Topography is a factor controlling the SPATIAL distribution of soil moisture, vegetation, soil salinity, soil texture and so on. It has an important role in changing the characteristics of the soil and hydrological processes. In recent years the topography have been used as an important factor for predicting the properties of soil, climate, geology, etc. According to the importance of topography to extract different information, use of satellite images with high SPATIAL RESOLUTION seems very necessary. Digital elevation models (DEM) have become a widely used tool and product in the last 20 years. They provide a snapshot of the landscape and landscape features while also providing elevation values. They have allowed us to better visualize and interrogate topographic features. In addition to increasing the SPATIAL RESOLUTION, information of the digital elevation model (DEM) that is the most important issues in quantitative geomorphology have increased. In order to increase the SPATIAL RESOLUTION several models have been proposed. Among the models, the attraction model as the newest model has very high accuracy. The sub-pixel attraction models convert the pixel towards sub-pixels based on the fraction values in neighboring pixels that can be attracted only by central pixel. Based on this approach only a maximum of eight neighboring pixels can be selected for the attraction. In the model, other pixels are supposed to be far from the central pixel to have any attraction. In this study by using sub-pixel attraction model, the SPATIAL RESOLUTION of digital elevation models (DEM) was increased (Sub-pixel mapping technology is apromising method of increasing the SPATIAL RESOLUTION of the classification results derived from remote sensing imagery). The design of the algorithm is accomplished by using digital elevation model (DEM) with SPATIAL RESOLUTION of 30 m (Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER)) and 90 m (Shuttle Radar Topography Mission (SRTM)). This study was carried out in the East Mount Sahand, Iran is located at the longitude of N 37° 31َto 37° 30 and latitude of E 45° 55َto 45° 58َ.It is expected that using attraction model increasesthe SPATIAL RESOLUTION of DEM. The attraction model does not need any calibration and training similar to the machine learning algorithms. So, to run the algorithm in the model, the computing time was reduced. In attraction model, scale factors of (2, 3 and 4) with two neighboring methods of touching and quadrant are applied to DEMs using Matlab software and then using RMSE (Root mean square error), determined the best model. The algorithm is evaluated using 2118 sample points that aremeasured by surveyors. As the result of Root mean square error (RMSE), it showed that the SPATIAL attraction model with scale factor of (S=2 and T=2) for digital elevation model (DEM) 30m and digital elevation model (DEM) 90mgives better results compared to scale factors that are greater than 2 and also touching neighborhood method proved to be more accurate than quadrant. In fact, subtracting each pixel tomore than two sub-pixels caused to decrease the accuracy of resulted DEM which makes the value ofroot mean square error (RMSE) to increase and showed that attraction models could not be used for S which is greater than 2. So, according to the results, it is suggested that themodel to be used for increasing SPATIAL RESOLUTION of DEM in the studies catchment. Comparing the digital elevation model (DEM) as inputs in the attraction models determined that digital elevation model (DEM) 30 m (root mean square error<5.54) has better SPATIAL RESOLUTION than digital elevation model (DEM) 90 m (root mean square error=9.13) to find the best model for increasing SPATIAL RESOLUTION. The results showed that by using the method, the SPATIAL RESOLUTION of digital elevation model (DEM) with lower time and cost could be increased. Digital elevation model (DEM) map with high RESOLUTION as a base can be used for finding more information from the Earth surface. For different study such as amount of vegetation, temperature, rainfall and hydrological status the results of sub-pixel attractions on digital elevation model (DEM) can be used and more details of study area could be found. Therefore, it is suggested that the same researches should be done in other areas with different topographic and geographical conditions in order to confirm the results of this study.

