Abstract
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 km(2). 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 km(2) ± 4,252.5 km(2)) 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.