The purpose of this research is to use granulometric analyzes in desertification studies in Gonabad city,  To achieve this goal, after the preparation of geomorphological maps of the region, 14 sediment samples were taken from the erosion-sensitive facies of the region. In the laboratory, a series of sieve with a diameter of 2000, 1000, 500, 250, and 64 micrometers, and a container Gatherings of sediment smaller than 64 μm were screened. The results showed that the highest frequency of particle diameter in sand samples was in the 250-225 micron class. And, given the average particle relationship with their transport intervals, it can be concluded that the distance between the particles was close to a point. Variants of different samples varied from 1.502 to 1.319. The most prevalent plots of ridge and the smallest slopes with low slope were the highest. In surveys in the skidding section, precipitates were mostly positive tilted, indicating the prevalence of fine particles in the region.

In recent years, the technology of crop production has been greatly expanded using satellite data. Today, Landsat 8 and OLI sensor data, with a spatial resolution of 30 meters, allow the discovery of factors that control phenology on a local scale. In this study, the remote sensing indices - NDVI, EVI, Greenness, and Brightness - obtained from the OLI sensor and the GCC index obtained from digital camera images were used to estimate the phenological stages of the rapeseed plant. The Savitzky-Goli filter was used to remove outlier data and to produce smooth curves of time series of plant indices. The results showed that the curves obtained from the indices of NDVI, EVI, GCC show all four stages of remote sensing phenology – green-up, dormancy, maturity, and senescence - well, but the Greenness index did not show the dormancy stage well. The Brightness index curve shows the inverse behavior to other curves. According to Pearsonchr('39')s correlation test, GCC index data are correlated with NDVI and Brightness index data .we used the ratio threshold, rate of change and first derivative methods, to estimate "start of season" and "end of season" and the results showed that the first derivative and ratio threshold methods with an average difference of 18 and 19 days in the "start of the season"  and the rate of change method, with an average difference of 8 days, has the best performance in estimating the “end of the season”. Also, the Brightness index with an average difference of 16 days and the EVI index with an average difference of 7 days have the best performance in estimating "start of season" and "end of season", respectively.