灌溉作为人类水资源消耗的主要形式，在提升作物产量和缓解干旱影响方面具有关键作用。准确绘制灌溉分布图对水资源高效管理和粮食安全评估至关重要。然而，现有全球灌溉耕地地图分辨率较粗（通常约10公里），且缺乏定期更新。本研究提出一种创新方法，通过干旱胁迫下的灌溉表现作为作物生产力与耗水量的指示因子，实现全球灌溉耕地的精准识别。在划分的每个灌溉制图区（IMZ）内，我们分别确定了2017-2019年生长季的干旱月份及2010-2019年最干旱月份。基于采集样本计算了两种指标：2017-2019年干旱月份的归一化植被指数（NDVI）阈值，以及最干旱月份NDVI相对于十年均值的偏离值。通过融合两种方法的最优结果，构建了100米分辨率的全球最大灌溉范围数据集（GMIE-100），总体精度达83.6%±0.6%。研究显示，全球灌溉耕地最大覆盖面积为403.17±9.82百万公顷，占全球耕地的23.4%±0.6%。这些耕地集中分布于肥沃平原及主要河流沿岸，印度、中国、美国和巴基斯坦的灌溉面积分列全球前四位。GMIE-100的空间分辨率显著优于主流灌溉地图，为农业用水估算和区域粮食安全评估提供了更精细的数据支撑。进一步地，本研究基于U-net架构开发了新型卷积神经网络Pivot-Net，通过深度学习方法首次实现了全球中心支轴灌溉系统（CPIS）的识别。结果显示全球CPIS面积为11.5±0.01百万公顷，约占灌溉总面积的2.90%±0.03%。纳米比亚、美国、沙特阿拉伯、南非、加拿大和赞比亚的CPIS占比均超过10%。这是国际上首次针对特定灌溉方式（CPIS）开展全球尺度识别的系统性研究。
", "ds_source": "基于灌溉的根本目的，本研究通过识别干旱胁迫期来凸显灌溉农田与雨养农田的作物生长差异。我们首先采用CropWatch建立的65个监测报告单元（MRUs）作为基础框架，这些单元综合考虑了作物类型、农业潜力和环境条件等因素。在此基础上，我们将全球耕地进一步细分为110个灌溉制图区（IMZs）：第一级65个农业生态区提供了全球宏观分区框架；为克服原有分区在水资源胁迫和灌溉特征表征上的局限性，我们引入第二级精细化分区标准，依据干旱指数、水资源可利用量、土壤类型和地形特征进行细分。最终形成的110个IMZs，作为确定干旱胁迫关键时段的基本单元，这一分级体系有效放大了灌溉与雨养农田的作物生长差异。
", "ds_process_way": "这项研究受到灌溉目的的启发，即灌溉可以减轻水胁迫的影响。基本上，我们假定水分胁迫可以是定期或不定期的。如果旱季有农作物，则应定期灌溉。否则，在极端干旱的年份，灌溉只是降雨的补充，这意味着灌溉是不定期的。对于定期灌溉，当降水无法满足作物的需水量时，我们可以检测旱季的植被信号（DM-NDVI）。对于不规则灌溉，我们将极旱年份的 NDVI 与 10 年平均水平进行比较，并计算偏差（NDVIdev），以确定是否灌溉。为了确定一个地区是定期灌溉还是不定期灌溉，我们使用了这两个指标，并选择了准确度较高的方法。然后，在 DL 模型的支持下，我们训练了一个以圆形为重点的 CPIS 识别模型，并将其应用于全球，以生成全球 CPIS 分布数据。CPIS 的范围被认定为用于更新全球灌溉范围的灌溉范围。最后，我们估算了 CPIS 在灌溉耕地中的灌溉类型比例。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 7034934393, "ds_files_count": 76, "ds_format": "", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "ff85297d-fc99-4ccd-9cd1-c2e8eeb45cc7.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "a3ce23a2-c353-4383-a544-65c8f218579f", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-04-29 11:34:06", "last_updated": "2025-04-29 11:34:06", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.DVN.DB6835.2025", "i18n": { "en": { "title": "GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods（2010-2019）", "ds_format": "", "ds_source": "Taking inspiration from the fundamental purpose of irrigation, our aim is to identify periods of drought stress to highlight disparities in crop conditions between irrigated and rainfed croplands. We began by utilizing the 65 monitoring and reporting units (MRUs) established by CropWatch. These MRUs, which account for factors such as crop types, agricultural potential, and environmental conditions, served as the foundation for dividing global cropland into 110 irrigation mapping zones (IMZs). The first-level 65 agroecological zones provide a broad global overview. To address limitations in representing water stress and irrigation within zones, we introduced a more detailed classification, creating second-level agroecological zones based on arid indices, water availability, soil types, and landforms. Ultimately, we utilized 110 IMZs as the foundational units for determining the specific timing of drought stress, as illustrated in Fig. 1. This comprehensive approach enabled us to capture and amplify the distinctions in crop conditions between irrigated and rainfed croplands. Irrigated cropland is defined as agricultural land that benefits from human interventions and is equipped with irrigation infrastructure, including facilities like canals and central pivot systems.