Is sub-metre resolution necessary for cocoa mapping? A landscape-stratified evaluation of very high resolution imagery, decametric Earth Observation inputs, and operational products in Cote d'Ivoire

2026-07-09

AI Summary

This study addresses the challenge of accurately mapping cocoa cultivation in smallholder agroforestry landscapes, where conventional medium-resolution remote sensing suffers from spatial aggregation effects that hinder deforestation monitoring and supply chain transparency. For the first time, it systematically evaluates cocoa mapping performance across a real-world gradient of smallholder landscape complexity using 0.5-meter Pleiades imagery, 10-meter Sentinel-2 annual composites, and foundation models (TESSERA and AlphaEarth). A stratified accuracy assessment based on 2,821 independent validation points reveals that sub-meter imagery achieves an F1 score of 0.92 with consistent performance across diverse landscapes. Among 10-meter approaches, TESSERA significantly outperforms others (F1 = 0.86), and embedding foundation models markedly enhances large-scale mapping accuracy—particularly in fragmented landscapes where their advantages are most pronounced.

Abstract

Accurate cocoa mapping is increasingly important for deforestation monitoring, supply-chain transparency, and regulatory applications. Spatial aggregation in conventional medium-resolution Earth observation (EO) imagery may limit cocoa detection in heterogeneous smallholder landscapes. In Cote d'Ivoire, we therefore evaluated how mapping performance varies across landscape conditions, whether very high resolution (VHR) imagery provides a meaningful advantage, and whether foundation-model embeddings improve decametric cocoa mapping. We developed models using 0.5 m Pleiades VHR imagery, a 10 m Sentinel-2 annual composite, and embeddings from TESSERA and AlphaEarth Foundations (AEF), and additionally assessed four publicly available cocoa mapping products. Performance was evaluated through a landscape-stratified accuracy assessment using 2,821 independently interpreted reference points distributed across gradients of tree cover density and landscape fragmentation. The VHR model achieved the highest performance (F1 = 0.92) and maintained F1-scores above 0.90 across all strata. Among the decametric inputs, TESSERA performed best (F1 = 0.86), followed by AEF (F1 = 0.82) and Sentinel-2 (F1 = 0.76). Of the existing cocoa products, the Kalischek product performed best (F1 = 0.83), comparable to the internally trained AEF model. Performance differences between VHR and decametric approaches increased with fragmentation and under low and high tree cover density conditions. Targeted VHR acquisition may therefore be particularly beneficial in complex cocoa landscapes, while foundation-model embeddings offer a scalable alternative for large-area mapping.

Problem

• cocoa mapping
• very high resolution imagery
• landscape heterogeneity
• deforestation monitoring
• Earth Observation

Innovation

• very high resolution imagery
• foundation model embeddings
• cocoa mapping
• landscape-stratified evaluation
• decametric Earth Observation