This document discusses how artificial intelligence and satellite remote sensing data can be leveraged to support decision making in the African agricultural sector. It outlines how AI can be used to improve climate modeling through techniques like multi-model ensembling, downscaling, and attribution. AI and remote sensing can also help monitor and measure carbon sequestration in agriculture to identify practices that reduce emissions. Additionally, AI can aid natural resource management through activities such as increasing forest data accuracy, finding vulnerable forest areas, stopping illegal logging, and monitoring reforestation efforts.

1. Leveraging Artificial Intelligence (AI) & Satellite Remote
Sensing Data for Decision-making in the African
Agricultural Sector
AFRICA AGRICULTURE WATCH
(AAgWa)
• AI TRENDS IN CLIMATE ACTION
• Dr. Mouhamadou Bamba Sylla
AIMS-Canada Research chair in climate change science
African Institute for Mathematical Sciences, AIMS Rwanda
sylla.bamba@aims.ac.rw; syllabamba@yahoo.fr

2. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
Outline
I/ Introduction
II/ Improving Climate Modeling
III/ Mitigation: Carbon Sequestration
IV/ Natural Resource Management
V/ Summary

3. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
I/ Introduction
Climate change hot spot where
increased hazards probability,
vulnerability and exposure meet:
climate risk is high
Climate change: human lives and
countries’ economies are at stake
Climate actions are needed for
effective adaptation and DRR
AI: Field combining computer
science and robust datasets to
enable problem-solving

4. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
II/ Climate Modeling
q Climate model
Duration and/or Ensemble size
Res
olu
tion
Computing
Resources
Complexity
1/120
Schematic of climate models
Improving climate models

5. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
II/ Climate Modeling
q Climate model
The potential of machine learning application
learning lies everywhere in the workflow
Ø Climate data monitoring, quality control,
data fusion from different sources
Ø Emulate model components, develop
improved parametrization schemes, learn
the underlying equations of motion
ØBias correction, feature detection,
multimodel ensembling, uncertainty
quantification, further downscaling
Ø Better-suite boundary forcings, physical
consistency

6. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
II/ Climate Modeling
q Multimodel ensembling
Jose et al. 2022
- All ML algorithms
perform better than the
arithmetic mean
- Precipitation: LSTM
- Temperature: RF and
LSTM

7. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
II/ Climate Modeling
q Further Downscaling
Substantial improvements captured when using DL CNN-based downscaled data

8. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
ØAttribution using climate models
IPCC 2021
ØAttribution using deep learning: CNN and VI
Bone et al. 2022
II/ Climate Modeling
q Attribution

9. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
III/ Mitigation: Carbon Sequestration
Ø Agriculture as a source of emissions

10. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
III/ Mitigation: Carbon Sequestration
Ø Agriculture fields can
act as a carbon sink
- Crops photosynthesis
sequesters carbon in the soil
- Any disturbance to the soil
releases carbon

11. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
III/ Mitigation: Carbon Sequestration
Ø Monitoring/measuring
- AI and remote sensing can be
leveraged to identify areas of large/low
emissions, policy and practices
causing it
Ø Reducing
- AI to be used (soil analysis, crop
selection, suitable areas, etc.) to
identify best practices to reduce
emissions
Ø Removing
- AI to detect areas of woody plants,
encroachment, suitable conditions for
agroforestry

12. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
IV/ Natural Resource Management
Reducing forest degradation and improving forest
management
Focus on conserving and restoring peatlands and coastal
wetlands, such as mangroves
Protect, restore and enhance marine and coastal ecosystems
including marine conservation.

13. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
IV/ Natural Resource Management
q Reducing forest degradation and improving forest management
Ø Increasing accuracy of forest data
- Satellite imagery, forest inventory, mapping species, tree growth, sequestration
Ø Finding vulnerable forest areas
- track illegal mining, roads, fires, housing
Ø Stopping illegal logging
- track the noises of chainsaws and logging trucks
Ø Reforestation
- Monitor tree growth, track optimal conditions, areas encroachment

14. Leveraging Artificial Intelligence (AI) & Satellite Remote Sensing Data for Decision-making in the African
Agricultural Sector
V/ Summary

15. Leveraging Artificial Intelligence (AI) & Satellite Remote
Sensing Data for Decision-making in the African
Agricultural Sector
AFRICA AGRICULTURE WATCH
(AAgWa)
THANK YOU!