From foundational models to interpretability: a comparative study of embeddings and feature engineering for smallholder irrigation detection in Sub-Saharan Africa

Smallholder irrigation is crucial to livelihoods and growth in rural agro-economies. Identifying where it occurs provides valuable insights for agriculture extension, water management, infrastructure investments, marketing and energy provision. Remote sensing methods can aid but have been stymied by the small plot sizes, especially of irrigated plots. Humid tropical climates are characterized by high temperatures and heavy seasonal or year-round rainfall. This adds an additional layer of challenge due to fewer cloud-free imagery from persistent cloud cover. This study focuses on these settings to compare the performance, interpretability and transferability of classification using embeddings versus a feature engineering approach. Geospatial foundation models such as AlphaEarth Foundations (AEF) have emerged to address the contrasting scarcity of high-quality labels in comparison to increasing volumes of earth observation data. The embeddings from these models have been shown to generate maps from sparse labels outperforming traditional machine learning approaches in certain cases. In this study, we utilize a nation-wide smallholder irrigation survey data from Uganda as a data-rich region and pilot surveys from Zambia as a data-sparse region. We present a reproducible cloud-native Earth observation workflow in Google Earth Engine that discusses the tradeoffs and performance of the feature engineering approach compared to foundational model embeddings. The results show that a model trained on AEF embeddings outperforms a traditional feature engineering approach on a rich label dataset. The performance is similar in a data-sparse region. However, the embedding models do not lend themselves to auditable methods for applicability to regions where no data is available.