Estimation of Rice Yield Considering Heading Stage Using Satellite Imagery and Ground-Based Data in Indonesia

Yuki Sofue, Chiharu Hongo, Naohiro Manago, Gunardi Sigit, Koki Homma and Budi Utoyo

Journal of Agricultural Science, 2024, vol. 14, issue 8, 1

Abstract: Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.

Date: 2024
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