Abstract
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.
