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Assessment of Methane Emission from Rice Paddies and Water Management Using Remote Sensing Technology

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 591

Special Issue Editors


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Guest Editor
Japan Aerospace Exploration Agency, Tsukuba 305-8505, Japan
Interests: image data analysis; SAR data analysis; agricultural research

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Guest Editor
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Toulouse, France
Interests: remote sensing; forest; agriculture; carbon cycle

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Guest Editor
Japan Aerospace Exploration Agency, Tsukuba 305-8505, Japan
Interests: earth observation satellite remote sensing; geoinformatics; environmental monitoring; spatio-temporal image processing; agriculture; public health

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Guest Editor
Institute of Industrial Science, The University of Tokyo, Japan Bw-602, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505, Japan
Interests: remote sensing of disasters and environment; wetland ecosystems; blue/green/black/brown carbon studies; socio-economic impact assessment with night-time light; flood/drought and cropland monitoring; global air pollutant emission inventory study
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Special Issue Information

Dear Colleagues,

Methane is one of the most important greenhouse gases causing climate change, and there are significant methane emissions from paddy fields in Asia and other regions. Recently, the water management method, called Alternate Wetting and Drying (AWD), has been drawing attention due to its potential to save fresh water resources and to reduce methane emissions. Furthermore, the amount that methane emissions are reduced can be used in carbon trading and provide money to farmers, but the Measurement, Reporting and Verification (MRV) systems used to account for and value the methane emission reduction efforts of farmers using AWD methods and to generate carbon credits saleable on the market can be costly and time-consuming.  Therefore, recently, there have been many studies assessing methane emissions from rice paddy fields and water management using remote sensing as a complemental MRV system in a cost- and time-effective way.

Within this context, this Special Issue aims to present articles that focus primarily on assessing methane emissions from rice paddy fields and water management using remote sensing and AI/ML. The Special Issue welcomes articles concerning novel approaches or case studies in the study of remote sensing. Topics can be related, but not limited, to the following:

  • Water level change detection in paddy fields using remote sensing technology;
  • Polarimetric SAR change detection of water level;
  • Hyperspectral or MSS remote sensing change detection of water level and rice crop growth or biomass;
  • Adversarial and/or IOT learning with field survey for change detection of water level or rice crop growth or biomass;
  • Explainable AI for change detection of water level with GHG emission assessment.

Dr. Shinichi Sobue
Dr. Thuy Le Toan
Dr. Kei Oyoshi
Prof. Dr. Wataru Takeuchi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • methane emission
  • water level
  • water management
  • polarimetric SAR change detection
  • remote sensing

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Published Papers (1 paper)

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Research

22 pages, 4380 KiB  
Article
Utilization of Multisensor Satellite Data for Developing Spatial Distribution of Methane Emission on Rice Paddy Field in Subang, West Java
by 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, Asmarhansyah, Shinichi Sobue, Kei Oyoshi, Goh Segami and Pegah Hashemvand Khiabani
Remote Sens. 2025, 17(13), 2154; https://doi.org/10.3390/rs17132154 - 23 Jun 2025
Viewed by 227
Abstract
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 [...] Read more.
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. Full article
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