The drought caused a series of effects on many sectors of economy, especially natural resources. During two last decades, Iran has suffered from several severe to extreme agricultural droughts which caused significant decreases in rangeland and agriculture yields. This paper discusses the detection of agricultural drought severity over the rangelands of Markazi Province between 2000 and 2014 using remotely sensed data. Vegetation Temperature Condition Index (VTCI) is a near-real time drought assessment and monitoring approach which have been developed using Terra-MODIS normalized difference vegetation index (NDVI) and Land Surface Temperature (LST) products. VTCI is defined as the ratio of LST differences among pixels with a specific NDVI value in a sufficiently large study area. VTCI has capability of drought stress classification which therein lower VTCI is for drought and higher one for wet conditions. The ground-measured precipitation data from the synoptic stations of Markazi Province are used to validate the VTCI drought monitoring approach (11 stations). For this objective, after the calculation of Standardized Precipitation Index ) SPI) with different periods and VTCI month of July during 2000 to 2014 (warm and cold edges from NDVI and LST scatter gram extracted), linear regression analysis between VTCI (15 maps) and SPI 1, 3,6,9,12,18 months were surveyed and finally, the best map was extracted. Based on the statistical analysis, higher correlations were found for July 2006 (R2 =0.73 for warm edge and R2=0.86 for cold edge) and the best linear correlation was created for SPI-18 month in July. Results showed that within VTCI classified map, moderate and low drought classes constituted most area of studied region. Also, the results showed that VTCI is closely related not only to recent rainfall events but also to past rainfall amount (18 month) indicating that VTCI is a better and near-real time drought monitoring approach for rangelands. Manuscript profile

Soil salinity has concerned people in arid and semi-arid rangelands. One of the most essential cases in relation to information for natural resource managers is preparation of soil salinity maps. Developing such maps, using traditional methods spends a lot of time and costs. Satellite data have broadened and integrated our vision for this purpose. This study was conducted in order to develop a model for providing a salinity map using ETM+ satellite data collected in 2012 and salinity values in Damghan rangelands, Iran. The geometric and atmospheric correction of satellite images was carried out. Necessary processing such as fusion of multispectral bands with panchromatic bands, tasseled cap transformation, the analysis of Principal Components Analysis (PCA), and rationing for composite bands creation were also performed. A total number of 114 surface soil sample points with the depth of 0-15 cm were taken through a random sampling method and their Electrical Conductivity (EC) was measured. Different bands extracted spectral values for each sample and the relation between spectral values (i.e. main bands, Tasseled Cap bands, and soil and vegetation index) with EC values of the samples was investigated. Using PCA analysis, the variables were categorized into four principle components to develop soil EC map according to the highest correlation. Results revealed that there was the highest correlation between PCA1 and variables of blue, green, red bands (R=0.7), Tasseled cap 1, 2, 4 (R=0.68) and indicators SI1, SI2, SI3 (R=0.7), GVI, BI (R=0.68), INT1, INT2, MND, WDVI (R=0.7). In PCA2, the variables of NIR,OSAVI, NDVI, SAVI, VNIR1 and TVI had a significant correlation with PCA2. Finally, using stepwise regression, three models were developed to determine soil salinity maps according to the utilized independent variables. Results showed that Landsat ETM+ images are good tools to estimate salinity maps of arid rangelands. Manuscript profile