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To mitigate CH4 emissions in paddy fields, Alternate Wetting Drying (AWD) techniques has been employed to manage the water usage in rice cultivation in Subang, West Java. Our preliminary study aims to develop a spatial model of CH4 emissions at AWD sites based on Landsat-8 and Sentinel-2 imageries. We hypothesized that CH4 flux from rice paddies exhibits a linear relationship with vegetation greenness level, as indicated by remote sensing indices. The dependent variable in the regression analysis is the CH4 flux, while the independent variables are the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2). Field measurements and satellite data were collected during the rice growing season, specifically in the vegetative phase between 22 February and 3 April 2024. Our findings indicate a positive correlation between CH4 flux with vegetation indices, notably NDVI (R2 = 0.563) and EVI2 (R2 = 0.614) during the vegetative phase. The estimated CH4 emission from AWD practice was 50–144 mg/day for each pixel (30 × 30 m). Further works should focus on collecting more CH4 measurements from the field and comparing CH4 fluxes between AWD site and continuous flooding site area.

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Spatial Modeling of CH4 Emissions from Rice Paddies Using Optical Remote Sensing Imagery and Field Measurement in Subang, Indonesia

  • 会议论文

  • 发表日期：

    2026年

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  • Khalifah Insan Nur Rahmi,
  • Terry Ayu Adriany,
  • Vidya Nahdhiyatul Fikriyah,
  • Dandy Aditya Novresiandi,
  • Parwati Sofan,
  • Wage Ratna Rohaeni,
  • Rahmat Arief,
  • Iman Muhardiono,
  • Destika Cahyana,
  • Helena Lina,
  • Asmarhansyah Asmarhansyah,
  • Hilda Ayu Pratikasiwi

摘要

To mitigate CH4 emissions in paddy fields, Alternate Wetting Drying (AWD) techniques has been employed to manage the water usage in rice cultivation in Subang, West Java. Our preliminary study aims to develop a spatial model of CH4 emissions at AWD sites based on Landsat-8 and Sentinel-2 imageries. We hypothesized that CH4 flux from rice paddies exhibits a linear relationship with vegetation greenness level, as indicated by remote sensing indices. The dependent variable in the regression analysis is the CH4 flux, while the independent variables are the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2). Field measurements and satellite data were collected during the rice growing season, specifically in the vegetative phase between 22 February and 3 April 2024. Our findings indicate a positive correlation between CH4 flux with vegetation indices, notably NDVI (R2 = 0.563) and EVI2 (R2 = 0.614) during the vegetative phase. The estimated CH4 emission from AWD practice was 50–144 mg/day for each pixel (30 × 30 m). Further works should focus on collecting more CH4 measurements from the field and comparing CH4 fluxes between AWD site and continuous flooding site area.