Background: A variable RESOLUTION X-ray (VRX) CT scanner provides a great increase in the SPATIAL RESOLUTION. In VRX CT scanners, the SPATIAL RESOLUTION of the system and its field of view (FOV) can be changed according to the object size. One of the main factors that limit the SPATIAL RESOLUTION of VRX CT scanner is the effect of the X-ray focal spot. Materials and Methods: A theoretical study of the effect of X-ray focal spot on the SPATIAL RESOLUTION of VRX CT is presented in this paper. In this study, we used the parameters of an actual VRX CT scanner. By using the relevant equations, the effects of foal spot sizes of 0.6 and 0.1 mm were calculated on SPATIAL RESOLUTION of the system at various opening half angles. Results: Focal spot size of 0.6 mm had no significant effect on SPATIAL RESOLUTION of the system for opening half angles of above 14°. Even focal spot sizes of larger than 0.6 mm could not affect the SPATIAL RESOLUTION of the system. For opening half angles of below 14°, foal spot size of 0.6 mm limited the SPATIAL RESOLUTION of the system to 5.7 cycle/mm and caused great SPATIAL RESOLUTION non-uniformity along the detector length. Conclusion: By focal spot size of 0.1 mm, the SPATIAL RESOLUTION varied as a function of the opening half angle and increased to more than 30 cycle/mm. Additionally, focal spot size of 0.1 mm minimized the SPATIAL RESOLUTION non-uniformity along the detector length.

Improving the SPATIAL RESOLUTION of multispectral images is one of the common pre-processing steps in reaching the maximum potential of these images in remote sensing applications. The presence of images with higher SPATIAL RESOLUTION along with multispectral images allows the process of improving SPATIAL RESOLUTION to be performed through image pan-sharpening methods. The lack of simultaneous panchromatic image sensors in the satellite platforms imposes challenges related to co-registration and asynchronies when using images of different satellite sensors in the process of image pan-sharpening. In such a situation, super-RESOLUTION techniques are considered as alternative approaches to improve SPATIAL RESOLUTION. Using the generative adversarial network (GAN) is one of the effective methods in this field that require the existence of multiple training data. Generally, it is not possible to prepare two satellite images with the same spectral RESOLUTION and different SPATIAL RESOLUTION from a specific region that is required for training the network. Therefore, in this research, an approach with two main steps is designed to improve the SPATIAL RESOLUTION of multispectral images. In the first step, a deep super-RESOLUTION generative adversarial network is used to improve the RESOLUTION of the true color composition of multispectral images. A boosting strategy is exploited to deeply train the GAN network using the resampled images extracted from the Google-Earth. In the second step, the spectral contents are added to the super-RESOLUTION images through the traditional pan-sharpening method. The results demonstrated that the proposed approach improved the SPATIAL RESOLUTION of multispectral images by 32. 85% better than the best comparative method and maintained the spectral content without the need to provide extensive training data.

Digital Elevation Model is one of the most important data for watershed modeling whit hydrological models that it has a significant impact on hydrological processes simulation. Several studies by the Soil and Water Assessment Tool (SWAT) as useful Tool have indicated that the simulation results of this model is very sensitive to the quality of topographic data. The aim of this study is evaluating the SPATIAL RESOLUTION effect of three type's digital elevation model such as ASTER (30 m), SRTM (90 m) and GTOPO30 (1000 m) on the uncertainty of results for flow and total nitrogen simulation. With increasing SPATIAL RESOLUTION of 30 to 1, 000 m physiographic characteristics such as the number HRU reduced but the average slope and the average minimum and maximum elevation increased. Furthermore, the channel drawing is heavily affected by the SPATIAL RESOLUTION of DEM. The Best results of monthly calibration and validation are obtained in Shirgah station for ASTER digital elevation model. R2 and NS coefficient obtained 0. 71 and 0. 68 for during calibration period and 0. 70 and 0. 54 during validation period, respectively. Finally, calculated relative error of SRTM and GTOPO30 simulation results compared with ASTER. The results shows that the model overestimated flow and nitrate by increasing SPATIAL RESOLUTION 30 to 90m and underestimated these two parameters by increasing SPATIAL RESOLUTION 90 to 1000m. The results of this study showed that the accuracy simulation of discharge and total nitrate with the ASTER with the highest SPATIAL RESOLUTION presented the best simulation compared to SRTM and GTOPO30 which this is due to the improvement of important physiographic properties, such as slope length and gradient and thus better simulation of hydrological processes.

