Evaluating the sequential masking classification approach for improving crop discrimination in the Sudanian Savanna of West Africa

• A variant of sequential masking classification is proposed for crop mapping.
• Individual crop types are classified using different image combinations.
• Accuracy of classifying individual crop classes improves by between 4% and 19%.
• Increased availability of satellite data can improve crop mapping in cloud-prone areas.

Classification of remotely sensed data to reveal the spatial distribution of crop types has high potential for improving crop area estimates and supporting decision making. However, remotely sensed crop maps still demand improvements as e.g. variations in farm management practices (e.g. planting and harvesting dates), soil and other environmental factors cause overlaps in features available for classification and thus confusion in error matrices. In this study, a variant of the sequential masking classification technique was applied to multi-temporal optical and microwave remote sensing data (RapidEye, Landsat, TerraSAR-X) to improve the accuracy of crop discrimination in West Africa. This approach employs different sets of multi-temporal images to sequentially classify individual crop classes. The efficiency of the sequential masking approach was tested by comparing the results with that of a one-step classification, in which all crop classes were classified at the same time. Compared to the one-step classification, the sequential masking approach improved overall classification accuracies by between 6% and 9% while increments in the accuracy of individual crop classes were between 4% and 19%. The McNemar’s statistical test showed that the observed differences in accuracy of the two approaches were statistically significant at the 1% significance level. The findings of this study are important for crop mapping efforts in West Africa, where data and methodological constraints often hinder the accurate discrimination of crops.