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

Abid Nazir,Zulfiqar Ahmad Saqib,Azhar Abbas,Khalid Hussain,Asad Ali,Munawar Shah,Muhammad Shakir,Muhammad Shahid Iqbal,Muhammad Usman Butt,Saleem Ullah

doi:10.3390/agriculture11101026

Abid Nazir, Zulfiqar Ahmad Saqib + Show 8 more

Open Access

https://doi.org/10.3390/agriculture11101026

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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 R2 = 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.

Highlights

IntroductionThe rapid increase in the world population exerts pressure on the agriculture sector and threatening the food security of the world [1]
This study aims to accurately quantify rice yield and identify the critical growth stages that influence the rice yield production
The selected critical growth stages were common in all four types of vegetation indices (i.e., NDVI, Enhanced Vegetation Index (EVI), SAVI, and REP) used

Summary

Introduction

The rapid increase in the world population exerts pressure on the agriculture sector and threatening the food security of the world [1]. Rice is one of the prime sources of food with high nutritive value (i.e., containing carbohydrate, vitamins (B, E, thiamine), and minerals (Ca, Mg, Fe). Rice is widely grown, consumed globally (i.e., daily food of 3.5 billion people worldwide), and accounts for 19% of the dietary energy [2]. 90% of the rice comes from Asia, which is approximately 640 million tons per annum [3,4]. Pakistan ranks 11th at the global rice production list and contributes 8% to the world’s total rice trade [3]. Pakistan produced seven (7) million tons of rice in the year

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