Large-area mapping of active cropland and short-term fallows in smallholder landscapes using PlanetScope data

Philippe Rufin; Adia Bey; Michelle Picoli; Patrick Meyfroidt

doi:10.1016/j.jag.2022.102937

Large-area mapping of active cropland and short-term fallows in smallholder landscapes using PlanetScope data

Int J Appl Earth Obs Geoinf. 2022 Aug:112:102937. doi: 10.1016/j.jag.2022.102937.

Authors

Philippe Rufin^{1

2}, Adia Bey¹, Michelle Picoli¹, Patrick Meyfroidt^{1

3}

Affiliations

¹ Earth and Life Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium.
² Geography Department, Humboldt-Universität zu Berlin, 10117 Berlin, Germany.
³ F.R.S.-FNRS, 1000 Brussels, Belgium.

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 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.

Keywords: Agriculture; Coregistration; Google Earth Engine; Land use; Mozambique; Sentinel-2; Shifting cultivation; Sub-Saharan Africa; Time series.