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Estimation of Net Rice Production by Remote Sensing and Multi Source Datasets

Estimation of Net Rice Production by Remote Sensing and Multi Source Datasets

Syed Muhammad Hassan Raza1*, Syed Amer Mahmood1, Syeeda Areeba Gillani1, Syed Shehzad Hassan1, Muneeb Aamir1, Muhammad Saifullah1, Mubashar Basheer1, Atif Ahmad1, Saif-ul-Rehman2 and Tariq Ali1 

1RS/GIS group, Space Science Department, Punjab University Lahore, Pakistan; 2Department of Geography Government College University Lahore.

smhn72@gmail.com  

ABSTRACT

Estimation of net crop production before harvest enables agronomists and decision makers to determine the volume of grain precisely. Yield estimation is one of the challenging tasks which is significant to evaluate accurately for farmers. This research was conducted in eastern Punjab Pakistan by incorporating yield/area as reported by Crop Reporting Service Department along with open source satellite datasets. We downloaded three images of each year (2008-2018) from Geological Survey of United States and applied geometric corrections. All the spectral wavelengths were transformed to Top of Atmosphere reflectance and processed the bands in infrared and red wavelengths to generate Ratio Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) datasets. The annual NDVI and RVI based rice yields were compared with CRS based yield records by applying linear regression and generated yield equations. Rice area was estimated using satellite datasets of 11-years by applying supervised classification to 15 bands composite. The satellite extracted areas under rice cultivation, were compared with CRS reported areas by applying linear regression and generated the regression equation. The NDVI of rice crop in 2018 was 0.72 and the predicted yield was estimated as 2.05 ton/ha. Satellite derived rice area in 2018 was 689580 ha, which was substituted in the regression equation to predict the CRS based area that was 654966 ha. The net rice production for the year 2019, was predicted as 1.42 m tons. The remote sensing tools, datasets and the methodology is easy to understand and apply throughout the world to estimate the net productions precisely. 

 

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Sarhad Journal of Agriculture

June

Vol. 36, Iss. 2, Pages 374-733

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