The SPATIAL RESOLUTION of satellite data in determining the area covered with snow was examined in this research. For this purpose, the Advanced Very High RESOLUTION Radiometer (AVHRR) of NOAA satellite, with a nominal RESOLUTION of 1,100 m and the TM radiometer of Landsat satellite, with the nominal RESOLUTION of 28.5 m, were chosen and the data provided by them were compared. According to this research, which focused on snowy areas of Karun river basin in Iranian Zagros mountain range, the approximate areas derived from images of snow-covered regions produced by NOAA and Landsat satellites in two different dates, one at the beginning of the snow melt season and another at the end of this season, show a discrepancy by 15% and 17%, respectively. Furthermore, the research shows the SPATIAL overlap of polygons by the two satellites is considerably less than the overlap of the images. However, the overlap area in various polygons is significantly correlated with the total area of the snow-covered region. Additionally, as the SPATIAL RESOLUTION of satellite data reduces the risk of overestimation of snow-covered area increases. Another issue that must be considered is that only if the size of snow fields must at least is equal to some pixels as viewed by the radiometer distinguishing the fields will be possible.

The BASINS model, developed by the United States EPA, is a popular simulation tool for predicting watershed responses, such as runoff, pollution exports, and water quality. It requires large amounts of data to set parameters. Many studies state that model input is a major source of model uncertainty. Thus, improvements to the quality and completeness of the data will improve the certainty of the model. The objective of this study is to discuss the effects of SPATIAL data, including digital elevation models (DEMs) and SPATIAL rainfall records, on predictions of runoff from the BASINS model. The result shows that both DEMs and rainfall data can significantly influence peak flow and runoff volume. Rainfall input has more influence on the curve shape of hydrograph than DEM RESOLUTION. DEM RESOLUTION can have more impact on peak flow predictions than rainfall input. Because the model uncertainties from DEMs and rainfall records influence each other, the prediction error does not always decrease when DEM RESOLUTION increases. The present results show that the BASINS model produces reliable answers in the case area when the grid size is less than 100 m×100 m and the precipitation records from the Bihu Rainfall Station are correct and complete.

