Name
Title | ||
|---|---|---|
Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa |
Abstract | ||
|---|---|---|
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 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-m resolution in both countries. The analysis reveals that smallholder plots with maize cultivation can be identified with up to 75% accuracy. 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% of the training data. Seemingly little erosion in accuracy under less preferable approaches to georeferencing plots results in the total area under maize cultivation being overestimated by 0.16-0.47 million hectares (8-24%) in Malawi. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13234749 | REMOTE SENSING |
Keywords | DocType | Volume |
agriculture, maize, crop type mapping, Sentinel-2, household surveys, training data, Malawi, Ethiopia | Journal | 13 |
Issue | Citations | PageRank |
23 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Azzari | 1 | 0 | 0.34 |
| Shruti Jain | 2 | 0 | 0.34 |
| Graham Jeffries | 3 | 0 | 0.34 |
| Talip Kilic | 4 | 0 | 0.34 |
| Siobhan Murray | 5 | 0 | 0.34 |