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学者姓名：邱炳文
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Tea trees (Camellia sinensis), a quintessential homestead agroforestry crop cultivated in over 60 countries, hold significant economic and social importance as a vital specialty cash crop. Accurate nationwide crop data is imperative for effective agricultural management and resource regulation. However, many regions grapple with a lack of agroforestry cash crop data, impeding sustainable development and poverty eradication, especially in economically underdeveloped countries. The large-scale mapping of tea plantations faces substantial limitations and challenges due to their sparse distribution compared to field crops, unfamiliar characteristics, and spectral confusion among various land cover types (e.g., forests, orchards, and farmlands). To address these challenges, we developed the Manual management And Phenolics substance-based Tea mapping (MAP-Tea) framework by harnessing Sentinel-1/2 time series images for automated tea plantation mapping. Tea trees, exhibiting higher phenolic content, evergreen characteristics, and multiple shoot sprouting, result in extensive canopy coverage, stable soil exposure, and radar backscatter signal interference from frequent picking activities. We developed three phenology-based indicators focusing on phenolic content, vegetation coverage, and canopy texture leveraging the temporal features of vegetation, pigments, soil, and radar backscattering. Characteristics of biochemical substance content and manual management measures were applied to tea mapping for the first time. The MAP-Tea framework successfully generated China's first updated 10 m resolution tea plantation map in 2022. It achieved an overall accuracy of 94.87% based on 16,712 reference samples, with a kappa coefficient of 0.83 and an F1 score of 85.63%. The tea trees are typically cultivated in mountainous and hilly areas with a relatively low planting density (averaging about 10%). Alpine tea trees exhibited a notably dense concentration and dominance, mainly found in regions with elevations ranging from 700 m to 2000 m and slopes between 2 degrees to 18 degrees. The areas with low altitudes and slopes hold the largest tea plantation area and output. As the slope increased, there was a gradual decline in the dominance of tea areas. The results suggest a good potential for the knowledge-based approaches, combining biochemical substance content and human activities, for national-scale tea plantation mapping in complex environment conditions and challenging landscapes, providing important reference significance for mapping other agroforestry crops. This study contributes significantly to advancing the achievement of the Sustainable Development Goals (SDGs) considering the crucial role that agroforestry crops play in fostering economic growth and alleviating poverty. The first 10m national Tea tree data products in China with good accuracy (ChinaTea10m) are publicly accessed at https://doi.org/10.6084/m9.figshare .25047308.
Keyword ：
Agroforestry crop mapping Agroforestry crop mapping Phenology-based algorithm Phenology-based algorithm Sentinel-1/2 Sentinel-1/2 Special cash crop Special cash crop Tea plantation Tea plantation
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| GB/T 7714 | Peng, Yufeng , Qiu, Bingwen , Tang, Zhenghong et al. Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images [J]. | REMOTE SENSING OF ENVIRONMENT , 2024 , 303 . |
| MLA | Peng, Yufeng et al. "Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images" . | REMOTE SENSING OF ENVIRONMENT 303 (2024) . |
| APA | Peng, Yufeng , Qiu, Bingwen , Tang, Zhenghong , Xu, Weiming , Yang, Peng , Wu, Wenbin et al. Where is tea grown in the world: A robust mapping framework for agroforestry crop with knowledge graph and sentinels images . | REMOTE SENSING OF ENVIRONMENT , 2024 , 303 . |
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The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018-2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R-2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.
