Rapid local ionosphere modeling based on Precise Point Positioning over Iran: A case study under 2014 solar maximum

Presently, the ionosphere effect is the main source of the error in the Global Positioning System (GPS) observations. This effect can largely be removed by using the two-frequency measurements, while to obtain the reasonable results in the single-frequency applications, an accurate ionosphere model is required. Since the global ionosphere models do not meet our needs everywhere, the local ionosphere models are developed. In this paper, a rapid local ionosphere model over Iran is presented. For this purpose, the GPS observations obtained from 40 GPS stations of the Iranian Permanent GPS Network (IPGN) and 16 other GPS stations around Iran have been used. The observations have been selected under 2014 solar maximum, from the days 058, 107, 188 and 271 of the year 2014 with different geomagnetic activities. Moreover, ionospheric observables based on the precise point positioning (PPP) have been applied to model the ionosphere. To represent our ionosphere model, the B-spline basis functions have been employed and the variance component estimation (VCE) method has been used to regularize the problem.To show the efficiency our PPP-derived local ionosphere model with respect to the International GNSS Service (IGS) global models, these models are applied on the single point positioning using single-frequency observations and their results are compared with the precise coordinates obtained from the double-differenced solution using dual-frequency observations. The results show that the 95th percentile of horizontal and vertical positioning errors of the single-frequency point positioning are about 3.1 and 13.6?m, respectively, when any ionosphere model are not applied. These values significantly improve when the ionosphere models are applied in the solutions. Applying CODE’s Rapid Global ionosphere map (CORG), improvements of 59% and 81% in horizontal and vertical components are observed. These values for the IGS Global ionosphere map (IGSG) are 70% and 82%, respectively. The best results are obtained from our local ionosphere model, where 84% and 87% improvements in horizontal and vertical components are observed. These results confirm the efficiency of our local ionosphere model over Iran with respect to the global models. As a by-product, the Differential Code Biases (DCBs) of the receivers are also estimated. In this line, we found that the intra-day variations of the receiver DCBs could be significant. Therefore, these variations must be taken into account for the precise ionosphere modeling.     

Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex,south Iran

Spectral transformation methods, including correlation coefficient (CC) and Optimum Index Factor (OIF), band ratio (BR) and principal component analysis (PCA) were applied to ASTER and Landsat TM bands for lithological mapping of Soghan ophiolitic complex in south of Iran. The results indicated that the methods used evidently showed superior outputs for detecting lithological units in ophiolitic complexes. CC and OIF methods were used to establish enhanced Red–Green–Blue (RGB) color combination bands for discriminating lithological units. A specialized band ratio (4/1, 4/5, 4/7 in RGB) was developed using ASTER bands to differentiate lithological units in ophiolitic complexes. The band ratio effectively detected serpentinite dunite as host rock of chromite ore deposits from surrounding lithological units in the study area. Principal component images derived from first three bands of ASTER and Landsat TM produced well results for lithological mapping applications. ASTER bands contain improved spectral characteristics and higher spatial resolution for detecting serpentinite dunite in ophiolitic complexes. The developed approach used in this study offers great potential for lithological mapping using ASTER and Landsat TM bands, which contributes in economic geology for prospecting chromite ore deposits associated with ophiolitic complexes.     

Extraction of hydrothermal alterations from ASTER SWIR data from east Zanjan,northern Iran

The use of satellite images for mineral exploration has been very successful in pointing out the presence of minerals such as smectite and kaolinite which are important in the identification of hydrothermal alterations. Shortwave infrared (SWIR) bands from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the wavelength of ASTER SWIR bands between 1.65 and 2.43 μm has a good potential for mapping a hydrothermal alteration minerals such as alunite, pyrophyllite, kaolinite, illite–muscovite–sericite, and carbonate. In this range, hydroxide minerals which have been produced by hydrothermal alteration exhibit good absorption compared to shorter or longer wavelengths. In this research which aims to remove atmospheric and topographic effects from ASTER SWIR data, the authors used the log-residual method (LRM) with the minimum noise fraction (MNF) transformation to create a pixel purity index (PPI) which was used to extract the most spectrally pure pixels from multispectral images. Spectral analyses of the clay mineralogy of the study area (east Zanjan, in northern Iran) were obtained by matching the unknown spectra of the purest pixels to the U.S. Geological Survey (USGS) mineral library. Three methods, spectral feature fitting (SFF), spectral angle mapping (SAM), and binary encoding (BE) were used to generate a score between 0 and 1, where a value of 1 indicates a perfect match showing the exact mineral type. In this way, it was possible to identify certain mineral classes, including chlorite, carbonate, calcite–dolomite–magnesite, kaolinite–smectite, alunite, and illite. In this research, two main propylitic and phyllic–argillic zones could be separated using their compositions of these minerals. These two alteration zones are important for porphyry copper deposits and gold mineralization in this part of Iran.     

Reliability of satellite-based precipitation products in capturing extreme precipitation indices over Iran

The goal of this study is to assess the performance of four widely-used satellite precipitation products in capturing extreme precipitation indices across Iran over the period 2001–2018; these products include GPM IMERG (Integrated Multi-Satellite Retrievals for Global Precipitation Measurement), TRMM 3B42 (Tropical Rainfall Measuring Mission), CHIRPS (Climate Hazards Center InfraRed Precipitation with Station data), and PERSIANN-CDR (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). For this aim, a national gridded precipitation dataset was developed using a dense network of rain gauges as a reference dataset. The results suggest that the IMERG product outperforms the other three precipitation products in capturing extreme precipitation indices both temporally and spatially. TRMM 3B42 data show promising results in identifying many extreme indices, while the CHIRPS and PERSIANN-CDR products show less performance in accurately generating many of the extreme precipitation indices.