Monitoring paddy rice cultivation adjustments in Northeast China through time series remote sensing and deep learning
Northeast China is one of China’s most important rice production bases, contributing about one-fifth of the country’s rice production. In recent years, several agricultural policies have been implemented in Northeast China to adjust crop structures, driven by economic and ecological benefits. Timely...
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| Format: | Article |
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Elsevier
2025-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003863 |
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| author | Shihao Wang Chong Huang Lingxiao Huang Xinliang Xu Huading Shi Qingbao Gu Qiang Xue Shiai Liu Chenchen Zhang |
| author_facet | Shihao Wang Chong Huang Lingxiao Huang Xinliang Xu Huading Shi Qingbao Gu Qiang Xue Shiai Liu Chenchen Zhang |
| author_sort | Shihao Wang |
| collection | DOAJ |
| description | Northeast China is one of China’s most important rice production bases, contributing about one-fifth of the country’s rice production. In recent years, several agricultural policies have been implemented in Northeast China to adjust crop structures, driven by economic and ecological benefits. Timely monitoring of the changed pattern of rice cultivation is a prerequisite for policy assessment. Current paddy rice mapping methods are experiencing uncertainties due to confusion with wetlands and are highly parameter-dependent. To these, in this study, we developed a paddy rice mapping framework that integrates automatically generated training samples, time series features from key cultivation stages, and a deep learning model to improve paddy rice identification accuracy in Northeast China, which is a typical rice-wetland coexisting area. We produced 10 m paddy rice maps for 2019–2023 in Northeast China and examined their changes under agricultural policy implementation. The resultant paddy rice maps have high accuracies, with overall accuracies >0.97, producer’s accuracies >0.94, user’s accuracies ≥0.93, and F1 scores of ≥0.95, respectively. Our proposed mapping method effectively identified small patches of paddy rice and reduced confusion with wetlands. Paddy rice areas in Northeast China estimated in this study continued to decrease annually from 71.1 × 103 km2 in 2019 to 56.4 × 103 km2 in 2023. Hot spots of rice conversion to other crops were found in the Sanjiang Plain, mainly due to the Soybean Revitalization Plan. The rice cultivation expansion was mainly found in the Songnen Plain, resulting from policies on Rehabilitation and Utilization of Saline Soils. Observed changes in paddy rice plantation emphasize the importance and necessity of timely and continuous crop cultivation monitoring under the influence of agricultural policy adjustment. |
| format | Article |
| id | doaj-art-aca7997a423e49348a809f64de42ed07 |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-aca7997a423e49348a809f64de42ed072025-08-20T02:57:35ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-08-0114210473910.1016/j.jag.2025.104739Monitoring paddy rice cultivation adjustments in Northeast China through time series remote sensing and deep learningShihao Wang0Chong Huang1Lingxiao Huang2Xinliang Xu3Huading Shi4Qingbao Gu5Qiang Xue6Shiai Liu7Chenchen Zhang8State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Corresponding authors.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaTechnical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, ChinaState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, ChinaGansu Province Geological Disaster Control Center, Lanzhou 730000, ChinaDongying Agriculture and Rural Bureau, Dongying 257091, ChinaSchool of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA; Corresponding authors.Northeast China is one of China’s most important rice production bases, contributing about one-fifth of the country’s rice production. In recent years, several agricultural policies have been implemented in Northeast China to adjust crop structures, driven by economic and ecological benefits. Timely monitoring of the changed pattern of rice cultivation is a prerequisite for policy assessment. Current paddy rice mapping methods are experiencing uncertainties due to confusion with wetlands and are highly parameter-dependent. To these, in this study, we developed a paddy rice mapping framework that integrates automatically generated training samples, time series features from key cultivation stages, and a deep learning model to improve paddy rice identification accuracy in Northeast China, which is a typical rice-wetland coexisting area. We produced 10 m paddy rice maps for 2019–2023 in Northeast China and examined their changes under agricultural policy implementation. The resultant paddy rice maps have high accuracies, with overall accuracies >0.97, producer’s accuracies >0.94, user’s accuracies ≥0.93, and F1 scores of ≥0.95, respectively. Our proposed mapping method effectively identified small patches of paddy rice and reduced confusion with wetlands. Paddy rice areas in Northeast China estimated in this study continued to decrease annually from 71.1 × 103 km2 in 2019 to 56.4 × 103 km2 in 2023. Hot spots of rice conversion to other crops were found in the Sanjiang Plain, mainly due to the Soybean Revitalization Plan. The rice cultivation expansion was mainly found in the Songnen Plain, resulting from policies on Rehabilitation and Utilization of Saline Soils. Observed changes in paddy rice plantation emphasize the importance and necessity of timely and continuous crop cultivation monitoring under the influence of agricultural policy adjustment.http://www.sciencedirect.com/science/article/pii/S1569843225003863Paddy riceAgricultural policyDeep learningDeep neural networks |
| spellingShingle | Shihao Wang Chong Huang Lingxiao Huang Xinliang Xu Huading Shi Qingbao Gu Qiang Xue Shiai Liu Chenchen Zhang Monitoring paddy rice cultivation adjustments in Northeast China through time series remote sensing and deep learning International Journal of Applied Earth Observations and Geoinformation Paddy rice Agricultural policy Deep learning Deep neural networks |
| title | Monitoring paddy rice cultivation adjustments in Northeast China through time series remote sensing and deep learning |
| title_full | Monitoring paddy rice cultivation adjustments in Northeast China through time series remote sensing and deep learning |
| title_fullStr | Monitoring paddy rice cultivation adjustments in Northeast China through time series remote sensing and deep learning |
| title_full_unstemmed | Monitoring paddy rice cultivation adjustments in Northeast China through time series remote sensing and deep learning |
| title_short | Monitoring paddy rice cultivation adjustments in Northeast China through time series remote sensing and deep learning |
| title_sort | monitoring paddy rice cultivation adjustments in northeast china through time series remote sensing and deep learning |
| topic | Paddy rice Agricultural policy Deep learning Deep neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225003863 |
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