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| GB/T 7714 | Qiu, Bingwen , Liu, Baoli , Tang, Zhenghong et al. National-scale 10-m maps of cropland use intensity in China during 2018-2023 [J]. | SCIENTIFIC DATA , 2024 , 11 (1) . |
| MLA | Qiu, Bingwen et al. "National-scale 10-m maps of cropland use intensity in China during 2018-2023" . | SCIENTIFIC DATA 11 . 1 (2024) . |
| APA | Qiu, Bingwen , Liu, Baoli , Tang, Zhenghong , Dong, Jinwei , Xu, Weiming , Liang, Juanzhu et al. National-scale 10-m maps of cropland use intensity in China during 2018-2023 . | SCIENTIFIC DATA , 2024 , 11 (1) . |
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CONTEXT: Long-term historical information on national -scale grain production is critical for ensuring food security but often limited by the lack of geospatial data. OBJECTIVE: This study aims to conduct the first systematic investigation of grain Cropping Patterns (CP) in China over the past two decades, shedding light on the roles of grain expansion and intensification in sustainable agriculture. METHODS: This study proposes a framework to fully characterize grain production patterns considering crop types, cropping intensity and patterns based on spatiotemporal continuous ChinaCP datasets (2005-2020). Four indicators were developed for measuring the Reality to Capability Ratio (RCR) of grain production regarding the total yield and sow area, the cropland extent and cropping intensity. The capability of grain production was derived based on grain cultivation history. RESULTS AND CONCLUSION: There was a huge gap between the reality and capability of grain production in China, which varied with grain crop types and cropping patterns. At national level, a vast majority (96%) of cropland was capable of grain production, and two fifths of cropland quantified for double grain cropping. However, only 46.65% and 24.89% of the capability was implemented for grain or double -grain cropping in 2020. Maize, rice, and wheat was ever cultivated in 76.88%, 57.05%, and 25.18% of national cropland, respectively. Winter wheat plays an important role in stabilizing grain production by double grain cropping, accounting for 7/8 continuously grain -cultivated areas. However, the RCR of double rice was only 7% in 2020. Bridging these gaps could potentially triple grain production, however, achieving this increase poses challenges due to a series of constraints related to cropland fraction, topographic conditions and lack of agricultural labors along with rapid urbanization. This study found that there was a continuous Northeastward movement & countryside shift in grain production. Continuous support for long-term active agricultural systems is crucial to ensure sustainable grain production in China, with a special emphasis on key grain productive regions, considering targeted cropping patterns and regional disparities. SIGNIFICANCE: This study enhances our understanding of grain production systems in China based on long-term cultivation histories. Findings can inform the development of more geographic -targeted policies concerning grain cropping intensifications to ensure food security and environmental sustainability in developing countries. The long term spatiotemporal continuous CPChina datasets during 2005-2020 was are publicly accessed at: https ://doi.org/10.6084/m9.figshare.25106948.
Keyword ：
China China Cropping patterns Cropping patterns Grain security Grain security Non-grain production Non-grain production Spatiotemporal process Spatiotemporal process
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| GB/T 7714 | Qiu, Bingwen , Jian, Zeyu , Yang, Peng et al. Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification [J]. | AGRICULTURAL SYSTEMS , 2024 , 216 . |
| MLA | Qiu, Bingwen et al. "Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification" . | AGRICULTURAL SYSTEMS 216 (2024) . |
| APA | Qiu, Bingwen , Jian, Zeyu , Yang, Peng , Tang, Zhenghong , Zhu, Xiaolin , Duan, Mingjie et al. Unveiling grain production patterns in China (2005-2020) towards targeted sustainable intensification . | AGRICULTURAL SYSTEMS , 2024 , 216 . |
