Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Assessing Cropland Area in West Africa for Agricultural Yield Analysis

Domaine:

agriculturegeospatial
Créateur:
KabNiaGraYac
Éditeur:
MDP
Hôte:
Accurate estimates of cultivated area and crop yield are critical to our understanding of agricultural production and food security, particularly for semi-arid regions like the Sahel of West Africa, where crop production is mainly rain-fed and food security is closely correlated with the inter-annual variations in rainfall. Several global and regional land cover products, based on satellite remotely-sensed data, provide estimates of the agricultural land use intensity, but the initial comparisons indicate considerable differences among them, relating to differences in the satellite data quality, classification approaches, and spatial and temporal resolutions. Here, we quantify the accuracy of available cropland products across Sahelian West Africa using an independent, high-resolution, visually interpreted sample dataset that classifies all points across West Africa using a 2-km sample grid (~500,000 points for the study area). We estimate the “quantity” and “allocation” disagreements for the cropland class of eight land cover products in five Western Sahel countries (Burkina Faso, Mali, Mauritania, Niger, and Senegal). The results confirm that coarse spatial resolution (300 m, 500 m, and 1000 m) land cover products have higher disagreements in mapping the fragmented agricultural landscape of the Western Sahel. Earlier products (e.g., GLC2000) are less accurate than recent products (e.g., ESA CCI 2013, MODIS 2013 and GlobCover 2009). We also show that two of the finer spatial resolution maps (GFSAD30, and GlobeLand30) using advanced classification approaches (random forest, decision trees, and pixel-object combined) are currently the best available products for cropland identification. However, none of the eight land cover databases examined is consistent in reaching the targeted 75% accuracy threshold in the five Sahelian countries. The majority of currently available land cover products overestimate cultivated areas by an average of 170% relative to the cropland area in the reference data.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

Similaires

Cropland Nutrient Balance — Cropland potassium per unit area | Africa (FAOSTAT)Cropland Nutrient Balance — Cropland nitrogen per unit area | Africa (FAOSTAT)Cropland Nutrient Balance — Cropland phosphorus per unit area | Africa (FAOSTAT)safariant/GEE-Cropland-Area-Analysis-over-time-in-Kenyakel1y/East-Africa-Cropland-AnalysisPesticides Use — Use per area of cropland | Africa (FAOSTAT)

Cropland Nutrient Balance — Cropland potassium per unit area | Africa (FAOSTAT)

🌍 25,384 observations · 53 Africa countries · 1961–2023 · Repackaged by Electric Sheep Africa This

Cropland Nutrient Balance — Cropland nitrogen per unit area | Africa (FAOSTAT)

🌍 37,887 observations · 53 Africa countries · 1961–2023 · Repackaged by Electric Sheep Africa This

Cropland Nutrient Balance — Cropland phosphorus per unit area | Africa (FAOSTAT)

🌍 25,384 observations · 53 Africa countries · 1961–2023 · Repackaged by Electric Sheep Africa This

safariant/GEE-Cropland-Area-Analysis-over-time-in-Kenya

This GEE code is designed to calculate and visualize the total area of cropland within Kenya from th

kel1y/East-Africa-Cropland-Analysis

Geospatial analysis of cropland dynamics in East Africa using MODIS Land Cover data and Google Earth

Pesticides Use — Use per area of cropland | Africa (FAOSTAT)

🌍 1,774 observations · 53 Africa countries · 1990–2023 · Repackaged by Electric Sheep Africa This d