本数据整合了多源数据，包括高分辨率遥感数据、模型模拟数据及历史农业普查数据，以1950年至2020年为时间尺度，以1公里×1公里空间分辨率，重建了南美洲四种主要经济作物（即大豆、玉米、小麦和水稻）的历史动态。研究结果表明，过去70年间，南美洲的农田面积通过侵占其他植被迅速扩张。具体而言，大豆是扩张最为显著的作物之一，其种植面积从1950年的几乎为零增长至2020年的4880万公顷，导致其他植被（即森林、牧场/草地和未管理草地/灌木丛）总计减少2392万公顷。此外，玉米种植面积从1950年的1270万公顷增加到2020年的2690万公顷，增长了2.1倍，而水稻和小麦的种植面积则相对稳定。新开发的作物类型数据为评估南美洲农业用地扩张对作物产量、温室气体排放以及碳和氮循环的影响提供了重要见解。此外，这些数据对于制定国家政策、可持续贸易、投资和开发战略至关重要，旨在确保南美洲及其他地区粮食供应以及实现其他人类和环境目标
\n", "ds_source": "\n包括高分辨率遥感数据、模型模拟数据及历史农业普查数据。具体如下：
\n（1）利用基于遥感和基于模型的LULC和作物类型155 - 160数据生成了南美洲的耕地密度图和作物类型底图；
\n（2）哥白尼全球土地服务土地覆盖地图（CGLS-LC100):PROBA-V 100m时间序列数据和高质量的土地覆盖训练样本，构建1级精度为80%的土地覆盖分类模型；
\n（3）全球30米土地覆盖动态监测数据集_FCS30D;
\n（4）全球环境历史数据库（HYDE 3.2版）;
\n（5）历史土地动力学评估+ (HILDA+);
\n（6）空间生产分配模型（SPAM 2010）;
\n（7）地球观测全球农业监测组织（GEOGLAM）;
\n（8）全球土地分析与发现 （GLAD）;
\n（9）MapBiomas 为阿根廷、巴西和乌拉圭提供分辨率为 30 m 的土地利用和土地覆盖图；
\n（10）阿根廷跨国公司提供了 2018 年至 2022 年阿根廷分辨率为 30 m 的详细作物类型地图。
", "ds_process_way": "\n为了重建历史耕地和作物类型动态，需要对所有数据集进行预处理。首先，将高分辨率数据集（即CGLS-LC100、GLC_FCS30D、GLAD、Argentina MNC、MapBiomas和Uruguay LC）聚合到1 km分辨率，以获得部分农田和作物类型。其次，采用双线性方法对HYDE3.2、SPAM2010和GEOGLAM进行重采样，达到1 km的空间分辨率。最后，投影数据集转换为WGS84进行进一步分析，所有过程在谷歌Earth Engine中进行。
", "ds_quality": "\n与现有数据相比，重构数据具有更高的时空分辨率，能够更好地捕捉历史时期作物类型变化的动态。
", "ds_acq_start_time": "1950-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "南美洲", "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": "login-access", "ds_total_size": 95159359, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1000", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "b931e053-b183-49e5-adc2-faf60cfe2218.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-08-28 16:07:13", "last_updated": "2026-01-14 10:58:47", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6963.2025", "i18n": { "en": { "title": "History of Human Land Transformation in South America (HISLAND-SA) Annual 1-kilometer Resolution Crop specific Grid Data (1950-2020)", "ds_format": "tif", "ds_source": "\nIncluding high-resolution remote sensing data, model simulation data, and historical agricultural census data. Specifically, as follows:
\n(1) Using remote sensing and model-based LULC and crop type 155-160 data, cultivated land density maps and crop type base maps of South America were generated;
\n(2) Copernicus Global Land Services Land Cover Map (CGLS-LC100): PROBA-V 100m time series data and high-quality land cover training samples are used to construct a land cover classification model with a first level accuracy of 80%;
\n(3) Global 30 meter Land Cover Dynamic Monitoring Dataset FCS30D;
\n(4) Global Environmental History Database (HYDE version 3.2);
\n(5) Historical Land Dynamics Assessment+(HILDA+);
\n(6) Spatial Production Allocation Model (SPAM 2010);
\n(7) Global Agriculture Monitoring Organization for Earth Observation (GEOGLAM);
\n(8) Global Land Analysis and Discovery (GLAD);
\n(9) MapBiomas provides land use and land cover maps with a resolution of 30 meters for Argentina, Brazil, and Uruguay;
\n(10) Argentine multinational companies have provided detailed crop type maps with a resolution of 30 meters for Argentina from 2018 to 2022.
", "ds_quality": "\nCompared with existing data, reconstructed data has higher spatiotemporal resolution and can better capture the dynamics of crop type changes during historical periods.
", "ds_ref_way": "", "ds_abstract": "This data integrates multiple sources of data, including high-resolution remote sensing data, model simulation data, and historical agricultural census data. Using 1950 to 2020 as the time scale and a spatial resolution of 1 kilometer by 1 kilometer, it reconstructs the historical dynamics of four major economic crops in South America, namely soybean, corn, wheat, and rice. The research results indicate that over the past 70 years, the farmland area in South America has rapidly expanded by encroaching on other vegetation. Specifically, soybean is one of the most significantly expanding crops, with its planting area increasing from almost zero in 1950 to 48.8 million hectares in 2020, resulting in a total reduction of 23.92 million hectares in other vegetation (i.e. forests, pastures/grasslands, and unmanaged grasslands/shrubs). In addition, the planting area of corn increased by 2.1 times from 12.7 million hectares in 1950 to 26.9 million hectares in 2020, while the planting area of rice and wheat remained relatively stable. The newly developed crop type data provides important insights for evaluating the impact of agricultural land expansion in South America on crop yield, greenhouse gas emissions, and carbon and nitrogen cycling. In addition, these data are crucial for formulating national policies, sustainable trade, investment, and development strategies aimed at ensuring food supply in South America and other regions, as well as achieving other human and environmental goals
", "ds_time_res": "年", "ds_acq_place": "South America", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "\nIn order to reconstruct the dynamics of historical farmland and crop types, preprocessing is required for all datasets. Firstly, the high-resolution datasets (i.e. CGLS-LC100, GLC_FCS30D, GLAD, Argentina MNC, MapBiomas, and Uruguay LC) were aggregated to a resolution of 1 km to obtain partial farmland and crop types. Secondly, the bilinear method was used to resample HYDE3.2, SPAM2010, and GEOGLAM, achieving a spatial resolution of 1 km. Finally, the projection dataset was converted to WGS84 for further analysis, and all processes were carried out in Google Earth Engine.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "南美洲", "作物地图", "玉米大豆小麦" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "南美洲" ], "ds_time_tags": [ 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "田汉琴", "email": "hanqin.tian@bc.edu", "work_for": "波士顿学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "田汉琴", "email": "hanqin.tian@bc.edu", "work_for": "波士顿学院", "country": "中国" } ], "ds_managers": [ { "true_name": "田汉琴", "email": "hanqin.tian@bc.edu", "work_for": "波士顿学院", "country": "中国" } ], "category": "水文" }