Introduction Evapotranspiration is one of the most important parts of the water cycle (Boegh and Soegaard 2004). Precise prediction of actual evapotranspiration () is essential for various fields, such as agriculture, water resource management, irrigation planning and plant growth modeling. Therefore, accurate determination of actual evapotranspiration has always been a major concern of experts in these fields. Due to the limited number of weather stations and the fact that collecting ground information is both time consuming and expensive, remote sensing and satellite imagerycan be a suitable tool in determination of actual evapotranspiration (Brisco et al., 2014). Satellite productions are usually divided into images with low, medium and high SPATIAL RESOLUTION (Rao et al., 2017). Surface energy balance is a method usually used in combination withremotely sensed SPATIAL data for estimation. Information collected from various sources, such as remotely sensedimageries and meteorological data, are used in this method. The present studyinvestigatesSPATIAL distribution on different scales (from field-to regional-) using remotely sensed imagerieswithdifferent SPATIAL and temporal RESOLUTION. TheSurface Energy Balance System (SEBS) is one of the most important methods used for the estimation of in remotely sensed images (Ochege et al., 2019). This model needs thermal maps produced using satellite images. Daily maps produced with RS are usually very large, and their pixelsize is usually so large that it can provide the SPATIAL diversity found in the basins with respect to the errors (Mahour et al., 2017). Material and Methods In order to estimate the actual evapotranspirationin satellite images collected from Zayanderud basin, the effects of Co-Kriging downscaling of surface temperature (LST) were investigated in June 2017 using two different methods. To reach this aim, we first applied a co-kriging downscaling method to a low-power LST product collected from MODIS at 1000 meters. Then based on the results and using the SEBS system, the daily was obtained from images with average SPATIAL RESOLUTION (250 m). In the second method, map produced usinghigh resoultion satellite imageswas downscaled to medium RESOLUTION (250 m). For both methods, 250 m RESOLUTIONMODIS NDVI products were used as co-variables. Then, validation was performed using Landsat-8 imagery, and land surface temperature was extracted from its thermal bands. SEBS algorithm was used to determine in Landsat 8 30-meter RESOLUTIONimagery. Accuracy of measurements wasexamined based on a comparison between down scaledLST and maps (250 meterRESOLUTION). Results and Discussion In the present study, mean LST equals 3/312 K (SD = 1. 74) and average daily equals 12. 5 mm / day (SD = 0. 86). In the downscaling phase, the relationship between LST parameters and and vegetation index(as a co-variable)was investigated. Moreover, to investigate the relation betweenhigh RESOLUTION variables and NDVI, we re-sampled LST and variables from a 1000 mRESOLUTION to 250 mRESOLUTION. In250 mRESOLUTION, there is a negative linear relation (r=-0. 85) between LST and NDVI, but the relation betweenand NDVI is positive (r = 0. 80). Thus, lower LST (> 305k) indicates more vegetation (NDVI >0. 3) inthe region, while higher LST results in lower NDVI or lack of vegetation. As a result, more vegetation can be observed in regions with higher(12 mm/day). Results indicated that the difference between average downscaled-SEBS (12. 56 mm/day) and reference (13. 11 mm/day) is negligible. The RMSE between the reference and the downscaled equaled 1. 66 mm/day (r = 0. 73), and RMSE between the reference LST and the downscaled LST equaled4. 36 K (r = 0. 78). Thus, values obtained from two downscaling methods were similar, but the obtained from downscaled LST showed a higher SPATIAL variation. Therefore, LST has greatly influenced the production of maps using remotely sensing images, and Co-Kriging downscaling has been useful for providing daily maps with intermediate SPATIAL RESOLUTION. Conclusion Evapotranspiration downscaling using the co-kriging method is not significantly different from the SEBS product and the results are similar. The results of-SEBS method isalso acceptable, but the derived from the SEBS algorithm is more variable due to the LST downscaling.

Soil moisture is a valuable parameter for water cycle over lands, controlling water fluxes between the atmosphere, land surface and subsurface, through evaporation and plant transpiration. Measurement and recording of soil moisture observations as in-situ data cannot meet human needs. The availability of global soil moisture maps which is possible using remote sensing sensors, will benefit many application, including precipitation forecasting, flood prediction, drought monitoring and agricultural related applications. These maps must be provided at suitable scale. This is done by SPATIALly downscaling of the soil moisture observations. SPATIAL downscaling of soil moisture measured by satellites should provide two purposes in order to use this parameter in hydrological, meteorological and agricultural applications: 1-Achieving to medium RESOLUTION (approximately 10 km), 2-Sufficient retrieval accuracy. It is evident that fulfilling both purposes using a single sensor is difficult. Therefore, complementary downscaling using a range of observation types has been proposed as an approach to overcome these scale and accuracy issues, by combining the merits from different sensors. In this paper, the soil moisture remote sensing techniques and as well, two basic available downscaling approaches which have the potential to fulfil the stated requirements on RESOLUTION and accuracy are introduced and their pros and cons are investigated.

This article is mainly motivated by the growing needs for highly resolved measurements for wall-bounded turbulent flows and aims to proposes a SPATIAL correction coefficient in order to increase the wall-shear stress sensors accuracy. As it well known for the hot wire anemometry, the fluctuating streamwise velocity measurement attenuation is mainly due to the SPATIAL RESOLUTION and the frequency response of the sensing element. The present work agrees well with this conclusion and expands it to the wall-shear stress fluctuations measurements using electrochemical sensors and suggested a correction method based on the spanwise correlation coefficient to take into account the SPATIAL filtering effects on unresolved wall-shear stress measurements due to too large sensor spanwise size.