Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques.

1. Assessment of wheat crop coefficient using remote sensing techniques
WRJAS
Assessment of wheat crop coefficient using remote sensing techniques Mohammed A. El-Shirbeny*1, Abd-Elraouf M. Ali2, Mahmoud A. Badr3, Esam M. Bauomy4 1*,2,3,4 National Authority for Remote Sensing and Space Sciences, Egypt Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques. Keywords: Normalized deference vegetation index (NDVI), landsat8; crop evapotranspiration (ETc), Egypt and arid region. INTRODUCTION
Determination of ETc at farm level has traditionally been made on the basis of a so called two steps approach. The evapotranspiration of a reference standard crop (ETo) is first estimated on the basis of a site meteorological variables. A semi-empirical coefficient (crop coefficient) is then applied to take into account all other crop and environmental factors (magliulo et al., 2003). ETc can be obtained from ETo using a stage- dependent Kc. Kc based estimation of ETc is one of the most commonly used methods for irrigation water management. Kc method of FAO 56 paper can contribute to ETc estimates that are substantially different from actual crop evapotranspiration (Kamble et al., 2013; Vicente et al., 2012). Similarities between the crop coefficient curve and a satellite-derived vegetation index showed potential for modeling a crop coefficient as a function of the vegetation index (Kamble et al., 2013).
Vegetation indices (VIs) were first developed in the 1970s to monitor terrestrial landscapes by satellite sensors and have been highly successful in assessing vegetation condition, foliage, cover, phenology, and processes related to the fraction of photosynthetically active radiation absorbed by a canopy (Huete et al., 2008; Edward et al., 2008). Tasumi and Allen (2007) reported that satellite-based remote sensing is a robust, economic and efficient tool for estimating actual ET and developing Kc curves. This technique can cover hundreds of sampled fields at a time so that large populations of ETo and Kc can be used to develop representative mean values. They used empirical equation for soil adjusted vegetation index (SAVI) to get crop coefficient through the regression analysis.
*Corresponding author: Mohammed A. El-Shirbeny, National Authority for Remote Sensing and Space Sciences (NARSS), 23 Joseph Tito St., El-Nozha El- Gedida, Cairo, Egypt. E-mail: mshirbeny@yahoo.com, Tel.: (+202) 26251267, Fax: (+202) 26225800 World Research Journal of Agricultural Sciences Vol. 1(2), pp. 012-016, September, 2014. © www.premierpublishers.org. ISSN: 2326-7266x
Research Article

2. Assessment of wheat crop coefficient using remote sensing techniques
El-Shirbeny et al. 012 Figure 1. Shows study area location.
Kc = a * SAVI + b (1) Where: SAVI, is soil adjusted vegetation index, a, and b, can be determined by regression analysis. The NDVI transformation is computed as a ratio of measured intensities in the red (R) and near infrared (NIR) spectral bands using the following formula: NDVI = (NIR - R) / (NIR + R) (2) The resulting index value is sensitive to the presence of vegetation on the Earth's land surface and can be used to address issues of vegetation type, amount, and condition. Many satellites have sensors that measure the red and near-infrared spectral bands, and many variations on the NDVI exist. The sensor that supplies one of the most widely used NDVI products is on board the Landsat8 with channels in the red (Band 4) and near infrared (Band 5).
Various studies on NDVI and LAI using remote sensing techniques were done in Egypt for agricultural sustainability purposes (Aboelghar et al., 2010; Aboelghar et al., 2011; El-Shirbeny et al., 2014a; Belal et al., 2014 and Mohamed et al., 2014). Yoder and Waring (1994) found that the sensitivity of NDVI to chlorophyll concentration varied depending on the choice of visible band used in the calculations. The visible band chosen, therefore, significantly changed the correlation between the NDVI and canopy properties. They also found that the NDVI tended to saturate as LAI increased. Satellite maps of vegetation show the density of plant growth. Very low values of NDVI (0.1 and below) correspond to arid areas of rock, sand, or snow. Moderate values represent shrub and grassland (0.2 to 0.3), while high values indicate temperate and tropical rainforests (0.6 to 0.8). Ryua et al. (2012) developed a temporal up-scaling scheme using satellite-derived instantaneous estimates of ET to produce a daily sum ET averaged over an 8 day interval. The main objective of this study is estimation the wheat crop coefficient using remote sensing techniques. MATERIALS AND METHODS Study area El-Salhia project is located at the South Western of Ismaillia city and to the East southern of El-Kassaseen city. It is bounded by 30O 22' 02" and 30O 31' 16" latitudes and 31O 52' 36" and 32O 06' 26" longitudes. Pivot No 1, 8 and 10 were studied (Fig. 1). Climate Conditions
The climate in Egypt is Dry Arid according to Köppen Climate Classification System, where precipitation is less than 50% of potential evapotranspiration, Annual average temperature is over 18°C. In study area, the average rainfall is approximately 20 mm/year. The maximum values of rainfalls are registered in January with average values of 6.9 mm. The average maximum values of

