How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914818218262528 |
|---|---|
| author | Kerner, Hannah Nakalembe, Catherine Yang, Adam Zvonkov, Ivan McWeeny, Ryan Tseng, Gabriel Becker-Reshef, Inbal |
| author_facet | Kerner, Hannah Nakalembe, Catherine Yang, Adam Zvonkov, Ivan McWeeny, Ryan Tseng, Gabriel Becker-Reshef, Inbal |
| contents | Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where there is high food insecurity and sparse agricultural statistics. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_02575 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? Kerner, Hannah Nakalembe, Catherine Yang, Adam Zvonkov, Ivan McWeeny, Ryan Tseng, Gabriel Becker-Reshef, Inbal Machine Learning Computers and Society Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where there is high food insecurity and sparse agricultural statistics. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions. |
| title | How accurate are existing land cover maps for agriculture in Sub-Saharan Africa? |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2307.02575 |