Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

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The main purpose of this study is to investigate temporal and spatial distribution of thunderstorm frequency in Iran. In order to do study, observed statistical data of the present weather codes of thunderstorm (17, 29, 91 -99) in 50 synoptic stations around the country were used in a 35 years statistic period . The results of the study showed that the phenomenon occurs mostly in southwest, west and northwest regions of Iran. Thunderstorm occurrence reduces when moving toward east. Maku station in northwest of Iran shows the maximum thunderstorm frequency during the statistical period (mean annual of 31). Jask station in southern Iran (mean annual of 2) has shown minimum thunderstorm occurrence during the statistical period. In the eastern regions of Iran, Torbat Heydarieh station revealed higher frequency than the other stations which was because of high mountains like Qaenat. In terms of temporal distribution, spring showed the highest frequency of the phenomenon. In monthly scale, the maximum frequency of hail downfall happens in April and May. In hourly scale, the maximum occurrence of thunderstorm observed at 12 -18 p.m. for UTC time.

Objective: This study aimed to assess the thermal comfort trend using a metrological parameter based on the Summer Simmer Index (SSI), for different climates of Iran between 1985 and 2014. Methods: This is a Time-series study. The new SSI was calculated using metrological data in a 30 year- period for illustrating the changes to the thermal comfort or discomfort level through summer months in different climates of Iran. Mann- Kendall test and Sen's Slope were used to compare the upward or downward trend of the index during this period. Results: The worst thermal condition was observed in the southern and central regions of the country. A few stations were put in comfort zone (77≤SSI<83°F). The fluctuations of SSI were not considerable during the study period (P<0.001). However, a commonly upward trend was observed, indicating rising temperature. This initial assessment, which can be reported daily along with other atmospheric parameters at meteorological stations, could play a significant role in reducing the heat-related complications among exposed individuals and a basis for taking appropriate protective measures. Conclusion: Based on the results obtained in this study, which has been investigated in Iran for a long period of time, considering intrinsic features of the SSI index, such as ease of calculation and interpretation and also the possibility of calculating the index using daily reported meteorological data, the use of SSI as a screening index of thermal stress in order to adopt preventive policies in outdoor settings in climates of Iran is recommended.