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学者姓名：邱炳文
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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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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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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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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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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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Maize yield in China accounts for more than one-fourth of the global maize yield, but it is challenged by frequent extreme weather and increasing food demand. Accurate and timely estimation of maize yield is of great significance to crop management and food security. Commonly applied vegetation indexes (VIs) are mainly used in crop yield estimation as they can reflect the greenness of vegetation. However, the environmental pressures of crop growth and development are difficult to monitor and evaluate. Indexes for water content, pigment content, nutrient elements and biomass have been developed to indirectly explain the influencing factors of yield, with extant studies mainly assessing VIs, climate and water content factors. Only a few studies have attempted to systematically evaluate the sensitivity of these indexes. The sensitivity of the spectral indexes, combined indexes and climate factors and the effect of temporal aggregation data need to be evaluated. Thus, this study proposes a novel yield evaluation method for integrating multiple spectral indexes and temporal aggregation data. In particular, spectral indexes were calculated by integrating publicly available data (remote sensing images and climate data) from the Google Earth Engine platform, and county-level maize yields in China from 2015 to 2019 were estimated using a random forest model. Results showed that the normalized moisture difference index (NMDI) is the index most sensitive to yield estimation. Furthermore, the potential of adopting the combined indexes, especially NMDI_NDNI, was verified. Compared with the whole-growth period data and the eight-day time series, the vegetative growth period and the reproductive growth period data were more sensitive to yield estimation. The maize yield in China can be estimated by integrating multiple spectral indexes into the indexes for the vegetative and reproductive growth periods. The obtained R-2 of maize yield estimation reached 0.8. This study can provide feature knowledge and references for index assessments for yield estimation research.
Keyword ：
combined index combined index maize yield maize yield multiple spectral indexes multiple spectral indexes temporal aggregation temporal aggregation yield estimation yield estimation
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| GB/T 7714 | He, Yuhua , Qiu, Bingwen , Cheng, Feifei et al. National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation [J]. | REMOTE SENSING , 2023 , 15 (2) . |
| MLA | He, Yuhua et al. "National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation" . | REMOTE SENSING 15 . 2 (2023) . |
| APA | He, Yuhua , Qiu, Bingwen , Cheng, Feifei , Chen, Chongcheng , Sun, Yu , Zhang, Dongshui et al. National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation . | REMOTE SENSING , 2023 , 15 (2) . |
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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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Multiple cropping is a widespread agricultural intensification for increasing food production. National-scale Cropping Intensity (CI) mapping is important for achieving sustainable development goals. However, previous studies have largely relied on vegetation indices (VI) waves for detecting valid cropping cycles, which is challenged by the complexity of agricultural systems. i.e. winter crops with multiple VI waves and double rice with less distinctive waves. This study proposed a robust framework for Mapping cropping Intensity through bettercharacterizing crop Life cycles based on combined considerations of vegetative and productive Stages (MILS). The number of cropping cycles was estimated by the frequency of valid coupled patterns of vegetation and brownness indices from Sentinel-2 (S2) MultiSpectral Instrument (MSI) time series and further improved through fusing Sentinel-1 (S1) SAR and S2 data to reduce the omission errors of double rice. The MILS algorithm was implemented using the Google Earth Engine platform and applied to China, which is dominated by smallholder farms with diverse cropping patterns. This study produced the first 10 m updated CI map over conterminous China (CIChina10m) in 2020 by fusing S1 and S2 time series. The CIChina10m had an overall accuracy of 93.93% when validated with 14,468 widely spread reference sites. The cropping intensity was 1.2769 on the national scale, which illustrated higher values in the Middle and lower reaches of the Yangtze River plain (CI = 1.5879) and South China (CI =1.5503). There were 1086,620, 412,620 and 1,441 km2 areas cultivated by single, double and triple cropping in China, which accounted for 72.4%, 27.5% and 0.1% of cropland, respectively. The proposed MILS algorithm showed good performances in detecting complex agricultural systems, which can be further applied to generate continental or global 10-m CI products with good quality. Codes of the MILS algorithm are publicly available at https://code.earthengine.google.com/a7f24f76291bf901ee25a130025a7ce6, and the first 10m national CI data products in China with good accuracy (ChinaCI10m) are also publicly accessed at https://doi.org/10.6084/m9.figshare.23939505.
Keyword ：
Conterminous China Conterminous China Crop phenology Crop phenology Cropping intensity Cropping intensity Google Earth Engine Google Earth Engine Senetinel-1/2 Senetinel-1/2 Time series Time series
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| GB/T 7714 | Qiu, Bingwen , Hu, Xiang , Yang, Peng et al. A robust approach for large-scale cropping intensity mapping in smallholder farms from vegetation, brownness indices and SAR time series [J]. | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2023 , 203 : 328-344 . |
| MLA | Qiu, Bingwen et al. "A robust approach for large-scale cropping intensity mapping in smallholder farms from vegetation, brownness indices and SAR time series" . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 203 (2023) : 328-344 . |
| APA | Qiu, Bingwen , Hu, Xiang , Yang, Peng , Tang, Zhenghong , Wu, Wenbin , Li, Zhengrong . A robust approach for large-scale cropping intensity mapping in smallholder farms from vegetation, brownness indices and SAR time series . | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING , 2023 , 203 , 328-344 . |
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