Crop type maps from satellite remote sensing are important tools for food security, local livelihood support and climate change mitigation in smallholder regions of the world, but most satellite-based methods are not well suited to smallholder conditions. To address this gap, we establish a four-part criteria for a useful embedding-based approach consisting of 1) performance, 2) plausibility, 3) transferability and 4) accessibility and evaluate geospatial foundation model (FM) embeddings -based approaches using TESSERA and AlphaEarth against current baseline methods for a region in the groundnut basin of Senegal. We find that the TESSERA -based approach to land cover and crop type mapping fulfills the selection criteria best, and in one temporal transfer example shows 28% higher accuracy compared to the next best method. These results indicate that TESSERA embeddings are an effective approach for crop type classification and mapping tasks in Senegal.

翻译：卫星遥感作物类型图是全球小农地区粮食安全、地方生计支持和气候变化缓解的重要工具，但大多数基于卫星的方法并不适合小农条件。为弥补这一不足，我们为一种实用的嵌入式方法建立了四部分标准，包括：1）性能，2）合理性，3）可迁移性，以及4）可访问性，并针对塞内加尔花生盆地的一个区域，使用TESSERA和AlphaEarth评估了基于地理空间基础模型（FM）嵌入的方法与当前基线方法的对比。我们发现，基于TESSERA的土地覆盖和作物类型制图方法最能满足选择标准，并且在一个时间迁移示例中，其准确率比次优方法高出28%。这些结果表明，TESSERA嵌入是塞内加尔作物类型分类与制图任务的一种有效方法。