Albedo is one of the key parameter in climatic studies. Albedo climatology investigation can be a tool to recognize environmental changes. The MODIS continuously produces the land surface Albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this research, in order to analyze the climatology Albedo of Iran, firstly, the data produced by the MODIS sensor MCD43A4 product in the range of Iran in the period from 1/1/2001 to 12/30/2021 with a spatial resolution of 500 meters and a daily temporal resolution was taken from the NASA website. After the necessary pre-processing, the long-term average monthly, seasonal and annual Albedo of Iran was calculated. The findings on a monthly scale showed that in the months of Jan, Feb, and Mar which are known as Iran's snow-covered months, Iran's Albedo is maximum and is decreased in the transition months, and then in the warm months of the year (June, July, and Aug) it is increased again due to the dryness of the land and the increase in the land surface temperature. This two-way behavior is also evident in the seasonal scale. These calculations are made in the worst conditions (July) over 98% of the area of Iran and in the best conditions (Jan) on 99.97% of the area of Iran. In other words, in the July, the Albedo time series data was complete for about 98% of Iran's area, and there was a statistical gap in about 2% of Iran's area. In the research of Kefayat Motlagh et al. (2021), the Albedo data gap values of the MODIS sensor have been investigated in different seasons and annually. Results show that the maximum distribution of Albedo in winter and autumn seasons corresponds to the snow-covered heights of Alborz, Zagros and the northwest of the country. But in the spring and especially in the summer, with the increase in air temperature and surface temperature, most of the wetlands dry up. With the drying of the bottom of Jazmurian, Hamon, Shadgan, Maharlo and salt lakes and Urmia, salt flats appear. These salt marshes also show a high Albedo due to their white color. On the coastal of the Caspian Sea, low Albedo is seen due to the decrease in land surface temperature and increase in soil moisture. This part of the research is in harmony with the findings of other studies conducted on the land surface Albedo of Iran (Soltani Akmal, 1397; Kefayat Motlagh, 1400; Karbalaee, 1399). Also, the findings showed that the long-term average Albedo of Iran is 12.5%, which is about half of the average planetary Albedo (24%) (Zhang et al., 2010). This part of the research is also in line with the research of Karbalaee et al. (2021).

The aim of this study is modeling spatiotemporal variations of Albedo. This study was conducted using simultaneous effects of several components, such as wetness of surface layer of soil, cloudiness, topography and vegetation density (NDVI), using MEERA2 model with a resolution of 50 in 50 km during 2000-2010 in Iran. The results of spatial analysis of Albedo values in Iran showed that the highest value is in 44 to 45 degrees of east longitude about 2.8 to 3.3 and the lowest value of Albedo is also in 52 to 53 degrees of east longitude, that is, the eastern slopes of the Zagros Mountains, have been recorded at 1 to 1.5 units. In terms of provincial rank, the largest Albedo is about 0.25 units in Ilam province and the Fars province is ranked next about 0.24 units. The lowest amount of Albedo also in the Gilan provinces and in next Mazandaran province are about 0.19 and 0.18 respectively. In addition, the results of temporal analysis in seasonal scale showed that the highest Albedo in Iran in winter was 0.26 and its lowest amount was recorded in spring with 0.23 units. In general, according to the factors used, it can be said that the western and central parts of the country have a highest Albedo, and the north and northwest regions of the country have a lowest Albedo.

Introduction The main sources of Albedo change are variations in snow cover, variations in soil moisture, droughts, and variations in vegetation phenology, forest fires, and land use/ cover changes directly related to human activities, such as deforestation, irrigation, and urbanization. Forests obtain lower Albedo values than shrubs, dry crops, grasslands, and bare soils. As a result, the conversion of forests to these land cover types leads to increases in surface Albedo. This potentially has local and regional feedback, since an increase in surface Albedo leads to a reduction in net radiation, turbulent heat fluxes, convective clouds, and precipitation, leading to a drier atmosphere Furthermore, black carbon decreases the surface Albedo when deposited on snow and glaciers because it is incorporated in snowflakes, darkening snow and ice surfaces and increasing surface melt. Aerosols like dust transferred into the atmosphere and transported by the wind into the mountains where it settles on snow and glaciers, reducing Albedo and leading to enhanced warming at higher elevations. It is noted that even though precipitation is the main driver of variations in soil moisture, its impact on Albedo is controlled by evaporation, soil type, irradiation, vegetation, and topography. The present paper aims to evaluate the spatiotemporal variations of white sky Albedo in Iran. For this, daily Albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied from 2000 to 2019 with a spatial resolution of 500 × 500 m. MODIS provides black-sky Albedo for direct and white-sky Albedo for isotropic diffuse radiation at local solar noontime. For this, daily white sky Albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied. One of the main applications of the principal component analysis (PCA) is climatic zoning which is a method of determining environmental changes in temporal dimensions. A plethora of studies have been conducted using principal component analysis in the field of climatology but little has been done in relation to the Albedo variation. To the best of the authors’,knowledge, this study uses a technique that has not been applied in scientific texts related to Modis Albedo data. The questions that we will address in this study include: what is the temporal-spatial behavior of white sky Albedo in Iran? How many components explain the variation of white Sky Albedo? What factors will distinguish white sky Albedo in Iran? Materials and methods In this investigation, daily white sky Albedo datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) from onboard Aqua and Terra (MCD43A3v006) were applied for the period of 2000-02-24 to 2019-06-03 (7040 days) with a spatial resolution of 500 × 500 m. Among the various MODIS datasets, white sky Albedo datasets were extracted. The daily white sky Albedo was averaged over the 19-year period for each pixel inside the border of Iran. The size of this array was 7541502 pixel *12month. Long-term monthly and seasonal means were also calculated by the available time series data. In the next step, the PCA method was applied to analyze the spatio-temporal variations of Albedo in Iran. PCA is a method to reduce the number of the data and convert them into several finite components so that these few components explain the largest amount of the variance. This procedure is searching for the variable with the largest amount of the variance in space (PCA was invented in 1901 by Karl Pearson, and it was later developed by Harold Hotelling in the 1930s. In this method, initial variables are converted into n principal components each being a linear combination of the variables. In this way, the first principal component has the largest possible variance, and the components afterward explain a smaller percentage of the variance. Principal component analysis leads to the analysis of space-time array into two time-array and space-array. In this case, it is possible to identify what important spatial patterns the primary data have and at what time periods each of these patterns has been active or inactive. Because the principal components are finite, the temporal and spatial patterns introduced by the first component are more important than the temporal and spatial patterns of subsequent components. Results and discussion The long-term average of Iran's white sky Albedo was calculated,The results showed that the average Albedo of spring, summer, autumn and winter in Iran 14. 99%, 16. 06%, 15. 53%, and 19. 58%, respectively. The evaluated long-term mean white sky Albedo for each season showed that the highest value had occurred in winter. The dramatic increase in this value was placed along the Zagros, Alborz, Sahand, and Sabalan Mountains which exceeds 90 to 100 percent in some places. In the next step, the temporal-spatial variations of white sky Albedo values in Iran were analyzed using principal component analysis, and the results showed that the three main components are able to explain 97. 7% of the data variation. The first component explains more than 73%, of the total changes, the second component more than 20. 8% and finally the third component explains more than 3. 9% of the changes. Conclusion Spatial analysis revealed that the values which are higher than the mean are places in highlands and mountainous regions of Iran, such as the Zagros and Alborz Mountains, Sabalan, Sahand mountains and Zard Kuh-e Bakhtiari, which are associated with snow cover Therefore, the first component was named as snow cover as the maximum variance of Albedo was explained by snow cover. The spatial analysis of the second component revealed that higher values were placed in small areas across Iran including, Hajaligholi desert Gavkhuni wetland, Qom salt lake, Sirjan salt lake parts of Loot desert. In the second component, most of the cell's scores upper of average in Iran corresponded to areas covered with salt. As the maximum variance is explained with salt cover, therefore, it can be named as the salt land. Spatial analysis indicated that in very limited parts of Alborz, Zagros, Alam-Kuh Mountain, Sahand, and Sabalan mountains Kino Mountain values are mostly positive which is related to the glaciers (regions with appropriate conditions to keep the snow cover in most of the year) and is the origin of the seasonal or permanent rivers Therefore, according to the cell scores (upper of average in Iran) in the third component, it was found that these cells corresponded to the, so it was named as the glacier component.