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Accurate and continuous maps of maize distribution are essential for food security and sustainable agricultural development. However, there are no continuous national-scale and fine-resolution maize maps and explicit updated information on the spatiotemporal dynamics of maize for most countries. Maize mapping at the national scale is challenging due to the spectral heterogeneity caused by crop growth conditions, cropping patterns, and inter-annual variations. To this end, this study developed a novel crop index-based algorithm for national-scale maize mapping. Compared to other crops, maize is characterized by large-leaf-dominated canopies and high photosynthetic efficiency. Maize shows significant changes in chlorophyll and anthocyanin content. Therefore, a robust maize index was established by exploring the temporal Variation of the Vegetation-Pigment index (VVP) during the growing period. A simple decision rule was coded on the Google Earth Engine (GEE) platform, which was used for maize mapping based on the Sentinel-2 time series in China and the contiguous United States (US) from 2018 to 2022. The national-scale 10 m annual maize maps for China and the contiguous US were developed and in good agreement with the corresponding agricultural statistics data for many years (R-2 > 0.94) and 9,412 reference points (overall accuracy of 90.09 %). Compared with simply applying the vegetation index, the VVP index took account of spectral heterogeneity caused by variations in crop growth conditions, cropping patterns, and inter-annual, and the omission error of maize was reduced by over 20 %. Moreover, the VVP index can significantly improve the spatial transferability of the Random Forest (RF) classifier. The first 10 m annual maize maps for China revealed that the planted area trend decreased and then increased from 2018 to 2022. The year 2020 was the turning point. The maize planted area consisted of 68 % single maize and 32 % double cropping with maize in 2020, with the northern boundary for double cropping with maize in the Yanshan Mountains. The maize planted area in China consistently decreased by 39,352 km(2) (about 9 %) from 2018 to 2020. This is mainly due to the adjustment of the maize-planted structure in the "Sickle Bend" region of China (the "Sickle Bend" policy). However, the maize planted area gradually recovered from 2020 to 2022, primarily concentrated in regions with ever-planted. This study will provide essential information for cropping structure adjustment and related agricultural policy formulation and contribute to sustainable agricultural development by mapping maize from a national to a global scale.
Keyword ：
Crop mapping Crop mapping Cross -region Cross -region Maize index Maize index National -scale National -scale Spatiotemporal variations Spatiotemporal variations
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| GB/T 7714 | Huang, Yingze , Qiu, Bingwen , Yang, Peng et al. National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2024 , 221 . |
| MLA | Huang, Yingze et al. "National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 221 (2024) . |
| APA | Huang, Yingze , Qiu, Bingwen , Yang, Peng , Wu, Wenbin , Chen, Xuehong , Zhu, Xiaolin et al. National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2024 , 221 . |
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Accurate mapping of winter wheat provides essential information for food security and ecosystem protection. Deep learning approaches have achieved promising crop discrimination performance based on multitemporal satellite imagery. However, due to the high dimensionality of the data, sequential relations, and complex semantic information in time-series imagery, effective methods that can automatically capture temporal -spatial features with high separability and generalizability have received less attention. In this study, we proposed a U-shaped CNN-Transformer hybrid framework based on an attention mechanism, named the U -TemporalSpatial -Transformer network (UTS-Former), for winter wheat mapping using Sentinel-2 imagery. This model includes an "encoder-decoder " structure for multiscale information mining of time series images and a temporalspatial transformer module (TST) for learning comprehensive temporal sequence features and spatial semantic information. The results obtained from two study areas indicated that our UTS-Former achieved the best accuracy, with a mean MCC of 0.928 and an F1 -score of 0.950, and the results of different band combinations also showed better performance than other popular time-series methods. We found that the MCC (MCC/All) of the UTS-Former using only RGB bands decreased by 4.53 %, while it decreased by 13.36 % and 35.02 % for UNet2dLSTM and CNN-BiLSTM, respectively, compared with that of all the band combinations. The comparison demonstrated that the proposed UTS-Former could capture more global temporal -spatial information from winter wheat fields and achieve greater precision in terms of local details than other methods, resulting in highquality mapping. The analysis of attention scores for the available acquisition dates revealed significant contributions of both beginning and ending growth images in winter wheat mapping, which is valuable for making appropriate selections of image dates. These findings suggest that the proposed approach has great potential for accurate, cost-effective, and high-quality winter wheat mapping.
