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学者姓名：邱炳文
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Abstract ：
MADS6, JAGGED (JAG), and DROOPING LEAF (DL) are key regulators of floral organ patterns in rice (Oryza sativa); however, how they work together in specifying floral organs remains to be determined. Here, we extensively analyzed the coordination mechanism. Genetic interactions showed that all double/triple mutant combinations of mads6-5 with jag and/or dl-sup7 generated an inflorescence from the spikelet center and lemma-like organs (LLOs) at the periphery, indicating that these genes synergistically promote floral organ specification, inhibit inflorescence initiation, and terminate the floral meristem (FM). Particularly, a fully developed mads6-5 jag spikelet appeared as a large bouquet composed of numerous multifloral complexes (MFC), while the triple mutant was generally similar to mads6-5 jag, except for a longer pedicel and fewer MFCs. Expression analysis revealed that JAG directly inhibits the transcription of MADS6 in stamens but not in pistils, as JAG and DL co-express in pistils and form a JAG-DL complex, indicating that JAG and DL may coordinate the transcription of MADS6 in sexual organs. Protein interactions revealed that MADS6 and JAG bind to 5 spikelet-related YABBY proteins (including DL), forming 10 heterodimers, suggesting that they may promote floral differentiation through various pathways. However, MADS6 and JAG neither bound together nor formed a heterotrimer with any of the 5 YABBY proteins. These findings revealed specific synergistic patterns between MADS6, JAGGED, and YABBY proteins, which may contribute to the unique characteristics of rice spikelets and provide insights into the diversity regulation mechanisms of floral specification in plants. The transcription factors MADS6 and JAGGED synergistically determine rice spikelet characteristics by regulating MADS6 transcription and genetic and physical interaction with YABBY family proteins.
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| GB/T 7714 | Cai, Zhengzheng , Li, Jieqiong , Su, Yuanyuan et al. The MADS6, JAGGED, and YABBY proteins synergistically determine floral organ development in rice [J]. | PLANT PHYSIOLOGY , 2025 , 197 (3) . |
| MLA | Cai, Zhengzheng et al. "The MADS6, JAGGED, and YABBY proteins synergistically determine floral organ development in rice" . | PLANT PHYSIOLOGY 197 . 3 (2025) . |
| APA | Cai, Zhengzheng , Li, Jieqiong , Su, Yuanyuan , Zheng, Lili , Zhang, Jianwei , Zhu, Miaomiao et al. The MADS6, JAGGED, and YABBY proteins synergistically determine floral organ development in rice . | PLANT PHYSIOLOGY , 2025 , 197 (3) . |
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Accurate crop type classification remains challenged by dependence on ground-based samples and the presence of ‘salt-and-pepper’ noise. This study presented a hierarchical parcel-level classification framework for multi-crop mapping, integrating the boundary-field interaction network (BFINet), the Red Edge Maize-Cotton Index (RMCI), and a random forest (RF) classifier. BFINet enables precise delineation of agricultural field boundaries, reducing the influence of non-cropland areas and minimizing pixel-level noise. RMCI is a new spectral index designing for maize and cotton classification. The RF classifier is used to separate cropland into dominant crops and minor crops, and subsequently to classify the minor crops into different crops. Applied to 2023 Sentinel-2 imagery in the Ebinur Lake Basin (ELB), the framework produced the region's first detailed crop type map. BFINet delineated agricultural parcels in ELB with IOU of 82.3 % and OA of 87.8 %. RMCI achieved an overall accuracy (OA) of 98.6 % for maize–cotton separation, outperforming RF classifier (98.4 %). For minor crops, the RF model attained an OA of 92.3 %. Compared to directly using standalone RF approach, The hierarchical framework outperformed the standalone RF classifier in classifying all crop types in the ELB with F1 for cotton (99.04 % vs. 87.28 %), maize (97.44 % vs. 96.22 %), wheat–maize (88.2 % vs. 82.0 %), grape (92.7 % vs. 89.0 %), and zucchini (94.4 % vs.75.6 %). This framework offers a scalable and accurate solution for crop mapping in complex agricultural landscapes of arid regions. © 2025
Keyword ：
BFINet BFINet Crop classification Crop classification Ebinur Lake Basin Ebinur Lake Basin Random forest Random forest Red Edge Maize-Cotton Index Red Edge Maize-Cotton Index
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| GB/T 7714 | Xie, Y. , Zeng, H. , Li, J. et al. Improving parcel level crop classification by integrating a novel red edge maize-cotton mapping index and machine learning: A case study in the Ebinur Lake Basin [J]. | International Journal of Applied Earth Observation and Geoinformation , 2025 , 143 . |
| MLA | Xie, Y. et al. "Improving parcel level crop classification by integrating a novel red edge maize-cotton mapping index and machine learning: A case study in the Ebinur Lake Basin" . | International Journal of Applied Earth Observation and Geoinformation 143 (2025) . |
| APA | Xie, Y. , Zeng, H. , Li, J. , Zhao, H. , Yu, Q. , Qiu, B. et al. Improving parcel level crop classification by integrating a novel red edge maize-cotton mapping index and machine learning: A case study in the Ebinur Lake Basin . | International Journal of Applied Earth Observation and Geoinformation , 2025 , 143 . |
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Context: Wheat, as the world's largest cereal crop, contributes significantly to agricultural intensification through crop rotation systems. Updated knowledge of cropping patterns (CP) describing crop rotations is crucial for the development of sustainable agricultural systems. However, there is a gap in data availability and finer resolution CP maps are not available for most countries, which hampers our knowledge of geographically targeted crop rotation for sustainable management. It is challenging to automatically map CP at large scales due to the lack of ground-truth datasets, the complexity of crop rotation systems, and the limited applicability of existing algorithms. Objective: This paper has three objectives: 1) propose approaches for automatic mapping of wheat cropping patterns; 2) assess its capability through its applications over conterminous China; 3) explore the distribution patterns for wheat of crop rotation systems in China. Methods: This study introduced a novel framework for automatic agricultural mapping by proposing CP indices based on coupled patterns of multi-source imagery and inter-seasonal variations. This study developed the first 10-m wheat Cropping Patterns (ChinaCP-Wheat10m) distribution map over conterminous China by proposing a robust algorithm for mapping Wheat cropping Patterns by fusing Sentinel-1 SAR and Sentinel-2 MSI data (WPSS). Results and conclusion: The ChinaCP-Wheat10m map showed that wheat dominated the north of the Yangtze River and east of the Taihang Mountain, with a distinctive spatial pattern of winter wheat-rice or upland crops divided by the Huaihe River. There was 206,919 km2 of wheat sown area in China in 2020, and over 90 % of national wheat cultivation was implemented by double cropping. More than half of national wheat farming was intensified through rotation by maize (51.39 %), followed by paddy rice (21.12 %) and other upland crops (18.90 %). There was a small proportion of single cropping by spring wheat (6.86 %) and winter wheat (1.73 %). The reliability of the WPSS was validated by 17,627 widely distributed reference sites with an overall accuracy of 92.57 % and good agreement with the agricultural census data (R2 = 0.96). Significance: This study opens a new direction to move from crop type identification to the automatic generation of crop rotation maps at the national scale, which would facilitate the progress of the Sustainable Development Goals (SDGs) to reduce poverty and hunger. The processing codes and wheat CP records produced in China can be downloaded from the following link: https://doi.org/10.6084/m9.figshare.28668173.v1
Keyword ：
Cropping pattern mapping Cropping pattern mapping Google earth engine (GEE) Google earth engine (GEE) National-scale National-scale Sentinel-1/2 Sentinel-1/2 Wheat Wheat
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| GB/T 7714 | Qiu, Bingwen , Li, Zhengrong , Yang, Peng et al. Towards automation of national scale cropping pattern mapping by coupling Sentinel-1/2 data: A 10-m map of crop rotation systems for wheat in China [J]. | AGRICULTURAL SYSTEMS , 2025 , 227 . |
| MLA | Qiu, Bingwen et al. "Towards automation of national scale cropping pattern mapping by coupling Sentinel-1/2 data: A 10-m map of crop rotation systems for wheat in China" . | AGRICULTURAL SYSTEMS 227 (2025) . |
| APA | Qiu, Bingwen , Li, Zhengrong , Yang, Peng , Wu, Wenbin , Chen, Xuehong , Wu, Bingfang et al. Towards automation of national scale cropping pattern mapping by coupling Sentinel-1/2 data: A 10-m map of crop rotation systems for wheat in China . | AGRICULTURAL SYSTEMS , 2025 , 227 . |
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Complex cropping patterns with crop diversity are an underexploited treasure for global food security. However, significant methodological and dataset gaps in fully characterizing cropland cultivated with multiple crops and rotation sequences hinder our ability to understand and promote sustainable agricultural systems. Existing crop mapping models are challenged by the deficiency of ground reference data and the limited transferability capabilities across large spatial domains. This study aimed to fill these gaps by proposing a robust Complex Cropping Pattern Mapping framework (CCPM) capable of national-scale automatic applications using the Sentinel-1 SAR and Sentinel-2 MSI time series datasets. The CCPM framework addresses these challenges by integrating knowledge-based approaches & data-driven algorithms (Dual-driven model) and Phenological Normalization. The CCPM framework was implemented over conterminous China with complex cropping systems dominated by smallholder farms, and the first national-scale 10-m Cropping pattern map with descriptions of cropping intensity and 10 crops in China (ChinaCP-T10) in 2020 was produced. The efficiency of the CCPM framework was validated when evaluated by 18,706 ground-truth reference datasets, with an overall accuracy of 91.47 %. Comparisons with existing crop data products revealed that the ChinaCP-T10 offered more comprehensive and consistent information on diverse cropping patterns. Dominant cropping patterns diversified from single maize in northern China, winter wheat-maize in North China Plain, single oilseeds in Western China, to single rice or double rice in Southern China. The key cropping patterns changed from double-grain cropping, single grain to single cash cropping with increasing altitudes. There were 151,744 km2 planted areas of double grain cropping patterns in China, and multiple cropping accounted for 36.1 % of grain cultivated area nationally. Over 80 % of grain production was mainly implemented at lower altitudes as the Non-Grain Production (NGP) ratio enhanced from 32 % within elevations below 200 m to over 70 % among elevations above 700 m. Consistent datasets on complex cropping patterns are essential, given the significant roles of diversification and crop rotations in sustainable agriculture and the frequently observed inconsistencies in existing crop data products based on thematic mapping.
Keyword ：
Crop diversity Crop diversity Cropping patterns mapping Cropping patterns mapping Dual-driven models Dual-driven models Model generalization Model generalization Sentinel-1/2 Sentinel-1/2
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| GB/T 7714 | Qiu, Bingwen , Wu, Fangzheng , Hu, Xiang et al. A robust framework for mapping complex cropping patterns: The first national-scale 10 m map with 10 crops in China using Sentinel 1/2 images [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2025 , 224 : 361-381 . |
| MLA | Qiu, Bingwen et al. "A robust framework for mapping complex cropping patterns: The first national-scale 10 m map with 10 crops in China using Sentinel 1/2 images" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 224 (2025) : 361-381 . |
| APA | Qiu, Bingwen , Wu, Fangzheng , Hu, Xiang , Yang, Peng , Wu, Wenbin , Chen, Jin et al. A robust framework for mapping complex cropping patterns: The first national-scale 10 m map with 10 crops in China using Sentinel 1/2 images . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2025 , 224 , 361-381 . |
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JAGGED (JAG) and DROOPING LEAF (DL) are two key regulators of floral organ pattern in rice, but how they work together in establishing reproductive organs and terminating FM remains to be determined. Here, we have analyzed the coordination mechanism in detail. While the floral meristem (FM) determinacy is severely compromised in null-allele jag and dl spikelets, jag dl exhibits a complete loss of FM determinacy, with a large bouquet composed of lemma-like organs sprouting from the center of spikelets replacing the stamen and pistil, indicating that they synergistically determine sexual organ identity and FM termination. Protein-protein interactions revealed that JAG binds to SUPERWOMAN1 (SPW1)/OsMADS16 and DL, forming two heterodimers, JAG-SPW1 and JAG-DL. Expression analysis revealed that JAG binds to the promoters of both DL and SPW1 and directly activates SPW1, while SPW1 and DL do not directly inhibit each other. However, JAG-SPW1 inhibits DL transcription, while JAG-DL inhibits SPW1 transcription, indicating that JAG may mediate the transcription antagonism through these two heterodimers to limit the transcription of DL in stamen and SPW1 in pistil, which is crucial for sexual organ origination in the appropriate location. Meanwhile, JAG and DL can directly inhibit OSH1 and OSH15, individually or synergistically, which is essential for timely termination of FM. These findings reveal the unique synergistic manner of JAG and DL in rice reproductive organ generation, providing insights into the regulatory mechanisms of floral morphogenesis in plants.
Keyword ：
DROOPING LEAF DROOPING LEAF floral meristem termination floral meristem termination floral organ specification floral organ specification JAGGED JAGGED KNOTTED-LIKE HOMEOBOX KNOTTED-LIKE HOMEOBOX rice rice SUPERWOMAN1 SUPERWOMAN1
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| GB/T 7714 | Cai, Zhengzheng , Su, Yuanyuan , Kong, Lan et al. JAGGED and DROOPING LEAF synergistically establish sexual organs and terminate floral meristem in rice [J]. | PLANT JOURNAL , 2025 , 123 (1) . |
| MLA | Cai, Zhengzheng et al. "JAGGED and DROOPING LEAF synergistically establish sexual organs and terminate floral meristem in rice" . | PLANT JOURNAL 123 . 1 (2025) . |
| APA | Cai, Zhengzheng , Su, Yuanyuan , Kong, Lan , Ren, Guangxin , Zheng, Lili , Zhu, Miaomiao et al. JAGGED and DROOPING LEAF synergistically establish sexual organs and terminate floral meristem in rice . | PLANT JOURNAL , 2025 , 123 (1) . |
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Accurate and timely crop mapping is essential for food security assessment, and high-quality feature factors are the core foundation for accurate mapping. However, deep learning model crop classification algorithms have achieved some success, while the models themselves struggle to explain the specific contribution and impact of different features on the results. In this study, a self-adaptive Feature-attention Kolmogorov-Arnold Network (FKAN) is proposed for interpretable and scalable crop mapping. The model integrated the adaptive weighted feature attention module (AWFA) and the interpretable KAN network, which can visualize the complex associations between features and target crops and automatically capture and filter effective key spatiotemporal features, thus enhancing the interpretability of the model. Experimental results demonstrate that integrating optical, radar, and terrain features yields superior performance in both sample prediction and crop mapping, surpassing existing methods. The proposed FKAN achieves an overall accuracy and F1 score exceeding 0.90. Optical and radar features contribute the most significantly to classification accuracy, while terrain data provides complementary enhancement. By aligning with key crop phenology and leveraging the Google Earth Engine (GEE), FKAN establishes the first operational platform for global winter wheat identification, enabling accurate and scalable crop mapping. The migrated model achieves over 85% accuracy across different regions and years, demonstrating strong robustness and generalization capability. The study identifies optimal phenological periods and feature indices for different crops, providing scientific guidance for future mapping efforts. The FKAN model demonstrated robustness, scalability, and interpretability, was able to automatically extract high-confidence pixels and generate crop planting probabilities, providing an efficient and scalable solution for large-scale crop monitoring. This study generated the first global winter wheat map GlobalWinterWheat10m dataset by the FKAN algorithm. The code and demo link is accessible at https://github.com/FZUcheng123/FKAN.
Keyword ：
Crop mapping Crop mapping Google Earth Engine Google Earth Engine Historical Data Historical Data Interpretability Interpretability Sample generation Sample generation
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| GB/T 7714 | Cheng, Feifei , Qiu, Bingwen , Yang, Peng et al. Crop sample prediction and early mapping based on historical data: Exploration of an explainable FKAN framework [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 237 . |
| MLA | Cheng, Feifei et al. "Crop sample prediction and early mapping based on historical data: Exploration of an explainable FKAN framework" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 237 (2025) . |
| APA | Cheng, Feifei , Qiu, Bingwen , Yang, Peng , Wu, Wenbin , Yu, Qiangyi , Qian, Jianping et al. Crop sample prediction and early mapping based on historical data: Exploration of an explainable FKAN framework . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 237 . |
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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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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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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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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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