Perennial commodity crops, such as coffee, often play a large role globally in agricultural markets and supply chains and locally in livelihoods, poverty reduction, and biodiversity. Yet, the production of spatial information on these crops are often overlooked in favor of annual food crops. Remote sensing detection of coffee faces a particular set of challenges due to persistent cloud cover in the tropical “coffee belt,” hilly topography in coffee growing regions, diversity of coffee growing systems, and spectral similarity to other tree crops and agricultural land. Looking at the major coffee growing region in Dak Lak, Vietnam, we integrate multi-temporal 10 m optical Sentinel-2 and Sentinel-1 SAR data in order to map three coffee production systems: i) open-canopy sun coffee, ii) intercropped and other shaded coffee and iii) newly planted or young coffee.

Leveraging Google Earth Engine (GEE), we compute five sets of features in order to best enhance separability between coffee and other land cover and within coffee production systems. The features include Sentinel-2 dry and wet season composites, Sentinel-1 texture features, Sentinel-1 spatiotemporal metrics, and topographic features. Using a random forest classification algorithm, we produce a 9-class land cover map including our three coffee production classes and a binary coffee/non-coffee map. The binary map has an overall accuracy of 89% and the three coffee production systems have user accuracies of 65, 56, 71% for sun coffee, intercropped coffee and newly planted coffee, respectively. This is a first effort at large-scale distinction of within-crop production styles and has implications across many applications. The binary coffee map can be used as a high-resolution crop mask, whereas the detailed land cover map can inform monitoring of deforestation dynamics, biodiversity, sustainability certification and implementation of climate adaptation strategies. This work offers a scalable approach to integrating optical and radar Sentinel data for production of spatially explicit agricultural information and contributes particularly to tree crop and agroforestry mapping, which often is overlooked in between agricultural and forestry sciences.

多年生商品作物，如咖啡，通常在全球农业市场和供应链以及当地的生计、减贫和生物多样性方面发挥着重要作用。然而，关于这些作物的空间信息的产生往往被忽视，而有利于一年生粮食作物。由于热带“咖啡带”的持续云层覆盖、咖啡种植区的丘陵地形、咖啡种植系统的多样性以及与其他树木作物和农田的光谱相似性，咖啡的遥感检测面临着一系列特殊的挑战。着眼于越南 Dak Lak 的主要咖啡种植区，我们整合了多时相 10 m 光学 Sentinel-2 和 Sentinel-1 SAR 数据，以绘制三个咖啡生产系统：i) 开放式阳光咖啡，

利用 Google Earth Engine (GEE)，我们计算了五组特征，以最好地增强咖啡和其他土地覆盖之间以及内部的可分离性。咖啡生产系统。这些特征包括 Sentinel-2 干湿季复合材料、Sentinel-1 纹理特征、Sentinel-1 时空指标和地形特征。使用随机森林分类算法，我们生成了一个 9 类土地覆盖图，包括我们的三个咖啡生产类和一个二元咖啡/非咖啡地图。二值图的整体准确度为 89%，三个咖啡生产系统的用户准确度分别为太阳咖啡、间作咖啡和新种植咖啡的 65%、56%、71%。这是大规模区分作物内生产方式的首次尝试，对许多应用都有影响。二元咖啡地图可用作高分辨率作物掩码，而详细的土地覆盖地图可以为森林砍伐动态、生物多样性、气候适应战略的可持续性认证和实施。这项工作提供了一种可扩展的方法来整合光​​学和雷达哨兵数据，以产生空间明确的农业信息，特别有助于树木作物和农林业测绘，这在农业和林业科学之间经常被忽视。