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Irrigation accounts for the major form of human water consumption and plays a pivotal role in enhancing crop yields and mitigating the effects of drought. Accurate mapping of irrigation distribution is essential for effective water resource management and the assessment of food security. However, the resolution of the global irrigated cropland map is coarse, typically approximately 10 km, and it lacks regular updates. In our study, we present a robust methodology that leverages irrigation performance during drought stress as an indicator of crop productivity and water consumption to identify global irrigated cropland. Within each irrigation mapping zone (IMZ), we identified the dry months of the growing season from 2017 to 2019 or the driest months from 2010 to 2019. To delineate irrigated cropland, we utilized the collected samples to calculate normalized difference vegetation index (NDVI) thresholds for the dry months of 2017 to 2019 and the NDVI deviation from the 10-year average for the driest month. By integrating the most accurate results from these two methods, we generated the Global Maximum Irrigation Extent dataset at 100 m resolution (GMIE-100), achieving an overall accuracy of 83.6 % ± 0.6 %. The GMIE-100 reveals that the maximum extent of irrigated cropland encompasses 403.17 ± 9.82 Mha, accounting for 23.4 % ± 0.6 % of the global cropland. Concentrated in fertile plains and regions adjacent to major rivers, the largest irrigated cropland areas are found in India, China, the United States, and Pakistan, which rank first to fourth, respectively. Importantly, the spatial resolution of GMIE-100 surpasses that of the dominant irrigation map, offering more detailed information essential to support estimates of agricultural water use and regional food security assessments. Furthermore, with the help of the deep learning (DL) method, the global central pivot irrigation system (CPIS) was identified using Pivot-Net, a novel convolutional neural network built on the U-net architecture. We found that there is 11.5 ± 0.01 Mha of CPIS, accounting for approximately 2.90 % ± 0.03 % of the total irrigated cropland. In Namibia, the United States, Saudi Arabia, South Africa, Canada, and Zambia, the CPIS proportion was greater than 10 %. To our knowledge, this is the inaugural study to undertake a global identification of specific irrigation methods, with a focus on the CPIS.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The study was inspired by the purpose of irrigation, i.e. that it mitigates the effect of water stress. Basically, we assume that water stress can be regular or irregular. If there are crops during the dry season, the irrigation should occur regularly. Otherwise, irrigation is just complementary to rainfall in extremely dry years, which means irrigation is irregular. For regular irrigation, we could detect vegetation signal in the dry season (DM-NDVI) when precipitation cannot meet water demand for crops. For irregular irrigation, we compare the NDVI in extremely dry years to the 10-year average level and calculate the deviation (NDVIdev) to determine whether it is irrigated or not. To determine whether a region has regular or irregular irrigation, we used both of these indicators and chose the method with the higher accuracy.Then, with the support of the DL model, a CPIS identification model focused on circular shapes was trained and applied to the entire world to generate global CPIS distribution data. The extent of the CPIS was recognized as the extent of irrigation used to update the global extent of irrigation. Finally, we estimated the proportion of irrigation types of CPIS within irrigated cropland.
", "ds_ref_instruction": "When using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "GMIE", "灌溉分布", "生长季", "干旱月份" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "吴炳方", "email": "wubf@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吴炳方", "email": "wubf@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "吴炳方", "email": "wubf@aircas.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "category": "生态" }