Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping : Evidence from Sub-Saharan Africa
With the surge in publicly available high-resolution satellite imagery, satellite-based monitoring of smallholder agricultural outcomes is gaining momentum. This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models f...
| Main Authors: | , , , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2021
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/339781617301798195/Understanding-the-Requirements-for-Surveys-to-Support-Satellite-Based-Crop-Type-Mapping-Evidence-from-Sub-Saharan-Africa http://hdl.handle.net/10986/35404 |
| Summary: | With the surge in publicly available high-resolution satellite
imagery, satellite-based monitoring of smallholder agricultural
outcomes is gaining momentum. This paper provides
recommendations on how large-scale household surveys
should be conducted to generate the data needed to train
models for satellite-based crop type mapping in smallholder
farming systems. The analysis focuses on maize cultivation
in Malawi and Ethiopia, and leverages rich, georeferenced
plot-level data from national household surveys that were
conducted in 2018–20 and that are integrated with Sentinel-2
satellite imagery and complementary geospatial
data. To identify the approach to survey data collection
that yields optimal data for training remote sensing models,
26,250 in silico experiments are simulated within a machine
learning framework. The best model is then applied to map
seasonal maize cultivation from 2016 to 2019 at 10-meter
resolution in both countries. The analysis reveals that smallholder
plots with maize cultivation can be identified with
up to 75 percent accuracy. However, the predictive accuracy
varies with the approach to georeferencing plot locations
and the number of observations in the training data. Collecting
full plot boundaries or complete plot corner points
provides the best quality of information for model training.
Classification performance peaks with slightly less than 60
percent of the training data. Seemingly small erosion in
accuracy under less preferable approaches to georeferencing
plots results in total area under maize cultivation being
overestimated by 0.16 to 0.47 million hectares (8 to 24
percent) in Malawi. |
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