Showing 4 results for Nazarifar
A. Rahimi Khoob, S.m.r Behbahani , M.h. Nazarifar,
Volume 11, Issue 42 (winter 2008)
Abstract
Air temperature prediction models using satellite data are based on two variables of land surface temperature and vegetation cover index. These variables are obtained by atmospheric corrections in the values for the above data. Water vapor, ozone, and atmospheric aerosol optical depth are required for the atmospheric correction of visible bands. However, no measurements are available for these parameters in most locations of Iran. Using the common methods, land surface temperature can be measured accurately at 2 ° C. Given these limitations, efforts are made in this study to evaluate the accuracy of predicting maximum air temperature when uncorrected atmospheric data from the NOAA Satellite are used by a neural network. For this purpose, various neural network models were constructed from different combinations of data from 4 bands of NOAA satellite and 3 different geographical variables as inputs to the model in order to select the best model. The results showed that the best neural network was the one consisting of 6 neurons as the input layer (including 4 bands of NOAA satellite, day of the year, and altitude) and 19 neurons in the hidden layer. In this structure, about 91.4% of the results were found to be accurate at 3 ° C and the statistical criteria of R2, RMSE, and MBE were found to be 0.62, 1.7 ° C, and -0.01 ° C, respectively.
M. H.nazarifar, R. Momeni,
Volume 15, Issue 56 (sumer 2011)
Abstract
Deficit irrigation is one of the strategies used to obtain products with maximum profits in recent years. In this context, research on determining appropriate levels of deficit irrigation is essential. Since determining the different levels of performance through field experiments is difficult, the use of simulation models is a strategy through which we can examine the water balance data, simulate the growth process, and to study different managerial scenarios. The purpose of this study was validation and evaluation of CropSyst, a plant growth model, to determine suitable cropping patterns in deficit irrigation conditions. Applying three deficit irrigation scenarios in model, with values of 10%, 20% and 30% on six crops, fava bean, bean, wheat, potato, sunflower and rice, we concluded that the applied deficit irrigation of 10% to bean, potato and beans, 20% to sunflower and 30% to wheat had been suitable, and it is better not to apply deficit irrigation in rice. Also, since in final selection, the rate of water productivity is one of the basic criteria in each crop mentioned above, determining net benefit based on drop index (NBPD) per cubic meter showed that the most NBPD is related to bean with 6853 Rials per cubic meters and the lowest amount is related to sunflower with a value equal to 2809 Rials per cubic meters.
A. Rahimikhoob, P. Saberi, S. M. Behbahani, M. H. Nazarifar,
Volume 15, Issue 56 (sumer 2011)
Abstract
In this study, the remote sensing statistical approach was used to determine the global solar radiation from NOAA-AVHRR satellite data in southeast of Tehran. This approach is based on the linear correlation between a satellite derived cloud index and the atmospheric transmission measured by the clearness index on the ground. A multiple linear regression model was also used to convert the five AVHRR data channels and extraterrestrial radiation to global solar radiation. The results of this study showed that multiple linear regression model estimated the solar radiation with an R2 of 0.93 and a root mean square error (RMSE) of 5.8 percent, which was better than the statistical approach.
H. Karimi Avargani, A. Rahimikhoob, M. H. Nazarifar,
Volume 23, Issue 3 (Fall 2019)
Abstract
In recent years, a lot of research has been done on the Aquacrop model, the results show that this model simulates the product performance for deficit irrigation conditions. But this model, like other models, is sensitive to values of independent variables (model inputs). In this research, the sensitivity of the Aquacrop model was analyzed for 4 input parameters of reference evapotranspiration, normalized water productivity, initial canopy cover percentage and maximum canopy cover for barley. Irrigation treatments included full irrigation and two deficit irrigation treatments of 80% and 60%, the experiment was done in 2014-15 growing season in the field of Abourihan College. The values of measured biomass were used as the base values for treatments. The Beven’s method (Beven et al., 1979) was used for sensitivity analysis of Aquacrop model. The results showed that the model is most sensitive to the reference crop evapotranspiration, So the sensitivity coefficient for this parameter for full irrigation treatments, 80% full irrigation and 60% full irrigation were -1.1, -1.2 and -2.3 respectively. The negative sign indicates that if the value of reference evapotranspiration input is exceeded the actual value into the model, Yield performance is simulated less than actual value. In the meantime, the higher the degree of deficit irrigation, the greater the sensitivity of the model.