Keyword ：
Deep learning Deep learning Sentinel-2 Sentinel-2 Temporal -spatial fusion Temporal -spatial fusion Time series Time series Wheat mapping Wheat mapping
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| GB/T 7714 | Fan, Lingling , Xia, Lang , Yang, Jing et al. A temporal-spatial deep learning network for winter wheat mapping using time-series Sentinel-2 imagery [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2024 , 214 : 48-64 . |
| MLA | Fan, Lingling et al. "A temporal-spatial deep learning network for winter wheat mapping using time-series Sentinel-2 imagery" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 214 (2024) : 48-64 . |
| APA | Fan, Lingling , Xia, Lang , Yang, Jing , Sun, Xiao , Wu, Shangrong , Qiu, Bingwen et al. A temporal-spatial deep learning network for winter wheat mapping using time-series Sentinel-2 imagery . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2024 , 214 , 48-64 . |
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Upland crop -rice cropping systems (UCR) facilitate sustainable agricultural intensification. Accurate UCR cultivation mapping is needed to ensure food security, sustainable water management, and rural revitalization. However, datasets describing cropping systems are limited in spatial coverage and crop types. Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples, which limits its applications over large regions. We describe a novel algorithm (RRSS) for automatic mapping of upland crop-rice cropping systems using Sentinel -1 Synthetic Aperture Radar (SAR) and Sentinel -2 Multispectral Instrument (MSI) data. One indicator, the VV backscatter range, was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR. The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions. This study developed 10-m UCR maps of a major rice bowl in South China, the Xiang -Gan -Min (XGM) region. The performance of the RRSS algorithm was validated based on 5197 ground -truth reference sites, with an overall accuracy of 91.92%. There were 7348 km 2 areas of UCR, roughly one-half of them located in plains. The UCR was represented mainly by oilseed-UCR and tobacco-UCR, which contributed respectively 69% and 15% of UCR area. UCR patterns accounted for only one -tenth of rice production, which can be tripled by intensification from single rice cropping. Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm, which could be further applied to generate 10-m UCR datasets for application at national or global scales. (c) 2024 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY -NCND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword ：
China China Cropping-pattern mapping Cropping-pattern mapping Paddy rice Paddy rice Sentinel-1/2 Sentinel-1/2 Sustainable intensification Sustainable intensification
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| GB/T 7714 | Qiu, Bingwen , Yu, Linhai , Yang, Peng et al. Mapping upland crop-rice cropping systems for targeted sustainable intensification in South China [J]. | CROP JOURNAL , 2024 , 12 (2) : 614-629 . |
| MLA | Qiu, Bingwen et al. "Mapping upland crop-rice cropping systems for targeted sustainable intensification in South China" . | CROP JOURNAL 12 . 2 (2024) : 614-629 . |
| APA | Qiu, Bingwen , Yu, Linhai , Yang, Peng , Wu, Wenbin , Chen, Jianfeng , Zhu, Xiaolin et al. Mapping upland crop-rice cropping systems for targeted sustainable intensification in South China . | CROP JOURNAL , 2024 , 12 (2) , 614-629 . |
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Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies. Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area. Although various methods, such as index-based methods, curve similarity-based methods and machine learning-based methods, have been developed for winter wheat mapping based on remote sensing, the former two often require satellite data with high temporal resolution, which are unsuitable for Landsat data with sparse time-series. Machine learning is an effective method for crop classification using Landsat data. Yet, applying machine learning for winter wheat mapping in the North China Plain encounters two main issues: 1) the lack of adequate and accurate samples for classifier training; and 2) the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area. To address these two issues, we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data, with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps. Then, we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion, which divided the study area into six subzones with uniform classification features. For each subzone, a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data. Field sample validation confirmed the high accuracy of the produced maps, with an average overall accuracy of 91.1% and an average kappa coefficient of 0.810 across different years. The derived winter wheat area also has a good correlation (R2 = 0.949) with census area at the provincial level. The results underscore the reliability of the produced annual winter wheat maps. Additional experiments demonstrate that our proposed optimal zoning method outperforms other zoning methods, including Köppen climate zoning, wheat planting zoning and non-zoning methods, in enhancing wheat mapping accuracy. It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping. © 2024 The Author(s). Published by Taylor & Francis Group and Science Press on behalf of the International Society for Digital Earth, supported by the International Research Center of Big Data for Sustainable Development Goals.
