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.

The main purpose of this research is maximum temperature prediction using neural network techniques. For this purpose, 70% of the data were allocated for network training and 30% of the data were devoted for testing and validation. The most appropriate neural network structure for predicting Tehran maximum winter temperature is a model with three neurons in the input layer, and a hidden layer with 9 neurons and the use of a hyperbolic tangent function in the hidden layer, that is, 3–9-1 arrangement in which the root mean of square error, correlation coefficient and the mean of absolute error for the training phase and the testing phase are respectively 0.001, 0.997, 0.61 and 0.104, 0.997, 0.311. The determination coefficient and correlation coefficients for both training and testing periods equal 0.99 and 0.99 and the correlation coefficient is significant at the level of 1%.

Background: The aim of this research was to assess the Humidex (HD) trends as a thermal discomfort index by analyzing meteorological data during a 30-year period of summertime in Iran. Methods: For this purpose, data regarding average temperature and relative humidity were collected daily from 40 different synoptic meteorological stations during a 30-year statistical period (1985-2014). The HD index was calculated based on temperature and relative humidity according to an equation introduced by Masterton and Richardson. The Mann-Kendall and Sen’s slope tests were performed to analyze the changing trend of the HD. Results: Based on the findings, in 72% of the meteorological stations, the HD followed an upward trend, so that 40% of them was statistically significant. The changing trends in temperature during summertime throughout the studied years fluctuated greatly but generally, in many regions such as the arid, semi-arid, and humid regions, this trend was mostly incremental. Also, the changing trends in relative humidity in all regions was decremental throughout the years under study. Conclusion: The changing trend of the HD, which is based on temperature and humidity, was incremental in arid and semi-arid regions and decremental in the Mediterranean and humid regions.