A. Rahimi Khoob, S.m.r Behbahani , M.h. Nazarifar,
Volume 11, Issue 42 (1-2008)
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 Modares Sanavi, M Amini Dehagh, M Gholamhoseni, M Panj Tan Dost,
Volume 13, Issue 48 (7-2009)
Abstract
In order to study the effect of air and root-zone temperature on yield, yield components, nodulation and nitrogen fixation of three annual medics, an experiment was conducted in controlled environment (growth chamber) at the Faculty of Agriculture, Tarbiat Modares University in 2006. The experiment was performed as a spilt split plot with the layout of completely randomized design with three replications. Air temperature at three levels including 15/10, 20/15 and 25/20ºC day/night, four levels of root-zone temperatures including 5, 10, 15 and 20ºC and three annual medics (Medicago polymorpha, M. radiata and M. rigidula) were randomized to main plot, sub plot and sub sub plot units, respectively. The results showed that there were significant differences among annual medics for dry matter production, yield components and nitrogen fixation. M. rigidula produced more leaves, stems and root dry matter, leaf and stem to root ratio, leaf number and area and forage yield than other annual medics. Also, three annual medics at 25/20ºC day/night air temperature (the highest one) produced more nodulation dry matter (8.85 mg/pot) and nitrogen fixation (7.7 mg/g dry matter) than other temperatures. Plants at the former temperature produced 8 and 2 times more nodulation and nitrogen fixation than 15/10ºC day/night air temperature (the lowest one), respectively. Low root-zone temperature up to 5ºC had severely negative effect on yield and nitrogen fixation in the three studied annual medics. Interaction among annual medics, air and root-zone temperatures showed that M. rigidula was better than other annual medics for yield, nodulation and nitrogen fixation at 25ºC air temperature and 15ºC root-zone temperature . The result showed that M. rigidula had normal growth and development compared with other annual medics at low root-zone temperatures. Thus, M. rigidula may be a better annual medic for cultivation in cold and moderate regions. Therefore, in the zones where soil temperature is lower than 5ºC during the season, cultivation of annual medics is not successful, but in the zones where soil temperature is greater than 10ºC, annual medics have normal growth and produce average yield due to better nitrogen fixation.
L. Parviz , M. Kholghi, Kh. Valizadeh,
Volume 15, Issue 56 (7-2011)
Abstract
The determination of air temperature is important in the energy balance calculation, hydrology and meteorological studies. In this regard, the limited number of meteorological stations is one of the serious problems for air temperature determination on a large spatial scale. The remote sensing technique by covering large areas and using updated satellite images might be appropriate for estimation of this parameter. In this research, the negative correlation between land surface temperature and vegetation index (NDVI) has been used for air temperature estimation through TVX method in which the inference of air temperature is based on the hypothesis that the temperature of the dense vegetation canopy is close to air temperature. For investigation the performance of TVX method, images of MODIS sensor have been applied for the Sefidrod River basin in the years 1381- 1382-1384. The spilt window technique which was developed by Price has been used for land surface temperature calculation. The mean difference between observed and estimated land surface temperature using Price algorithm was about 6.2Co. This error can affect the air temperature values. Because of using NDVI index in TVX method, this method has the sensitivity to the vegetation density, though in the parts with sparse vegetation, the value of error increases. 4 percent variation of air temperature against the 0.05 increasing of maximum NDVI indicates the high performance of TVX method for air temperature estimation in large areas.
H. Siasar, T. Honar, M. Abdolahipour,
Volume 23, Issue 4 (2-2020)
Abstract
The estimation of reference crop evapotranspiration (ETo) is one the important factors in hydrological studies, irrigation planning, and water resources management. This study attempts to explore the possibility of predicting this key component using three different methods in the Sistan plain: Generalized Linear Models (GLM), Random Forest (RF) and Gradient Boosting Trees (GBT). The maximum and minimum temperature, mean temperature, maximum and minimum humidity, mean humidity, rainfall, sunshine hours, wind speed, and pan evaporation data were applied for years between 2009 to 2018. Using various networks, the ETo as output parameter was estimated for different scenarios including the combination of daily scale meteorological parameters. In order to evaluate the capabilities of different models, results were compared with the ETo calculated by FAO Penman-Monteith as the standard method. Among studied scenarios, M1 covering the maximum number of input parameters (10 parameters) showed the highest accuracy for GBT model, with the lowest RMSE (0.633) and MAE (0.451) and the maximum coefficient of regression (R = 0.993). Air temperature was found as the most sensitive parameters during sensitivity analysis of studied models. It indicated that accuracy and precision of temperature data can improve the results. Application of the GBT model could decrease the time consumed to run the model by 70%. Therefore, the GBT model is recommended for estimation of ETo in the Sistan plain.
