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CH4Rice Project: 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 April 2026 | Viewed by 5368

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 (CH4) is a powerful greenhouse gas and methane emissions from rice paddies are estimated to make up approximately 8% of total global anthropogenic methane emissions. Reducing methane emissions could make an effective contribution to rapid climate change mitigation on the decadal timescale. Methane emissions from rice paddies can be reduced by methods such as Alternate Wetting and Drying (AWD).

The CH4Rice project was approved during the Asia Pacific Regional Space Agency Forum (APRSAF)-28 in Hanoi, Vietnam, in November 2022. It has two main aims: contributing to climate change mitigation and promoting sustainable agriculture. The project seeks to achieve these goals by encouraging the adoption of AWD and other water-saving irrigation techniques.

CH4Rice seeks to integrate satellite and in situ data and other field observations to develop methodologies which allow the monitoring of water levels in many rice paddies across wide areas and at scale. It is hoped that these methodologies will ultimately underpin improved water management practice and work towards the realization of reduced methane emissions and the resulting generation of carbon credits that can be applied in national Monitoring, Reporting, and Verification (MRV) systems.

This Special Issue for the CH4Rice project aims to present articles that focus primarily on assessing methane emissions from rice paddy fields and water management using remote sensing and AI/ML. This 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 250 words) can be sent to the Editorial Office for assessment.

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.

Publisher’s notice

As stated above, the central purpose of this Special Issue is to present research from the “CH4Rice project”. Given this purpose, the Guest Editors’ contribution to this Special Issue may be greater than standard Special Issues published by MDPI. Further details on MDPI's Special Issue guidelines can be found here: https://www.mdpi.com/special_issues_guidelines. The Editorial Office and Editor-in-Chief of Remote Sensing has approved this and MDPI’s standard manuscript editorial processing procedure (https://www.mdpi.com/editorial_process) will be applied to all submissions. As per our standard procedure, Guest Editors are excluded from participating in the editorial process for their submission and/or for submissions from persons with whom a potential conflict of interest may exist. More details on MDPI’s Conflict of Interest policy for reviewers and editors can be found here: https://www.mdpi.com/ethics#_bookmark22.

Keywords

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

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Published Papers (3 papers)

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Research

36 pages, 9007 KB  
Article
Automated Machine Learning for High-Resolution Daily and Hourly Methane Emission Mapping for Rice Paddies over South Korea: Integrating MODIS, ERA5-Land, and Soil Data
by Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong and Yangwon Lee
Remote Sens. 2026, 18(5), 753; https://doi.org/10.3390/rs18050753 - 2 Mar 2026
Viewed by 255
Abstract
Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by [...] Read more.
Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine learning (AutoML) framework to compare their performance and process-level interpretability. The daily model demonstrated high predictive accuracy with correlation coefficients (CC) of 0.897 in 5-fold cross-validation and 0.819 in Leave-One-Year-Out (LOYO) cross-validation. Shapley Additive Explanations (SHAP) analysis revealed that while soil temperature is the dominant predictor for daily emissions (explaining ~50% of the variance), variable importance shifts significantly at finer resolutions. The hourly model exhibited a more complex multivariate structure. In this high-resolution context, although Normalized Difference Vegetation Index (NDVI) remains constant diurnally, its importance strengthens as a critical regulator of emission sensitivity, interacting with hourly meteorological fluctuations to capture short-term dynamics. The resulting 500 m daily gridded maps provide a robust foundation for national inventory refinement and spatially targeted mitigation planning. Our findings suggest that while the daily model offers optimal computational efficiency for long-term monitoring, the hourly model is superior for mechanistic understanding and detecting episodic emission events. This multi-resolution framework establishes an empirical basis for selecting appropriate temporal scales in operational greenhouse gas monitoring systems. Full article
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24 pages, 5216 KB  
Article
Characterizing L-Band Backscatter in Inundated and Non-Inundated Rice Paddies for Water Management Monitoring
by Go Segami, Kei Oyoshi, Shinichi Sobue and Wataru Takeuchi
Remote Sens. 2026, 18(2), 370; https://doi.org/10.3390/rs18020370 - 22 Jan 2026
Viewed by 957
Abstract
Methane emissions from rice paddies account for over 11% of global atmospheric CH4, making water management practices such as Alternate Wetting and Drying (AWD) critical for climate change mitigation. Remote sensing offers an objective approach to monitoring AWD implementation and improving [...] Read more.
Methane emissions from rice paddies account for over 11% of global atmospheric CH4, making water management practices such as Alternate Wetting and Drying (AWD) critical for climate change mitigation. Remote sensing offers an objective approach to monitoring AWD implementation and improving greenhouse gas estimation accuracy. This study investigates the backscattering mechanisms of L-band SAR for inundation/non-inundation classification in paddy fields using full-polarimetric ALOS-2 PALSAR-2 data. Field surveys and satellite observations were conducted in Ryugasaki (Ibaraki) and Sekikawa (Niigata), Japan, collecting 1360 ground samples during the 2024 growing season. Freeman–Durden decomposition was applied, and relationships with plant height and water level were analyzed. The results indicate that plant height strongly influences backscatter, with backscattering contributions from the surface decreasing beyond 70 cm, reducing classification accuracy. Random forest models can classify inundated and non-inundated fields with up to 88% accuracy when plant height is below 70 cm. However, when using this method, it is necessary to know the plant height. Volume scattering proved robust to incidence angle and observation direction, suggesting its potential for phenological monitoring. These findings highlight the effectiveness of L-band SAR for water management monitoring and the need for integrating crop height estimation and regional adaptation to enhance classification performance. Full article
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22 pages, 4380 KB  
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
Cited by 2 | Viewed by 2794
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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