Introduction: Today, the assessment and control of environmental and climate change at the regional and global levels is of particular importance for monitoring the current situation and for predicting future changes. Albedo (or solar reflectivity) plays an important role in the thermal behavior of pavements and other ground surfaces and their resultant impacts on humans and the environment (Li, 2016). Therefore, the study of its temporal and spatial behavior can be a tool for understanding environmental changes. Albedo, as one of the important components in the Earth's Radiation balance, is the ratio of reflected flux density to incident flux density, referenced to some surface. Albedos commonly tend to be broadband ratios, usually referring either to the entire spectrum of solar radiation or just to the visible portion. More precise work requires the use of spectral Albedos, referenced to specific wavelengths. Visible Albedos of natural surfaces range from low values of ∼ 0. 04 for calm, deep water and overhead sun, to > 0. 8 for fresh snow or thick clouds. Many surfaces show an increase in Albedo with increasing solar zenith angle (American Meteorological Society, 2019). Remote sensing is one of the most suitable tools for measuring Albedo. So many researchers have used it and also evaluated its algorithm (Jackson et al., 1987; Schaaf et al., 2002; Wang et al., 2018). Data and methods: In this study, for the Assessment long-term mean value of Albedo Iran from the first band of MCD43A4, the Modis sensor was used in the range of 0. 62-0. 67 microns during the period from 2000/03/20 to 2018/03/20 for 6574 days. The reason for choosing this product is because it has the Bidirectional Reflectance Distribution Function (BRDF). Indeed BRDF determines that when the radiation energy reaches a certain level (an opaque surface), it is reflected in the other direction. OThe data of this sensor is available as separate tiles at 1200 x 1200 km and Iran generally falls into six tiles. Of course, based on the Inpolygon function in MATLAB software, grid points outside of Iran were clipped. Iran was divided into 4 regions based on digital number of pixels. The basis of division is the quart values (first, second, and third quartiles). After zoning, statistical characteristics including the average, minimum, maximum, variance, range of changes and coefficient of skewness and Kurtosis of the cells located in each region were calculated. These statistical characteristics provide an opportunity to compare Albedo in the different regions of Iran. In the end, four seaboard areas in Iran were mapped and its relation with height was calculated. Results and Discussion: Table 1 shows the statistical characteristics of the red band Albedo Iran during the study period. The average, minimum, maximum of the first quartile is 53. 15, 1. 45 and 85. 18 respectively. The coefficient of variation in this class is 21. 76%. The highest coefficient of variation is seen in the first quartile, but this may be due to the number of pixels affected. In general, the coefficient of variation shows that the highest coefficient of variation occurs at very low or high levels of Albedo. A long-term Albedo map was developed for the time period (2000-2018) based on remote sensing data. . . After preparing the aforementioned map based on the thresholds obtained from the quartile method, Iran was divided into four distinct areas in terms of Albedo. The first area covers about 2% of the total area of the country where is most commonly found in the south-east of the country, the Caspian coast and the water areas within the land, such as Lake Urmia. The second zone covers the range of 22-19 percent and is seen in different parts of the country. The third zone is in the range of 26-22% and is further dispersed in the center of the country. The fourth region has a range of 62-26%, which shows the highest Albedo. In this area, snow-covered mountains such as the Alborz and Zagros heights and the zones that have been stained with white evaporation deposits over time are seen. Larger parts of this area, due to permanent snow or evaporation deposits, have Albedo more than the average planet Earth (24%). There is a good relationship between Albedo and height, although this is a completely nonlinear relationship. . . As up to 1200 m altitude, with increasing altitude, Albedo decreased and at an altitude of 1400 to 1200 m varied from 12 to 36% due to different land use. From a height of 1, 400 meters, the strong link between Albedo and the altitude of the sea level in Iran is seen. As Albedo increased with altitude due to lower temperature and also snowfall, it reaches nearly 60% at Altitude 4000 meters. In general, it can be said that this relative relationship between Albedo and altitude of sea level in Iran is due to the complexity of topography and land use. As a result, the relationship is straight above altitudes of 1, 400 meters, and with increasing altitude, the Albedo is elevated, and at altitudes less than 1000 meters indirectly, and with increasing altitude, there is a decrease in Albedo. Conclusions: The MODIS sensor produces Albedo the surface of the earth continuously on a global scale with low spatial resolution and provides free access to the public. In this study, for measuring the average long-term Albedo of Iran, the daily data of Albedo in the region of Iran was extracted from the MODIS website during the period from 2000/03/20 to 2018/03/20 for 6574 days. Then, based on nearly 45 billion cells, the long-term average of Iran's Albedo was calculated. The results showed that Albedo of Iran with an average of 21% is close to the Albedo of the planet average which depends on latitude and topography and land surface conditions in Iran. The relationship between Albedo and altitude from sea level was studied. The results of this section indicate that this relationship is a completely nonlinear relationship. So in the first altitudes up to 1200 meters, the Albedo has a decreasing behavior, and between the altitudes of 1200 to 1400 meters there is a steady trend and from 1400 to higher the Albedo behavior is quite increasing. The increasing behavior of Albedo well illustrates the role of snow cover in the highlands. In general, it can be concluded that this relationship is due to the diversity of topography and the type of the earth's surfaces. For this reason, this relationship is direct in elevations above 1400 meters. As the altitude increases, the Albedo is increased, and at elevations less than 1000 meters the relations indirectly (by increasing the elevation, a decrease in Albedo is observed).

Albedo is determined in new and old method based on SEBAL algorithm. In the new method that has been used since 2004, the extracting Albedo was based on the experimental models. To determine the Albedo of the object to the surface in large-scale and with consideration of the varied topography at the surface, the measurement of object’s Albedo was impossible, thus they should use remote sensing methods. In this research, the surface Albedo was determined by use of ETM+ and MODIS images and tries to compare the obtained results from these two devices in various land uses. To determine the abode, we used the following programs, ERDAS® 9.1 and ArcGIS® 10.1. The result of study shows the average Albedo in agricultural land extracted from ETM+ and MODIS images are 0.186 and 0.344, respectively, that the lowest, between all land use categories the highest Albedo belonged to watercourse land use with the 0.242.  Furthermore, the minimum Albedo in follow agriculture in the ETM+ and MODIS image was 0.088, 0.274 and 0.374, 0.464 relatively. By determining Albedo in different land use, we can determine the difference between net received energy and use it as one of the factors in determining the evapotranspiration with using the METRIC or SEBAL remote sensing algorithm.

Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R =-0. 85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0. 58, indicating good accuracy of the model.

Albedo is one of the parameters needed in environmental and climate studies. Therefore, examining its temporal and spatial behavior can be a tool for understanding environmental changes. The MODIS sensor produces Albedo the surface of the earth continuously on a global scale with low spatial resolution and provides free access to the public. In this study, for measuring the Analysis of Barriers to Albedo Observations in Iran, The first daily data of Albedo MODIS Sensor in the kernel of Iran was downloaded from the MODIS website during the period from 2000/03/20 to 2018/12/31 for 6867 days. After mosaic tiles, based on 48 billion observations, the long term frequency of land surface Albedo Iran was calculated separately for each season. The results showed that the limiting factors of satellite view were different at times and places. Humidity has a limiting role in summer, especially on the coast of Oman. In the winter, especially in the Alborz and the Zagros Mountains, cloudiness is a limiting factor. In addition to the humidity and cloudiness factors, Dust storms are also known to limit Albedo harvest. Surveys of 394 ground stations proved that more than 70 percent of the factors listed were reported when the satellite was unable to measure Albedo.

Development of low-cost small satellites has been at the center of attention in recent years. Concurrent Orbit and Attitude Estimation (COAE) requires fewer sensors onboard and subsequently results in some cost reductions. In this regard, the present paper has focused on addressing the importance of COAE utilizing temperature rate on satellite surfaces. To this end, the thermal model for a low Earth orbiting satellite is introduced first. A three-axis stabilized spacecraft is assumed equipped with small measurement plates that are isolated from each other and from the internal heat sources of the satellite. As the Sun and the Earth are the significant sources of radiation for a near Earth space system, the view factor is the key parameter for observability of the orbital elements, while the Sun radiation is responsible for the attitude observability. The Earth Albedo factor is a major uncertain parameter required for the thermal analysis of low Earth orbiting satellites. This parameter is greatly dependent on the Earth’s local terrain and climatic conditions such as instantaneous cloud coverage. To address the problem of Albedo factor uncertainty, it is estimated simultaneously with the attitude and orbit of the satellite. NASA's CERES project provides satellite-based observations of the Earth’s radiation budget and clouds over almost 18 years. In this paper, CERES data tables for the Earth’s thermal flux and Albedo factor have been used to produce more realistic measurement data. The nonlinear filter of Unscented Kalman Filter (UKF) is also exploited for the state estimation. Lack of sun radiation during the satellite’s eclipse intervals results in the loss of orbit and attitude observability. The performance and viability of the proposed COAE algorithm are verified by Monte Carlo simulations. Moreover, a sensitivity analysis is conducted within a wide range of semi major axes, eccentricities, and inclinations. The results demonstrate the high sensitivity of the algorithm to the orbit altitude and the sun rays direction.

Albedo is one of the important factors affecting the energy balance of lakes. This study investigates the spatiotemporal variation of shortwave Albedo over the entire boundary of Lake Urmia. For this purpose, first, the boundaries of the lake water body, salt flats, and bare lands have been extracted. Secondly, monthly time series of shortwave Albedo have been analyzed using the MODIS Albedo product (MCD43A3). The validation results showed that MODIS data underestimates the lake surface Albedo. Moreover, along with the significant lake level drop from 2008 to 2017 (compared to the previous decade), the surface Albedo of Lake Urmia has extremely increased (up to 150%). This, in turn, can lead to a decrease in surface temperature, evaporation, and thermal storage capacity of the lake and impair its function in regulating the micro-climate of its surrounding region. Despite an increase in the bare land and salt flat areas, Albedo of these surfaces have remained pretty constant and no trend has been observed during the study period. Results of this study indicated the inefficiency of the MODIS Albedo data (MCD43A3 v. 6) in monitoring Lake Urmia Albedo. Findings can also provide insights into the performance improvement of the next version of MODIS Albedo products, particularly over saline lakes.