Cropland mapping in smallholder landscapes is challenged by complex and fragmented landscapes, labor-intensive and unmechanized land management causing high within-field variability, rapid dynamics in shifting cultivation systems, and substantial proportions of short-term fallows. To overcome these challenges, we here present a large-area mapping framework to identify active cropland and short-term fallows in smallholder landscapes for the 2020/2021 growing season at 4.77 m spatial resolution. Our study focuses on Northern Mozambique, an area comprising 381,698 km². The approach is based on Google Earth Engine and time series of PlanetScope mosaics made openly available through Norwaýs International Climate and Forest Initiative (NICFI) data program. We conducted multi-temporal coregistration of the PlanetScope data using seasonal Sentinel-2 base images and derived consistent and gap-free seasonal time series metrics to classify active cropland and short-term fallows. An iterative active learning framework based on Random Forest class probabilities was used for training rare classes and uncertain regions. The map was accurate (area-adjusted overall accuracy 88.6% ± 1.5%), with the main error type being the commission of active cropland. Error-adjusted area estimates of active cropland extent (61,799.5 km² ± 4,252.5 km²) revealed that existing global and regional land cover products tend to under-, or over-estimate active cropland extent, respectively. Short-term fallows occupied 28.9% of the cropland in our reference sample (13% of the mapped cropland), with consolidated agricultural regions showing the highest shares of short-term fallows. Our approach relies on openly available PlanetScope data and cloud-based processing in Google Earth Engine, which minimizes financial constraints and maximizes replicability of the methods. All code and maps were made available for further use.

小农景观中的农田测绘面临以下挑战：复杂且分散的景观、劳动密集型和非机械化的土地管理导致田间的高度变异性、耕作系统的快速动态变化以及大量的短期休耕。为了克服这些挑战，我们在这里提出了一个大面积测绘框架，以 4.77 m 空间分辨率识别 2020/2021 生长季小农景观中的活跃农田和短期休耕地。我们的研究重点是莫桑比克北部，面积为 381,698 km ² 。该方法基于 Google Earth Engine 和通过挪威国际气候和森林倡议 (NICFI) 数据计划公开提供的 PlanetScope 马赛克时间序列。我们使用季节性 Sentinel-2 基础图像对 PlanetScope 数据进行多时相配准，并得出一致且无间隙的季节性时间序列指标，以对活跃农田和短期休耕地进行分类。基于随机森林类别概率的迭代主动学习框架用于训练稀有类别和不确定区域。地图准确（面积调整后的总体精度为 88.6% ± 1.5%），主要误差类型是活跃农田的委托。活动农田范围的误差调整面积估计（61,799.5 km ² ± 4,252.5 km ² ）表明，现有的全球和区域土地覆盖产品往往分别低估或高估了活动农田范围。在我们的参考样本中，短期休耕占耕地的 28.9%（占地图耕地的 13%），其中综合农业区域显示短期休耕所占比例最高。 我们的方法依赖于公开可用的 PlanetScope 数据和 Google Earth Engine 中基于云的处理，这最大限度地减少了财务限制并最大限度地提高了方法的可复制性。所有代码和地图都可供进一步使用。