Abstract Remote sensing-derived cropland products have depicted the location and extent of agricultural lands with an ever increasing accuracy. However, limited attention has been devoted to distinguishing between actively cropped fields and fallowed fields within agricultural lands, and in particular so in grass fallow systems of semi-arid areas. In the Sahel, one of the largest dryland regions worldwide, crop-fallow rotation practices are widely used for soil fertility regeneration. Yet, little is known about the extent of fallow fields since fallow is not explicitly differentiated within the cropland class in any existing remote sensing-based land use/cover maps, regardless of the spatial scale. With a 10 m spatial resolution and a 5-day revisit frequency, Sentinel-2 satellite imagery made it possible to disentangle agricultural land into cropped and fallow fields, facilitated by Google Earth Engine (GEE) for big data handling. Here we produce the first Sahelian fallow field map at a 10 m resolution for the baseline year 2017, accomplished by designing a remote sensing driven protocol for generating reference data for mapping over large areas. Based on the 2015 Copernicus Dynamic Land Cover map at 100 m resolution, the extent of fallow fields in the cropland class is estimated to be 63% (403,617 km2) for the Sahel in 2017. Similar results are obtained for five contemporary cropland products, with fallow fields occupying 57–62% of the cropland area. Yet, it is noted that the total estimated area coverage depends on the quality of the different cropland products. The share of cropped fields within the Copernicus cropland area is found to be higher in the arid regions (200–300 mm rainfall) as compared to the semi-arid regions (300–600 mm rainfall). The woody cover fraction within cropped and fallow fields is found to have a reversed pattern between arid (higher woody cover in cropped fields) and semi-arid (higher woody cover in fallow fields) regions. The method developed, using cloud-based Earth Observation (EO) data and computation on the GEE platform, is expected to be reproducible for mapping the extent of fallow fields across global croplands. Future applications based on multi-year time series is expected to improve our understanding of crop-fallow rotation dynamics in grass fallow systems being key in teasing apart how cropland intensification and expansion affect environmental variables, such as soil fertility, crop yields and local livelihoods in low-income regions such as the Sahel. The mapping result can be visualized via a web viewer ( https://buwuyou.users.earthengine.app/view/fallowinsahel ).

中文翻译：

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被遗忘的土地利用类别：使用 Sentinel-2 绘制整个萨赫勒地区的休耕地图

摘要 遥感衍生的农田产品越来越准确地描绘了农田的位置和范围。然而，对于区分农用地内积极耕作的田地和休耕地的关注有限，尤其是在半干旱地区的草地休耕系统中。在萨赫勒地区，世界上最大的旱地地区之一，作物休耕轮作实践被广泛用于土壤肥力再生。然而，关于休耕地的范围知之甚少，因为在任何现有的基于遥感的土地利用/覆盖地图中，无论空间尺度如何，休耕地都没有明确区分农田类别。空间分辨率为 10 m，重访频率为 5 天，Sentinel-2 卫星图像可以将农田划分为耕地和休耕地，谷歌地球引擎 (GEE) 为大数据处理提供了便利。在这里，我们为 2017 基线年制作了第一张分辨率为 10 m 的萨赫勒休耕地地图，这是通过设计遥感驱动的协议来完成的，该协议用于生成用于绘制大面积地图的参考数据。根据分辨率为 100 m 的 2015 年哥白尼动态土地覆盖图，2017 年萨赫勒地区农田类别的休耕面积估计为 63%（403,617 平方公里）。 五个当代农田产品获得了类似的结果，其中休耕地占耕地面积的 57-62%。然而，需要注意的是，估计的总面积覆盖率取决于不同农田产品的质量。与半干旱地区（300-600 毫米降雨量）相比，干旱地区（200-300 毫米降雨量）中哥白尼农田面积的耕地比例更高。已发现耕地和休耕地的木本覆盖率在干旱（耕地的木本覆盖率较高）和半干旱（休耕地的木本覆盖率较高）区域之间具有相反的模式。开发的方法使用基于云的地球观测 (EO) 数据和 GEE 平台上的计算，预计可重复用于绘制全球农田的休耕范围。基于多年时间序列的未来应用有望提高我们对草地休耕系统中作物休耕轮作动态的理解，这是梳理农田集约化和扩张如何影响环境变量的关键，例如萨赫勒等低收入地区的土壤肥力、作物产量和当地生计。映射结果可以通过 Web 查看器 (https://buwuyou.users.earthengine.app/view/fallowinsahel) 进行可视化。