الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
As results population increase water demand had increased the Government had to formulate policies and programs to improve water management for limited water resources. In particular agriculture sector, which uses more than 80 percent of available water. The per capita water resources are expected to DROP from about 922 m3 per/year at1990 to about 337 m3 per/year in 2025. Furthermore improved planning and management procedures for appropriation water use and allocation are main key measures generally prescribed to make the optimum use of available water. Moreover essentially optimal water management is a prerequisite for sustainable development in Egypt.
Remote sensing techniques offer solution to the limitations short comings of conventional methods for estimating crop evapotranspiration by providing real time information on the daily crop water use as influenced by development pattern of the crop, the crop coverage, local atmospheric conditions and field spatial variability. Also remotely sensed data can therefore give a real time mean of instantaneous estimation of energy balance and therefore the crop evapotranspiration, together with the percent of the crop stand. Sensible heat flux methodology using the optical satellite imagery found to be efficient to estimate the crop evapotranspiration as a residual of the latent heat flux. Traditional methods for center pivot evaluation are time and effort consuming; moreover it requires a high experience for field work. Estimation and mapping the collector’s water volume under pivot irrigation systems using remote sensing data is essential for calculating the coefficient uniformity (CU) of water distribution. The study oriented mainly to achieve the following objectives:
1. Calculating the different vegetation and water indices from different remote sensing data.
2. Compare between the traditional and remote sensing methods in evaluating the water distribution efficiency.
3. Estimation of the collector’s water volume for pivot evaluation from landsat 8 satellite using multiple-linear regression (stepwise selection).
4. Testing the most sensitive bands to water deficit and validation of the water indices under the local conditions.
The Research work was undertaken to study estimating and mapping collector’s water volume using Landsat OLI 8 satellite data integrated with Heermann and Hein (1968) modified equation for center pivot evaluation. The study area located in New Salhyia, Ismailia governorate in eastren Nile Delta, Egypt. Landsat OLI 8 image was geometrically and radiometrically corrected to calculate the vegetation and water indices (NDVI, NDWI) in addition to land surface temperature (LST). The Agro-meteorological station data (maximum air temperature – minimum air temperature – relative humidity – wind speed – average sun shine hours – average solar radiation) were used to calculate the reference evapotranspiration ETO According to modified FAO Penmann - Montieth. In addition to collector’s water volume were collected synchronized by remote sensing data to calculate coefficient of uniformity (CU) According to Heermann and Hein (1968).
On the basis of the obtained results it is clearly evident that estimation collector’s water volume distribution using vegetation and water indices and land surface temperature derived from remote-sensing data, which used to calculate coefficient of water distribution uniformity under pivot system is essentially significant either by using multispectral or hyper-spectral data.
Also, the results showed the higher collected collector’s water volume had high reflection from the plant canopy at the near infrared and shrot wave infrared. That is the cause of near infrared is sensitive to leaf structure which changes with the change of water content. The short wave infrared is most sensitive wavelength to leaf water content. Moreover peanut crop had highest reflection overall the visible range (blue, green and red) because of influence of soil reflection with the canopy.
Multi linear regression MLR analysis using stepwise selection was applied to develop collector’s water volume prediction equations for the different crops using NDVI, NDWI and LST. The results showed R2 and adjusted R2 0.93 and 0.88 respectively. Study area or field level verification was applied for estimation equation with correlation 0.93 between the collected collector’s water volume and estimated values.