Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area – defined as continental to global – cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 countries covering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) platform. We used a pixel-based Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 93.8% with roughly 14% omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of net croplands (nearly 30% of global net cropland areas) occupying 18% of the study land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. This Landsat-derived 30-m cropland product (GFSAD30) provided 10–30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30 m global land cover (GLC30) product providing an overall similarity of 88.8% (Kappa 0.7) with producer’s cropland similarity of 89.2% (errors of omissions = 10.8%) and user’s cropland similarity of 81.8% (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strengths of GFSAD30 product, compared to other products, were: 1. identifying precise location of croplands, and 2. capturing fragmented croplands. The cropland extent map dataset is available through NASA’s Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global Croplands <https://croplands.org> website, GEE code is accessible at: https://code.earthengine.google.com/1666e8bed34e0ce2b2aaf1235ad8c6bd.

准确及时的耕地信息对于环境，粮食安全和政策研究至关重要。还需要空间明确的农田数据集，以获取有关作物类型，作物产量，耕种强度以及灌溉面积的信息。由于耕地的差异表现，广泛的耕作方式以及有限的参考数据可用性，大面积（定义为大陆到全球）的耕地制图面临挑战。这项研究显示了覆盖欧洲，中东，俄罗斯和中亚大部分地区的64个国家的农田范围图的结果。为了覆盖如此广阔的区域，我们在Google Earth Engine（GEE）平台中处理了2014年至2016年之间3351个足迹中的大约160,000个Landsat场景。我们使用了基于像素的随机森林（RF）机器学习算法，并使用了一组卫星数据输入来捕获跨越十二个农业生态区（AEZ）的各种光谱，时间和地形特征。训练分类模型的参考数据是从非常高的空间分辨率图像（VHRI）和辅助数据集中收集的。结果是二进制图，显示了耕种/非耕种区域ca。2015年。根据大量独立的验证样本，该地图对农田类别的总体准确性为93.8％，遗漏和委托误差约为14％。该地图表明，整个研究区域总计有5.46亿公顷（Mha）的净耕地（占全球净耕地面积的近30％），占研究用地面积的18％。联合国粮食及农业组织（粮农组织）对国家耕地面积的估计与这项工作得出的估计值之间的比较也显示出0.95的R平方值。与64个国家的联合国粮农组织相比，这种源自Landsat的30米农田产品（GFSAD30）提供了10-30％的农田面积。最后，GFSAD30与其他几种农田产品之间的地图对地图比较显示，最好的相似度矩阵使用30m全球土地覆盖（GLC30）产品提供的整体相似度为88.8％（Kappa 0.7），生产者的耕地相似度为89.2％（遗漏误差= 10.8％），用户的耕地相似度为81.8％（佣金错误= 8.1％）。GFSAD30在德国和比利时的大量灌溉农业地区以及意大利的雨养农业中捕获了GLC30产品中缺失的耕地。这项研究还确定，与其他产品相比，GFSAD30产品的真正优势在于：1.确定农田的精确位置，以及2.捕获零散的农田。耕地范围图数据集可通过NASA的土地过程分布式主动存档中心（LP DAAC）获得，网址为https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001，