An operational framework for large-area mapping of active cropland and short-term fallows in smallholder landscapes using PlanetScope data

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Authors

Philippe Rufin, Adia Bey, Michelle Picoli, Patrick Meyfroidt

Abstract

Cropland mapping in complex 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 openly available PlanetScope mosaics made available through the NICFI data program. We conducted multi-temporal co-registration 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 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 are made available for further use.

DOI

https://doi.org/10.31223/X5P62F

Subjects

Environmental Studies, Geographic Information Sciences, Nature and Society Relations, Remote Sensing, Spatial Science

Keywords

Mozambique, sub-Saharan Africa, Shifting Cultivation, agriculture, remote sensing, land use, sentinel-2, time series, Google Earth Engine, Co-Registration

Dates

Published: 2022-03-17 04:44

Last Updated: 2022-03-17 08:44

License

CC BY Attribution 4.0 International

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