USE OF CANOPY REFLECTANCE AT DIFFERENT GROWTH STAGE FOR ESTIMATION WHEAT YIELD

S.K. PYASI¹*, R. BAGHEL², A. MISHRA³, R. SHARMA^4
1College of Agricultural Engineering, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Adhartal, Jabalpur, 482004, Madhya Pradesh, India
²College of Agricultural Engineering, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Adhartal, Jabalpur, 482004, Madhya Pradesh, India
³ICAR-Krishi Vigyan Kendra, Narsinghpur, 487551, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Adhartal, Jabalpur, 482004, Madhya Pradesh, India
⁴MPCouncil of Science and Technology, Remote Sensing Application Centre, Bhopal, Bhopal, 462 003, Madhya Pradesh, India
* Corresponding Author : skpyasi@gmail.com

Received : 03-05-2019     Accepted : 12-05-2019     Published : 15-05-2019
Volume : 11     Issue : 9       Pages : 8397 - 8404
Int J Agr Sci 11.9 (2019):8397-8404

Keywords : LAI meter, Chlorophyll meter and Spectroradiometer
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are thankful to Director, Regional National Institute of Hydrology, Bhopal and College of Agricultural Engineering, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Adhartal, Jabalpur, 482004, Madhya Pradesh, India.
Author Contribution : All authors equally contributed

It is difficult to predicting grain yield of wheat for a large area because the relationship may not be stable even if information on surface cover type is used. Remote sensing observations were found successful for reliable and quantitative estimates of canopy biophysical properties. Keeping this in view a study was planned in village Halali of district Raisen. The study area belongs to eastern part of the fertile Vindyanchal Plateau. This study has been done for the data collected during humid subtropical climate with cool, dry winter’s a hot summer and a humid monsoon season. The plant bio physical parameters were taken from LAI meter, Chlorophyll meter and Spectroradiometer. These parameters were taken as input parameters for PROSIAL model. The output of this model was recorded as simulated data. The simulated data & ground data were used to get R2 by linear correlation. Relationships between wave length and spectral response were drawn by relative spectral response (RSR) for 2nm intervals using Lagrange’s interpolation scheme. The empirical regression models were developed for the study area by using in situ field observation and LAI was calculated during growing to harvesting crop season 2015-2016. The spatial resolution of AWiFS (56m) was adequate enough to ensure relatively accurate retrials of LAI of wheat crop at regional scale. The AWiFS has a 5- days revisit period which may cause loss of data due to persistent cloud or fog and to assess. However, the Resoures at-2 increases the possibility to get clear sky data availability. The linear correlation between simulated and ground data during the wheat growing season gave high coefficient of determination (R2= 0.99) in SWIR band.

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