Senegal Cropland Mapping Training 2024

Schedule | Mapping Project | Helmets Labeling Crops | Resources

Dates: June 3-7, 2024

Pre-training Survey | Post-training Survey

This project aims to advance national agriculture monitoring in West Africa using Earth observation data through the collaborative development of semi- automated approaches for generating foundational datasets, emphasizing capacity development and co-production. This training, therefore, seeks to transfer the required skill set for developing cropland maps in support of food security assessments using Google Earth Engine.

Team

Catherine Nakalembe, PI, University of Maryland (UMD)
Ivan Zvonkov, UMD
Hannah Kerner, Arizona State University
Adebowale Daniel, UMD
Diana Frimpong, UMD
Alana Ginsburg, UMD
Kevin Horn, NASA/SCO

Schedule: June 3-7, 2024

Cropland Mapping Project

Google Earth Engine Repository containing all relevant scripts

Presentation

1. Understand crops within the area of interest

Overview of region (geography, location, population)
Climatology (climate zones, precipitation patterns)
Physical characteristics (soils, elevation)
Socioeconomic factors (economics, agriculture, food security, staple foods)
Crop dynamics (challenges in crop production, crop calendars)

2. Analysis of two existing maps

Visual Assessment [relevant script: 1_Worldcover_cropland, 2_visualizing_all_maps]
Accuracy Assessment [relevant script: 3_Worldcover_cropland_accuracy]

3. Create cropland for the region

Overview of the mapping approach
Datasets used (satellite imagery [relevant script: 4_Sentinel2], ground truth data)
Models and algorithms employed
Visual Assessment [relevant script: 5_classification]
Accuracy Assessment [relevant script: 3_Worldcover_cropland_accuracy]
Conclusion (key findings, future directions and recommendations)

Regions of Interest

Group 1 Google Earth Engine Boundary code:

    
    var AEZ = ee.FeatureCollection("users/izvonkov/Senegal/ZEF_AgroEcoZones")

    function getAOIFromAEZ(zoneList){
    return AEZ.filter(ee.Filter.inList('Zone', ee.List(zoneList)))
    }

    var roi = getAOIFromAEZ(["Senegal River Valley", "Ferlo"])
    Map.addLayer(roi, {}, "River Valley and Ferlo")

Group 2 Google Earth Engine Boundary code:

    

    var AEZ = ee.FeatureCollection("users/izvonkov/Senegal/ZEF_AgroEcoZones")

    function getAOIFromAEZ(zoneList){
    return AEZ.filter(ee.Filter.inList('Zone', ee.List(zoneList)))
    }

    var roi = getAOIFromAEZ(["Niayes", "Groundnut Basin"])
    Map.addLayer(roi, {}, "Niayes and Groundnut Basin")

Group 3 Google Earth Engine Boundary code:

    
    var AEZ = ee.FeatureCollection("users/izvonkov/Senegal/ZEF_AgroEcoZones")

    function getAOIFromAEZ(zoneList){
    return AEZ.filter(ee.Filter.inList('Zone', ee.List(zoneList)))
    }

    var roi = getAOIFromAEZ(["Casamance", "Eastern Senegal"])
    Map.addLayer(roi, {}, "Casamance and Eastern")

NASA Harvest

Machine Learning