Keyword ：
Landsat Landsat machine learning machine learning North China Plain North China Plain optimal zoning optimal zoning Winter wheat mapping Winter wheat mapping
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| GB/T 7714 | Liu, Y. , Chen, X. , Chen, J. et al. Long-term (2013–2022) mapping of winter wheat in the North China Plain using Landsat data: classification with optimal zoning strategy [J]. | Big Earth Data , 2024 , 8 (3) : 494-521 . |
| MLA | Liu, Y. et al. "Long-term (2013–2022) mapping of winter wheat in the North China Plain using Landsat data: classification with optimal zoning strategy" . | Big Earth Data 8 . 3 (2024) : 494-521 . |
| APA | Liu, Y. , Chen, X. , Chen, J. , Zang, Y. , Wang, J. , Lu, M. et al. Long-term (2013–2022) mapping of winter wheat in the North China Plain using Landsat data: classification with optimal zoning strategy . | Big Earth Data , 2024 , 8 (3) , 494-521 . |
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Timely and accurate mapping of paddy rice cultivation is needed for maintaining sustainable rice production, ensuring food security, and monitoring water usage. Synthetic Aperture Radar (SAR) remote sensing plays an important role in the continuous monitoring and mapping of rice cultivation in cloudy regions since it is not affected by weather conditions. To date, most SAR imagery-based rice mapping methods rely on prior knowledge (e.g., the planting date) and empirical thresholds for specific regions, which limits their applications in large spatial scales. To tackle this limitation, this study proposed a new SAR-based Paddy Rice Index (SPRI) to quantify the probability of land patches planted paddy rice. SPRI fully uses unique features of paddy rice during the transplanting-vegetative period in the Sentinel-1 VH backscatter time series. With the assistance of cloud-free Sentinel-2 images, SPRI can be calculated for each cropland object with adaptive parameters. Then, SPRI values of cropland objects can be converted to paddy rice maps using the binary-classification threshold. The proposed SPRI method was tested at five sites with diverse climate conditions, landscape complexity and cropping systems. Results show that the SPRI was able to produce an accurate classification map with an overall accuracy of over 88% and an F1 score of over 0.86 at all sites. Compared with the existing SAR-based rice mapping methods, our method performed much better in heterogeneous agricultural areas where rice is mosaiced with other crops. As SPRI does not need any prior knowledge, reference samples and many predefined parameters, it has high flexibility and applicability to support paddy rice mapping in large areas, especially for cloudy regions where optical remote sensing data is often not available.
Keyword ：
Mapping Mapping Paddy rice Paddy rice Rice index Rice index SAR SAR Sentinel-1 Sentinel-1 SPRI SPRI
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| GB/T 7714 | Xu, Shuai , Zhu, Xiaolin , Chen, Jin et al. A robust index to extract paddy fields in cloudy regions from SAR time series [J]. | REMOTE SENSING OF ENVIRONMENT , 2023 , 285 . |
| MLA | Xu, Shuai et al. "A robust index to extract paddy fields in cloudy regions from SAR time series" . | REMOTE SENSING OF ENVIRONMENT 285 (2023) . |
| APA | Xu, Shuai , Zhu, Xiaolin , Chen, Jin , Zhu, Xuelin , Duan, Mingjie , Qiu, Bingwen et al. A robust index to extract paddy fields in cloudy regions from SAR time series . | REMOTE SENSING OF ENVIRONMENT , 2023 , 285 . |
