fall into four categories: emissivity-dependent models, two-factor models, complex models, and radio-based models. The results of the comparisons between different algorithms shows that different algorithms perform differently in different situations with different geographical climates. Therefore, the present study aims to compare the types of LST calculation algorithms for MODIS sensor images and determine the best algorithm for East Azarbaijan province.

 Convert digital numbers (DN) to spectral radiation. The following equation was used to convert the numerical values to spectral radiation for thermal bands of MODIS sensor images. Planck's equation was used to convert spectural radation to spectral reflection when the radiant power of thermak data of MODIS sensor is considered to be a maximum of one. In order to estimate the surface emissivity, the Normalized Difference Vegetation Index (NDVI) thresholding method is used. The radiant power is divided into three categories to determine the soil characteristics in each pixel and to calculate the emissivity rate and emissivity difference; 0.2>NDVI, it is considered as dry soil and its radiant power is considered to be equal to 0.978. 0.5 NDVI, it is related to pixels with higher vegetation density and its radiant power is considered 0.985. 0.5>NDVI<0.2, it is based on a combination of pixels relating to vegetation and soil and the radiant power for them can be calculated. The vegetation ratio, that its value can be calculated. The value of each scientific finding depends on its accuracy. To compare the obtained results from the algorithms used to calculate the land surface temperature with the recorded temperature in meteorological station.

The results of the present study show that among the 18 algorithms for the land surface temperature estimation for MODIS sensor images, the Sobrino algorithm with RMSE value of 1.79 has the highest accuracy, Cole Casillas and Prata algorithm with RMSE value of 2.85 is in the second position, and also the Salisbury and Sobrino algorithms with RMSE values of 2.39 have the third place for LST calculation among the other algorithms. The Qin algorithm with a RMSE value of 5.28 has the lowest accuracy for LST estimation.

A review of the data obtained from comparing split-window algorithms shows the overall compliance of the calculated temperatures with the topographic conditions of the region, so that almost the lowest temperature values in all algorithms are related to the parts having more height (mountainous) and green cover of the region and also, temperature values have risen in low-lying areas lacking dense vegetation.

Drought as a natural hazard has always affected some parts of the country. Nowadays, it is possible to study drought using remote sensing techniques through its effects on the plants and achieve more accurate and efficient results for drought modelling. This study aims to investigate the relationship between meteorological, hydrological and agricultural drought using drought indices and remote sensing method in Gharehsou watershed. For this purpose, MODIS images (satellite Terra, product MODO9Q1) and rainfall and discharge data of five meteorological and hydrometric stations for 2000 to 2015 time period were used. The results of the comparison of meteorological, hydrological and agricultural drought represent conformity of the three types of drought in the years 2000, 2001, 2004, 2005, 2007, 2009, 2010, 2011, 2012, 2013, 2014 and 2015. Although the SPI and NDVI values were positive in the years 2002, 2003 and 2008, SDI index showed the occurrence of hydrological drought (negative values). In the year 2006 despite the rainfall increase, but this year has been facing the agricultural and hydrological drought. So the results simultaneity of drought there does not exist in all the years. The results of Pearson correlation showed there is a high correlation between mean NDVI and SPI and SDI indices equal to 0.706 and 0.788 respectively at the significance level of 0.01. Generally, the results of SDI and SPI indices largely confirm the results of the NDVI index.

In recent years, dust storms have been one of the most important air pollution crises in Khuzestan province. According to various studies, the countries of Iraq and Syria is reported as the main sources of dust over Khuzestan, it is necessary to monitor the spatial patterns of dust sources in the western region of Iran in different years. In this study, using Aerosol Optical Depth (AOD) from the MODIS, annual trend of dust in Khuzestan province was determined from March 2000 through the end of 2016. Three years 2005, 2009, and 2015 as representative of the main changes in the Khuzestan dust index, the spatial variation of dust in the neighbouring western region of Iran were investigated. According to the results, the dust indexes of the western region of Iran in 2005 were similar to 2015. Nevertheless, in 2015, the activities of dust source points in the eastern and northern part of Iraq (north of the Tharthar Lake) were reduced, and the activity of dust source points in the south of Kuwait and northeastern Arabia increased. The activity of dust source points in areas in the northwest of Iraq and eastern Syria in 2009 has temporarily increased significantly, which has led to an increase in dust index of Khuzestan province that year. Along with these changes, the increase of dust activity in southeast Ahwaz in 2015 compared with the years 2005 and 2009, due to the proximity to the city of Ahwaz, could play a significant role in the dust waves of the recent years.

Using renewable energy, particularly solar radiation is considered today as one of the most important energy sources. Therefore the study of solar energy is very important. Although the sun is a great source of energy, but the energy output at ground level does not act alike, so that the amount of solar radiation in different parts of the world is changing and it is high in low latitudes. Thus, identifying appropriate locations for implementation of solar energy is necessary. The best and most accurate way to measure this parameter is using devices that measure radiation in stations, but because of natural, economic conditions and other conditions we cannot use them On the other hand, these devices have a limited radius of point measurements. So, in recent years using satellite images these problems have been resolved partially. Therefore, in this study using satellite images of MODIS sensor and narrow band to broadband albedo conversion method, the instantaneous short-wave solar radiation in the central Iranian province of Qom, Isfahan, Tehran and Semnan was estimated. To analyze the results of the coefficient of determination (R2) root mean square error (RMSe) and the mean average error (MAE) was used. Consequently the rate of RMSE was about 42 watts per square meter and MAE rate was about 40.75 watts per square meter indicating the high accuracy of the method used to estimate radiation.

The dust haze phenomenon is one of the most environmental challenges in the West and South-West of Iran. To detect the phenomenon of dust haze the images with a repeat in day and night, wide coverage and high spectral bands is needed therefore the images related to MODIS sensor is suitable. The purpose of this study is identifying sources producing dust haze entering in this region in 2011 by using visible bands and thermal bands of MODIS sensor and with the use of Ackermann index and synoptic analysis to identify the movement of dust haze from the origin to Iran By using 500 hectopascal geopotential height synoptic data, sea level pressure and 500 and 1000 hectopascal wind direction maps. Finally, its movement from source to Iran was monitored by using GIS and Spatial Analysis Tools. The results showed that source of the phenomena of dust haze entering the West and South West of the country is from Syria, Iraq and part of Saudi Arabia, which the major origin of dust haze is north of Iraq and the border between Iraq and Syria. Combining the results of detection of dust haze and synoptic maps has optimized found the origin and mode of transmission of dust hazes and provides a more suitable prediction of the movement path of dust haze.