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:
[email protected]
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.
Figure 10:
MCARI map of 2017-18.