3. Assessment of wheat crop coefficient using remote sensing techniques
World Res. J. Agric. Sci. 013
temperatures reach (34.6°C) in June. January represents
the coldest month (19.0 °C). The minimum temperatures
range between 8.0 °C in January to 21.5 °C in August.
Remote Sensing Data Availability
Landsat8 data (path 176/row 039) collected around 10
a.m. local time with 30 meter ground resolution, during
winter season was used in current study. Landsat8
satellite data were used to calculate NDVI. The data
acquired on Dec. 09th, 2013, Dec. 25th, 2013, Jan. 10th,
2014, Feb. 11th, 2014, Mar. 15th, 2014 and Mar. 31st,
2014.
NDVI and KcSat
The relation between Kc and NDVI is clear. Similarities
between Kc curve and a satellite-derived vegetation
index showed potential for modeling Kc as a function of
the vegetation index. Therefore, the possibility of directly
estimating Kc from satellite reflectance of a crop was
investigated (magliulo et al., 2003). Landsat8 bands 4
and 5 provide R and NIR measurements and therefore
can be used to generate NDVI data with the following
formula:
NDVI = (Band 5 - Band 4) / (Band 5 + Band 4) (3)
Kc used with ETo to estimate ETc. Kc is a dimensionless
number (usually between 0.1 and 1.2) that is multiplied
by the ETo value to calculate (ETc). The resulting ETc
can be used to help an irrigation manager schedule when
irrigation should occur and how much water should be
put back into the soil. The relation between KcSat and
NDVI represented by equation (4) which established by
(El-Shirbeny et al., 2014b).
( 0.2) 0.6
Kc  1.2 NDVI  Sat (4)
Where: 1.2 is the maximum Kc for wheat under Egyptian
conditions; 0.6 is difference between minimum and
maximum NDVI value for vegetation and 0.2 is minimum
NDVI value for vegetation.
KcFAO Calculation
The Kc is defined as the ratio of ETc to ETo. It is affected
by the local climate conditions, crop characteristics,
length of growing season, soil moisture and the time of
planting (Doorenbos and Pruitt, 1977; Allen et al., 1998).
To estimate Kc follow equation will be used:-
Kc = ETc / ETo (5)
The single KcFAO is described in Allen et al. (1998), where
the effect of both crop transpiration and soil evaporation
are integrated into a single crop coefficient. The Kc
incorporates crop characteristics and averaged effects of
evaporation from the soil. The Kc curve for wheat
represents deferent stages of crop growth. The Kc for
initial stage is referred as Kcini. Similarly Kc for mid and
end stages are designated as Kcmid and Kcend
respectively. (Allen et al., 1998) tabulated the values of
Kcini, Kcmid and Kcend for different crops under standard
growing conditions. Kc affects with climate conditions, so
Kcini, Kcmid, and Kcend calibrated to local climate
conditions according to Allen et al., (1998) method.
RESULTS AND DISCUSSION
Normalized Deference Vegetation Index (NDVI)
Menenti (1986) used NDVI to enhance the ability to
classify crops into various condition groups. Satellite data
used to detect vegetation cover changing from stage to
stage, growing season to next, from year to year, and
from decade to decade. These information help us to
understand the phonological stages for every crop or
natural vegetation at large scale. NDVI calculated from
Red and NIR bands in Landsat8 data which acquired on
Dec. 09th, 2013, Dec. 25th, 2013, Jan. 10th, 2014, Feb.
11th, 2014, Mar. 15th, 2014 and Mar. 31st, 2014. The NDVI
values ranged from -1.0 to 1.0, where vegetated areas
typically have values greater than 0.2 and less values
indicate non-vegetated surface features such as water,
barren, ice, snow, or clouds.
NDVI for cultivated areas vary according to crop age,
planting density and chlorophyll activity. It seems like Kc
varying from planting to senescence. NDVI used as input
in Equation (4) to estimate Kc from satellite data. Figure
(2) shows NDVI changing during the growing season.
Figure 2. Shows changing in NDVI values during growing season.
Crop coefficient (Kc)
Kc is an important parameter for irrigation scheduling and

