Forecasting Transplanted Rice Yield at the Farm Scale Using Moderate-Resolution Satellite Imagery and the AquaCrop Model: A Case Study of a Rice Seed Production Community in Thailand

Kulapramote Prathumchai,Nophea Sasaki,Nitin Tripathi,Masahiko Nagai

doi:10.3390/ijgi7020073

Kulapramote Prathumchai, Nophea Sasaki + Show 2 more

Open Access

https://doi.org/10.3390/ijgi7020073

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Abstract

Thailand has recently introduced agricultural policies to promote large-scale rice farming through supporting and integrating small-scale farmers. However, achieving these policies requires agricultural tools that can assist farmers in rice farming planning and management. Crop models, along with remote sensing technologies, can be useful for farmers and field managers in this regard. In this study, we used the AquaCrop model along with moderate-resolution satellite images (30 m) to simulate the rice yield for small-scale farmers. We conducted field surveys on rice characteristics in order to calibrate the crop model parameters. Data on rice crop, leaf area index (LAI), canopy cover (CC) and agricultural practices were used to calibrate the model. In addition, the optimal rice constant value for conversion of CC was investigated. HJ-1A/B satellite images were used to calculate the CC value, which was then used to simulate yield. The validated results were applied to 126 sample pixels within transplanted rice fields, which were extracted from satellite imagery of activated rice plots using equivalent transplanting methods to the study area. The rice yield simulated using the AquaCrop model and assimilated with the results of HJ-1A/B, produced a satisfactory outcome when implemented into the validated rice plots, with RMSE = 0.18 t ha−1 and R2 = 0.88. These results suggest that integration of moderate-resolution satellite imagery and this crop model are useful tools for assisting rice farmers and field managers in their planning and management.

Highlights

Rice farming is one of the most critical activities in the global agriculture sector, producing a staple food for the majority of the world’s increasing population, which is expected to exceed 9 billion by 2050 and will require approximately 60% more food
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Summary

Introduction

Rice farming is one of the most critical activities in the global agriculture sector, producing a staple food for the majority of the world’s increasing population, which is expected to exceed 9 billion by 2050 and will require approximately 60% more food. Accurate and up-to-date assessments of the spatial distribution of rice cultivation areas are a vital requirement for all stakeholders including policy makers, rice farmers and consumers. Assessment with high precision is crucial for water resource management, market price control and handling of humanitarian food crises [1,2,3]. To maintain stable rice production, Thailand has recently introduced community-based policies to create regional agricultural plantations. The concept of these policies is to encourage aggregation of small rice fields (farmer-based) into one large (community-based) field. There are three major requirements of this policy: expert field managers, an action plan and site-specific technology [4,5]

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