Showing 3 results for Rahimikhoob
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.
M. A. Moradi, A. Rahimikhoob,
Volume 16, Issue 62 (Winte - 2013 2013)
Abstract
Reference evapotranspiration (ET0) is a necessary parameter for calculating crop water requirements and irrigation scheduling. In this study, a method was presented as ET0 is estimated with NOAA satellite imagery in the irrigation network. In this method, a pixel from a set of pixels within the irrigation network was chosen with the highest vegetation index, and its surface temperature (Ts) with extraterrestrial radiation parameter (Ra) was used as inputs of the model. The M5 model tree for converting Ta and Ra to ET0 was used as input variables. In this research, Gazvin irrigated area was selected as a case study. A total of 231 images of NOAA satellite related to irrigation season of the study area were used. The results obtained by the M5 model were compared with the Penman–Monteith results, and error values were found within acceptable limits. The coefficient of determination (R2), percentage root mean square error (PRMSE) and the percentage mean bias error (PMBE) were found to be 0.81, 8.5% and 2.5%, respectively, for the testing data set.
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.
