Soil moisture is generally regarded as the limiting factors in rangeland production. Although many studies have been conducted to estimate soil moisture in semiarid areas, there is little information on mountainous rangelands in west of Iran. The present study aims to investigate the soil moisture estimation in rangelands as compared to the other land uses over a mountainous area in central Zagros, Iran using remote sensing. The Surface Energy Balance Algorithm for Land was used to compute actual evapotranspiration (ET) and soil moisture by the means of Landsat8 images of March, April, May, June, July, August and September 2013 for diverse land uses in Malayer, Iran. SEBAL algorithm estimates the ET using net radiation flux, soil heat flux and sensible heat flux. The results showed that there was no significant difference between daily ET computed by SEBAL method and Penn man Monteith. Mean Bias Error (MBE) and Root Mean Square Error (RMSE) for daily and hourly ET were 0.15 and 0.39, respectively. The spatial regression was used to detect the relationship between soil moisture index (SMI) and Temperature dryness vegetation index (TDVI) as dependent variables and daily evapotranspiration (ET24) as independent variable. The results revealed that the correlation between SMI and ET24 was positively significant (0.78 to 0.49) and between TDVI and ET24 was negatively significant (-0.74 to -0.46) during the period of rangeland vegetation growth in this area (March to June). SMI in rangelands had the strongest correlation as compared to the other land uses. Thus, SEBAL model is a robust tool to calculate the soil moisture in rangelands by the means of remote sensing. پرونده مقاله

Dust events of Iran mainly originate from Iraq, Syria, Saudi Arabia, Kuwait and inland territories which are influenced by droughts and Land Use/Land Cover (LU/LC) degradation in regional scale. The aim of this research was to investigate the impact of Regional Vegetation Cover Degradation (RVCD), particularly Regional Rangeland Cover Degradation (RRCD) on frequency of dusty days in western provinces of Iran (Khorramabad, Ahvaz, Hamadan and Kermanshah) since 2000. Therefore, the LU/LC and RRCD were evaluated with respect to time-series MODIS satellite images and NDVI Index. The trend of RRCD was predicted by Markov chain analysis for 2030, 2060 and 2100. The accuracy analysis of comparing the observed and predicted LU/LC classes to 2000 and 2016 indicated the absolute value of error around 5.49%. The findings showed that probability of changing from water body, high-cover and low-cover classes to non-cover class would probably be 55% and 62% during 2016-2030 and 2030-2060, respectively. Durability of non-cover class was 89% during 2000-2016. Thus, the area of non-cover class increased to 410 km2 in the study region. In general, it could be noted that increasing of RRCD and drought are the main causes of dusty days increasing from 2000 to 2060. پرونده مقاله