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及时准确地获取水稻种植模式变化对于有效防控“非粮化”和完成“双碳”目标具有重要意义。现有研究多基于固定时间窗口挖掘水稻生长期特征，且多使用的是单一卫星影像数据，难以应用于大范围水稻制图。本文通过Sentinel-1/2数据构建动态窗口提取移栽期光学/雷达特征，利用其耦合关系实现水稻种植模式制图。将该算法应用于湖南和江西两省水稻制图。基于1402个地面参考点位对水稻提取结果进行验证，总体精度达92.80%；在县域尺度上，湖南和江西两省水稻制图面积与农业统计数据也具有高度一致性，R~2达0.85以上。相比于用固定窗口进行水稻特征提取，该方法具有较强的鲁棒性和迁移能力，为实现更大范围作物制图提取提供新的思路和参考依据。2018—2021年江西省水稻制图结果表明，水稻总种植面积减少9.47%，约3460 km~2，水稻种植强度从1.62下降至1.49；在种植模式上，“双改中”趋势明显，双季稻种植面积锐减21.61%，其中约84%改种中稻。
Keyword ：
Google Earth Engine Google Earth Engine Sentinel-1/2 Sentinel-1/2 “V”形特征 “V”形特征 动态窗口 动态窗口 水稻 水稻 物候 物候 移栽期 移栽期 遥感 遥感
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| GB/T 7714 | 甘聪聪 , 邱炳文 , 张建阳 et al. 基于Sentinel-1/2动态耦合移栽期特征的水稻种植模式识别 [J]. | 地球信息科学学报 , 2023 , 25 (01) : 153-162 . |
| MLA | 甘聪聪 et al. "基于Sentinel-1/2动态耦合移栽期特征的水稻种植模式识别" . | 地球信息科学学报 25 . 01 (2023) : 153-162 . |
| APA | 甘聪聪 , 邱炳文 , 张建阳 , 姚铖鑫 , 叶智燕 , 黄姮 et al. 基于Sentinel-1/2动态耦合移栽期特征的水稻种植模式识别 . | 地球信息科学学报 , 2023 , 25 (01) , 153-162 . |
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Accurate and timely spatiotemporal change information of rice planting patterns is significant for effectively preventing "no- grain" and achieving carbon peak and carbon neutrality goals. However, it is challenging to establish a rice mapping method at large spatial domain. This study developed a novel paddy rice planting mapping method based on dynamic coupling optical/radar features of transplanting period. The proposed algorithm was applied to paddy rice mapping in Jiangxi and Hunan province. The derived paddy rice planting map was evaluated using 1402 ground reference sites, and it had an overall accuracy of 92.80%. The paddy rice planting area was also highly consistent with the agricultural census data (R2 > 0.85) at the county level. Compared with rice feature extraction using a fixed window, the proposed method has strong robustness and migration ability, and provides a new idea and reference for crop mapping at large spatial domain. The result showed that the paddy rice planting area in Jiangxi province decreased by 3460 km2 (9.47%) from 2018 to 2021. The rice cropping intensity in Jiangxi province had decreased by 0.13 due to the change of double-cropping rice to medium rice. The double- cropping rice planting area decreased by 21.61%, with 84% shifted to single cropping rice. © 2023 Journal of Geo-Information Science. All rights reserved.
Keyword ：
dynamic window dynamic window Google Earth Engine Google Earth Engine phenology phenology remote sensing remote sensing rice rice Sentinel- 1/2 Sentinel- 1/2 transplanting period transplanting period "V" shape feature "V" shape feature
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| GB/T 7714 | Gan, C. , Qiu, B. , Zhang, J. et al. Mapping Paddy Rice Planting Patterns based on Sentinel-1/2 [基于 Sentinel-1/2 动态耦合移栽期特征的水稻种植模式识别] [J]. | Journal of Geo-Information Science , 2023 , 25 (1) : 153-162 . |
| MLA | Gan, C. et al. "Mapping Paddy Rice Planting Patterns based on Sentinel-1/2 [基于 Sentinel-1/2 动态耦合移栽期特征的水稻种植模式识别]" . | Journal of Geo-Information Science 25 . 1 (2023) : 153-162 . |
| APA | Gan, C. , Qiu, B. , Zhang, J. , Yao, C. , Ye, Z. , Huang, H. et al. Mapping Paddy Rice Planting Patterns based on Sentinel-1/2 [基于 Sentinel-1/2 动态耦合移栽期特征的水稻种植模式识别] . | Journal of Geo-Information Science , 2023 , 25 (1) , 153-162 . |
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