Estimation and Forecasting of Rice Yield Using Phenology-Based Algorithm and Linear Regression Model on Sentinel-II Satellite Data

Abid Nazir, Saleem Ullah, Zulfiqar Ahmad Saqib, Azhar Abbas, Asad Ali, Muhammad Shahid Iqbal, Khalid Hussain, Muhammad Shakir, Munawar Shah and Muhammad Usman Butt
Additional contact information
Abid Nazir: Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan
Saleem Ullah: Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan
Zulfiqar Ahmad Saqib: Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad 38040, Pakistan
Azhar Abbas: Institute of Agriculture and Resource Economics, University of Agriculture, Faisalabad 38040, Pakistan
Asad Ali: Department of Applied Mathematics and Statistics, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan
Muhammad Shahid Iqbal: Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan
Khalid Hussain: Department of Agronomy, Faculty of Agriculture, University of Agriculture, Faisalabad 38040, Pakistan
Muhammad Shakir: Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan
Munawar Shah: Department of Space Science, Institute of Space Technology, P.O. Box 2750, Islamabad 44000, Pakistan
Muhammad Usman Butt: Sustainable Rice Production, Galaxy Rice Mills Pvt Ltd., Gujranwala 52230, Pakistan

Agriculture, 2021, vol. 11, issue 10, 1-14

Abstract: Rice is a primary food for more than three billion people worldwide and cultivated on about 12% of the world’s arable land. However, more than 88% production is observed in Asian countries, including Pakistan. Due to higher population growth and recent climate change scenarios, it is crucial to get timely and accurate rice yield estimates and production forecast of the growing season for governments, planners, and decision makers in formulating policies regarding import/export in the event of shortfall and/or surplus. This study aims to quantify the rice yield at various phenological stages from hyper-temporal satellite-derived-vegetation indices computed from time series Sentinel-II images. Different vegetation indices (viz. NDVI, EVI, SAVI, and REP) were used to predict paddy yield. The predicted yield was validated through RMSE and ME statistical techniques. The integration of PLSR and sequential time-stamped vegetation indices accurately predicted rice yield (i.e., maximum R 2 = 0.84 and minimum RMSE = 0.12 ton ha −1 equal to 3% of the mean rice yield). Moreover, our results also established that optimal time spans for predicting rice yield are late vegetative and reproductive (flowering) stages. The output would be useful for the farmer and decision makers in addressing food security.

Keywords: rice yield; vegetation indices; hyper-temporal data; PLSR (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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