Utilization of Multisensor Satellite Data for Developing Spatial Distribution of Methane Emission on Rice Paddy Field in Subang, West Java - Khalifah Insan Nur Rahmi; Parwati Sofan; Hilda Ayu Pratikasiwi; Terry Ayu Adriany; Dandy Aditya Novresiandi; Rendi Handika; Rahmat Arief; Helena Lina Susilawati; Wage Ratna Rohaeni; Destika Cahyana; Vidya Nahdhiyatul Fikriyah; Iman Muhardiono; et al. (Remote Sensing, 2025)
Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH4) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH4 emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH4 emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH4 flux rate on the local sites. The results revealed spatial variability in CH4 emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH4 emissions than those under AWD, underscoring the critical role of water management in mitigating CH4 emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH4 emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies.