Abstract. Soil moisture estimation is essential for optimal water and soil resources management. Surface soil moisture is an important variable in the natural water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological and meteorological processes. Soil moisture changes due to the variability of soil characteristics, topography and vegetation in time and place. Soil moisture measurements are performed directly using in situ methods and indirect, by means of transfer functions or remote sensing. Since in-site measurements are usually costly and time-consuming in large areas, we can use methods such as remote sensing to estimate soil moisture at very large scales. The purpose of this study is to estimate soil moisture using surface temperature and vegetation indices for large areas. In this paper, ground temperature was calculated using Landsat-8 thermal band for Mashhad city and was used to estimate the soil moisture content of the study area. The results showed that urban areas had the highest temperature and less humidity at the time of imaging. For this purpose, using the LANDSAT 8 images, the indices were extracted and validated with soil moisture data. In this research, the study area was described and then, using the extracted indices, the estimated model was obtained. The results showed that there is a good correlation between surface soil moisture content with LST and NDVI indices (95%). The results of the verification of the soil moisture estimation model also showed that this model with a mean error of less than 0.001 can predict the surface moisture content, this small amount of error indicates the precision of the proposed model for estimating surface moisture.
Background and aims: Physical and social environments are effective on personality traits. What is in the framework of medical geography, is physical environment that can have positive effect on the human psyche. It can also have negative effects whose investigation is in the field of medical geographers.
Methods:The present study is a descriptive analytical research that discusses the modeling of the vulnerability of mood disorders (depression, bipolar) by using meta-ranking PROMETHEE, and ArcGIS software to study climatic parameters on the spatial distribution of these disorders in Isfahan Province from 2007 to 2011. Results: The prevalence of mood disorders (depression and bipolar disorder) in all the province had a direct correlation with each other. Isfahan, Lenjan, and Shahin Shahr were very high-risk cities and Nain and Semirom were low-risk cities. The prevalence and incidence of these disorders have a direct correlation with temperature, precipitation and humidity. There was no significant correlation between sunshine hours and incidence of mood disorders in Isfahan province. The percentage of incidence of these disorders was almost twice higher in men than women. Conclusion: Climatic parameters can be one of those factors that are effective in incidence and increasing of mood disorders (depression, bipolar). This issue highlights the need for more study and research in this field.
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