4. Assessment of wheat crop coefficient using remote sensing techniques
El-Shirbeny et al. 014
water allocation. (Kang et al., 2003) Investigated crop coefficients and the ratio of transpiration to evapotranspiration (Tp/ETc) of winter wheat and maize based on lysimeter data for 10 years. They analyzed several relationships, Kc and days after sowing (DAS), Kc and leaf area index (LAI), and Tp/ETc and LAI. (Attarod et al., 2009) calculated Kc using the relation of AET/ETo, where; AET was the measured actual evapotranspiration (mm/d) and the ETo was the FAO reference crop evapotranspiration (mm/d), and the daily average of Kc for the winter season crops was between 1.2 and 0.2. Equation NO (4) used to estimate Kc from Landsat8 data. Figure (3) shows KcSat changing during the growing season. Figure 3. Shows changing in KcSat values during growing season. The Pivot NO 1 cultivated on November 11th 2013 and average of NDVI, KcSat and KcFAO were 0.57, 0.72 and 0.80 respectively While, Pivot NO 8 cultivated on December 1st 2013 and average of NDVI, KcSat and KcFAO were 0.49, 0.59 and 0.71 respectively. The Pivot NO 10 cultivated on November 21th 2013 and average of NDVI, KcSat and KcFAO were 0.55, 0.70 and 0.77 respectively. Figures 4, 5 and 6 show changing in NDVI, KcSat and KcFAO values during growing season for Pivots 1, 8 and 10 respectively. Crop coefficient (Kc) validation Kc depends on stage of canopy height, crop growth, architecture and cover (Allen et al., 1998). The relation between Kc and NDVI is highly correlated. Empirical Kc have been criticized as regards their meaning and use, because their values vary according to the conditions Figure 4. Shows changing in NDVI, KcSat and KcFAO values during growing season for Pivot NO 1. Figure 5. Shows changing in NDVI, KcSat and KcFAO values during growing season for Pivot NO 8.
of both climate and crop stage under which they were derived. (Doorenbos and Pruitt, 1977) in FAO-24 and (Allen et al., 1998) in FAO-56 suggested Kc values for a large number of crops under different climatic conditions which are commonly used in places where the local data is not available. However, there is a need for local calibration of Kc under given climatic conditions (Kashyap and panda, 2001).

5. Assessment of wheat crop coefficient using remote sensing techniques
World Res. J. Agric. Sci. 015
Figure 6. shows changing in NDVI, KcSat and KcFAO values during growing
season for Pivot NO 10.
KcFAO = 0.803*KcSat + 0.2256
R2 = 0.9647
Figure 7. Represents the relation between KcSat and KcFAO.
Table 1. Illustrates KcSat and KcFAO simple model description.
Model
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
Constant 0.226 0.030 7.407 0.000
KcSat 0.803 0.038 0.982 20.903 0.000
a. Dependent Variable: KcFAO
Exploring the regression strength of the relationship
between KcSat and KcFAO, Diagram shows that the model
produced has significant correlation between the KcSat
and KcFAO. Model has R2 = 0.965 with standard error of
0.06% at KcSat (Fig. 7 and Table, 1).
CONCLUSION
Landsat8 data were acquired on Dec. 09th, 2013, Dec.
25th, 2013, Jan. 10th, 2014, Feb. 11th, 2014, Mar. 15th,
2014 and Mar. 31st, 2014. Landsat8 bands 4 and 5 used
to calculate Normalized Deference Vegetation Index
(NDVI). KcSat=2*NDVI-0.2 represented the relation
between crop coefficient (Kc) and NDVI. Linear
relationship between KcFAO and KcSat was established
(KcFAO = 0.803*KcSat + 0.2256) and R2 was 0.96.
ACKNOWLEDGEMENT
I would like to thank NASA for data availability and I
would like to thank 6th of October for Agricultural Projects

6. Assessment of wheat crop coefficient using remote sensing techniques
El-Shirbeny et al. 016
Company, Egypt for their kind support and
encouragement.
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Accepted 01 September, 2014.
Citation: El-Shirbeny MA, Ali AM, Badr MA, Bauomy EM
(2014). Assessment of wheat crop coefficient using
remote sensing techniques. World Research Journal of
Agricultural Sciences 1(2): 012-016.
Copyright: © 2014 El-Shirbeny et al. This is an open-access
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