Rice (Oryza sativa) is a key food commodity in Indonesia and plays a crucial role in ensuring national food security. South Lampung Regency, particularly Natar and Jati Agung Sub-districts, significantly contributes to rice production in the province. However, conventional rice yield estimation methods face limitations in terms of time, labor, and cost. This study aims to analyze the relationship between vegetation indices—Modified Soil Adjusted Vegetation Index (MSAVI), Soil Adjusted Vegetation Index (SAVI), and Rice Growth Vegetation Index (RGVI)—and rice productivity, as well as estimate rice yield using Sentinel-2A imagery in the study area. Data used include 2024 Sentinel-2A imagery and productivity measurements from 53 field observation points. The methods involve image classification, vegetation index calculation (SAVI, MSAVI, RGVI), Pearson correlation analysis, and yield estimation using multiple linear regression. The correlation results show r values of 0.870 (SAVI), 0.852 (MSAVI), and 0.667 (RGVI). The regression model yields an R² of 0.823 and an adjusted R² of 0.812. Yield estimates were classified into three categories: low (3.39–4.76 tons/ha), medium (4.76–6.13 tons/ha), and high (>6.13 tons/ha). This study demonstrates that remote sensing has strong potential to support sustainable agricultural practices and accurate, continuous rice yield estimation.
Sijabat et al. (Mon,) studied this question.
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