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Real Time Estimation of Wheat Chlorophyll Content Retrieve from Landsat 8 Imagery Under Rainfed Condition

Real Time Estimation of Wheat Chlorophyll Content Retrieve from Landsat 8 Imagery Under Rainfed Condition

Syed Shah Mohioudin Gillani1, Muhammad Naveed Tahir1, Adeel Anwar1*, Syed Ijaz Ul Haq2, Muhammad Awais3, Mujahid Iqbal4, Javed Iqbal5, Hina Ahmed Malik3, Syed Muhammad Zaigham Abbas Naqvi6, Raza Ullah7 and Muhammad Abdullah Khan1

1Department of Agronomy, PMAS-Arid Agriculture University, Rawalpindi, 46300, Pakistan; 2School of Agriculture Engineering and Food Sciences, Shandong University of Technology, Zibo, China, 255000; 3Institute of Soil and Environmental Sciences University of Agriculture, Faisalabad, PAkistan; 4Agriculture Research Institute Sariab Quetta, Pakistan; 5Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan; 6Department of Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China; 7Agronomy College, Jilin Agriculture University, Changchun, China.

 
*Correspondence | Adeel Anwar, Department of Agronomy, PMAS-Arid Agriculture University, Rawalpindi, 46300, Pakistan; Email: dradeelanwar@uaar.edu.pk

Figure 1:

Land Cover classification of Chakwal district 2017-18.

Figure 2:

GNDVI map of 2017-18.

Figure 3:

GNDVI graph and ground chlorophyll content.

Figure 4:

NDVI map of 2017-18.

Figure 5:

NDVI graph and ground chlorophyll content.

Figure 6:

CARI map of 2017-18.

Figure 7:

CARI graph and ground chlorophyll content.

Figure 8:

TCARI map of 2017-18.

Figure 9:

TCARI graph and ground chlorophyll content.

Figure 11:

MCARI graph and ground chlorophyll content.

Figure 12:

Probability map of for the prediction wheat chlorophyll content.

Equation 1
Equation 2
Figure 10:

MCARI map of 2017-18.

Sarhad Journal of Agriculture

September

Vol.40, Iss. 3, Pages 680-1101

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