Abstract Accurate estimation of cropping intensity (CI), an indicator of food production, is well aligned with the ongoing efforts to achieve sustainable development goals (SDGs) under diminishing natural resources. The advancement in satellite remote sensing provides unprecedented opportunities for capturing CI information in a spatially continuous manner. However, challenges remain due to the lack of generalizable algorithms for accurately and efficiently mapping global CI with a fine spatial resolution. In this study, we developed a 30-m planetary-scale CI mapping framework with the reconstructed time series of Normalized Difference Vegetation Index (NDVI) from multiple satellite images. Using a binary crop phenophase profile indicating growing and non-growing periods, we estimated pixel-by-pixel CI by enumerating the total number of valid cropping cycles during the study years. Based on the Google Earth Engine cloud computing platform, we implemented the framework to estimate CI during 2016–2018 in eight geographic regions across continents that are representative of global cropping system diversity. Comparison with PhenoCam network data in four cropland sites suggests that the proposed framework is capable of capturing the seasonal dynamics of cropping practices. Spatially, overall accuracies based on validation samples range from 80.0% to 98.9% across different regions worldwide. Regarding the CI classes, single cropping systems are associated with more robust and less biased estimations than multiple cropping systems. Finally, our CI estimates reveal high agreement with two widely used land surface phenology products, including Vegetation Index and Phenology V004 (VIP4) and Moderate Resolution Imaging Spectroradiometer Land Cover Dynamics (MCD12Q2), meanwhile providing much more spatial details. Due to its robustness, the developed CI framework can be potentially generalized to produce global fine resolution CI products for food security and other applications.

中文翻译：

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绘制全球精细分辨率裁剪强度的新框架：算法、验证和含义

摘要 准确估算作为粮食生产指标的耕作强度 (CI) 与在自然资源减少的情况下为实现可持续发展目标 (SDG) 所做的努力非常吻合。卫星遥感的进步为以空间连续的方式捕获 CI 信息提供了前所未有的机会。然而，由于缺乏可准确有效地映射具有良好空间分辨率的全局 CI 的通用算法，挑战仍然存在。在这项研究中，我们开发了一个 30 米行星尺度 CI 制图框架，其中包含来自多个卫星图像的归一化差异植被指数 (NDVI) 的重建时间序列。使用指示生长期和非生长期的二元作物物候特征，我们通过枚举研究年份中有效种植周期的总数来估计逐像素 CI。基于谷歌地球引擎云计算平台，我们实施了框架来估计 2016-2018 年期间在代表全球作物系统多样性的八个地理区域的 CI。与四个农田站点的 PhenoCam 网络数据进行比较表明，所提出的框架能够捕捉种植实践的季节性动态。在空间上，基于验证样本的总体准确率在全球不同地区的 80.0% 到 98.9% 之间。关于 CI 类，与多重裁剪系统相比，单一裁剪系统与更稳健且偏差更小的估计相关联。最后，我们的 CI 估计显示与两种广泛使用的地表物候产品高度一致，包括植被指数和物候 V004 (VIP4) 和中分辨率成像光谱仪土地覆盖动力学 (MCD12Q2)，同时提供更多空间细节。由于其稳健性，开发的 CI 框架可以潜在地推广到生产用于食品安全和其他应用的全局精细分辨率 CI 产品。